Sample test system for measuring red blood cell characteristics

Abstract

The present disclosure relates to systems, devices, and methods for measuring analyte levels. A glycosylated hemoglobin level measurement system includes a sample test cartridge having microchannels that can passively transfer and compress cells. The system may execute one or more calibration or normalization routines on the sensor data to generate calibrated or normalized data. The calibrated or normalized data may be used to determine one or more analyte levels.

Description

[0001] Cross - Reference to Related Applications

[0002] This application claims the benefit of U.S. Provisional Patent Application No. 63 / 386,887, filed on December 9, 2022, the entire content of which is incorporated herein by reference. Technical Field

[0003] The present disclosure relates to analyte detection and sample testing systems configured to measure one or more characteristics associated with an analyte, such as glycated hemoglobin A1c. Background Art

[0004] Blood glucose testing is generally used to diagnose diabetes. It measures the glucose level in the blood and gives a blood glucose value. However, blood glucose levels are transient values that can change before or after meals or due to other factors.

[0005] In contrast, glycated hemoglobin testing measures the level of glucose attached or bound to hemoglobin within red blood cells. When red blood cells are in the blood, they can bind to glucose in the blood. By measuring or estimating the average amount of glucose attached to hemoglobin over time, glycated hemoglobin testing can measure the glucose level accumulated over the average lifespan of red blood cells (e.g., three months). Therefore, compared to other blood glucose tests, glycated hemoglobin testing is less affected by physical activity or food intake. That is, glycated hemoglobin levels are more stable than blood glucose levels and may be a better reference for diagnosing diabetes.

[0006] However, traditional glycated hemoglobin level measurement devices require complex technologies and equipment, which limits their usability (e.g., they can only be used by institutions at the hospital and laboratory levels). Although continuous management of glycated hemoglobin levels is needed to control diabetes and its prognosis, patients have few options for tracking such management. In addition, work on developing such available management methods has only recently begun. Similar to blood glucose measurement devices that have become popular for home use, efforts are underway to enable home measurement of glycated hemoglobin levels without the need to visit a clinic (e.g., see Korean Patent Registration Publication KR2281500 (registration date: July 20, 2021)). However, these efforts have mainly focused on biochemical methods, which have several drawbacks, including limited lifetimes of required components, difficult storage methods, and low or unreliable measurement accuracy due to poor storage conditions or poor user skills. Brief Description of the Drawings

[0007] The features, aspects, and advantages of the present disclosure can be better understood in conjunction with the following drawings.

[0008] Figure 1is a block diagram illustrating an environment in which some embodiments of an analyte level measurement system can operate.

[0009] Figure 2 is a partially transparent isometric view of a sample test cartridge according to some embodiments of the present technology.

[0010] Figure 3 is Figure 2 a plan view of a part of the sample test cartridge.

[0011] Figures 4A - 4C illustrates various features related to the Figure 2 sample test cartridge according to some embodiments of the present technology.

[0012] Figure 5A and 5B illustrates hemoglobin A1c levels relative to normal red blood cells at different positions along a microchannel according to some embodiments of the present technology.

[0013] Figure 6A and 6B illustrates hemoglobin A1c levels relatively high in red blood cells at different positions along a microchannel according to some embodiments of the present technology.

[0014] Figure 7A and 7B illustrates steps for measuring an analyte according to some embodiments of the present technology.

[0015] Figure 7C and 7D illustrates steps for obtaining a measurement result from a signal according to some embodiments of the present technology.

[0016] Figure 8 is a red blood cell velocity map in a capillary sensor according to some embodiments of the present technology.

[0017] Figures 9A - 9C are graphs of velocity data, time-calibrated velocity data, and time- and velocity-calibrated velocity data of a user's red blood cells, respectively, according to some embodiments of the present technology.

[0018] Figure 10 is a graph of time- and velocity-calibrated red blood cell velocity data of three users according to some embodiments of the present technology.

[0019] Figure 11A and 11B is a graph of time- and velocity-calibrated red blood cell velocity data of seven users according to some embodiments of the present technology.

[0020] Figure 12 is a graph of red blood cell velocity data of seven different samples according to some embodiments of the present technology.

[0021] Figure 13 is a flowchart illustrating a method for measuring glycated hemoglobin levels according to some embodiments of the present technology.

[0022] Figures 14A - 14D is a graph of normalized red blood cell velocity data for four samples according to some embodiments of the present technology.

[0023] Figure 15 illustrates sensor readings according to some embodiments of the present technology.

[0024] Figures 16A - 16C is a graph of normalized red blood cell sensor readings for three samples according to some embodiments of the present technology.

[0025] Figure 17 is a flowchart illustrating a method for analyzing red blood cells in a patient's blood sample according to some embodiments of the present technology.

[0026] Figure 18 is a flowchart illustrating a method for analyzing cells in a patient's body fluid sample according to some embodiments of the present technology.

[0027] Figure 19 is a block diagram illustrating an example of a processing system according to some embodiments of the present technology.

[0028] Those skilled in the relevant art will understand that the features shown in the figures are for illustration purposes only and that various changes are possible, including different and / or additional features and their arrangements. Detailed Description

[0029] Introduction

[0030] The following disclosure describes systems, devices, and methods for measuring analyte levels. More specifically, the present technology relates to a device for measuring analyte levels (such as glycated hemoglobin levels or analyte levels) at home using microchannel manufacturing technology. More specifically, one or more embodiments of the present technology include measuring glycated hemoglobin levels based on one or more physical characteristics of a sample (such as a fingerstick blood sample or other sampling techniques). For example, glycated hemoglobin levels can be determined based on measured changes in one or more physical characteristics (such as mechanical characteristics, deformability, rigidity, etc.) of glycated red blood cells. The system can include a pump-free cartridge having a particulate analysis sensor that uses capillary action for cell movement. The system can execute one or more routines (e.g., calibration routines, normalization routines) to, for example, operate the sensor, improve analyte detection accuracy, and process the collected data.

[0031] In some embodiments, a glycated hemoglobin level measurement system includes a sample test cartridge having microchannels for lysing cells. The system may perform one or more calibration routines using sensor data to generate a processed output (e.g., calibrated data). The calibrated data may be used to determine one or more analyte levels. Alternatively or additionally, the system may perform one or more normalization routines using sensor data to generate a processed output (e.g., normalized data).

[0032] For illustrative purposes, this technology describes one or more aspects related to glycation of red blood cells. However, it is understood that this technology may also be used to measure or analyze other fluid suspended particles and / or their characteristics.

[0033] Operating Environment

[0034] Figure 1 FIG. 100 is a block diagram illustrating an operating environment of some embodiments of an analyte level measurement system. The analyte level measurement system may be an analyte measurement system (e.g., a glycated hemoglobin level measurement system) and may include a sample test device or cartridge 102 (“cartridge 102”) and an analysis device 104 (e.g., via a connector 106) that may be coupled to the cartridge 102. The cartridge 102 may interrogate a sample based on received input. In some embodiments, the cartridge 102 may include an inlet for receiving a blood sample (e.g., a diluted blood sample) and an outlet for releasing the blood sample. The width and / or depth of the inlet may be greater than the outlet to facilitate entry of the blood sample. The analyte level measurement system may perform one or more calibration and / or normalization routines (e.g., sensor calibration routines, electrode calibration routines, data normalization routines), signal processing parameters (e.g., calibration parameters), and other routines to adjust performance. Figures 2 - 6B Exemplary features of the cartridge 102 are discussed.

[0035] The analysis device 104 can communicate with one or more client computing devices 120 via a direct wired or wireless communication link 108 and / or a network 130. The client computing devices 120 include an imaging device 120A, a smartphone or tablet 120B, a desktop computer 120C, a computer system 120D, a laptop computer 120E, and a wearable device 120F. These are just examples of some of the devices, and other embodiments may include other computing devices, such as other types of personal and / or mobile computing devices. The client computing devices 120 can collect various data from users (e.g., analytical data from a wearable analyte monitor (e.g., a continuous glucose monitor (CGM)), sleep data, heart rate data, blood pressure data, dietary information, exercise data, health metrics, etc.) and transmit the collected data to the analysis device 104 and / or a service provider (e.g., a remote device / system, such as a server). The collected data can be used in the testing / measurement process. For example, the analysis device 104 can include a processing system programmed to provide an output based on the correlation between real-time CGM data and glycated hemoglobin A1c levels. For example, the processing system can include a controller with one or more processors, a memory storing programs for calibrating and / or analyzing the collected data, and these programs can be used to identify individual cells, cell overlap, cell movement speed, sample flow rate, etc. Examples of calibration routines will be discussed in conjunction with FIGS. 9-13. The analysis device 104 can execute one or more sensor calibration routines to adjust signal processing parameters (e.g., thresholds, filtering parameters, calibration parameters, etc.), test settings, routines, and / or algorithms based on the collected data. The analysis device 104 can transmit data (such as raw data, processed data, sensor signals, etc.) to a remote device and receive data (such as calibration parameters, signal processing parameters, algorithms, firmware updates) from the remote device.

