Urine sample analysis method and urine sample analyzer

Abstract

To precisely determine the possibility of trichomoniasis even when there are only a small number of tangible elements differentiated from trichomonads in a urine sample.SOLUTION: The present invention relates to a method for analyzing a urine sample which acquires information on tangible elements in a urine sample 5. The method includes the steps of acquiring counting information 70 about the number of trichomonads and first information 71 made of counting information on at least one of squamous cells and white cells on the basis of detection data of tangible elements in the urine sample 5; and outputting information 80 about the possibility of trichomoniasis on the basis of the counting information 70 about the number of trichomonads and the first information 71.SELECTED DRAWING: Figure 17

Description

[Technical Field]

[0001] The present invention relates to a urine sample analyzing method and a urine sample analyzer for obtaining information about formed elements in a urine sample. [Background technology]

[0002] Patent Document 1 discloses a urine sample analyzer that acquires detection data of formed elements in a urine sample using flow cytometry and counts the formed elements by type based on the detection data. This urine sample analyzer identifies Trichomonas protozoa from among the formed elements based on the fluorescence intensity and forward scattered light intensity of the formed elements acquired as detection data, and counts the identified Trichomonas protozoa. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2015-163855 Summary of the Invention [Problem to be solved by the invention]

[0004] Because Trichomonas protozoa are morphologically similar to other formed elements in urine, such as white blood cells and deep squamous epithelial cells, there are limitations to the accuracy of counting Trichomonas protozoa separately from other elements.

[0005] One aspect of the present invention is directed to accurately determining whether a person is suspected of having a trichomoniasis infection. [Means for solving the problem]

[0006] In order to achieve the above object, a urine sample analyzing method according to a first aspect of the present invention comprises, as shown in FIGS. 1, 13 and 17, A urine sample analyzer (100)A urine sample analysis method for obtaining information about formed elements in a urine sample (5) (see FIG. 2), which obtains (S5) count information (70) about the number of Trichomonas protozoa and first information (71) consisting of count information about the number of at least one of squamous epithelial cells and white blood cells based on detection data of formed elements in the urine sample (5), and outputs information (80) (see FIG. 1) about suspected Trichomonas infection based on at least the count information (70) about the number of Trichomonas protozoa and the first information (71). death (S7 and S8) and comparing a count value X of the count information regarding the number of Trichomonas protozoa under non-hemolytic conditions with a first threshold value, and comparing a count value Y of the first information with a second threshold value equal to or greater than the first threshold value, and determining that there is a suspicion of Trichomonas infection when the conditions X>first threshold value and Y>second threshold value are met. .

[0007] As described above, the urine sample analysis method according to the first invention acquires (S5) count information (70) relating to the number of Trichomonas protozoa and first information (71) comprising count information relating to the number of at least one of squamous epithelial cells and white blood cells based on detection data of formed elements in a urine sample (5). In a urine sample (5) from a patient suffering from Trichomonas infection, the number of squamous epithelial cells and white blood cells appear significantly increased along with Trichomonas protozoa. Therefore, if the numerical value of the count information acquired as the first information (71) is high, it can be determined that the reliability of the count information (70) relating to the number of Trichomonas protozoa is high. Conversely, if the numerical value of the count information acquired as the first information (71) is low, it can be determined that the reliability of the count information (70) relating to the number of Trichomonas protozoa is low. Therefore, the urine sample analyzing method according to the first invention outputs information (80) regarding a suspected trichomoniasis infection based on at least the count information (70) regarding the number of trichomoniasis protozoa and the first information (71) (S7 and S8). As a result, even from detection data of a urine sample (5) containing not only trichomoniasis protozoa but also other formed elements with morphological characteristics similar to trichomoniasis protozoa, the reliability of the count information (70) regarding the number of trichomoniasis protozoa can be improved based on the first information (71), which serves as an indicator of the reliability of the count information (70) regarding the number of trichomoniasis protozoa. As a result, the suspicion of trichomoniasis infection can be accurately determined.

[0008] As shown in Figures 1, 13 and 17, a urine sample analyzer (100) according to a second aspect of the invention comprises a detection unit (30) for detecting formed elements in a urine sample (5) (see Figure 2), and an analysis unit (20) for acquiring count information (70) relating to the number of Trichomonas protozoa and first information (71) comprising count information relating to the number of at least one of squamous epithelial cells and white blood cells based on the detection data of the formed elements in the urine sample (5), and outputting information (80) relating to a suspected Trichomonas infection based on at least the count information (70) relating to the number of Trichomonas protozoa and the first information (71). The analysis unit (20) executes a process of comparing a count value X of the count information (70) regarding the number of Trichomonas protozoa under non-hemolytic conditions with a first threshold value, and a process of comparing a count value Y of the first information (71) with a second threshold value that is equal to or greater than the first threshold value, and determines that there is a suspicion of Trichomonas infection when the conditions X>first threshold value and Y>second threshold value are satisfied. . As shown in Figures 1, 13 and 17, the urine sample analyzer (100) according to the third invention includes a detection unit (30) that detects formed elements in a urine sample (5) (see Figure 2), and obtains count information (70) relating to the number of Trichomonas protozoa and first information (71) comprising count information relating to the number of at least one of squamous epithelial cells and white blood cells based on the detection data of the formed elements in the urine sample (5), and calculates the count information (70) relating to at least the number of Trichomonas protozoa. and an analysis unit (20) that outputs information (80) related to suspected trichomoniasis infection based on the count information (70) and the first information (71), wherein the analysis unit (20) executes a process of comparing a count value X of the count information (70) related to the number of trichomoniasis protozoa with a first threshold value and a process of comparing a count value Y of the first information (71) with a second threshold value that is equal to or greater than the first threshold value, and determines that there is a suspicion of trichomoniasis infection when the conditions X>first threshold value and Y>second threshold value are satisfied.

[0009] No. 2 and the third The urine sample analyzer (100) according to the present invention includes an analysis unit (20) that acquires count information (70) regarding the number of Trichomonas protozoa and first information (71) including count information regarding the number of at least one of squamous epithelial cells and white blood cells based on detection data of formed elements in a urine sample (5), as described above, and outputs information (80) regarding a suspected Trichomonas infection based on at least the count information (70) regarding the number of Trichomonas protozoa and the first information (71). As a result, similar to the urine sample analysis method according to the first invention, the reliability of the count information (70) regarding the number of Trichomonas protozoa can be improved based on the first information (71), which serves as an indicator of the reliability of the count information (70) regarding the number of Trichomonas protozoa, even from detection data of a urine sample (5) containing not only Trichomonas protozoa but also other formed elements morphologically similar to Trichomonas protozoa. As a result, the suspected Trichomonas infection can be accurately determined. [Effects of the Invention]

[0010] According to the first and second inventions, suspicion of trichomoniasis infection can be determined with high accuracy. [Brief explanation of the drawings]

[0011] [Figure 1] 1 is a schematic diagram showing an example of the configuration of a urine sample analyzer. [Figure 2] FIG. 2 is a block diagram showing an example of the configuration of a sample preparation unit. [Figure 3] FIG. 2 is a block diagram showing an example of the configuration of a detection unit. [Figure 4] FIG. 2 is a block diagram for explaining a configuration related to control of a measurement unit. [Figure 5] FIG. 2 is a block diagram showing an example of the configuration of an analysis unit. [Figure 6] Graphs showing (A) peak intensity, (B) pulse width, and (C) pulse area as examples of feature quantities of detected signals. [Figure 7] Distribution data showing the distribution area of ​​red blood cells (A), distribution data showing the distribution area of ​​white blood cells (B), distribution data showing the distribution area of ​​squamous epithelial cells (C), distribution data showing the distribution area of ​​bacteria (D), and distribution data showing the distribution area of ​​yeast-like fungi (E). [Figure 8] Distribution data (A) to (D) for detecting Trichomonas protozoa. [Figure 9] 8(A) and 8(B) show distribution data (A) and (B), respectively, obtained by measuring a Trichomonas-positive sample and corresponding to FIG. 8(A) and FIG. 8(B). [Figure 10] This is distribution data corresponding to FIG. 8(D) obtained by measuring Trichomonas-positive samples. [Figure 11] Scattergrams (A) and (B) of specimens confirmed to be positive for Trichomonas. [Figure 12] Scattergrams (A) and (B) of specimens that were not Trichomonas positive. [Figure 13] FIG. 10 is a schematic diagram for explaining an outline of a determination process for suspected trichomoniasis infection in an analysis unit. [Figure 14] This is distribution data for explaining a method for acquiring information showing the bias in the distribution of Trichomonas protozoa. [Figure 15] FIG. 10 is a diagram for explaining threshold sets for each mode of the analysis unit. [Figure 16] FIG. 10 is a diagram showing an example of an analysis result screen. [Figure 17] FIG. 4 is a flow chart illustrating the operation of the urine sample analyzer. [Figure 18] FIG. 18 is a flow chart for explaining the measurement sample preparation process of FIG. [Figure 19] FIG. 18 is a flow chart for explaining the first measurement sample measurement process of FIG. 17. [Figure 20] FIG. 18 is a flow chart for explaining the second measurement sample measurement process of FIG. 17. [Figure 21] FIG. 18 is a flowchart for explaining determination processing based on the count information, first information, and second information of Trichomonas protozoa in FIG. 17. [Figure 22] FIG. 18 is a flow diagram for explaining the output process of information relating to suspected trichomoniasis infection in FIG. 17. [Figure 23] FIG. 10 is a flowchart illustrating a determination process based on count information of Trichomonas protozoa and first information according to a second embodiment. [Figure 24] FIG. 11 is a flowchart illustrating a determination process based on count information of Trichomonas protozoa and first and second information according to a third embodiment. [Figure 25] FIG. 10 is a flowchart illustrating a determination process based on count information of Trichomonas protozoa and first information according to a fourth embodiment. [Figure 26] FIG. 10 is a flow diagram for explaining a modified determination rule 1A. DETAILED DESCRIPTION OF THE INVENTION

[0012] First Embodiment The first embodiment will now be described with reference to the drawings. First, the overall configuration of a urine sample analyzer 100 according to the first embodiment will be described with reference to FIG. 1. The urine sample analyzer 100 is an analyzer that analyzes formed elements in a urine sample. The formed elements in urine may include, for example, red blood cells, white blood cells, epithelial cells, casts, bacteria, etc. The urine sample analyzer 100 is further configured to detect Trichomonas protozoa as formed elements in urine and perform analysis for suspected Trichomonas infection.

[0013] (Overall configuration of urine sample analyzer) As shown in Figure 1, the urine sample analyzer 100 is primarily composed of at least a measurement unit 10 and an analysis section 20. The measurement unit 10 includes a detection section 30 and a mechanism for aspirating a urine sample to prepare a measurement sample and supplying the prepared measurement sample to the detection section 30. The measurement unit 10 includes a main body section 11 that houses the detection section 30, and a sample transport section 12 that transports a sample rack 13. The analysis section 20 is provided separately from the measurement unit 10 and is connected to the measurement unit 10 so that data communication is possible.

[0014] The sample rack 13 can hold a plurality of sample containers 14 in an upright position with the openings of the sample containers 14 facing upward. The sample containers 14 contain urine samples 5 (see FIG. 2). The sample containers 14 are set in the sample rack 13 and then installed by the user at a predetermined loading position in the sample transport unit 12. The sample transport unit 12 is configured to transport the sample containers 14 held in the sample rack 13 installed at the loading position to a position where the measurement unit 10 aspirates the urine samples 5.

[0015] (Sample Preparation Department) 2, the main body 11 of the measurement unit 10 includes a sample preparation section 40 for preparing a measurement sample, and a sample distribution section 45 for supplying a urine sample 5 to the sample preparation section 40. The sample distribution section 45 aspirates the urine sample 5 in the sample container 14 transported to the aspirating position using an aspirating tube 46 with a syringe pump (not shown), and supplies the aspirated urine sample 5 to the sample preparation section 40.

[0016] The sample preparation unit 40 includes a mixing unit 41 that mixes the urine sample 5 with a staining reagent. The mixing unit 41 is a reaction vessel having a predetermined volume. By mixing the urine sample 5 with the staining reagent in the mixing unit 41, the formed elements in the urine sample 5 are stained by the staining reagent.

[0017] 2, the mixing unit 41 includes a first mixing unit 41a which is a first reaction tank for mixing the urine sample 5 with a first reagent 42, and a second mixing unit 41b which is a second reaction tank for mixing the urine sample 5 with a second reagent 43. The sample distributing unit 45 distributes a portion of the urine sample 5 obtained from the sample container 14 to the first mixing unit 41a and another portion to the second mixing unit 41b.