[0036] The client computing device 120 can also pass information (such as test results or other notifications) from the analysis device 104 and / or the service provider to the user. Thus, the computing device 120 can operate in a network environment using a logical connection through the network 130 to the analysis device 104 and / or one or more remote computers (such as a server computing device or a cloud computing environment). The network environment can also be used to provide software updates for the algorithms used in the analysis device 104 and / or one or more client computing devices 120.

[0037] In some embodiments, the analysis can be performed by or shared with a backend system (e.g., one or more computing devices such as servers, and / or a database configured to perform the analysis of the collected data). For example, the computing environment can include one or more computing devices (e.g., servers 140 and / or 150A-C, databases 155A-C, or similar devices) that are communicatively coupled to client computing device 120 and / or analysis device 104. In an example, server 140 can be an edge server that receives client requests and coordinates the fulfillment of these requests through other servers such as servers 150A-C. Server computing devices 140 and 150 can include computing systems. Although each server computing device 140 and 150 is logically shown as a single server, each server computing device can be a distributed computing environment including multiple computing devices located at the same or geographically distinct physical locations. In some implementations, each server 150 corresponds to a group of servers.

[0038] Client computing device 120 and server computing devices 140 and 150 can each connect to other server / client devices as either a server or a client. Server 140 can be connected to database 145. For example, servers 150A-C can be respectively connected to corresponding databases 155A-C. As described above, each server 150 can correspond to a group of servers, where each server can share a database or can have its own database. Databases 145 and 155 can warehouse (e.g., store) information. Although databases 145 and 155 are logically shown as a single unit, databases 145 and 155 can each be a distributed computing environment including multiple computing devices, which can be located within their corresponding servers or at the same or geographically distinct physical locations.

[0039] Network 130 can be a local area network (LAN), a wide area network (WAN), and / or other wired, wireless, or combined network. A portion of network 130 can be the Internet or other public or private network. Client computing device 120 can be connected to network 130 through a network interface (e.g., through wired or wireless communication). Although the connections between server 140 and servers 150 are shown as separate connections, these connections can be any type of local, wide area, wired, or wireless network, including network 130 or a separate public or private network.

[0040] In some embodiments, the analysis device 104 may initiate one or more tests on a blood sample collected by the cartridge 102. The analysis device 104 may interact with the cartridge 102 to collect and analyze one or more measurements regarding the blood sample. The analysis device 104 may transmit the analysis results to a server 140 corresponding to other entities (such as a healthcare provider, a further health tracking or comprehensive health analysis service, etc.). Alternatively, the analysis device 104 may provide the measurement results to the server 140 (e.g., without performing local analysis on the analysis device 104), and a remote service provider may analyze the provided measurement results.

[0041] Sample Test Kit

[0042] Figures 2 - 6B Illustrates various embodiments of a sample test cartridge according to some embodiments of the present technology. Figure 2 Is a partially transparent isometric view of the cartridge 102 according to some embodiments of the present technology. The cartridge 102 may include a plate-like chip or substrate 210 and a sensor body 220 disposed thereon. The substrate 210 and / or the sensor body 220 may be made of an elastomer (such as polydimethylsiloxane (PDMS)), glass (such as borate glass, soda-lime glass), or other suitable materials. The sensor body 220 may define an opening or cavity 222 into which a user may place a sample (such as a drop of blood). A microchannel or other microfluidic pattern 230 (the "microchannel 230") extending from the cavity 222 may be formed on the substrate 210 and / or the sensor body 220. In some embodiments, electrodes 240 (also referred to as a blood cell analyzer) are patterned onto the substrate 210 (e.g., the top surface of the substrate 210) by lithography, chemical vapor deposition, and / or other techniques, such that the electrodes 240 are positioned near the microchannel 230.

[0043] In some embodiments, prior to attachment to the substrate 210, the sensor body 220 (such as liquid PDMS) is applied to a patterned wafer and cured (e.g., cured at 70 - 150 °C for 1 - 4 hours). Using a biopsy punch and / or other tools, the patterned wafer and the curing process may be used to create specific patterns (such as the microchannel 230, the microchannel inlet, the microchannel outlet) on the sensor body 220 (such as the bottom surface of the sensor body 220). In some embodiments, the microchannel 230 is formed by lithography techniques (e.g., soft lithography, photolithography). The patterned sensor body 220 may then be connected to the substrate 210, for example, such that the microchannel 230 on the sensor body 220 is properly aligned with the electrodes 240 on the substrate 210.

[0044] In some embodiments, the substrate 210 (which may include electrodes 240) is further patterned to include microchannels 230. For example, the substrate 210 may be made of glass, and the microchannels 230 may be formed by patterning polyimide. Then, the sensor body 220 (e.g., without any patterning) may be placed on the substrate 210. Fabricating the cartridge 102 in this manner is advantageous because by patterning the microchannels 230 and electrodes 240 on the substrate, the microchannels 230 and electrodes 240 can be pre-aligned on the substrate 210 when the substrate 210 and the sensor body 220 are connected. In contrast, forming the electrodes 240 on the substrate 210 and forming the microchannels 230 separately on the sensor body 220 may require precise alignment of the substrate 210 and the sensor body 220, which is difficult to achieve with conventional manufacturing techniques.

[0045] In the illustrated embodiment, the microchannels 230 extend from the cavity 222 in a generally linear direction to the edge of the sensor body 220. In particular, the microchannels 230 have an inlet region 232 that is fluidly connected to the cavity 222, an outlet region 236 that is fluidly connected to the environment and / or a collection reservoir (not shown), and an observation window 234 that extends therebetween. Red blood cells (or other particles) in the sample received in the cavity 222 may move along the movement direction TD of the microchannels 230, pass through the inlet region 232, the observation window 234, and flow out through the outlet region 236. Details of the microchannels 230 and the electrodes 240 will be described in further detail below with reference to Figures 3 - 6B Further detail.

[0046] In some embodiments, the cartridge 102 is reusable or disposable. As used herein, the term "disposable", when applied to a system or component (or combination of components), such as a cartridge or a sensor, is a broad term and is not limited to referring to a related component having a limited number of uses and then being discarded. Some disposable components are used only once and then become unusable. Other disposable components are used more than once and then discarded. For example, a single-sample disposable cartridge can be used to analyze a single sample and then discarded. The system or cartridge destroys or prevents the operation of the components after analyzing a single sample, thereby preventing multi-sample use. In other embodiments, the system can be programmed to identify a disposable cartridge and then authorize the cartridge for limited use (e.g., the number of samples that can be analyzed).

[0047] Figure 3 is a plan view of a portion of the cartridge 102 for illustrating the microchannels 230 and the corresponding sensor mechanism. The boundaries between the inlet region 232, the observation window 234, and the outlet region 236 of the microchannels 230 are generally indicated by markings 331 on the cartridge 102. As shown, the inlet region 232 of the microchannels 230 may be formed to narrow in the movement direction TD (average angle θ 入), and the outlet region 236 of the microchannel 230 can be formed to widen in the moving direction TD (average angle θ 出 ). In some embodiments, the angle θin and / or θ 出 can be 1 degree, 2 degrees, 3 degrees, 4 degrees, 5 degrees, 6 degrees, 7 degrees, 8 degrees, 9 degrees, 10 degrees, or greater. In some embodiments, the flow of the sample (e.g., blood containing red blood cells) through the microchannel 230 is initiated by capillary action. In some embodiments, the material of the microchannel 230 can be selected according to hydrophilicity to initiate and control the flow rate through the microchannel 230. For example, the material forming the walls of the microchannel 230 can be glass (such as soda-lime glass) with a contact angle of 48 - 49°; or it can be PDMS (such as DBE-712, because DBE chemicals can be added to form a hydrophilic surface of PDMS, and PDMS is hydrophobic) with a contact angle of 85 - 86°. The surface finish and surface composition can be selected according to the target contact angle, hydrophobic / hydrophilic surface properties, capillary action, frictional effects, etc. In some embodiments, the user can add a liquid (e.g., water, physiological saline, etc.) to the sample (e.g., in the cavity 222) to further facilitate the fluid flow through the microchannel 230. Additionally or alternatively, the microchannel 230 can also be fluidly connected to a pump configured to control the pressure before, during, and / or after the microchannel 230, thereby facilitating the passage of the sample through the microchannel 230.