[0018] The first reagent 42 includes a first staining liquid 42a and a first diluent 42b. A reagent container for the first staining liquid 42a and a reagent container for the first diluent 42b are placed in the sample preparation unit 40. The second reagent 43 includes a second staining liquid 43a and a second diluent 43b. A reagent container for the second staining liquid 43a and a reagent container for the second diluent 43b are placed in the sample preparation unit 40.

[0019] The urine sample 5 distributed to the first mixing section 41a is mixed with the first reagent 42 (first staining solution 42a and first diluent 42b). The first reagent 42 stains non-nuclear formed elements in urine. The urine sample 5 treated with the first reagent 42 in the first mixing section 41a is primarily used to analyze non-nuclear particles such as red blood cells and casts. Hereinafter, the urine sample 5 treated with the first reagent 42 in the first mixing section 41a will be referred to as the first measurement sample.

[0020] The urine sample 5 distributed to the second mixing section 41b is mixed with the second reagent 43 (second staining solution 43a and second diluent 43b). The second reagent 43 dissolves red blood cells and crystals in the urine sample 5 and stains nucleic acids. The urine sample 5 treated with the second reagent 43 in the second mixing section 41b is primarily used to analyze cells with nuclei, such as white blood cells, epithelial cells, bacteria, and fungi. Hereinafter, the urine sample 5 treated with the second reagent 43 in the second mixing section 41b will be referred to as the second measurement sample.

[0021] The first mixing section 41a and the second mixing section 41b are each connected to the flow cell 31 of the detection section 30 via a liquid supply tube 41c. During measurement, the sample preparation section 40 supplies the first measurement sample in the first mixing section 41a and the second measurement sample in the second mixing section 41b to the flow cell 31 separately.

[0022] The first staining solution 42a contains a fluorescent dye that stains formed components that do not contain nucleic acids.

[0023] The first diluent 42b is a reagent whose main component is a buffer, and contains an osmotic pressure compensator so that a stable fluorescent signal can be obtained without hemolyzing red blood cells.

[0024] The second staining solution 43a contains a dye that stains nucleic acids. The second staining solution 43a contains an intercalator for specifically staining nucleic acids and a fluorescent dye that binds to the minor groove. Examples of intercalators include known cyanine, acridine, and phenanthridium dyes. For example, cyanine intercalators include SYBR Green I (SYBR is a registered trademark) and Thiazole Orange. Examples of acridine intercalators include Acridinorange. Examples of phenanthridium intercalators include propidium iodide and ethidium bromide. Examples of dyes that bind to the minor groove include known dyes such as DAPI and Hoechst (registered trademark). For example, dyes that bind to the Hoechst minor groove include Hoechst 33342 and Hoechst 33258. In the first embodiment, cyanine intercalators are preferred, and SYBR Green I and Thiazole Orange are particularly preferred.

[0025] The second diluent 43b contains a hemolyzing agent. The second diluent 43b damages the cell membrane to promote membrane passage of the second staining solution 43a, and also contains a cationic surfactant for hemolyzing red blood cells and shrinking impurities such as red blood cell fragments. The type of surfactant is not limited to a cationic surfactant, and may be a nonionic surfactant.

[0026] The detection unit 30 detects formed elements in the urine specimen 5 mixed with the first reagent 42 and formed elements in the urine specimen 5 mixed with the second reagent 43. Trichomonas protozoa can be detected as a signal that can be distinguished from other formed elements in both measurements using the first measurement sample and measurements using the second measurement sample.

[0027] (Detection unit) FIG. 3 shows a simplified view of the main components of the detection unit 30. FIG. 3 is a plan view schematically illustrating the configuration of the detection unit 30, in which a measurement sample flows from bottom to top through a flow cell 31 along an axis perpendicular to the plane of the page. The detection unit 30 includes a flow cell 31 through which a urine sample 5 flows, a light source 32 that irradiates the flow cell 31 with light, and light-receiving units (33a-33d) that receive light from the flow cell 31. In other words, the detection unit 30 is a flow cytometer that individually detects formed elements in a urine sample 5 using flow cytometry. Flow cytometers can detect a large number of formed elements contained in urine in a short period of time, making them suitable for detecting a small number of formed elements from a large number of formed elements. The number of Trichomonas protozoa in urine is limited in the early stages of infection. Therefore, flow cytometers are particularly advantageous in that they can quickly determine whether a person has a suspected Trichomonas infection, even in the early stages of infection.

[0028] The light receiving units (33a-33d) receive fluorescence and scattered light from the particles. This allows for obtaining multiple types of light receiving signals that serve as the basis for identifying various particles, including Trichomonas protozoa. As a result, a light receiving signal suitable for identifying particles can be selected from the multiple types of light receiving signals and used, thereby improving the accuracy of identifying particles. The detection unit 30 also includes lenses 34a, 34b, and 34c, mirrors 35a and 35b, a polarizing filter 36, and a spectral filter 37.

[0029] The first and second measurement samples sent to the detection unit 30 each form a thin stream surrounded by sheath liquid in the flow cell 31. This sample stream is irradiated with laser light from the light source 32. This operation is performed automatically by operating a pump, an electromagnetic valve, and the like (not shown) under the control of a microcomputer 51 (control device) described below.

[0030] Light source 32 is a semiconductor laser light source that outputs laser light with a center wavelength of 488 nm. Lens section 34a focuses the laser light emitted from light source 32 onto the sample flow in flow cell 31. Lens section 34a forms a beam spot on the sample flow in flow cell 31. When the laser light is irradiated onto particles (solid components) in the sample flow, forward scattered light is generated in front of flow cell 31 (F direction), and side scattered light and side fluorescent light are generated to the side of flow cell 31 (S direction).

[0031] Lens 34b focuses forward scattered light emitted from the solid components in the measurement sample onto light-receiving unit 33a. Light-receiving unit 33a is, for example, a photodiode. Lens 34c focuses side scattered light and side fluorescent light emitted from the solid components onto mirror 35a. Mirror 35a is a dichroic mirror that reflects the side scattered light to mirror 35b and transmits the side fluorescent light to spectral filter 37. The side fluorescent light that passes through spectral filter 37 is received by light-receiving unit 33b, which is a photomultiplier. Mirror 35b is a half mirror that splits the side scattered light from mirror 35a into two, transmits it to light-receiving unit 33c, which is also a photomultiplier, and reflects it to polarizing filter 36. The side scattered light that passed through mirror 35b is received by light-receiving unit 33c. The side-scattered light that passes through the polarizing filter 36 is received by the light-receiving unit 33d, which is a photomultiplier. The polarizing filter 36 transmits only the side-scattered light whose polarization plane has changed to a specific direction from the polarization state of the laser light irradiating the sample flow.

[0032] The light receiving units 33a, 33b, 33c, and 33d convert the received optical signals into electrical signals and output a forward scattered light signal (hereinafter referred to as "FSC"), a fluorescent light signal (hereinafter referred to as "FL"), a side scattered light signal (hereinafter referred to as "SSC"), and a depolarized side scattered light signal (hereinafter referred to as "DSS"), respectively. The light receiving unit 33b, which generates the fluorescent light signal (FL), can change its sensitivity to incident light by switching the drive voltage. The light receiving unit 33b selectively operates between normal sensitivity operation and high sensitivity operation for bacteria detection under the control of a microcomputer 51, which will be described later.

[0033] (Configuration related to control of measurement unit) As shown in FIG. 4, the main body 11 of the measurement unit 10 includes a signal processing circuit 50 for processing the output signal of the detection unit 30, a microcomputer 51, and a communication interface 52.

[0034] The signal processing circuit unit 50 includes an amplifier circuit 50a that amplifies the output signal of the detection unit 30, a filter circuit 50b that performs filtering on the output signal from the amplifier circuit 50a, an A / D converter 50c that converts the output signal (analog signal) of the filter circuit 50b into a digital signal, a digital signal processing circuit 50d that performs predetermined waveform processing on the digital signal, and a memory 50e connected to the digital signal processing circuit 50d.

[0035] The detector 30 amplifies the FSC, SSC, FL, and DSS signals using a preamplifier 38. The amplified signals are input to an amplifier circuit 50a. The amplifier circuit 50a amplifies the four types of signals, FSC, SSC, FLH, and FLL, according to a set gain. The amplifier circuit 50a can be set to a number of different gains under the control of a microcomputer 51.

[0036] The amplifier circuit 50a amplifies the fluorescent signal (FL) acquired in normal sensitivity operation at two different amplification factors: a high amplification factor and a low amplification factor. Therefore, from the fluorescent signal (FL) corresponding to one particle, a fluorescent signal amplified at a high amplification factor (hereinafter referred to as "FLH") and a fluorescent signal amplified at a low amplification factor (hereinafter referred to as "FLL") are acquired. Furthermore, the amplifier circuit 50a amplifies the fluorescent signal (FL) acquired in high sensitivity operation for bacteria detection at the same amplification factor as the high amplification factor. The resulting high-sensitivity, high-amplification fluorescent signal is hereinafter referred to as "FLH(B)." The high-sensitivity setting value for bacteria detection is five times the normal sensitivity setting value. Therefore, FLH(B) corresponds to a signal amplified at an amplification factor five times that of FLH.

[0037] Furthermore, the amplifier circuit 50a amplifies the side scattered light signal (SSC) at two types of amplification factors: a high amplification factor and a low amplification factor. Therefore, from the side scattered light signal (SSC) corresponding to one particle, a side scattered light signal (high sensitivity) amplified at a high amplification factor (hereinafter referred to as "SSH") and a side scattered light signal (low sensitivity) amplified at a low amplification factor (hereinafter referred to as "SSL") are obtained.

[0038] The microcomputer 51 is connected to the sample preparation unit 40, the specimen distribution unit 45, the detection unit 30, the amplifier circuit 50a, the digital signal processing circuit 50d, and the communication interface 52. The microcomputer 51 controls the specimen distribution unit 45, the specimen preparation unit 40, the amplifier circuit 50a, and the digital signal processing circuit 50d to measure the measurement sample and generate detection data. The communication interface 52 is connected to the analysis unit 20 via a LAN cable so as to be able to communicate with the analysis unit 20. The microcomputer 51 transmits the detection data to the analysis unit 20 via the communication interface 52.

[0039] (Analysis Department) 5, the analysis unit 20 is a personal computer. The analysis unit 20 includes a main body 20a, an input unit 20b, and a display unit 20c. The main body 20a includes a processor 21 including a CPU, a storage device 22, an input / output interface 23, and a communication interface 24.

[0040] The processor 21 executes computer programs stored in the ROM and computer programs loaded into the RAM. The storage device 22 includes a ROM and a hard disk for recording computer programs, and is used as a work area for the processor 21 when the processor 21 executes the computer programs recorded on the hard disk.

[0041] Various computer programs, such as an operating system and application programs, to be executed by the processor 21, and data used to execute the computer programs are installed in the storage device 22. A computer program for analyzing the detection data acquired from the measurement unit 10 and outputting the analysis results is installed in the storage device 22. The computer program may be downloaded via a network, or may be installed in the storage device 22 from a recording medium such as an optical disk or flash memory.

[0042] An input unit 20b consisting of a mouse and keyboard is connected to the input / output interface 23. A user operates the analysis unit 20 and inputs data to the analysis unit 20 by using the input unit 20b. The input / output interface 23 is connected to a display unit 20c consisting of a liquid crystal display, and outputs a video signal corresponding to the image data to the display unit 20c. The display unit 20c displays an image based on the input video signal. The analysis unit 20 is also connected to a communication interface 52 (see Figure 4) of the measurement unit 10 via a communication interface 24. The processor 21 can send and receive various data, including detection data, to the measurement unit 10 via the communication interface 24.

[0043] (Analysis processing in the analysis unit) The analysis section 20 analyzes the sediment components based on the detection data acquired from the measurement unit 10. The detection data generated by the measurement unit 10 includes the FSC, SSH, SSL, FLH, FLL, FLH(B), and DSS signals detected from each sediment component contained in the measurement sample. The detection data generated by the measurement unit 10 includes detection data acquired from the first measurement sample (hereinafter referred to as "detection data (SF)") and detection data acquired from the second measurement sample (hereinafter referred to as "detection data (CR)").

[0044] The analysis unit 20 extracts a plurality of characteristic parameters that indicate the characteristics of the particles in the urine sample 5 based on the detection data, and obtains count information for each type of particle based on the extracted characteristic parameters. This effectively improves the accuracy of identifying each type of particle by combining appropriate characteristic parameters according to the characteristics of each type of particle. The count information for particles is, specifically, the number of detected particles. The number of detected particles is called the "count value."

[0045] The feature parameter is defined by a combination of the type of the detection signal, the type of the feature amount obtained from the detection signal, and the type of the detection data.

[0046] The type of detection signal is a concept that indicates whether it is an FSC, SSH, SSL, FLH, FLH(B), FLL, or DSS signal. There are three types of feature quantities obtained from the detection signal: "peak intensity" shown in Figure 6(A), "pulse width" shown in Figure 6(B), and "pulse area" shown in Figure 6(C). Hereinafter, peak intensity will be represented by "P," pulse width by "W," and pulse area by "A." Hereinafter, feature parameters will be represented by listing the type of detection signal and the type of feature quantity consecutively, such as FSCP (peak intensity of the forward scattered light signal), SSHW (pulse width of the side scattered light signal (high sensitivity)), and FLLA (pulse area of ​​the low-sensitivity side fluorescent signal).

[0047] The detection signal from the detector 30 is generated when solid components in the sample flow passing through the flow cell 31 pass through the beam spot, resulting in a pulse-like signal waveform, as shown in Figures 6(A) to 6(C). In the graphs of Figures 6(A) to 6(C), the vertical axis represents signal intensity and the horizontal axis represents time. As shown in Figure 6(A), the peak intensity "P" of each optical signal is obtained as the height of the pulse peak, PP. As shown in Figure 6(B), the pulse width "W" of each optical signal is obtained as the interval, PW, from time T1 when the pulse exceeds a predetermined detection threshold to time T2 when it falls below the detection threshold. As shown in Figure 6(C), the pulse area "A" of each optical signal is obtained as the area (hatched area) PA surrounded by the signal pulse waveform line L1, lines L2 and L3 indicating the times when the pulse height reaches the predetermined detection threshold, and line L4 indicating the optical signal intensity value of 0, i.e., the time integral of the signal intensity. Note that feature quantities other than the above-mentioned peak intensity, pulse width, and pulse area (for example, peak kurtosis, aspect ratio, differential value (slope of waveform line), etc.) may also be obtained.

[0048] When the types of optical signals are seven types, FSC, SSH, SSL, FLH, FLL, FLH(B), and DSS, and the types of feature quantities are three types, peak intensity P, pulse width W, and pulse area A, there are 21 possible combinations of feature parameters. The detection data includes two types of detection data: detection data (SF) of the first measurement sample and detection data (CR) of the second measurement sample. Therefore, for each type of component, a combination of feature parameters suitable for distinguishing one component from another is preset from a total of 42 possible combinations.

[0049] The analysis unit 20 combines two or more characteristic parameters to identify the type of sediment in the urine sample 5 from the detection data, and calculates the number of each type of sediment to generate count information (count values) for each sediment. The analysis unit 20 identifies the type of sediment based on distribution data (see Figures 7 and 8) combining two or more characteristic parameters. The distribution data is a scattergram in which each sediment is plotted at coordinates corresponding to the characteristic parameters on a scattergram with each characteristic parameter as the coordinate axis.

[0050] <Examples of feature parameters> The following describes an example of a combination of characteristic parameters used to identify, from among the formed elements detectable by the urine sample analyzer 100, those formed elements that are particularly relevant to the analysis of suspected Trichomonas infection.

[0051] <Red blood cells (RBC): SF_DSSP × FSCP> 7(A), red blood cells (RBCs) are formed elements that appear in region R1 of distribution data 60a of the depolarized side-scattered light signal peak intensity (DSSP) and the forward-scattered light signal peak intensity (FSCP) in the detection data (SF) of the first measurement sample. Particles plotted in region R1 are counted as red blood cells.

[0052] <White blood cells (WBC):CR_FLLA×FSCW> As shown in Figure 7(B), white blood cells (WBCs) are formed elements that appear in region R2 of distribution data 60b of the side fluorescence signal pulse area (FLLA) and forward scattered light signal pulse width (FSCW) at normal sensitivity and low amplification in the detection data (CR) of the second measurement sample. Particles plotted in region R2 are counted as white blood cells.

[0053] <Squamous epithelial cells (squaEC):CR_SSHA×FSCW> As shown in Figure 7(C), squamous epithelial cells (squaECs) are formed elements that appear in region R3 of distribution data 60c of the pulse area (SSHA) of the highly amplified side-scattered light signal and the pulse width (FSCW) of the forward-scattered light signal in the detection data (CR) of the second measurement sample. Particles plotted in region R3 are counted as squamous epithelial cells.

[0054] <Bacteria (BACT):CR_FLH(B)P×FSCP> As shown in Figure 7(D), bacteria (BACT) are formed elements that appear in region R4 of distribution data 60d of the peak intensity of the high-sensitivity, high-amplification side fluorescence signal (FLH(B)P) for bacteria detection and the peak intensity of the forward scattered light signal (FSCP) in the detection data (CR) of the second measurement sample. Particles plotted in region R4 are counted as bacteria.

[0055] <Yeast-like fungi (YLC):CR_FLHP×FSCP> As shown in Figure 7(E), yeast-like fungi (YLC) are formed elements that appear in region R5 of the distribution data 60e of the side fluorescence signal peak intensity (FLHP) and forward scattered light signal peak intensity (FSCP) at normal sensitivity and high amplification rate in the detection data (CR) of the second measurement sample. Particles plotted in region R5 are counted as yeast-like fungi.

[0056] <Trichomania protozoa (Trich)> In the first embodiment, for Trichomonas protozoa (Trich), count information 70 relating to the number of Trichomonas protozoa is acquired from the detection data (SF) of the first measurement sample and the detection data (CR) of the second measurement sample.

[0057] (a) Detection data (SF) of the first measurement sample As shown in Figures 8(A) to (C), Trichomonas protozoa (Trich) can be distinguished from other formed elements by a combination of multiple distribution data (61a, 61b, 61c) in the detection data (SF) of the first measurement sample.

[0058] 8(A) is data of SF_DSSP×FSCP. That is, region R6 of distribution data 61a of the peak intensity of the depolarized side scattered light signal (DSSP) and the peak intensity of the forward scattered light signal (FSCP) in the detection data (SF) of the first measurement sample is set as the first detection region for Trichomonas protozoa (Trich).

[0059] 8(B) is data of SF_FLLP×FSCP. That is, region R7 of distribution data 61b of the peak intensity (FLLP) of the side fluorescence signal and the peak intensity (FSCP) of the forward scattered light signal at normal sensitivity and low amplification factor in the detection data (SF) of the first measurement sample is set as the second detection region for Trichomonas protozoa (Trich).

[0060] The distribution data 61c shown in FIG. 8(C) is SF_FLHP×FSCP data. That is, region R8 of the distribution data 61c of the peak intensity (FLHP) of the side fluorescence signal and the peak intensity (FSCP) of the forward scattered light signal at normal sensitivity and high amplification factor in the detection data (SF) of the first measurement sample is set as the third detection region for Trichomonas protozoa (Trich). In the distribution data 61a in FIG. 8(A), region R6 is close to the distribution region of red blood cells (RBC). In the distribution data 61c shown in FIG. 8(C), the distribution region of red blood cells (RBC) and region R8 are clearly separated. By considering the distribution data 61c, Trichomonas protozoa and red blood cells can be clearly distinguished.

[0061] In the first embodiment, particles plotted in the detection data (SF) of the first measurement sample in region R6 of distribution data 61a, region R7 of distribution data 61b, and region R8 of distribution data 61c are counted as Trichomonas protozoa detected from the first measurement sample. The obtained count value is defined as first count information 70a (see FIG. 13).

[0062] (b) Detection data of the second measurement sample: CR_FSCW × FLHP The distribution data 61d shown in Figure 8(D) is CR_FSCW x FLHP data. That is, particles plotted in region R9 of distribution data 60d of the pulse width (FSCW) of the forward scattered light signal and the peak intensity (FLHP) of the side fluorescence signal with normal sensitivity and high amplification factor in the detection data (CR) of the second measurement sample are counted as Trichomonas protozoa detected from the second measurement sample. The obtained count value is defined as second count information 70b. Region R9 may include not only Trichomonas protozoa but also epithelial cells (EC).

[0063] With the above configuration, the analysis section 20 acquires count information for each type of formed component in the urine sample 5 based on the detection data acquired from the measurement unit 10.

[0064] (Scattergram obtained by measuring an actual sample) Figure 9(A) is a scattergram of SF_DSSP×FSCP corresponding to Figure 8(A) obtained by measuring a Trichomonas-positive sample. Figure 9(B) is a scattergram of SF_FLLP×FSCP corresponding to Figure 8(B) obtained by measuring a Trichomonas-positive sample. As shown in Figures 9(A) and 9(B), the Trichomonas protozoan cluster (TRICH) and the white blood cell cluster (WBC) appear close to each other. This is due to the morphological similarity between Trichomonas protozoan and white blood cells. Furthermore, in the scattergram shown in Figure 9(A), the Trichomonas protozoan and the red blood cell (RBC) clusters are plotted far apart, but as the number of RBCs increases, the Trichomonas protozoan cluster and the RBC cluster become closer to each other.

[0065] Figure 10 shows a CR_FSCW×FLHP scattergram corresponding to Figure 8(D), obtained by measuring a Trichomonas-positive sample. As explained in the explanation of Figure 8(D), in this scattergram, Trichomonas protozoa appear within the area indicated by the square (see the dotted line), but epithelial cells also appear in the same area.

[0066] As such, because it is difficult to distinguish and detect Trichomonas protozoa from other formed elements, it is possible that not only Trichomonas protozoa but also other formed elements will appear in regions R6 to R9 shown in Figures 8(A) to 8(D). Therefore, if a suspected Trichomonas infection is determined based on the plots included in these regions R6 to R9, a so-called false positive may occur, in which particles that are not actually Trichomonas protozoa are detected as Trichomonas protozoa.

[0067] Figures 11(A) and 11(B) are scattergrams of samples confirmed to be Trichomonas positive. Figures 12(A) and 12(B) are scattergrams of samples not confirmed to be Trichomonas positive. Figures 11(A) and 12(A) are scattergrams of SF_DSSP×FSCP corresponding to Figure 8(A). Figures 11(B) and 12(B) are scattergrams of CR_SSHA×FSCW corresponding to Figure 7(C).

[0068] In both Figure 11(A) and Figure 12(A), a significant number of plots appear in region R6, where Trichomonas is present. In these two samples, plots similarly appeared in all of regions R6 to R9. Therefore, based solely on the number of plots in regions R6 to R9, a sample like that shown in Figure 12(A) may be determined to be Trichomonas positive.

[0069] As a result of research conducted by the present inventors, it was found that the number of squamous epithelial cells in urine is significantly increased in true-positive samples infected with Trichomonas protozoa, while the number of squamous epithelial cells is not necessarily increased in Trichomonas-negative samples. For example, referring to FIG. 11(B), squamous epithelial cells are plotted over a wide area in Trichomonas-positive samples. On the other hand, in FIG. 12(B), squamous epithelial cells are hardly plotted. Therefore, in the first embodiment, the accuracy of determining Trichomonas infection is improved by combining the first information on the number of squamous epithelial cells with the count information on the number of Trichomonas protozoa acquired from regions R6 to R9.

[0070] (Method for determining suspected trichomoniasis infection) Next, a method for determining whether a patient has a suspected trichomoniasis infection will be described with reference to Figures 13 and 14. As shown in Figure 13, the analysis unit 20 acquires count information 70 relating to the number of trichomoniasis protozoa and first information 71 comprising count information relating to the number of at least one of squamous epithelial cells and white blood cells as count information related to the determination of whether a patient has a suspected trichomoniasis infection. In the first embodiment, the analysis unit 20 further acquires second information 72 that is different from the first information 71.

[0071] <Counting information on Trichomonas protozoa numbers> In the first embodiment, the count information 70 regarding the number of Trichomonas protozoa includes first count information 70a regarding the number of Trichomonas protozoa detected from the urine specimen 5 treated with the first reagent 42 (i.e., the detection data (SF) of the first measurement sample), and second count information 70b regarding the number of Trichomonas protozoa detected from the urine specimen 5 treated with the second reagent 43 (i.e., the detection data (CR) of the second measurement sample). Hereinafter, the count value of the first count information 70a will be referred to as Xsf, and the count value of the second count information 70b will be referred to as Xcr.

[0072] In this way, by obtaining count information 70 (first count information 70a, second count information 70b) regarding the number of Trichomonas protozoa from samples treated with different reagents for the same urine specimen 5, it is possible to improve the accuracy of distinguishing Trichomonas protozoa from other formed components in the urine specimen 5. As a result, it is possible to effectively improve the reliability of the count information 70 regarding the number of Trichomonas protozoa.

[0073] As mentioned above, some or all of the particles counted as the first count information 70a and the second count information 70b may be formed elements other than Trichomonas protozoa. In this specification, "count information related to the number of Trichomonas protozoa" means the number of particles having specific characteristic parameters set by the urine sample analyzer to identify Trichomonas protozoa, that is, in the first embodiment, the number of particles that appear in regions R6 to R9 set to count Trichomonas protozoa, and does not necessarily mean the number of particles identified as Trichomonas protozoa.