[0048] In some embodiments, once the microchannel 230 is substantially filled with the sample flow passing through it, the sample reaching the end of the microchannel 230 (e.g., Figure 2 the edge of the sensor body 220 as shown) can enter the collection pool and / or evaporate to continue the flow. The outlet region 236 can have a predetermined geometry (e.g., the cross-sectional dimensions of the edge of the sensor body 220) to affect the evaporation rate and thus the flow rate. The evaporation rate is also affected by other factors such as the fluid properties of the sample and the ambient pressure. The evaporation rate can be calculated by the following equation:

[0049]

[0050] where, Q evap is the evaporation rate, k c is the mass transfer coefficient, A s is the evaporation surface area, M is the molecular mass of the sample solution, P s is the saturation pressure, T s is the saturation temperature, P ∞ is the ambient pressure, and T ∞ is the ambient temperature. When using capillary action to pass through the microchannel 230 and evaporate at the outlet, the cartridge 102 can passively facilitate the sample flow without using a pump or other mechanism to drive the sample through the microchannel 230.

[0051] In the illustrated embodiment, six (or portions of) electrodes 240 extend through or adjacent to the viewing window 234 of the microchannel 230. A first group of the electrodes 240 (e.g., three strips) (labeled 352a, 352b, 352c respectively) may define a first region 350 along the microchannel 230, and a second group of the electrodes 240 (e.g., three different strips) (labeled 362a, 362b, 362c respectively) may define a second region 360 along the microchannel 230. Although both the first and second regions 350, 360 are within the viewing window 234, the first region 350 is closer to the inlet region 232 than the second region 360, and conversely, the second region 360 is closer to the outlet region 236 than the first region 350. Thus, a sample passing through the microchannel 230 may enter the first region 350 first and then enter the second region 360 before reaching the outlet region 236.

[0052] Figures 4A - 4C Illustrates various features associated with the cartridge 102 according to some embodiments of the present technology. First referring to Figure 4A , this figure shows a plan view of the viewing window 234, and each electrode strip (e.g., strips 352a, 352b, 352c, 362a, 362b, 362c) adjacent to the microchannel 230 may have a thickness D1. For example, the thickness D1 may be 10 μm, 15 μm, 20 μm, any value therebetween, or other values. In some embodiments, the thickness D1 of each electrode strip may be selected according to the required analysis. For example, a relatively thin electrode strip (e.g., D1 equal to about 10 μm) may produce a signal with narrower peaks and / or valleys (e.g., see the exemplary signal difference readings in Figure 7B ), while a relatively thick electrode strip (e.g., D1 equal to about 20 μm, the signal measurement result can be) may produce a signal with wider peaks, valleys, midpoints, and / or other parts of the signal. Additionally, the signal produced by a thicker / wider electrode may have (wider) flat (e.g., horizontal) portions at the peaks, valleys, and / or transitions between the peaks / valleys. Thus, the thickness D1 can be selected according to the type of signal measurement targeted by the analysis. For example, when calculating the glycation level using the peaks of the signal, a narrower or thinner electrode may be used.

[0053] Each pair of adjacent electrode strips within the same region (e.g., the first region 350, the second region 360) may be separated by a gap D2 beside the microchannel 230. Exemplary dimensions of the gap D2 may include 10 μm, 15 μm, 20 μm, any value therebetween, or other values. The thickness D1 and / or the gap D2 may be controlled according to the target physical characteristics of the sample being tested. For example, the thickness D1 and / or the gap D2 may be controlled according to the compressibility of blood cells, the different shapes of blood cells (natural or compressed), the corresponding dimensions of the microchannel 230, or a combination thereof. In some embodiments, asFigure 2 As illustrated, when the electrode strip is away from the microchannel 230 and extends towards the edge of the substrate 210, the thickness of the electrode strip and / or the gap between the electrode strips can be increased to a size suitable for the electrode to be a contact pad.

[0054] The first and second regions 350 and 360 (e.g., measured between the intermediate strips 352b and 362b or between another set of reference positions) can be separated by a distance D3. The distance D3 can be 200 μm, 225 μm, 250 μm, any value therebetween, or other values. Next, referring to Figure 4B , Figure 4B shows a top view of the electrode 240 in the cavity 222. The first electrode strip 422a enters the cavity 222, and the second electrode strip 422b extends around the first electrode strip 422a. The separation distance D3 can be controlled according to the required test accuracy, the target test duration, the physical characteristics of the sample to be tested, or the like.

[0055] Next, referring to Figure 4C , which illustrates the cross-sectional shape of the microchannel 230. The viewing window 234 can have a width W and a height H. In some embodiments, the width W is less than the average diameter of uncompressed red blood cells (or other particles). In some embodiments, the height H is greater than the average height or thickness of uncompressed red blood cells. Alternatively, the height H can be less than the average height or thickness of uncompressed red blood cells. Thus, the microchannel 230 can be configured to allow a particle of interest (e.g., a single red blood cell) to enter and pass through at any given cross-section. Depending on at least one insufficient dimension, the microchannel 230 can compress the moving particle of interest (such as a red blood cell). For example, the average diameter of human red blood cells is about 7 - 8 μm, and the thickness is about 2 - 3 μm. Thus, the width W of the microchannel 230 can be between 2 and 12 μm (e.g., 6 μm), and the height H of the microchannel 230 can be between 1 and 5 μm (e.g., 3 μm). The dimensions can be selected according to the shape (such as rectangular, square, oval, circular, etc.) and size of the cross-section of the microchannel 230.

[0056] In some embodiments, the width W and / or the height H may vary (e.g., decrease) along the length of the microchannel 230. For example, the entrance portion of the viewing window 234 (e.g., around the first region 350) may have a first width, and the width of the microchannel 230 may gradually (e.g., linearly, exponentially) decrease such that the exit portion of the viewing window 234 (e.g., around the second region 360) has a second width that is smaller than the first width. The first width may be about 8 - 12 μm (e.g., 10 μm), and the second width may be about 2 - 7 μm (e.g., 4.5 μm). When measuring the effect of compressing red blood cells in the microchannel, for example, by comparing the characteristics of red blood cells at the first region 350 and the second region 360, a microchannel with a gradually narrowing width W is advantageous. For example, if the microchannel 230 has a constant width W along the viewing window 234, the red blood cells may be compressed at both the first and second regions 350, 360, resulting in a relatively small difference in the measured characteristics between the first and second regions 350, 360. In contrast, if the width W of the microchannel 230 narrows along the viewing window 234, the red blood cells may not be compressed or may be compressed to a small extent at the first region 350, while being compressed to a greater extent at the second region 360, resulting in a larger difference in the measured characteristics between the first and second regions 350, 360. Additionally or alternatively, a plurality of sensor regions may also be included along the gradually decreasing width of the microchannel 230. Measurement results of different regions (such as rates and comparisons between different rates) can be used to compare rates and / or changes in rates as the width decreases.

[0057] For illustrative purposes, the cross-sectional shape shown is rectangular. However, it is understood that the cross-sectional shape can be different. For example, the cross-sectional shape of the viewing window 234 can be oval, circular, different polygons, or polygons with rounded corners or arcs between two sides. Additionally, the shape of the transition portion (e.g., the compression portion) entering and / or leaving the viewing window 234 can geometrically facilitate the entry of a sample into the viewing window 234 one at a time.

[0058] Figure 5A and 5B Illustrates a first red blood cell 505 located at the first and second positions along the microchannel 230, respectively. Figure 6A and 6B Illustrates a second red blood cell 605 located at the first and second positions along the microchannel 230, respectively. Compared to the first position ( Figure 5A and 6A ), the second position ( Figure 5B and Figure 6B ) is further downstream along the microchannel 230. As described above with respect to Figure 4C , the size of the microchannel 230 can compress a single red blood cell. Thus, Figure 5A and Figure 6AThe corresponding blood cells in a relatively normal and uncompressed state can be illustrated, while Figure 5B and 6B the corresponding blood cells in a compressed state can be illustrated.

[0059] When hemoglobin in red blood cells binds to and glycates with glucose, the elasticity of the red blood cells decreases, causing the red blood cells to become stiffer. Compared with red blood cells having non-glycated hemoglobin, red blood cells with a higher level of glycated hemoglobin move more slowly and require more time to pass through part or the entire length of the microchannel 230. Therefore, the transit time, velocity / speed, acceleration, stiffness, deformation, other measured movement parameters of red blood cells passing through the microchannel 230, their changes over time, the ratio of such changes, and / or their combination (e.g., weighted average, ratio, and other comparative measurements) can be used to determine the glycated hemoglobin level of these cells.

[0060] The first red blood cell 505 can have a glycated hemoglobin level that is relatively normal (e.g., within the range recommended by medical staff), such as 5%. Therefore, the compliance of the first red blood cell 505 is relatively good, and it will undergo significant deformation when passing through the microchannel 230, as can be seen by comparing Figure 5A and Figure 5B On the other hand, the second red blood cell 605 can have a relatively higher glycated hemoglobin level, such as 8.2%. Therefore, the second red blood cell 605 is relatively more rigid and will not undergo much deformation when passing through the same microchannel 230, as can be seen by comparing Figure 6A and 6B In addition, by comparing Figure 5B and Figure 6B it can be seen that the first red blood cell 505 can be asymmetric and / or elongated as Figure 5B shown, while the second red blood cell 605 can remain symmetric and / or not elongated as Figure 6B shown, which corresponds to a higher stiffness and thus a higher glycated hemoglobin level.

[0061] Measurement and Analysis Examples

[0062] Figure 7A and 7B illustrate the steps of measuring an analyte according to some embodiments of the present technology. Specifically, Figure 7A illustrates the situation of the red blood cell 705 at five different positions and times in the first region 350 along the microchannel 230 when the red blood cell 705 passes through the electrode strips 352a, 352b, 352c in the moving direction TD. Figure 7B illustrates in connection with Figure 7ASensor readings of electrode strips 352a, 352b, 352c corresponding to the five positions and times shown. During the operation of cartridge 102, a reference signal can be generated and passed to (e.g., via the Figure 1 analyzer device 104 therein) electrode strip 352b (represented by voltage 入 ). In some embodiments, the reference signal includes an AC voltage (e.g., amplitude of 800 mV, frequency of 60 kHz). Depending on the position of red blood cells 705, the signal can pass through red blood cells 705 and / or electrolytes in microchannel 230 and return via electrode strips 352a and 352c (represented by voltage 出 ).

[0063] Figure 7B The illustrated graph can represent the difference in signals received at or via electrode strips 352a and 3352c. When red blood cells 705 are in the first illustrated position at time t0, first region 350 still has no red blood cells 705 (e.g., initial state, such as when the voltage has not yet risen before t0), and the signal received at electrode strip 352a has not been affected by red blood cells 705. The signals received at electrode strips 352a and 352c can be equal to each other, and the difference can correspond to a predetermined or expected voltage level (e.g., 0 V or DC offset voltage).

[0064] When red blood cells 705 are in the second illustrated position at time t1, red blood cells 705 are located between electrode strips 352a and 352b. Although the signal received at electrode strip 352c remains substantially the same as before, the change in the signal at electrode strip 352a causes a change in the difference between the received voltages, and the change in the difference increases as more red blood cells 705 are located between electrode strips 352a and 352c. In some embodiments, the maximum or positive peak of the sensor readings (as shown in the second graph of Figure 7B ) can correspond to when red blood cells 705 are approximately halfway between electrode strips 352a and 352b.

[0065] When red blood cells 705 are in the third illustrated position at time t2, the signals received at electrode strips 352a and 352c are uniformly affected by the presence of red blood cells 705, resulting in a zero reading of the signal difference (e.g., corresponding sensor readings), as shown in Figure 7BAs shown in the third figure. Generally, time t2 may represent the transition point from when red blood cell 705 has a greater impact on the signal passing through electrode 352a to having a greater impact on the signal of electrode 352c. The resulting voltage level may be equal to the level that occurred during the initial state (e.g., 0V). For illustrative purposes, time t2 is shown as the central part of red blood cell 705 reaching the midpoint of the first region 350 and centrally covering electrode 352b. However, it can be understood that time t2 may correspond to different or non - central situations, such as when red blood cell 705 has a different or irregular shape due to a lower glycosylation level or a blood disease (such as sickle cell disease).

[0066] When red blood cell 705 passes over electrode 352b, red blood cell 705 begins to have a greater impact / change on the signal received by electrode 352c and a smaller impact / change on the signal received by electrode 352a. When more red blood cells 705 overlap onto electrode 352c, the difference in the signals increases in the direction opposite to time t0. For example, when red blood cell 705 is in the fourth illustrated position at time t3, red blood cell 705 is located between electrode strips 352b and 352c. Although the signal received at electrode strip 352a returns to the signal received before time t0, the change in the signal at electrode strip 352c causes a change in the difference between the received voltages, and as more red blood cells 705 are located between electrode strips 352b and 352c, the change in the difference increases. In some embodiments, as Figure 7B shown in the fourth figure, the negative value, minimum value, or trough in the sensor reading may correspond to when red blood cell 705 is approximately halfway between electrode strips 352b and 352c.

[0067] When red blood cell 705 is in the fifth illustrated position at time t4, the first region 350 is again free of red blood cells 705 (e.g., the terminal state), and the signal received by electrode strip 352c is no longer affected by red blood cell 705. The signals received by electrode strips 352a and 352c may be equal to each other, and the difference may correspond to a predetermined or expected voltage level (e.g., 0V or a DC offset voltage).

[0068] Sensor readings can be used to determine various movement parameters of red blood cells 705 (such as speed / velocity, movement time, acceleration, ratio, and other combinations or comparisons), which can then be used to determine, for example, HbA1c levels. In some embodiments, a bridge circuit forms part of the sensor circuit for obtaining sensor readings. Additionally, the shape of the resulting graph can be analyzed to evaluate the shape of red blood cells 705 or corresponding blood diseases. For example, the symmetry of the time / duration and / or size before and after t1 can be compared. The difference in crossing the midpoint at time t1 can be used to further determine or verify HbA1c levels. Additionally, the overall shape and / or symmetry of the graph can be used (e.g., by comparison with one or more corresponding models or templates) to evaluate, diagnose, confirm, or track one or more blood diseases.

[0069] Figure 7C and 7D illustrates steps for obtaining measurements from a signal according to some embodiments of the present technology. In some embodiments, sensor measurements can be sampled at a specific frequency (e.g., 2 kHz) to obtain Figure 7B the sensor readings shown. Thus, the sampled sensor readings can be separated by a corresponding period (e.g., 500 μm).

[0070] The voltage output (such as Figure 7A the voltage of 出 and / or Figure 7B the calculated difference) can vary depending on the movement speed of red blood cells, electrode thickness, and other factors. The resulting signal may be asymmetric, including signal noise, and / or have other features that increase the difficulty of extracting target patterns / features (such as peaks, valleys, midpoints, start points, end points). Therefore, as Figure 7C shown, it may be advantageous to plot the difference between adjacent data points in time. In other words, each data point can be the difference between the currently sampled measurement result (e.g., the voltage 出 value of a specific red blood cell at t = n) and the sampled measurement result obtained at the previous adjacent time point (e.g., the voltage 出 value at t = n - 1). Such differences can be calculated using registers, shift registers, rolling windows, or the like, which retain and update the previous and current values for each sampling period.

[0071] Figure 7CThe graph 710 therein shows a first peak 712, a first trough 714, a second trough 716, and a second peak 718 corresponding to the calculated difference when a blood cell passes through the sensing region. The graph 710 also shows noise that effectively forms a band of random data points between the peaks and troughs and throughout the graph. By plotting the differences between subsequent data points at constant time intervals, the graph 710 can also represent the derivative graph of the difference signal graph (e.g., Figure 7B the mathematical derivative graph of the signal example shown). In other words, the device 104 and / or the server 140 / 150 can calculate the instantaneous slope or rate of change of the signal difference readings (e.g., the voltage 出 difference at electrodes 352a and 352c).

[0072] For illustrative purposes, the graph 710 illustrates two red blood cells successively passing through Figure 3 the first test region 350 and Figure 3 the second test region 360. The set of peaks and troughs 712, 714, 716, 718 may correspond to the first red blood cell passing through the first test region 350, and the next set of peaks and troughs may correspond to the second red blood cell passing through the first test region 350. The next two sets of peaks and troughs may correspond to the first and second red blood cells passing through the second test region 360, respectively.

[0073] Figure 7D Illustrated is a graph 710 aligned with the graph 720, which may include an exemplary difference signal (e.g., Figure 7B the signal example shown in). For illustrative purposes, the graph 720 shows a smoothed graph of the signal (e.g., with obvious signal noise removed). As shown, the signal in the graph 720 includes various data points, including a starting point 721, a peak 722, a midpoint 723, a trough 724, and an ending point 725. As described above, signal noise, signal asymmetry, and other factors make it difficult to determine the exact times at which the various data points 721 - 725 occur. Therefore, the graph 710 can be used to help determine the times of the respective data points 721 - 725.

[0074] For example, the time of the starting point 721 can be confirmed by determining when the graph 710 starts to rise above the signal noise (e.g., the calculated signal-to-noise ratio (SNR) or a predetermined threshold) by a certain degree (e.g., 3%, 5%, 7%). By determining the midpoint (along the x-axis) between the first peak 712 and the first trough 714 of the graph 710, the time of the peak 722 of the graph 720 can be confirmed. Since the graph 710 effectively represents the mathematical derivative or instantaneous slope of the graph 720, Figure 7CThe first peak 712 and the associated "positive" values (values above the noise level) in can correspond to the rising half of the first peak 722 in graph 720 (e.g., between the starting point 721 and the peak 722). Similarly, the first trough 714 and the corresponding "negative" values (values below the noise band) can correspond to the falling half of the peak 722 (e.g., between the peak 722 and the midpoint 723). Similarly, the second trough 716 can correspond to the falling half of the trough 724, and the second peak 718 can correspond to the rising / returning half of the trough 724. Since the shape of the voltage difference signal for each blood cell passing through the sensor area is known, the time of the peak 722 can be calculated from the graph 710 as described above.