[0074] <First information> In the first embodiment, the first information 71 includes count information 71a relating to the number of squamous epithelial cells. As described above, in a Trichomonas-positive sample, the number of squamous epithelial cells significantly increases along with the number of Trichomonas protozoa. In the first embodiment, the accuracy of determining whether or not the first information 71 relating to the number of squamous epithelial cells exceeds a threshold is determined in accordance with determination rule 1, which will be described later. In the first embodiment, count information 71b relating to the number of white blood cells is also used as the first information 71. However, the count information 71b relating to the number of white blood cells differs from the count information 71a in that it is used in determination rule 2, which will be described later, to determine whether white blood cells are present in urine in such an excessive amount that they interfere with the determination of Trichomonas infection. This increases the number of types of first information 71 that serve as an indicator of the reliability of the count information 70 relating to the number of Trichomonas protozoa. Hereinafter, the count value of the squamous epithelial cell count information 71a is referred to as Ysec, and the count value of the white blood cell count information 71b is referred to as Ywbc.

[0075] <Second information> The second information 72 includes information for suppressing false positives in determining whether a person has a suspected trichomoniasis infection. By taking the second information 72 into consideration, it is possible to suppress false positives in determining whether a person has a suspected trichomoniasis infection.

[0076] Specifically, the second information 72 includes count information on the number of other formed elements different from Trichomonas protozoa, squamous epithelial cells, and white blood cells. By taking the count information on the number of other formed elements into consideration as the second information 72, the reliability of the count information 70 on the number of Trichomonas protozoa can be evaluated based on the number of interfering substances that may cause a false positive determination.

[0077] The information for suppressing false positives in determining a suspected trichomoniasis infection includes count information regarding the number of at least one other formed component that interferes with the detection of the Trichomonas protozoan. These other formed components are present in the urine sample 5 together with the target component (here, the Trichomonas protozoan) and act as interfering substances that may change the count value of the target component. Therefore, when it is determined that an interfering substance is affecting the detection of the Trichomonas protozoan, information 80 regarding a suspected trichomoniasis infection can be output taking into account the influence of the interfering substance.

[0078] The at least one other formed element that interferes with the detection of Trichomonas protozoa includes at least one of red blood cells, bacteria, and yeast-like fungi. In the first embodiment, the second information 72 includes multiple types of these other formed elements. Specifically, the second information 72 includes red blood cell count information 72a, bacteria count information 72b, and yeast-like fungi count information 72c. This prevents the reliability of the information 80 regarding suspected Trichomonas infection from being reduced due to the presence of these interfering substances. Hereinafter, the count value of the red blood cell count information 72a will be referred to as Zrbc, the count value of the bacteria count information 72b will be referred to as Zbact, and the count value of the yeast-like fungi count information 72c will be referred to as Zylc.

[0079] The second information 72 also includes information relating to the distribution state of Trichomonas protozoa in the distribution data 60 that indicates the distribution of formed elements in the urine sample 5. This makes it possible to determine the reliability of the count information 70 relating to the number of Trichomonas protozoa based on how the Trichomonas protozoa are distributed in the distribution data 60. Therefore, by taking into account the information relating to the distribution state of Trichomonas protozoa, it is possible to accurately determine whether or not there is a Trichomonas infection.

[0080] The information on the distribution state of Trichomonas protozoa is, specifically, information 73 indicating the bias of the distribution of Trichomonas protozoa toward the side closer to the distribution area of ​​other formed elements within the distribution area R6 of Trichomonas protozoa.

[0081] 14 is an enlarged view of the periphery of the distribution region (region R6) of Trichomonas protozoa in the distribution data 61a used to acquire the first count information 70a of Trichomonas protozoa shown in FIG. 8(A). Region R6 is located below and to the right of the distribution data 61a (low FSCP and high DSSP), close to the distribution region Rw of white blood cells (WBC). Therefore, plots of formed elements that are actually white blood cells may be mixed into region R6 in the distribution data 61a. Therefore, in the first embodiment, the distribution regions of other formed elements include the distribution region Rw of white blood cells in the distribution data 61a.

[0082] To evaluate the possibility of white blood cells being mixed into region R6, a virtual line Lg is assumed in the distribution data 61a to indicate the upper limit of the FSCP of the white blood cell (WBC) distribution region Rw. The virtual line Lg indicates that if white blood cells were plotted in the distribution data 61a, they would be plotted below the virtual line Lg. Therefore, partial regions Rg1 and Rg2 are set by dividing region R6 by the virtual line Lg. Partial region Rg1 is the portion of region R6 farther from the white blood cell distribution region Rw, and partial region Rg2 is the portion of region R6 closer to the white blood cell distribution region Rw. If white blood cells are mixed into region R6, they may be plotted in partial region Rg2 below the virtual line Lg, but they are almost never plotted in partial region Rg1 above the virtual line Lg.

[0083] For this reason, if the plots belonging to region R6 appear disproportionately in partial region Rg2, there is a possibility that white blood cells have been mixed into region R6, and therefore the reliability of the first count information 70a counting the Trichomonas protozoa can be evaluated as low. Conversely, if the plots belonging to region R6 appear disproportionately in partial region Rg1, there is a low possibility that white blood cells have been mixed into region R6, and therefore the reliability of the first count information 70a can be evaluated as high.

[0084] Then, the analysis unit 20 acquires the distribution ratio Ztrich of the plots belonging to the distribution region R6 of the trichomonas protozoa from the distribution region Rw of the white blood cells to the subregion Rg1 as information 73 indicating the bias of the distribution of the trichomonas protozoa toward the side closer to the distribution region Rw of other formed elements within the distribution region R6 of the trichomonas protozoa.

[0085] The distribution ratio Ztrich is expressed as {number of plots belonging to the partial region Rg1 / (number of plots belonging to the partial region Rg2)+(number of plots belonging to the partial region Rg1)}. Note that {(number of plots belonging to partial region Rg2) + (number of plots belonging to partial region Rg1)} means all plots belonging to region R6. The lower the distribution ratio Ztrich, the more the plots belonging to region R6 appear biased toward partial region Rg2, and the higher the likelihood of white blood cell contamination is evaluated. The higher the distribution ratio Ztrich, the less the plots belonging to region R6 appear biased toward partial region Rg2, and the lower the likelihood of white blood cell contamination is evaluated.

[0086] (Decision rule) In the first embodiment, a determination rule regarding suspicion of Trichomonas infection is set by combining the above information, as shown in Figure 13. The determination rule is a rule for determining that the urine sample 5 is suspected of having Trichomonas infection (positive) based on the obtained detection data.

[0087] In the first embodiment, the analysis unit 20 acquires counting information 70 regarding the number of Trichomonas protozoa and first information 71 consisting of counting information regarding the number of at least one of squamous epithelial cells and white blood cells based on detection data of formed elements in the urine sample 5, and outputs information 80 regarding suspected Trichomonas infection based on at least the counting information 70 regarding the number of Trichomonas protozoa and the first information 71.

[0088] In urine samples 5 from patients suffering from trichomoniasis, the number of squamous epithelial cells and white blood cells appear significantly higher, along with the number of trichomonas protozoa. Therefore, if the numerical value of the count information acquired as first information 71 is high, it can be determined that the reliability of count information 70 regarding the number of trichomonas protozoa is high. Conversely, if the numerical value of the count information acquired as first information 71 is low, it can be determined that the reliability of count information 70 regarding the number of trichomonas protozoa is low. The reliability of count information 70 regarding the number of trichomonas protozoa can be improved by using first information 71 as an index of the reliability of count information 70 regarding the number of trichomonas protozoa.

[0089] In the first embodiment, three determination rules are set for determining whether or not there is a suspicion of trichomoniasis infection. Determination rule 1 is a determination rule for determining whether or not there is a suspicion of trichomoniasis infection based on count information 70 related to the number of trichomoniasis protozoa and first information 71. Specifically, determination rule 1 determines that there is a suspicion of trichomoniasis infection as information 80 related to the suspicion of trichomoniasis infection when count information 70 related to the number of trichomoniasis protozoa meets a condition and when count information belonging to the first information 71 meets a condition. Information indicating a suspicion of trichomoniasis infection is output when not only count information 70 related to the number of trichomoniasis protozoa meets the condition but also first information 71, which is an index of the reliability of count information 70 related to the number of trichomoniasis protozoa, meets the condition, thereby increasing the reliability of the information indicating a suspicion of trichomoniasis infection.

[0090] Determination rules 2 and 3 are determination rules that include the second information 72. In this way, in the first embodiment, information 80 regarding a suspected trichomoniasis infection is output based on the second information 72 in addition to the count information 70 regarding the number of trichomoniasis protozoa and the first information 71. This makes it possible to determine the reliability of the information 80 regarding a suspected trichomoniasis infection by further considering the second information 72 in addition to the first information 71, which is an indicator of the reliability of the count information 70 regarding the number of trichomoniasis protozoa.

[0091] Furthermore, determination rules 2 and 3 are determination rules that reduce the possibility of a false positive from the determination result based on determination rule 1, based at least on second information 72. Therefore, determination rules 2 and 3 are determination rules that prohibit the output of information indicating a suspicion of trichomoniasis infection when their respective conditions are met. In other words, even if determination rule 1 determines that there is a suspicion of trichomoniasis infection, if the conditions of either determination rule 2 or determination rule 3 are met, there is a possibility of a false positive, and therefore the result is determined to be non-positive (negative or indeterminable). However, for ease of understanding, the conditions for outputting a "suspected" determination of trichomoniasis infection for determination rule 2 and determination rule 3 will also be described below.

[0092] <Judgment rule 1> In the determination rule 1 in the example of FIG. 13, the first count information 70a, the second count information 70b, and the count information of squamous epithelial cells as the first information 71 are compared with the corresponding thresholds.

[0093] That is, determination rule 1 includes the following conditions (1) to (3). (1) The count value Xsf of the first count information 70a is greater than the threshold value N1 (Xsf>N1), (2) The count value Xcr of the second count information 70b is greater than the threshold value N2 (Xcr>N2). (3) The count value Ysec of the count information of squamous epithelial cells is greater than the threshold value N3 (Ysec>N3). If all of the conditions (1) to (3) of Judgment Rule 1 are met, a "suspected" judgment of Trichomonas infection is made according to Judgment Rule 1. If any of the three conditions of Judgment Rule 1 are not met, a "not suspected or undeterminable" judgment is made according to Judgment Rule 1.

[0094] The threshold values ​​N1 to N3 applied to determination rule 1 will be described later. As a basic concept, the threshold value N3 of the count value Ysec of the first information 71 is at least greater than the threshold value N1 of the count value Xsf of the first count information 70a under non-hemolytic conditions. This principle applies to the first and third modes described later.

[0095] That is, determination rule 1 includes comparing the count value X (Xsf) of the count information (first count information 70a) regarding the number of Trichomonas protozoa under non-hemolytic conditions (first measurement sample) with a first threshold value N1, and comparing the count value Y (Ysec) of the first information 71 with a second threshold value N3 that is greater than the first threshold value N1. If the conditions of count value X (Xsf) > threshold value N1 and count value Y (Ysec) > threshold value N3 are met, a determination is made that there is a suspicion of Trichomonas infection.

[0096] In other words, because Trichomonas protozoa appear in urine in small numbers, it is preferable to set a threshold (low threshold) that allows detection even when they are present in small quantities. However, because Trichomonas protozoa have a shape similar to white blood cells and red blood cells, setting the threshold low may increase the number of false positive urine samples 5. Therefore, by adding the first information 71 (whether squamous epithelial cells or white blood cells appear in urine) to the criteria for determining whether or not there is a suspected Trichomonas infection, the accuracy of the determination can be improved. However, because squamous epithelial cells and white blood cells can be found in urine even when there is no abnormality in the urinary tract, by making the second threshold (N3) for the count value of the first information 71 larger than the first threshold (N1) for the count value of Trichomonas protozoa, false positives can be reduced in cases where the count value of the first information 71 increases due to factors other than Trichomonas infection.

[0097] <Judgment rule 2> In the example of determination rule 2 in FIG. 13, red blood cell (RBC) count information 72a, bacteria (BACT) count information 72b, and yeast-like fungus (YLC) count information 72c are compared with the corresponding thresholds as second information 72. Furthermore, in determination rule 2, white blood cell (WBC) count information 71b is compared with the threshold as first information 71. This threshold comparison of first information 71 may be included in determination rule 1 above. As described above, first, the conditions for outputting a "suspected" determination of Trichomonas infection according to determination rule 2 are described.