[0075] Similarly, the time of the trough 724 in graph 720 can be confirmed by determining the midpoint (along the x-axis) between the second trough 716 and the second peak 718 of graph 720. Once the times of the peak 722 and the trough 724 in graph 720 are confirmed, the time of the midpoint 723 in graph 720 can be confirmed by determining the midpoint (along the x-axis) between the peak 722 and the trough 724.

[0076] As discussed above with respect to Figure 4A the thicknesses of the electrodes 352a, 352b, 352c affect the signal. For example, relatively thick electrodes can cause the peak 722 or the trough 724 to have a flat portion, making it difficult to confirm the exact times of these data points from the graph 720 alone. Additionally, if there are flat portions or other irregularities in the signal, simply obtaining the maximum or minimum value of the graph may result in inaccurate analysis results. Therefore, using the graph 710 to assist in confirming the times of various data points can yield more accurate analysis results.

[0077] The steps illustrated and discussed above can be used for Figure 3The electrode strips 362a, 362b, 362c of the second region 360, so that the first rate / speed (e.g., inlet or entrance rate / speed) of the first region 350 and the second rate / speed (e.g., outlet or exit rate / speed) of the second region 360 can be determined for the same target. Thus, the cartridge 102 can determine changes, ratios, or other comparisons between different points along the microchannel 230. The cartridge 102 can also measure other parameters, such as fluid velocity, volumetric flow rate, or similar parameters. Examples of sensor circuits and details of their measurement operations are further disclosed in U.S. Patent No. 11,747,348, filed on December 9, 2022, with the invention title "Apparatus and Related Methods for Measuring Red Blood Cell Glycation and Glycated Hemoglobin Levels Using Physical and Electrical Properties of Cells", and U.S. Patent Application Publication No. US2023 / 0105313, filed on December 9, 2022, with the invention title "Apparatus and Related Methods for Measuring Particle Properties in a Solution", the disclosures of which are incorporated herein by reference in their entirety. For example, a first resistor can be coupled to the electrode 352a, and a second resistor having the same resistance value can be coupled to the electrode 352c. The sensor circuit can combine the voltages measured at the first and second resistors (e.g., subtraction, such as using a negating circuit and an adding circuit, etc.) to determine the difference between the two signals. Thus, together with the electrodes and the inherent resistance between the individual electrodes, the first and second resistors can effectively form a bridge circuit.

[0078] Figure 8 is a graph of the velocity (in mm / s) of multiple red blood cells over time (in minutes) in a capillary sensor according to some embodiments of the present technology. More specifically, Figure 8 illustrates how the velocity of red blood cells (e.g., measured at a location along the microchannel 230) changes as more red blood cells are introduced and expelled Figure 2 from the microchannel 230 over the entire measurement period. As shown, the velocity can exhibit an initial increase in velocity, followed by an exponential decay in velocity. In other words, at the start of the test, a group of initial blood cells may move faster, while as the test progresses, subsequent samples may move slower. This may be attributed to, for example, capillary action that initiates sample flow, the amount of sample in the opening and collection pool, and evaporation at the end of the microchannel, as described above with respect to Figure 2As discussed. In some cases, measurements of different samples, measurements of different particles (such as white blood cells), and / or measurements using different types of sensors may exhibit different velocity curves during measurement, and are also affected by different environmental factors (such as temperature, humidity, pressure), experimental conditions (channel size error, surface roughness, and other factors that may affect the rate). In addition, the collected samples include younger or newer blood cells, which have a lower degree of glycation than other blood cells (for example, due to less exposure) and can more accurately reflect the patient's condition. One method is to focus the analysis on a part of the velocity / time graph, such as the descending or end part (for example, the percentage delay or predetermined offset after the peak velocity). Another method, as further discussed herein, may be to calibrate and / or normalize the data to compensate for the above variations (such as variations between samples).

[0079] Figures 9A - 9C are graphs of red blood cell velocity data, time-calibrated velocity data, and time- and velocity-calibrated velocity data for multiple samples from a single user, respectively, according to some embodiments of the present technology. As Figure 9A shown, the raw red blood cell velocity data for different samples, even from the same user, may exhibit different patterns, for example, due to the above-mentioned environmental or other factors. As Figure 9B shown, for time calibration, the x-axis of each sample data can be adjusted so that the peak velocities are aligned. After time calibration, the velocity data for different samples may exhibit a generally similar decay trend (such as shape, slope, or their rate of change, etc.). For velocity calibration, the velocity can be related to 1 / t^0.5 according to the Washburn equation. After time and velocity calibration, as Figure 9C shown, other variations, such as environmental effects and experimental errors, can be removed for data analysis, comparison between samples, comparison between different users, etc.

[0080] In some embodiments, Figure 1 the device 104 and / or Figure 1 the server 140 / 150 may interact with the user to analyze multiple samples. The device 104 and / or the server 140 / 150 can perform velocity and / or time calibration for the user using the results of multiple samples.

[0081] Figure 10This is a graph of red blood cell velocity data calibrated for time and velocity from three users according to some embodiments of the present technology. Specifically, the HbA1c level of the first user is 7.2% according to the National Glycohemoglobin Standardization Program (NGSP), the HbA1c level of the second user is 7.3% NGSP, and the HbA1c level of the third user is 8.1% NGSP. As shown, the red blood cell velocity of users with higher HbA1c levels is generally lower than that of users with lower HbA1c levels. This can be attributed to, for example, higher glycosylation levels causing red blood cells to become more rigid and thus slower when passing through the microchannel 230. In other words, the entry rate of more rigid red blood cells (e.g., the rate of passing through Figure 3 the first region 350) may be higher than the exit rate (e.g., the rate of passing through Figure 3 the second region 360).

[0082] Figure 11A and Figure 11B This is a graph of red blood cell velocity data calibrated for time and velocity from seven users according to some embodiments of the present technology. Specifically, the HbA1c level of the first user is 6.1%, the HbA1c level of the second user is 6.6%, the HbA1c level of the third user is 7.1%, the HbA1c level of the fourth user is 7.2%, the HbA1c level of the fifth user is 7.3%, the HbA1c level of the sixth user is 7.9%, and the HbA1c level of the seventh user is 8.1% (all calculated according to NGSP). Figure 11A The graphs in focus on the period from [N] to [N + 1] minutes after the peak velocity (e.g., the period at least 5 minutes after the start of the sample flow), which may roughly correspond to the time when the velocity data tends to stabilize. As shown, the samples of users with higher HbA1c levels exhibit generally slower velocities.

[0083] Figure 11B This is a comparison graph of the HbA1c levels measured by seven users through embodiments of the present technology with known HbA1c levels (e.g., measured using a protein quantification-based method). As shown, the coefficient of determination is close to 1, indicating the accuracy and reliability of the present technology in measuring HbA1c values. In other words, the glycosylation levels calculated using the above-mentioned device and / or method are close enough to the actual known glycosylation levels of the tested patients (according to the R 2 value of 0.9985).

[0084] Figure 12It is a graph of red blood cell velocity data for seven different samples according to some embodiments of the present technology. As shown in the figure, the higher the HbA1c level, the higher the red blood cell hardness, the lower the peak velocity, and the later the time of occurrence. Therefore, calibration and corresponding processing based on the above peak velocity values can improve the accuracy of calculating the glycosylation level of the tested patient.

[0085] Figure 13 It is a flowchart illustrating method 1300 for measuring the glycated hemoglobin level according to some embodiments of the present technology. Although method 1300 will be described below with reference to the components illustrated and described above Figures 1 - 12 Method 1300 may include transmitting one or more signals to cartridge 102 in block 1310. For example, the signal may be a reference signal generated by analysis device 104. The signal may pass through the electrodes as described above. Method 1300 may include receiving a sample at cartridge 102 in block 1320. For example, a user may provide a drop of blood to chamber 222. In some embodiments, blocks 1310 and 1320 may be swapped, for example, the patient user provides blood into chamber 222 and then inserts cartridge 102 into device 104. Method 1300 may include initiating by capillary action to cause or allow the sample to flow through microchannel 230 in block 1330. As described above, microchannel 230 may include some materials having a desired level of hydrophilicity and geometry such that the sample flow can be initiated solely by capillary action.

[0086] Method 1300 may include measuring the movement parameters of red blood cells (or other particles in the received sample) and / or other characteristics of the sample flow in block 1340. As described above, the movement parameters may include velocity / speed, movement time, acceleration, combinations thereof, etc., and other characteristics of the sample flow may include total flow rate. In some embodiments, the method may further include waiting until the sample flow reaches the end of the microchannel and / or until the velocity data stabilizes (e.g., using a predetermined test duration or measuring relative to the peak rate measured in real time). Method 1300 may include calibrating data in block 1350. In some embodiments, the measured flow rate is used to calibrate the data. In some embodiments, according to the above regarding Figures 9A - 9CStep calibration data, for example, using one or more previous tests and / or relative to peak velocity. Method 1300 may include determining the Hb1Ac level in block 1360. In some embodiments, the glycated hemoglobin level is determined based on the measured and calibrated data. For example, device 104 and / or server 140 / 150 may compare one or more rates (e.g., entry and exit rates and / or red blood cell rates measured during a target time window) with a set of predetermined glycation levels, such as using a look-up table. Additionally or alternatively, device 104 and / or server 140 / 150 may use a predetermined equation or method to calculate the glycation level of the patient user based on the rate of red blood cells and their corresponding stiffness.

[0087] Figures 14A - 14D is a graph of red blood cell velocity data normalized for four samples according to some embodiments of the present technology. As described above, the velocity or rate of red blood cells passing through microchannel 230 can vary due to the rigidity of the cells as well as environmental and / or experimental factors. Additionally, since there is no active pumping mechanism to control the flow rate, Figure 1 the cartridge 102 in [reference] may be more susceptible to velocity variations under the passive movement mechanism of microchannel 230. Additionally, the velocity, whether it is the inlet velocity, outlet velocity, or total velocity, etc., is generally affected by environmental factors. Comparing the velocities at different points in microchannel 230, such as calculating the ratio of the rates through the first and second regions, can eliminate or at least partially eliminate one or more factors of variation, mainly retaining the effect of cell stiffness in the comparison result. Therefore, using the comparison result for glycation calculation is another method of calibrating or normalizing data to address the variability between different samples and / or users. Compared with the Figures 9A - 9C and Figure 13 calibration methods discussed above, one advantage of using the comparison between different velocities is that the analysis does not need to wait until the velocity stabilizes. For example, as Figure 8 shown, the velocity may stabilize 5 minutes or more after receiving the sample.

[0088] Specifically, Figures 14A - 14D are the histogram distributions of the red blood cell velocity ratios for users with HbA1c levels of 5.0%, 5.6%, 6.0%, and 7.0% respectively. The velocity data is normalized using the ratio of the first velocity and the second velocity. In the illustrated graph, the first velocity corresponds to the inlet velocity (V 入 ), which is taken from, for example, Figure 3 the first region 350 of [reference], and the second velocity corresponds to the outlet velocity (V 出 ), which is taken from, for example, Figure 3 the second region 360 of [reference]. As shown, red blood cells associated with higher HbA1c levels generally exhibit lower velocity ratios (measured as V 出 / V入 )。 The observed pattern can be attributed to the fact that red blood cells with higher glycated hemoglobin levels are more rigid and thus move slower when moving along the microchannel 230. Therefore, Figure 1 device 104 and / or Figure 1 server 140 / 150 can use the comparison results (such as speed ratio) of the patient user to calculate the glycation level of the patient user.

[0089] In operation, cartridge 102 can be used to generate a speed ratio distribution map of a specific sample (e.g., by analyzing device 104) or corresponding results (e.g., shape, peak, mean, or a combination thereof), and can determine the glycated hemoglobin level of the specific sample based on the generated map / results (e.g., by considering mean, median, standard deviation, skewness).

[0090] Figure 15 Illustrates sensor readings according to some embodiments of the present technology. Specifically, Figure 15 shows a first voltage reading voltage 1 and a second voltage reading voltage 2, where voltage 1 corresponds to the sensor reading at the first region 350 when a single red blood cell passes through Figure 3 the first region 350, and voltage 2 corresponds to the sensor reading at the second region 360 when the same red blood cell passes through Figure 3 the second region 360. The generation of these voltage signals has been described in detail above with reference to Figure 7A and Figure 7B has been described in detail.

[0091] As shown, various measurements can be made based on the voltage 1 and voltage 2 signals. For example, the time required for a red blood cell to reach the next electrode strip from one electrode strip (e.g., T 入1 、T 入2 、T 出1 、T out2), the time required for a red blood cell to pass through a region (e.g., T 入总 、T 出总 ), and the time required for a red blood cell to move from the first region 350 to the second region 360 (e.g., T 移动) can be measured based on the wavelength, partial wavelength, and / or time period between two voltage signals. Other measurement methods also fall within the scope of the present technology. For example, the time period between two peaks, the time period between two valleys, the width of the peak and / or valley, the general shape or contour of the peak and / or valley, and / or any combination of the above values can be used. Since the specific distance along the microchannel is known and / or can be measured, the velocity or speed of red blood cells at a specific point along the microchannel 230, or the average velocity or speed through a specific portion of the microchannel 230, can be determined. Therefore, the velocity ratios that can be determined and plotted can include the ratio between the inlet velocity and the total velocity (such as the average velocity), the ratio between the outlet velocity and the total velocity, etc. The type of ratio can be selected based on the characteristics of the measurement sensor, the magnitude of the error, etc. As described above Figures 14A - 14D The velocity ratio distribution map can be used to determine the HbA1c level of an individual.

[0092] In addition, the shape and width of the peaks and valleys can be used to evaluate the shape profile of blood cells. For example, T 入1 can be compared with T 入2 or similarly compared with the magnitude and / or curve shape of the corresponding time period to calculate the symmetry of blood cells. Additionally or alternatively, the shape of the peak or valley can be compared with one or more predetermined template shapes, and corresponding measurement results can be generated (e.g., using the differences between the shapes). The symmetry measurement and / or shape comparison measurement can be used to further calculate or verify the HbA1c level of the patient user. For example, more rigid blood cells will experience relatively larger velocity changes and decelerate more throughout the microchannel while maintaining a more symmetrical shape as described above. Therefore, the resulting velocity ratio can have a correlation with the symmetry measurement. The device 104 and / or the server 140 / 150 can use a look-up table or a predetermined equation / process to define the threshold of this correlation. When the red blood cell measurement deviates from the expected correlation, the device 104 and / or the server 140 / 150 can classify the measurement as abnormal. The abnormal cases can be excluded from the overall calculation of the HbA1c level and / or trigger a separate analysis, such as for evaluating other blood diseases. For such evaluations, the device 104, the server 140 / 150, or a combination thereof can analyze the shape and / or symmetry measurement as described above. In addition, in some embodiments, the amplitude or ratio (or other combinations) of the signal can be used to determine the size and / or other characteristics of red blood cells.

[0093] Figures 16A - 16C is a graph of the normalized red blood cell sensor readings of three samples according to some embodiments of the present technology. As described above regarding Figures 5A - 6BDescription, red blood cells with lower glycated hemoglobin levels have higher compliance. Therefore, due to, for example, high fluid velocity, they may deform more than red blood cells with higher glycated hemoglobin levels. This deformation causes red blood cell asymmetry. In contrast, red blood cells with higher glycated hemoglobin levels are more rigid and thus deform less when passing through Figure 2 the microchannel 230 and can better maintain symmetry. Therefore, it may be advantageous to plot the ratio distribution between the peak time period (such as T Figure 15 ) and the valley time period (such as T 出1 ) of the voltage signal related to one or each region. As an example, Figure 15 shown in T 出2 ). The ratio distribution between the peak time period (such as T Figures 16A - 16C ) and the valley time period (such as T 出1 ) of the red blood cell time period for users with HbA1c levels of 4.8%, HbA1c levels of 6.3%, and HbA1c levels of 10.7% is plotted respectively in the histogram (see 出2 ). As shown in the figure, samples with higher HbA1c levels (such as Figure 15 ) include more rigid red blood cells with less deformation, so the ratio distribution is smaller and closer to 1. On the other hand, samples with lower HbA1c levels (such as Figure 16C ) include more flexible red blood cells with greater deformation, so the ratio distribution is larger and farther from 1. Therefore, the analysis device can also determine the glycated hemoglobin level based on the distribution of the time period ratio. For example, Figure 16A the device 104 in Figure 1 , the server 140 / 150 in Figure 1 , or a combination thereof can use the ratio of the red blood cell time period between T 出1 and T 出 2 to calculate the symmetry measurement of each blood cell reading. The device 104, the server 140 / 150, or a combination thereof can calculate the distribution of the symmetry measurement, for example, using a histogram or a scatter plot, for analysis. The device 104, the server 140 / 150, or a combination thereof can include a predetermined relationship or pattern, for example, through a look-up table or a predetermined equation, representing the correlation or connection between different symmetry measurements and the corresponding stiffness and glycated hemoglobin levels. Therefore, the device 104, the server 140 / 150, or a combination thereof can use the calculated distribution of the symmetry measurement or its statistical results (such as mean, median, deviation, peak, etc.) to a predetermined relationship or pattern to calculate the glycated hemoglobin level of the patient user.