[0098] That is, determination rule 2 includes the following conditions (4) to (7). (4) The white blood cell count value Ywbc is smaller than the threshold value N4 (Ywbc <N4)、 (5) The red blood cell count value Zrbc is smaller than the threshold value N5 (Zrbc <N5)、 (6) The bacterial count value Zbact is smaller than the threshold value N6 (Zbact <N6)、 (7) The count value Zylc of yeast-like fungi is smaller than the threshold value N7 (Zylc <N7)。 In Judgment Rule 2, if all conditions (4) to (7) are met, a "suspected" judgment of Trichomonas infection is made according to Judgment Rule 2. If any one of the four conditions (4) to (7) of Judgment Rule 2 is not met, a "not suspected or undetermined" judgment of Trichomonas infection is made.

[0099] Specific examples of the threshold values ​​in the first embodiment are N4=10000 / μL, N5=1000 / μL, N6=100000 / μL, and N7=300 / μL.

[0100] Therefore, the "condition for prohibiting output of a 'suspected' determination of trichomoniasis infection" in determination rule 2 is that one or more of conditions (4) to (7) are not satisfied. In this way, in the first embodiment, when the count information of any of the multiple types of formed elements matches the condition, output of information indicating a suspicion of trichomoniasis infection is prohibited as information 80 regarding the suspicion of trichomoniasis infection. In this way, when a large number of formed elements that hinder the identification of trichomoniasis protozoa are present, it becomes difficult to identify trichomoniasis protozoa, and therefore, when a determination regarding trichomoniasis infection is made based on count information 70 regarding the number of trichomoniasis protozoa, the determination result will be uncertain. Therefore, by determining whether each of the count information of the multiple types of interfering substances matches the condition and prohibiting output of a 'suspected' determination of trichomoniasis infection if any of the count information matches the condition, a decrease in the reliability of information 80 regarding the suspicion of trichomoniasis infection can be suppressed.

[0101] <Decision Rule 3> 13, the second information 72, which is information 73 indicating a bias in the distribution of Trichomonas protozoa toward the side closer to the distribution area of ​​other formed elements, is compared with a threshold value. As described above, first, the conditions for outputting a "suspected" determination of Trichomonas infection according to determination rule 3 are described.

[0102] That is, the determination rule 3 includes the following condition (8). (8) The distribution ratio Ztrich of Trichomonas protozoa belonging to the subregion Rg2 is greater than the threshold N8 (Ztrich>N8). (where Ztrich = {number of plots belonging to subregion Rg1 / (number of plots belonging to subregion Rg2)+(number of plots belonging to subregion Rg1)}). If condition (8) is satisfied, a "suspected" determination of Trichomonas infection is made according to determination rule 3. If condition (8) of determination rule 3 is not satisfied, a "not suspected or undeterminable" determination of Trichomonas infection is made. A specific example of the threshold value in the first embodiment is N8 = 0.1.

[0103] Therefore, the "condition for prohibiting the output of a 'suspected' determination of trichomoniasis infection" in determination rule 3 is that condition (8) is not satisfied. As described above, in the first embodiment, when the distribution data 61a indicates that the distribution of trichomoniasis protozoa is biased toward the side of the distribution region R6 of trichomoniasis protozoa (see FIG. 14) closer to the distribution region Rw of other formed elements (white blood cells), the output of information indicating a suspicion of trichomoniasis infection is prohibited as information 80 regarding the suspicion of trichomoniasis infection. This makes it possible to prevent unreliable information from being output when the obtained distribution data 61a indicates that other formed elements similar in size, shape, and other characteristics to trichomoniasis protozoa are likely to be mixed in the distribution region (region R6) of trichomoniasis protozoa.

[0104] Furthermore, as described above, the distribution region of other formed elements includes the distribution region Rw of white blood cells in the distribution data 60. This makes it possible to prevent unreliable information from being output in consideration of the possibility of white blood cell contamination when white blood cells similar in size, shape, and other characteristics to those of Trichomonas protozoa appear mixed in the distribution region of Trichomonas protozoa.

[0105] <Specific examples of thresholds N1 to N3 for decision rule 1> In the first embodiment, it is possible to accept a mode selection from a plurality of modes with different threshold setting values ​​for the threshold values ​​N1 to N3 (see FIG. 13) used in determination rule 1. The analysis unit 20 accepts the mode selection by an input operation via the input unit 20b (see FIG. 1).

[0106] 15, the plurality of modes include a first mode, a second mode, and a third mode. Each of the plurality of modes has a threshold set that defines thresholds N1, N2, and N3. The threshold set for each of the plurality of modes is stored in advance in the storage device 22.

[0107] The first mode, which is the standard mode, has a first threshold set 75a. The first threshold set 75a includes thresholds N1=1.0 / μL, N2=5.0 / μL, and N3=5.0 / μL. When the first mode is selected, the analysis unit 20 applies the first threshold set 75a as the thresholds N1 and N2 for the count information 70 regarding the number of Trichomonas protozoa and as the threshold N3 for the first information 71.

[0108] The second mode, which is a sensitivity-oriented mode, has a second threshold set 75b including thresholds set to values ​​lower than those of the first threshold set 75a. The second threshold set 75b includes thresholds N1 = 1.0 / μL, threshold N2 = 1.0 / μL, and threshold N3 = 1.0 / μL. The second threshold set 75b has smaller thresholds N2 and N3 than the first threshold set 75a. In response to the selection of the second mode, the analysis unit 20 applies the second threshold set 75b as the thresholds N1 and N2 for the count information 70 regarding the number of Trichomonas protozoa and the threshold N3 for the first information 71.

[0109] The third mode, which is a specificity-oriented mode, has a third threshold set 75c including thresholds set to higher values ​​than the first threshold set 75a. The third threshold set 75c includes a threshold N1 of 10.0 / μL, a threshold N2 of 20.0 / μL, and a threshold N3 of 20.0 / μL. The third threshold set 75c has higher values ​​for the thresholds N1, N2, and N3 than the first threshold set 75a. When the third mode is selected, the analysis unit 20 applies the third threshold set 75c as the thresholds N1 and N2 for the count information 70 related to the number of Trichomonas protozoa and the threshold N3 for the first information 71.

[0110] Thus, the second threshold set 75b and the third threshold set 75c are set to have higher sensitivity or specificity for suspected Trichomonas infection than the first threshold set 75a.

[0111] As described above, determination rule 1 determines that a patient is suspected of having a trichomoniasis infection if the count value of the count information 70 regarding the number of trichomoniasis protozoa and the count value of the first information 71 are both equal to or greater than the applied thresholds N1, N2, and N3. Therefore, by providing multiple modes with different threshold sets, the user can proactively select a mode to determine whether or not a patient is suspected of having a trichomoniasis infection. Therefore, for example, by selecting threshold set 75b, which has a higher sensitivity (lower threshold) for determining whether or not a patient is suspected of having a trichomoniasis infection, from among first threshold set 75a and second threshold set 75b, information that contributes to earlier detection of a trichomoniasis infection can be provided. For example, by selecting threshold set 75a, which has a higher specificity (higher threshold) for determining whether or not a patient is suspected of having a trichomoniasis infection, from among first threshold set 75a and second threshold set 75b, information that further reduces the possibility of a false positive for a trichomoniasis infection can be provided.

[0112] In a configuration that provides a standard first mode (first threshold set 75a), a sensitivity-oriented second mode (second threshold set 75b), and a specificity-oriented third mode (third threshold set 75c), the user can simply select the mode to switch between the standard first mode, the second mode that emphasizes sensitivity and contributes to early detection, and the third mode that emphasizes specificity and prioritizes the reliability of the analysis results. This makes it possible to provide information about suspected trichomoniasis infection that meets the user's needs.

[0113] (Information about suspected trichomoniasis infection) Fig. 16 is a schematic diagram showing an example of a screen on which the display unit 20c displays the analysis results of the analysis unit 20. As shown in Fig. 16, the analysis unit 20 outputs information 80 related to suspected Trichomonas infection to the display unit 20c. That is, the analysis unit 20 causes the display unit 20c to display an analysis result screen 81 including information 80 related to suspected Trichomonas infection.

[0114] The analysis result screen 81 is a screen that displays the detection results and analysis results for one urine sample 5 that has been analyzed. It includes a count information display field 82 and an information display field 83. The count information display field 82 displays count information, which is the detection result of formed elements detected in the urine sample 5, individually for each type of formed element. In the example of FIG. 16, the count information display field 82 includes an item field 82a that displays the item names of red blood cells (RBC), white blood cells (WBC), epithelial cells (EC), squamous epithelial cells (SquaEC), non-squamous epithelial cells (NonSEC), casts (CAST), BACT (bacteria), and yeast-like fungi (YLC), and a result field 82b that displays the count values ​​of the formed elements displayed in the item field 82a. The rightmost field of the count information display field 82 displays the unit of the count value. Note that specific examples of the counting methods for epithelial cells (EC), non-squamous epithelial cells (NonSEC), and casts (CAST) shown in item column 82a will not be explained in this specification, and the numerical values ​​in result column 82b will not be shown.

[0115] The information display field 83 displays information regarding suspicion of various diseases, etc., determined based on the count information of formed elements detected from the urine sample 5. In the first embodiment, information 80 regarding suspicion of Trichomonas infection is displayed in this information display field 83.

[0116] In the first embodiment, the information 80 regarding the suspicion of trichomoniasis infection includes information indicating that there is a suspicion of trichomoniasis infection. When the detection data of formed elements in the urine sample 5 satisfies all of the conditions of the above-mentioned determination rules 1 to 3 (see FIG. 13), the analysis unit 20 outputs information indicating that there is a suspicion of trichomoniasis infection to the information display field 83.

[0117] 16, the display "Trich," which indicates trichomoniasis as an analysis item, and the display "Trichomonas?", which indicates that there is a suspicion of trichomoniasis, are displayed in the information display field 83. As a result, information indicating that there is a suspicion of trichomoniasis regarding the analysis item of trichomoniasis is provided to the user.

[0118] In the first embodiment, if the detection data for formed elements in the urine sample 5 does not satisfy any of the above-mentioned determination rules 1 to 3, the analysis unit 20 does not output information 80 regarding suspected Trichomonas infection to the information display field 83. In other words, in this case, neither "Trich" nor "Trichomonas?" is displayed in the information display field 83.

[0119] In addition, if the detection data for formed elements in the urine sample 5 does not satisfy any of the above-mentioned determination rules 1 to 3, the analysis unit 20 may output information indicating that there is no suspicion of Trichomonas infection or that it is impossible to determine the presence of Trichomonas infection to the information display field 83.

[0120] (Urine sample analyzer operation) The operation of the urine sample analyzer 100 of the first embodiment will be described below with reference to FIGS.

[0121] Figure 17 is a flowchart showing the flow of the sample measurement process of the urine sample analyzer 100. Control process of the analysis section 20 is executed by the processor 21. Control process of the measurement unit 10 is executed by the microcomputer 51. In the following description, for each part of the urine sample analyzer 100, please refer to Figures 1 to 5.

[0122] First, in step S1, the processor 21 of the analysis unit 20 receives an instruction to perform measurement from the user through an input operation via the input unit 20b. The processor 21 also receives a mode selection for identifying a threshold set from among the first to third modes shown in FIG. 15 through an input operation via the input unit 20b. The processor 21 stores information identifying the selected mode in the storage device 22. The mode selection may be received at any time before the measurement is performed. In step S2, the processor 21 transmits instruction data to the measurement unit 10 to instruct it to start measurement.

[0123] In step S11, the microcomputer 51 receives instruction data to start measurement from the analysis section 20. Upon receiving the instruction data to start measurement, the microcomputer 51 operates the measurement unit 10 to execute a measurement sample preparation process (step S12), a first measurement sample (SF) measurement process (step S13), and a second measurement sample (CR) measurement process (step S14). Detection data for one urine specimen 5 is generated by the first measurement sample (SF) measurement process and the second measurement sample (CR) measurement process. The detection data is stored in memory 50e.

[0124] In step S15, the microcomputer 51 transmits the detection data (SF) and the detection data (CR) stored in the memory 50e to the analysis unit 20.

[0125] In step S3, the processor 21 of the analysis section 20 receives the detection data transmitted from the measurement unit 10.

[0126] In step S4, processor 21 executes counting processing of the particles based on the detection data. That is, processor 21 classifies the signals of individual particles into types of particles from the detection data using the combinations of feature parameters shown in Figures 7 and 8, and generates count information for each particle.

[0127] In step S5, processor 21 acquires the count information 70 (first count information 70a, second count information 70b) and information (first information 71, second information 72) of Trichomonas protozoa generated in step S4.

[0128] In step S6, the mode selection information received in step S1 is obtained from the storage device 22, and thresholds N1 to N3 defined in the threshold set corresponding to the selected mode from among threshold sets 75a to 75c (see FIG. 15) are set.