[0094] Such as Figures 14A - 14DAs shown in FIGS. 16A - 16C, the number of red blood cells used to plot the ratio distribution can vary. For example, in some embodiments, the number of red blood cells measured and plotted can be at least 50, 100, 150, 200, 250, 300, 350, 400, 450, 500, 50 - 500, 150 - 300, or any other value. In some cases, the number of red blood cells available for analysis may be limited by other factors. For example, when measuring the deformability and / or symmetry of red blood cells, as described above Figures 16A - 16C shown, the influence of fluid velocity can be significant, so only red blood cells within a specified fluid velocity range are suitable for analysis.

[0095] Figure 17 FIG. X is a flowchart illustrating a method 1700 for analyzing red blood cells in a patient's blood sample according to some embodiments of the present technology. Method 1700 may include transferring red blood cells in a patient's blood sample through a microchannel sized to compress the red blood cells in block 1710. As described above with respect to Figure 4C FIG. X, the microchannel 230 can be narrower than the average red blood cell. Method 1700 may include analyzing the movement of individual red blood cells that move along and are compressed by the microchannel using one or more sensor assemblies disposed along the microchannel in block 1720. In some embodiments, the sensor assembly can include electrodes 240 and / or electrode strips (e.g., strips 352a, 352b, 352c, 362a, 362b, 362c), electrically coupled to the detection portion (e.g., a resistor connected to ground) as described above, such that a reference input signal can be sent through portions of the microchannel and / or the red blood cell and read by the detection portion.

[0096] Method 1700 may include determining a ratio between a first movement parameter and a second movement parameter of an individual red blood cell based on the analyzed movement of the red blood cell in block 1730. As described above, each of the first and second movement parameters can include movement time (wavelength or a portion thereof), speed / rate (instantaneous or average), acceleration, stiffness, deformability, symmetry, etc. In some embodiments, the sensor circuit can generate a signal indicating the rate of the corresponding red blood cell at each sensing region in the microchannel 230. The ratio can be derived from the rates of the same red blood cell at different sensing regions. Method 1700 may include determining an analyte characteristic of the red blood cell based on the determined ratio in block 1740. In some embodiments, the analyte characteristic can indicate a glycated hemoglobin level (e.g., HbA1c percentage).

[0097] In the event of an error condition in the microchannel 230 (e.g., a second red blood cell enters the observation window 234 before the first red blood cell leaves the observation window 234, causing an overlap), the electrodes of the first region 350 may detect two separate red blood cells, but the electrodes of the second region 360 may detect only one red blood cell due to the error condition. In this case, the next reading of the electrodes of the second region 360 may actually correspond to a third red blood cell, but may be interpreted as the second red blood cell. In some embodiments, given the average speed of red blood cells passing through the microchannel (e.g., 0.2 to 6.0 mm / sec), such an error condition is automatically detected based on at least the observed abnormal passing speed. Therefore, the glycated hemoglobin level measurement system can ignore these abnormal signal readings instead of pairing erroneous inlet and outlet measurements. Such abnormal detection can also be based on a predetermined average time period required for red blood cells to move from the first region 350 to the second region 360.

[0098] Figure 18 is a flow chart illustrating a method 1800 for analyzing cells in a patient's bodily fluid sample according to some embodiments of the present technology. The method 1800 may be used to diagnose or otherwise determine the risk of various diseases or conditions. For example, sickle red blood cells may be shaped differently than normal red blood cells, and thus an analysis device may be used to determine the above-mentioned Figure 15 A ratio or other combination (e.g., difference, average, weighted average, product) of any two or more sensor readings discussed, and correlating the determined ratio (or other combination) of sensor readings with data to diagnose or otherwise determine the risk of sickle cell disease. Other parameters, such as the number of red blood cells counted, may also be used to assess underlying disease. Thus, the present technology may be used to diagnose anemia, sickle cell disease, thalassemia, polycythemia vera, G6PD deficiency, autoimmune hemolytic anemia, hereditary spherocytosis, paroxysmal nocturnal hemoglobinuria, hemolytic uremic syndrome, malaria, leukemia, lymphoma, myelodysplastic syndrome, HIV / AIDS, neutropenia, mononucleosis, chronic granulomatous disease, hematologic cancers, autoimmune neutropenia, rheumatoid arthritis, systemic lupus erythematosus, and the like.

[0099] Method 1800 may include transferring cells (e.g., red blood cells, white blood cells, etc.) in block 1810 through a microchannel sized to compress the cells. Method 1800 may include using, in block 1820, one or more sensor circuits disposed along the microchannel to analyze the movement of individual cells moving along and compressed by the microchannel. Method 1800 may include sending, in block 1830, the analyzed movement of the individual cells to a machine learning model trained to recognize a medical condition based on the analyzed movement. In some embodiments, the machine learning model (hereinafter referred to as the "ML model") is trained on a cell movement data training set (e.g., fluid sample data of individuals with known diseases or medical conditions) to recognize a medical condition based on one or more movement parameters. The training data and the input data may be specific sensor reading measurements (e.g., time period of a signal, minimum and / or maximum voltage, shape of peaks and valleys, etc.), ratio distributions, or other combinations of measured sensor readings (e.g., symmetry), as discussed above with respect to Figures 14A - 16C and so on.

[0100] As used herein, "machine learning model" or "model" refers to a structure that is trained using training data to make predictions or provide probabilities for new data items, whether or not the new data items are included in the training data. For example, training data for supervised learning may include positive and negative items with various parameters and specified classifications. The new data item may have parameters that the model can use to assign a classification to the new data item. Another example, the model may be a probability distribution derived from an analysis of the training data, e.g., based on an analysis of a large corpus of events with corresponding times, the likelihood that a person has or will develop a particular medical condition within a given time frame. Examples of models include: neural networks, support vector machines, decision trees, Parzen windows, Bayesian, clustering, reinforcement learning, probability distributions, decision trees, forests of decision trees, etc. The model can be configured for various scenarios, data types, sources, and output formats.

[0101] In some embodiments, the condition recognition model can be a neural network having multiple input nodes that receive fluid sample data. The input nodes can correspond to functions that receive inputs and produce results. These results can be provided to one or more intermediate-level nodes, each of which produces further results based on a combination of the results of the lower-level nodes. A weighting factor can be applied to the output of each node before passing the results to the next layer of nodes. At the last layer ("output layer"), one or more nodes can produce values that classify the input, which, once the model is trained, can be used as an indicator of a potential condition. In some embodiments, such a neural network, known as a deep neural network, can have multiple layers of intermediate nodes with different configurations, can be a combination of models that receive different parts of the input and / or input from other parts of the deep neural network, or can be a convolution that partially uses the output of a previous application of the model iteration as further input to produce the result of the current input.

[0102] The condition recognition model can be trained through supervised learning, where the training data includes positive and negative training items as fluid sample data paired with other personal data (such as family history, demographic information) as input, and the desired output (such as the presence or absence of a specific condition). The model output can be compared with the desired output for that individual, and the model can be modified based on the comparison results, for example, by changing the weights between the neural network nodes or the function parameters used by each node in the neural network (e.g., applying a loss function). After applying each pair in the training data and modifying the model in this way, the model can be trained to evaluate new fluid sample data, thereby generating a list of current or potential conditions (and associated probabilities). In some embodiments, the present technology can store the analysis results of the user's fluid sample as further training data and use this data to update the machine learning model.

[0103] The condition recognition model can be trained through unsupervised learning, where the ML model can identify patterns, relationships, or other unique features in the data. For example, clustering can be used, where the ML model groups similar data points together based on some features or characteristics. In another example, dimensionality reduction (such as principal component analysis (PCA)) can be used, which involves simplifying the dataset and reducing its complexity by extracting the basic features.

[0104] Figure 19 is a block diagram illustrating an example of a processing system 1000 in which at least some of the operations described herein can be implemented. For example, a computing device (e.g., Figure 1 the analysis device 104 in, one or more client computing devices 120, or a combination thereof) can be implemented using the processing system 1000.

[0105] The processing system 1000 may include one or more central processing units 1002 (“processors”), main memory 1004, non-volatile memory 1006, network adapter 1008 (e.g., network interface), video display 1010, input / output device 1012, control device 1014 (e.g., keyboard and pointing device), drive unit 1016 including a storage medium, and / or signal generation device 1018 communicatively connected to bus 1020. Bus 1020 is an abstract concept representing one or more physical buses and / or point-to-point connections connected via appropriate bridges, adapters, or controllers. Thus, bus 1020 may include a system bus, a Peripheral Component Interconnect (PCI) bus or PCI-Express bus, a HyperTransport or Industry Standard Architecture (ISA) bus, a Small Computer System Interface (SCSI) bus, a Universal Serial Bus (USB), an IIC (I2C) bus, or an Institute of Electrical and Electronics Engineers (IEEE) standard 1394 bus (also known as “FireWire”).