[0129] In step S7, processor 21 executes a determination process regarding trichomoniasis infection based on the acquired trichomonas protozoan count information 70 (first count information 70a, second count information 70b), first information 71, and second information 72. The determination process is executed based on determination rules 1, 2, and 3 shown in Fig. 13. As a result of the determination process, processor 21 generates information 80 regarding suspicion of trichomoniasis infection.

[0130] In step S8, the processor 21 outputs information relating to the suspicion of Trichomonas infection 80. With the above, the sample measurement process for one urine sample 5 is completed.

[0131] <Measurement sample preparation process> The measurement sample preparation process shown in step S12 of Figure 17 will now be described. The measurement sample preparation process is executed by the microcomputer 51 of the measurement unit 10. In the following description, the components of the urine sample analyzer 100 will be described with reference to Figures 1 to 5.

[0132] In step S12a of Figure 18, the microcomputer 51 controls the sample distribution unit 45 to cause the aspirating tube 46 to aspirate a predetermined amount of urine sample 5 from the sample container 14 and dispense a predetermined amount of urine sample 5 into each of the first mixing unit 41a and the second mixing unit 41b.

[0133] In step S12b, the microcomputer 51 controls the sample preparation unit 40 to dispense a predetermined amount of the first reagent 42 (first staining solution 42a and first diluent 42b) into the first mixing unit 41a. In step S12c, the microcomputer 51 controls the sample preparation unit 40 to dispense a predetermined amount of the second reagent 43 (second staining solution 43a and second diluent 43b) into the second mixing unit 41b.

[0134] The first mixing section 41a and the second mixing section 41b are each heated to a predetermined temperature by a heater (not shown). In this state, in step S12d, the microcomputer 51 controls the sample preparation section 40 to stir the mixture in each mixing section using a propeller-shaped stirrer (not shown).

[0135] As a result, a first measurement sample for measuring the nucleus-free component is prepared in the first mixing section 41a, and a second measurement sample for measuring the nucleus-containing component is prepared in the second mixing section 41b. When the processing of step S12d is completed, the microcomputer 51 returns the processing to the main routine of FIG.

[0136] <First measurement sample (SF) measurement process> The first measurement sample (SF) measurement process shown in step S13 of Figure 17 will be described. The first measurement sample (SF) measurement process is executed by the microcomputer 51 of the measurement unit 10. In the following description, the components of the urine sample analyzer 100 will be described with reference to Figures 1 to 5.

[0137] 19, the microcomputer 51 supplies the first measurement sample together with the sheath liquid from the first mixing unit 41a to the flow cell 31. The microcomputer 51 drives a compressor (not shown) to send the sheath liquid to the flow cell 31. While the sheath liquid is being supplied to the flow cell 31, the microcomputer 51 drives a compressor (not shown) to supply the first measurement sample from the first mixing unit 41a to the flow cell 31. This forms a sample flow of the first measurement sample surrounded by the sheath liquid in the flow cell 31.

[0138] In step S13b, the microcomputer 51 controls the detection unit 30 to irradiate the sample flow formed in the flow cell 31 with a laser beam from the light source 32. Each time a particle passes through the beam spot formed in the flow cell 31, forward scattered light, fluorescence, and side scattered light are generated. In step S13c, the forward scattered light, fluorescence, a portion of the side scattered light, and another portion of the side scattered light that passed through the polarizing filter 36 are received by the light receiving units 33a, 33b, 33c, and 33d. The light incident on each of the light receiving units 33a, 33b, 33c, and 33d is converted into an electrical signal, and each of the light receiving units 33a, 33b, 33c, and 33d outputs FSC, FL, SSC, and DSS. The output FSC, FL, SSC, and DSS are amplified by the amplifier circuit 50a. FL becomes FLL and FLH depending on the difference in amplification factor.

[0139] The FSC, FLH, FLL, SSC, and DSS signals amplified by the amplifier circuit 50a are filtered by the filter circuit 50b, converted into digital signals by the A / D converter 50c, and then processed by the digital signal processing circuit 50d. As a result, for each particle passing through the flow cell 31, signal data such as a forward scattered light signal (FSC), a side scattered light signal (high sensitivity) (SSH), a side scattered light signal (low sensitivity) (SSL), a high-sensitivity fluorescent light signal (FLH), a low-sensitivity fluorescent light signal (FLL), and a depolarized side scattered light signal (DSS) are extracted, along with characteristic parameters consisting of feature quantities (peak intensity P, pulse width W, pulse area A) for each signal data. As a result, in step S13d, detection data (SF) including the characteristic parameters for each particle is stored in memory 50e, and the measurement process for the first measurement sample (SF) is completed.

[0140] <Second measurement sample (CR) measurement process> The second measurement sample (CR) measurement process shown in step S14 of Figure 17 will be described. The second measurement sample (CR) measurement process is executed by the microcomputer 51 of the measurement unit 10. In the following description, the components of the urine sample analyzer 100 will be described with reference to Figures 1 to 5.

[0141] 20, the microcomputer 51 supplies the second measurement sample together with the sheath liquid from the second mixing unit 41b to the flow cell 31. The microcomputer 51 drives a compressor (not shown) to send the sheath liquid to the flow cell 31. While the sheath liquid is being supplied to the flow cell 31, the microcomputer 51 drives a compressor (not shown) to supply the second measurement sample from the second mixing unit 41b to the flow cell 31. This forms a sample flow of the second measurement sample surrounded by the sheath liquid in the flow cell 31.

[0142] In step S14b, the microcomputer 51 controls the light source 32 of the detection unit 30 to irradiate the sample flow formed in the flow cell 31 with a laser beam from the light source 32. Each time a particle passes through the beam spot formed in the flow cell 31, forward scattered light, fluorescence, and side scattered light are generated. In step S14c, the forward scattered light, fluorescence, a portion of the side scattered light, and another portion of the side scattered light that has passed through the polarizing filter 36 are received by the light receiving units 33a to 33d. In step S14c, the microcomputer 51 operates the light receiving unit 33b for fluorescence at normal sensitivity. The light incident on each of the light receiving units 33a to 33d is converted into an electrical signal, and each of the light receiving units 33a to 33d outputs FSC, FL (normal sensitivity), SSC, and DSS. The output FSC, FL, SSC, and DSS are amplified by the amplifier circuit 50a. For FL (normal sensitivity), it becomes FLL and FLH depending on the amplification factor.

[0143] The FSC, FLH, FLL, SSC, and DSS amplified by the amplifier circuit 50a are filtered by the filter circuit 50b, converted into digital signals by the A / D converter 50c, and then subjected to signal processing by the digital signal processing circuit 50d. As a result, for each particle passing through the flow cell 31, signal data such as a forward scattered light signal (FSC), a side scattered light signal (high sensitivity) (SSH), a side scattered light signal (low sensitivity) (SSL), a high-sensitivity fluorescent light signal (FLH), a low-sensitivity fluorescent light signal (FLL), and a depolarized side scattered light signal (DSS) are extracted, along with feature parameters consisting of feature quantities (peak intensity P, pulse width W, pulse area A) for each signal data. As a result, in step S14d, detection data (CR) including the feature parameters for each particle is stored in memory 50e.

[0144] In steps S14e to S14g, bacteria detection data is collected by operating the light receiving unit 33b at a high sensitivity setting for bacteria measurement. In step S14e, when a predetermined time has elapsed since the second measurement sample began to be supplied to the flow cell 31, the microcomputer 51 changes the sensitivity of the light receiving unit 33b to the high sensitivity setting for bacteria measurement.

[0145] In step S14f, the microcomputer 51 causes the measurement unit 10 to measure the second measurement sample with the sensitivity of the light-receiving unit 33b set to the high sensitivity setting for bacteria measurement. The measurement operation is the same as in step S14c. As a result, FL(B) is output from the light-receiving unit 33b at the high sensitivity setting for bacteria measurement, and the other light-receiving units 33a, 33c, and 33d each output signals (FSC, SSC, DSS) at the same sensitivity setting as in step S14c. The signals FSC, FL(B), SSC, and DSS are amplified by the amplifier circuit 50a. The FL(B) output from the high-sensitivity light-receiving unit 33b is amplified by the amplifier circuit 50a at the same amplification factor as the high amplification factor, and a high-sensitivity fluorescent signal (FLH(B)) is acquired.

[0146] The amplified FSC, FLH(B), SSH, SSL, and DSS are filtered by the filter circuit 50b, then converted into digital signals by the A / D converter 50c, and subjected to predetermined signal processing by the digital signal processing circuit 50d. By the signal processing, FLH(B)P is extracted from FLH(B) and FSCP is extracted from FSC as characteristic parameters. In step S14g, the data of the characteristic parameters extracted for each particle is stored in the memory 50e as detection data (CR). When the above processing is completed, the microcomputer 51 returns the processing to the main routine.

[0147] From the detection data (SF) of the first measurement sample and the detection data (CR) of the second measurement sample obtained as described above, in step S4 (see FIG. 17), based on combinations of two or more preset characteristic parameters for each type of formed component, the processor 21 acquires count information.

[0148] (Determination process regarding suspected trichomonas infection) Next, the details of the determination process regarding trichomonas protozoa count information and trichomonas infection shown in step S7 of FIG. 17 will be described. The determination process regarding trichomonas infection is executed by the processor 21 of the analysis unit 20. In the example shown in FIG. 21, the processing is executed in the order of determination rule 2, determination rule 1, and determination rule 3. In the following description, reference is made to FIGS. 1 to 5, FIGS. 13, and FIGS. 15 for each part of the urine sample analyzer 100.

[0149] In step S7a, the processor 21 performs a determination process based on determination rule 2. That is, the processor 21 determines whether all of the conditions (4) Ywbc < N4, (5) Zrbc < N5, (6) Zbact < N6, and (7) Zylc < N7 are satisfied.

[0150] If all of the conditions (4) to (7) are satisfied, processor 21 proceeds to step S7b. If one or more of the conditions (4) to (7) are not satisfied, processor 21 proceeds to step S7e, where it generates a flag indicating "negative or indeterminable" for the suspected Trichomonas infection and stores it in storage device 22.

[0151] In step S7b, processor 21 performs a determination process based on determination rule 1. That is, processor 21 determines whether or not all of the following conditions are satisfied: (1) Xsf>N1, (2) Xcr>N2, and (3) Ysec>N3. The threshold values ​​N1 to N3 are the values ​​of the threshold set set in step S6 as described above.

[0152] If all of the conditions (1) to (3) are satisfied, processor 21 proceeds to step S7c. If one or more of the conditions (1) to (3) are not satisfied, processor 21 proceeds to step S7e, where it generates a flag indicating "negative or indeterminable" for the suspected Trichomonas infection and stores it in storage device 22.

[0153] In step S7c, processor 21 performs a determination process based on determination rule 3. That is, processor 21 determines whether or not the condition (8) Ztrich>N8 is satisfied.

[0154] If all of the conditions in (8) are satisfied, the processor 21 proceeds to step S7d. If the conditions in (8) are not satisfied, the processor 21 proceeds to step S7e, where it generates a flag indicating "negative or indeterminable" for the suspected Trichomonas infection and stores it in the storage device 22.

[0155] In step S7d, the processor 21 generates a positive flag indicating a “suspected” Trichomonas infection and stores it in the storage device 22.

[0156] When a positive flag is stored in the storage device 22 in step S7d, or when a "negative or indeterminable" flag is stored in the storage device 22 in step S7e, the processor 21 returns the process to the main routine. Note that, although the example shown in Fig. 21 shows an example in which the determination process is executed in the order of determination rule 2, determination rule 1, and determination rule 3, the determination order of determination rule 2, determination rule 1, and determination rule 3 is arbitrary. Either determination rule 1 or determination rule 3 may be executed first, and the determination result will not change regardless of the order in which they are executed.

[0157] (Output processing of information regarding suspected trichomoniasis infection) Next, details of the output process of information related to suspected Trichomonas infection shown in step S8 of Fig. 17 will be described with reference to Fig. 22. The output process of information related to suspected Trichomonas infection is executed by the processor 21 of the analysis unit 20.

[0158] In step S8a, processor 21 determines whether or not a positive flag for suspected Trichomonas infection is stored in storage device 22 in the detection result of this urine sample 5. That is, if a positive flag is generated and stored in storage device 22 in step S7d of the determination process of Fig. 21 (Yes in step S8a), processor 21 proceeds to step S8b. On the other hand, if a "negative or indeterminable" flag is generated and stored in storage device 22 in step S7e of the determination process of Fig. 21 (No in step S8a), processor 21 proceeds to step S8c.

[0159] In step S8b, processor 21 outputs analysis result screen 81 (see FIG. 16) to display unit 20c. Processor 21 generates analysis result screen 81 including information 80 related to suspected trichomoniasis infection along with the count information for each type of formed element generated in step S4, and outputs this to display unit 20c. As shown in FIG. 16, as a result, information display field 83 of analysis result screen 81 displays information indicating that there is a suspicion of trichomoniasis infection (analysis item "Trich" and "Trichomonas?" indicating that there is a suspicion of trichomoniasis infection).

[0160] In step S8c, processor 21 outputs analysis result screen 81 to display unit 20c. Processor 21 generates analysis result screen 81 that includes the count information for each type of formed element generated in step S4 but does not include information 80 related to suspected trichomoniasis infection, and outputs it to display unit 20c. Therefore, in analysis result screen 81 output in step S8c, information related to suspected trichomoniasis infection is not displayed in information display field 83, unlike the display mode in FIG.

[0161] As described above, the urine sample analysis method according to the first embodiment acquires count information 70 regarding the number of Trichomonas protozoa and first information 71 consisting of count information regarding the number of squamous epithelial cells and / or white blood cells based on detection data of formed elements in the urine sample 5 (S5). Even in the early stages of Trichomonas infection, the number of squamous epithelial cells and white blood cells appear significantly increased in addition to Trichomonas protozoa in a urine sample 5 from a patient suffering from Trichomonas infection. Therefore, if the numerical value of the count information acquired as the first information 71 is high, it can be determined that the reliability of the count information 70 regarding the number of Trichomonas protozoa is high. Conversely, if the numerical value of the count information acquired as the first information 71 is low, it can be determined that the reliability of the count information 70 regarding the number of Trichomonas protozoa is low. Therefore, the urine sample analyzing method according to the first embodiment outputs information 80 regarding a suspected trichomoniasis infection based on at least the count information 70 regarding the number of trichomoniasis protozoa and the first information 71 (S7 and S8). This allows the reliability of the count information 70 regarding the number of trichomoniasis protozoa to be improved even from detection data of a urine sample 5 that may contain not only trichomoniasis protozoa but also other formed elements with morphological characteristics similar to those of trichomoniasis protozoa, based on the first information 71, which serves as an indicator of the reliability of the count information 70 regarding the number of trichomoniasis protozoa. As a result, even when the number of formed elements identified as trichomoniasis protozoa in the urine sample 5 is small, the suspicion of trichomoniasis infection can be accurately determined based on the count information 70 regarding the number of trichomoniasis protozoa and the first information 71. In other words, the suspicion of trichomoniasis infection can be accurately determined even at an early stage of trichomoniasis infection.

[0162] (Other embodiments) Next, another embodiment different from the first embodiment will be described.

[0163] For example, in the first embodiment described above, an example was shown in which information 80 regarding suspicion of trichomoniasis infection was output using three determination rules, but the present invention is not limited to this. In the present invention, it is not necessary to provide three determination rules. Below, an embodiment of a method for determining suspicion of trichomoniasis infection will be described.

[0164] Second Embodiment Fig. 23 shows a specific example of the determination process regarding suspicion of trichomoniasis infection shown in step S7 of Fig. 17, and is a second embodiment executed in place of the flow (S7a to 7e) of the first embodiment shown in Fig. 21. In the second embodiment, the determination process regarding suspicion of trichomoniasis infection is performed only by step S7b, which corresponds to determination rule 1.

[0165] In step S7b, processor 21 performs a determination process based on determination rule 1. That is, processor 21 determines whether or not all of the following conditions (1) to (3) are satisfied. (1)Xsf>N1, (2)Xcr>N2, (3)Ysec>N3

[0166] If all of the conditions (1) to (3) are satisfied, processor 21 proceeds to step S7d. If one or more of the conditions (1) to (3) are not satisfied, processor 21 proceeds to step S7e, where it generates a flag indicating "negative or indeterminable" for the suspected Trichomonas infection and stores it in storage device 22.

[0167] In step S7d, processor 21 generates a positive flag indicating "suspected" of Trichomonas infection and stores it in memory device 22. When the positive flag is stored in memory device 22 in step S7d, or when a "negative or indeterminable" flag is stored in memory device 22 in step S7e, processor 21 returns the process to the main routine.

[0168] As in this second embodiment, in the present invention, it is sufficient to output information 80 regarding suspected trichomoniasis infection based on at least counting information 70 regarding the number of trichomoniasis protozoa and first information 71, and there is no need to acquire and analyze second information 72.

[0169] Third Embodiment FIG. 24 is a specific example of the determination process regarding the suspicion of trichomonas infection shown in step S7 of FIG. 17, and is the third embodiment executed instead of the flow (S7a to 7e) of the first embodiment shown in FIG. 21. In the third embodiment, the determination process regarding the suspicion of trichomonas infection is performed by two steps, step S7b corresponding to determination rule 1 and step S7a corresponding to determination rule 2.

[0170] In step S7a, the processor 21 performs a determination process based on determination rule 2. That is, the processor 21 determines whether all of the conditions (4) Ywbc < N4, (5) Zrbc < N5, (6) Zbact < N6, and (7) Zylc < N7 are satisfied.

[0171] If all of the conditions (4) to (7) are satisfied, the processor 21 proceeds to step S7b. If one or more of the conditions (4) to (7) are not satisfied, the processor 21 proceeds to step S7e and generates a flag of "negative or undeterminable" regarding the suspicion of trichomonas infection and stores it in the storage device 22.

[0172] In step S7b, the processor 21 performs a determination process based on determination rule 1. That is, the processor 21 determines whether all of the conditions (1) Xsf > N1, (2) Xcr > N2, and (3) Ysec > N3 are satisfied.

[0173] If all of the conditions (1) to (3) are satisfied, the processor 21 proceeds to step S7d. If at least one of the conditions (1) to (3) is not satisfied, the processor 21 proceeds to step S7e and generates a flag of "negative or undeterminable" regarding the suspicion of trichomonas infection and stores it in the storage device 22.

[0174] In step S7d, processor 21 generates a positive flag indicating "suspected" of Trichomonas infection and stores it in memory device 22. When the positive flag is stored in memory device 22 in step S7d, or when a "negative or indeterminable" flag is stored in memory device 22 in step S7e, processor 21 returns the process to the main routine.

[0175] Fourth Embodiment Fig. 25 shows a specific example of the determination process regarding a suspicion of trichomoniasis infection shown in step S7 of Fig. 17, and is a fourth embodiment executed instead of the flow (S7a to 7e) of the first embodiment shown in Fig. 21. In the fourth embodiment, the determination process regarding a suspicion of trichomoniasis infection is performed by step S7b corresponding to determination rule 1 and step S7c corresponding to determination rule 3.

[0176] In step S7b, processor 21 performs a determination process based on determination rule 1. That is, processor 21 determines whether or not all of the following conditions are satisfied: (1) Xsf>N1, (2) Xcr>N2, and (3) Ysec>N3.

[0177] If all of the conditions (1) to (3) are satisfied, processor 21 proceeds to step S7c. If one or more of the conditions (1) to (3) are not satisfied, processor 21 proceeds to step S7e, where it generates a flag indicating "negative or indeterminable" for the suspected Trichomonas infection and stores it in storage device 22.

[0178] In step S7c, processor 21 determines whether condition (8) Ztrich>N8 is met, based on determination rule 3. If condition (8) is met, processor 21 proceeds to step S7d. If condition (8) is not met, processor 21 proceeds to step S7e, where it generates a flag indicating "negative or indeterminable" for the suspected Trichomonas infection and stores it in storage device 22.

[0179] In step S7d, processor 21 generates a positive flag indicating "suspected" of Trichomonas infection and stores it in memory device 22. When the positive flag is stored in memory device 22 in step S7d, or when a "negative or indeterminable" flag is stored in memory device 22 in step S7e, processor 21 returns the process to the main routine.

[0180] (Modification of Judgment Rule 1) In the first embodiment described above, an example of determination rule 1 based on count information 70 regarding the number of Trichomonas protozoa and count information 71a of squamous epithelial cells (see FIG. 13) as first information 71 has been described, but the present invention is not limited to this. In the present invention, as shown in FIG. 26, determination rule 1A based on count information 71b of white blood cells may be set instead of count information 71a of squamous epithelial cells.

[0181] Determination rule 1A includes the following conditions (1), (2), and (3A). (1) The count value Xsf of the first count information 70a is greater than the threshold value N1 (Xsf>N1), (2) The count value Xcr of the second count information 70b is greater than the threshold value N2 (Xcr>N2). (3A) The count value Ywbc of the white blood cell count information 71b is greater than the threshold value N11 (Ywbc>N11). The analysis unit 20 makes a "suspected" determination of Trichomonas infection according to the determination rule 1A when all of the conditions (1), (2), and (3A) of the determination rule 1A are satisfied. If one or more of the three conditions of the determination rule 1A are not satisfied, the analysis unit 20 makes a "not suspected or undeterminable" determination according to the determination rule 1A.

[0182] Specific examples of the threshold values ​​in determination rule 1A are N1=1.0 / μL, N2=5.0 / μL, and N11=25.0 / μL. Therefore, the determination conditions (1), (2), and (3) in step S7b shown in Figures 21, 23, 24, and 25 may be replaced with the conditions (1), (2), and (3A) in determination rule 1A.

[0183] For example, Figure 26 shows an example in which step S7b of the determination processing flow regarding suspicion of Trichomonas infection according to the second embodiment shown in Figure 23 is replaced with step S107b corresponding to determination rule 1A (see the area surrounded by dotted lines in Figure 26).

[0184] Alternatively, determination rule 1 and determination rule 1A may be combined, and determination conditions (3) and (3A) for both count information regarding the number of squamous epithelial cells and count information regarding the number of white blood cells may be set in determination rule 1.

[0185] (Example) Next, the results of experiments conducted to confirm the effects of the present invention will be described.

[0186] First Example First, the results of an experiment conducted to verify the accuracy of determining whether a person has a suspected Trichomonas infection using count information 70 relating to the number of Trichomonas protozoa and first information 71 will be described.

[0187] In the experiment, a urine sample 5 was analyzed by the urine sample analyzer 100 and subjected to a visual microscopic examination (visual inspection) to verify the accuracy of the analysis results. In other words, the degree of agreement between the results of the suspected Trichomonas infection determination by the analysis unit 20 and the results of the visual microscopic inspection was verified.

[0188] In the experiment, in Example 1, to verify the accuracy of determination using count information 70 and first information 71 related to the number of Trichomonas protozoa, the determination process by the analysis unit 20 was performed using the simplest determination process shown in the second embodiment shown in FIG. 23 (determination process based only on determination rule 1). That is, in Example 1, only determination rule 1 based on the above conditions (1), (2), and (3) using first count information 70a, second count information 70b, and count information of squamous epithelial cells was executed. For the determination, threshold set 75a shown in FIG. 15 was used.

[0189] In Example 2, the analysis unit 20 performed a determination process using the determination process (determination process based only on determination rule 1A) shown in the modified example of Fig. 26. That is, in Example 2, determination rule 1A was executed based on the above conditions (1), (2), and (3A) using the first count information 70a, the second count information 70b, and the white blood cell count information.

[0190] As a comparative example, the analysis unit 20 performed a determination process using only the count information 70 related to the number of Trichomonas protozoa. In other words, the comparative example is a determination process based only on conditions (1) and (2) of determination rule 1. The threshold values ​​N1 and N2 for conditions (1) and (2) are the same in Example 1, Example 2, and the comparative example.

[0191] For Example 1 and the Comparative Example, experiments were conducted on the same population of 1200 urine samples. For Example 2, experiments were conducted separately from Example 1 and the Comparative Example, and the experiment was conducted on a population of 1234 urine samples. Although the sample populations for Example 1 and the Comparative Example are the same, the sample population for Example 2 may differ from the populations for Example 1 and the Comparative Example.

[0192] The experimental results of Example 1 are shown in Table 1. [Table 1]

[0193] The analytical results of Example 1 showed a sensitivity of 74.2%, a specificity of 93.3%, and a positive predictive value of 22.8% compared to visual inspection.

[0194] The experimental results of Example 2 are shown in Table 2. [Table 2]

[0195] The analytical results of Example 2 showed a sensitivity of 73.5%, a specificity of 91.2%, and a positive predictive value of 19.1% compared to visual inspection.

[0196] Table 3 shows the experimental results for the comparative example. [Table 3]

[0197] The analytical results of the comparative example showed a sensitivity of 80.6%, a specificity of 85.5%, and a positive predictive value of 12.9% compared to visual inspection.

[0198] As described above, it was confirmed that in Examples 1 and 2, which take into account the first information 71 including count information on squamous epithelial cells or white blood cells, specificity was improved and false positives were significantly reduced compared to the comparative example. That is, while there were 169 false positives in the comparative example, the number of false positives was reduced to 78 in Example 1 and 106 in Example 2. This confirmed the effect of the present invention, in that the accuracy of determination can be improved by adding the first information 71 (whether squamous epithelial cells or white blood cells are present in urine) to the determination conditions for suspected Trichomonas infection.

[0199] Second Example Next, a comparative experiment of the analysis results using the three modes shown in Fig. 15 will be described. That is, the determination results regarding suspected Trichomonas infection using three modes with different threshold settings were compared.

[0200] In the experiment, similar to the first embodiment, analysis was performed on a urine sample 5 using the urine sample analyzer 100 and a visual microscopic examination (visual inspection) to verify the accuracy of the analysis results. In other words, the degree of agreement between the results of the suspected Trichomonas infection determination by the analysis unit 20 and the results of the visual microscopic inspection was verified.

[0201] In Example 3, a determination process for suspected trichomoniasis infection was carried out in the first mode (standard setting). In Example 4, a determination process for suspected trichomoniasis infection was carried out in the second mode (sensitivity-oriented setting). In Example 5, a determination process for suspected trichomoniasis infection was carried out using the third mode (specificity-oriented setting). The number of urine samples 5 used in the experiment was 1232. Experiments in Examples 3 to 5 were each performed on the same group of urine samples. In Examples 3 to 5, as shown in FIG. 23 , only determination rule 1 based on the above conditions (1), (2), and (3) using the first count information 70a, the second count information 70b, and the count information of squamous epithelial cells was executed.

[0202] The experimental results of Example 3 are shown in Table 4. [Table 4]

[0203] The experimental results of Example 4 are shown in Table 5. [Table 5]

[0204] The experimental results of Example 5 are shown in Table 6. [Table 6]

[0205] With reference to Tables 4 to 6, it was confirmed that the second mode (sensitivity-emphasized setting) has higher sensitivity than the first mode (standard setting), and is capable of providing useful information to users who prioritize early detection.The third mode (specificity-emphasized setting) has higher specificity than the first mode (standard setting), and is capable of providing useful information to users who prioritize accuracy, especially in the early stages of trichomoniasis infection.In addition, since the specificity of the first mode (standard setting) is higher than the second mode and the sensitivity is higher than the third mode, it was confirmed that it is possible to provide information with a good balance of sensitivity and specificity, especially for the purpose of early detection of trichomoniasis infection.

[0206] (Variation) The embodiments disclosed herein should be considered to be illustrative and not restrictive in all respects. The scope of the present invention is defined by the claims rather than the description of the above embodiments, and further includes all modifications (variations) within the meaning and scope of the claims.

[0207] In the first embodiment described above, an example was shown in which the detection unit 30 was provided as a flow cytometer, but the present invention is not limited to this. For example, the detection unit 30 may be configured as an imaging device that captures microscopic images of particles in a measurement sample that has been stained by mixing a urine specimen with a reagent. In this case, the particles can be identified by type based on the morphological characteristics and staining state (stained location, color development, etc.) of the particles captured in the image captured by the detection unit 30, and counting information can be obtained. The detection unit 30, which is configured as an imaging device, may capture images of particles in a sample that is stationary in a sample container for imaging, or may continuously capture images of particles in the sample flowing through a flow cell.

[0208] In the first embodiment described above, an example was shown in which three mode selections, namely, first mode, second mode, and third mode, each having a different threshold set, were accepted, but the present invention is not limited to this. In the present invention, mode selection may not be accepted and only one threshold set may be set. For example, the threshold set for determination rule 1 may be the first mode threshold set only and may not be changeable. Furthermore, when mode selection is accepted, the number of selectable modes is not limited to three, and two, four, or more modes may be selectable.

[0209] In the first embodiment described above, an example was shown in which the measurement unit 10 of the urine sample analyzer 100 is provided with the sample transport section 12, sample preparation section 40, and sample distribution section 45, but the present invention is not limited to this. In the present invention, the measurement unit 10 does not need to be provided with the sample transport section 12, sample preparation section 40, and sample distribution section 45. In that case, a measurement sample prepared in advance can be prepared and supplied to the detection section 30 of the measurement unit 10. [Explanation of symbols]

[0210] 5: urine sample, 20: analysis unit, 30: detection unit, 31: flow cell, 32: light source, 33a to 33d: light receiving unit, 41: mixing unit, 41a: first mixing unit, 41b: second mixing unit, 42: first reagent, 43: second reagent, 61a to 61d: distribution data, 70: counting information regarding the number of Trichomonas protozoa, 70a: first counting information, 70b: second counting information, 71: first information, 71a: counting information regarding the number of squamous epithelial cells, 71b: counting information regarding the number of white blood cells Counting information, 72: second information, 72a: counting information on the number of red blood cells, 72b: counting information on the number of bacteria, 72c: counting information on the number of yeast-like fungi, 73: information indicating bias in distribution of Trichomonas protozoa, 75a: first threshold set, 75b: second threshold set, 80: information regarding suspected Trichomonas infection, 100: urine sample analyzer, N1 to N8, N11: threshold, R6: distribution area of ​​Trichomonas protozoa, Rw: distribution area of ​​white blood cells

Claims

1. A urine sample analysis method in which a urine sample analyzer acquires information about formed elements in a urine sample, comprising: obtaining first information including count information on the number of Trichomonas protozoa and count information on the number of at least one of squamous epithelial cells and white blood cells based on the detection data of formed elements in the urine sample; outputting information relating to a suspicion of Trichomonas infection based on at least the count information relating to the number of Trichomonas protozoa and the first information; comparing a count value X of the count information regarding the number of Trichomonas protozoa under a non-hemolytic condition with a first threshold value; and further comprising comparing the count value Y of the first information with a second threshold value that is equal to or greater than the first threshold value; X>first threshold and Y>second threshold A urine sample analysis method that determines whether there is a suspicion of trichomoniasis infection if the following conditions are met.

2. The urine sample analyzing method according to claim 1 , wherein the first information includes both count information regarding the number of squamous epithelial cells and count information regarding the number of white blood cells.

3. 3. The urine sample analysis method of claim 1, wherein, when the counting information regarding the number of Trichomonas protozoa meets a condition and the counting information belonging to the first information meets a condition, information indicating that there is a suspicion of Trichomonas infection is output as information regarding the suspicion of Trichomonas infection.

4. acquiring second information different from the first information based on the detection data; A urine sample analysis method according to any one of claims 1 to 3, wherein information regarding the suspected Trichomonas infection is output based on the second information in addition to the count information regarding the number of Trichomonas protozoa and the first information.

5. The urine sample analyzing method according to claim 4 , wherein the second information includes information for suppressing false positives in determining the suspicion of Trichomonas infection.

6. The urine sample analyzing method according to claim 4 , wherein the second information includes count information regarding the number of the Trichomonas protozoa, the squamous epithelial cells, and other formed elements different from the white blood cells.

7. The urine sample analyzing method according to claim 4 , wherein the second information includes information on a distribution state of the Trichomonas protozoa in distribution data indicating a distribution of formed elements in the urine sample.

8. The urine sample analysis method of claim 7, wherein when the distribution data indicates that the distribution of the Trichomonas protozoa is biased toward a side of the distribution area of ​​the Trichomonas protozoa that is closer to the distribution area of ​​other formed elements, output of information indicating a suspicion of Trichomonas infection is prohibited as information regarding the suspicion of Trichomonas infection.

9. The urine sample analyzing method according to claim 8 , wherein the distribution region of the other formed elements includes a distribution region of white blood cells in the distribution data.

10. The urine sample analysis method of claim 5, wherein the information for suppressing false positives in determining suspected Trichomonas infection includes count information regarding the number of at least one other formed element that interferes with the detection of the Trichomonas protozoan.

11. 11. The method for analyzing a urine sample according to claim 10, wherein the at least one other formed element that interferes with the detection of Trichomonas protozoa includes at least one of red blood cells, bacteria, and yeast-like fungi.

12. the at least one other formed element that interferes with the detection of Trichomonas protozoa includes a plurality of types of formed elements; The urine sample analysis method of claim 11, wherein when the count information of any of the plurality of types of formed elements meets a condition, output of information indicating a suspicion of Trichomonas infection is prohibited as information regarding the suspicion of Trichomonas infection.

13. The count information regarding the number of Trichomonas protozoa is First count information regarding the number of Trichomonas protozoa detected in the urine specimen treated with a first reagent; and and second count information relating to the number of Trichomonas protozoa detected from the urine sample treated with the second reagent.

14. accepting a mode selection from a plurality of modes including a first mode and a second mode; applying a first threshold set as a threshold for the count information regarding the number of Trichomonas protozoa and a threshold for the first information in response to the first mode being selected; and applying a second threshold set, which is set to have a higher sensitivity or specificity for suspected Trichomonas infection than the first threshold set, as a threshold for the count information regarding the number of Trichomonas protozoa and a threshold for the first information in response to the second mode being selected; The urine sample analysis method according to any one of claims 1 to 13, wherein if the count value of the count information regarding the number of Trichomonas protozoa and the count value of the first information are both equal to or greater than an applied threshold, it is determined that there is a suspicion of Trichomonas infection.

15. a detection unit for detecting formed elements in a urine sample; an analysis unit that acquires count information on the number of Trichomonas protozoa and first information consisting of count information on the number of at least one of squamous epithelial cells and white blood cells based on the detection data of formed elements in the urine specimen, and outputs information on a suspicion of Trichomonas infection based on at least the count information on the number of Trichomonas protozoa and the first information, The analysis unit A process of comparing a count value X of the count information regarding the number of Trichomonas protozoa under non-hemolytic conditions with a first threshold value; and Execute a process of comparing the count value Y of the first information with a second threshold value that is equal to or greater than the first threshold value; X>first threshold and Y>second threshold A urine sample analyzer that determines whether there is a suspicion of trichomoniasis infection if the following conditions are met:

16. The urine sample analyzer of claim 15 , wherein the first information includes both count information regarding the number of squamous epithelial cells and count information regarding the number of white blood cells.

17. 17. The urine sample analyzer of claim 15, wherein the analysis unit outputs information indicating a suspicion of Trichomonas infection as information regarding the suspicion of Trichomonas infection when the count information regarding the number of Trichomonas protozoa meets a condition and the count information belonging to the first information meets a condition.

18. The analysis unit acquiring second information different from the first information based on the detection data; A urine sample analyzer according to any one of claims 15 to 17, which outputs information regarding the suspected Trichomonas infection based on the second information in addition to the count information regarding the number of Trichomonas protozoa and the first information.

19. The urine sample analyzer of claim 18 , wherein the second information includes information for suppressing false positives in determining whether or not there is a suspected Trichomonas infection.

20. a mixing unit for mixing the urine sample with a staining reagent; 20. The urine sample analyzer according to claim 15, wherein the detection section detects formed elements in the urine sample mixed with the staining reagent.

21. the mixing unit includes a first mixing unit that mixes the urine sample with a first reagent and a second mixing unit that mixes the urine sample with a second reagent; 21. The urine sample analyzer of claim 20, wherein the detection unit detects formed elements in the urine sample mixed with the first reagent and formed elements in the urine sample mixed with the second reagent.

22. A urine sample analyzer according to any one of claims 15 to 21, wherein the detection unit includes a flow cell through which the urine sample flows, a light source that irradiates light onto the flow cell, and a light receiving unit that receives light from the flow cell.

23. The urine sample analyzer of claim 22, wherein the light receiving section receives fluorescent light and scattered light from formed components.

24. The analysis unit extracting a plurality of characteristic parameters indicating characteristics of formed elements in the urine sample based on the detection data; The urine sample analyzer of any one of claims 15 to 23, wherein count information relating to the number of Trichomonas protozoa and the first information are each obtained based on the extracted plurality of characteristic parameters.

25. The analysis unit Accepting a mode selection from a plurality of modes including a first mode and a second mode; applying a first threshold set as a threshold for the count information regarding the number of Trichomonas protozoa and a threshold for the first information in response to the first mode being selected; a second threshold set different from the first threshold set is applied as a threshold for the count information regarding the number of Trichomonas protozoa and a threshold for the first information in response to the second mode being selected; A urine sample analyzer according to any one of claims 15 to 24, wherein if the count value of the count information relating to the number of Trichomonas protozoa and the count value of the first information are both equal to or greater than an applied threshold, it is determined that there is a suspicion of Trichomonas infection.

26. A detection unit for detecting formed elements in a urine sample; an analysis unit that acquires count information on the number of Trichomonas protozoa and first information consisting of count information on the number of at least one of squamous epithelial cells and white blood cells based on the detection data of formed elements in the urine specimen, and outputs information on a suspicion of Trichomonas infection based on at least the count information on the number of Trichomonas protozoa and the first information, The analysis unit A process of comparing a count value X of the count information regarding the number of Trichomonas protozoa with a first threshold value; and Execute a process of comparing the count value Y of the first information with a second threshold value that is equal to or greater than the first threshold value; X>first threshold and Y>second threshold A urine sample analyzer that determines whether there is a suspicion of trichomoniasis infection if the following conditions are met:

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