[0106] The processing system 1000 may operate as a server or client machine in a client-server network environment, or as a peer machine in a peer-to-peer network environment. The processing system 1000 may be a medical device, a server, a personal computer, a tablet, a personal digital assistant (PDA), a mobile phone, a game control platform, a game device, a music player, a wearable electronic device, a network-connected (“smart”) device, a virtual / augmented reality system, or an analysis circuit in any other machine capable of executing a set of instructions (sequentially or otherwise) that specify actions to be taken by the processing system 1000.

[0107] Although main memory 1004, non-volatile memory 1006, and the storage medium (also referred to as “machine-readable medium”) are shown as a single medium, the terms “machine-readable medium” and “storage medium” should be considered to include a single medium or multiple media (e.g., a centralized or distributed database, and / or associated caches and servers) that store a set of or multiple sets of instructions. The terms “machine-readable medium” and “storage medium” should also include any medium that is capable of storing, encoding, or carrying a set of instructions for execution by the processing system 1000.

[0108] In general, routines executed to implement embodiments of the present disclosure may be implemented as part of an operating system or a specific application, component, program, object, module, or sequence of instructions (collectively referred to as “computer program”). A computer program typically includes one or more instructions (e.g., instructions) that are set at different times in different memories and storage devices of a computing device. When one or more processors read and execute the instructions, the instructions cause the processing system to perform operations to execute elements related to aspects of the present disclosure.

[0109] In addition, although the embodiments have been described in the context of a full-featured computing device, those skilled in the art will appreciate that the various embodiments can be distributed as a program product in a variety of forms. The present disclosure applies regardless of the specific type of machine or computer-readable medium used to actually effect the distribution.

[0110] Other examples of machine-readable storage media, machine-readable media, or computer-readable media include recordable media such as volatile and non-volatile storage devices, floppy disks and other removable disks, hard disk drives, optical disks (such as compact disc read-only memories (CD ROMs), digital versatile discs (DVDs)), and transmission media such as digital and analog communication links.

[0111] Network adapter 1008 enables the processing system to interact with external entities of the processing system in a network through any communication protocol supported by the processing system and the external entity. Network adapter 1008 may include one or more of a network adapter card, a wireless network interface card, a router, an access point, a wireless router, a switch, a multilayer switch, a protocol converter, a gateway, a bridge, a bridge router, a hub, a digital media receiver, and / or a repeater.

[0112] Network adapter 1008 may include a firewall for controlling and / or managing data access / proxy permissions in a computer network and tracking different trust levels between different machines and / or applications. The firewall can be any number of modules with any combination of hardware and / or software components capable of enforcing a set of predefined access permissions (e.g., regulating traffic and resource sharing between these entities) between a specific set of machines and applications, machines and machines, and / or applications and applications. The firewall can also manage and / or access an access control list that details various permissions, including access and operation permissions of individuals, machines, and / or applications to objects, as well as the context in which the permissions exist.

[0113] The techniques introduced here can be implemented by programmable circuitry (such as one or more microprocessors), software, and / or firmware, dedicated hardwired (i.e., non-programmable) circuitry, or combinations of these forms. The dedicated circuitry can be one or more application specific integrated circuits (ASICs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), etc.

[0114] As described above, in some embodiments, the degree of glycation can be measured by changes in the physical characteristics of red blood cells caused by glycation. In some embodiments, compared to devices using biochemical techniques, a system using the disclosed techniques (e.g., calculating mechanical properties such as stiffness or hardness based on the microchannel transit time of each individual red blood cell) can more stably determine the degree of glycation in response to external and human factors. In some embodiments, a glycated hemoglobin level measurement system can utilize a circuit configuration to detect minute electrical changes occurring due to the passage of red blood cells and determine the degree of glycation of the red blood cells. In some embodiments, by correcting the initial calculation of the glycated hemoglobin level using a reference value of an individual user, the glycated hemoglobin level measurement system can be directly used for clinical diagnosis.

[0115] The system can store one or more analyte management programs, calibration routines, or protocols. In some embodiments, an analyte management program can indicate whether the measured analyte level is within a target or healthy range (e.g., an HbA1c level of 4%-6% of total hemoglobin). The HbA1c level can indicate the effectiveness of subject-specific blood glucose management over a period of time (such as the previous month or months prior to analysis). If a subject's level is high (e.g., the HbA1c level exceeds 8% of total hemoglobin), the subject may have diabetes or prediabetes. The subject can take measures to reduce the HbA1c level to an acceptable target level (e.g., the HbA1c level is equal to or lower than 5%, 6%, or 7% of total hemoglobin). The healthy range and target levels can be input by the user, a healthcare provider, or other sources.

[0116] In addition, the glycated hemoglobin level measurement system can be implemented via a computer-readable storage medium or a similar device, such as using software, hardware, or a combination thereof. In a hardware implementation, the glycated hemoglobin level measurement system can be implemented using at least one of an application-specific integrated circuit (ASIC), a digital signal processor (DSP), a digital signal processing device (DSPD), a programmable logic device (PLD), a field-programmable gate array (FPGA), a processor, a controller, a microcontroller, a microprocessor, and an electrical unit that performs other functions. In some embodiments, the glycated hemoglobin level measurement system can be implemented by the control module itself. In a software implementation, one or more aspects of the glycated hemoglobin level measurement system, such as the above programs and functions, can be implemented as separate software modules. Each software module can perform one or more functions and operations described in this specification. The software code can be implemented as a software application written in a suitable programming language. The software code can be stored in a storage module and executed by the control module.

[0117] The embodiments, features, systems, devices, materials, methods, and techniques described herein may be similar to any one or more of the embodiments, features, systems, devices, materials, methods, and techniques described below in some embodiments:

[0118] Korean Patent Application No. 10-2021-0128520, filed on September 29, 2021, published as Korean Patent No. 10-2439474;

[0119] International Application PCT / KR2021 / 018280, filed on December 3, 2021;

[0120] U.S. Application filed on December 9, 2022, with the invention name "Device and Related Methods for Measuring Glycation of Red Blood Cells and Glycated Hemoglobin Levels Using Physical and Electrical Properties of Cells"; Attorney Docket No.: 149800-8001.US01, List of Inventors: Ung-Hyeon Ko, Seung-Jin Kang, and Eun-Young Park;

[0121] International Application PCT / KR2022 / 019905, filed on December 8, 2022;

[0122] Korean Patent Application No. 10-2022-0031378, filed on March 14, 2022; and

[0123] U.S. Application filed on December 9, 2022, with the invention name "Device and Related Methods for Measuring Characteristics of Particles in a Solution"; Attorney Docket No.: 149800-8002.US00, List of Inventors: Ung-Hyeon Ko, Seung-Jin Kang, and Eun-Young Park.

[0124] The entire contents of all the above patents and applications are incorporated herein by reference. In addition, the embodiments, features, systems, devices, materials, methods, and techniques described herein may be applied to any one or more of the embodiments, features, systems, devices, or other substances, or used in combination therewith, in some embodiments.

[0125] The above description is only for explaining the technical idea of the present disclosure, and those skilled in the art can make various modifications, changes, and substitutions without departing from the basic features of the present disclosure. Therefore, the embodiments described above and in the accompanying drawings are intended to describe the present technology, rather than limiting the related technical idea. The scope of the present technology is not limited by any of the above embodiments and drawings.

[0126] It will be apparent to those skilled in the art that details of the above embodiments may be changed without departing from the basic principles of the present disclosure. In some cases, well-known structures and functions are not shown or described in detail in order to avoid unnecessarily obscuring the description of the embodiments of the present technology. Although the steps of the method may be presented herein in a particular order, alternative embodiments may execute these steps in a different order. Similarly, some aspects of the present technology disclosed in a particular embodiment may be combined or removed in other embodiments. In addition, although the advantages associated with some embodiments of the present technology may be disclosed in the context of these embodiments, other embodiments may also exhibit these advantages, and not all embodiments must exhibit these advantages or other advantages disclosed herein to fall within the scope of the present technology. Accordingly, the present disclosure and related technologies may include other embodiments not expressly shown or described herein, and the invention is not limited except as by the appended claims.

[0127] In the present disclosure, unless the context clearly indicates otherwise, the singular terms "a", "an", and "the" include the plural. Further, the terms "comprising", "including", and "having" shall be construed to include at least the stated features, and thus do not preclude any greater number of the same features and / or other types of features.

[0128] References herein to "one embodiment", "an embodiment", "some embodiments", or similar expressions mean that a particular feature, structure, operation, or characteristic associated with the embodiment may be included in at least one embodiment of the present technology. Thus, such phrases or expressions appearing herein do not necessarily all refer to the same embodiment. Further, the various specific features, structures, operations, or characteristics may be combined in any suitable manner in one or more embodiments.

[0129] Unless stated to the contrary, the numerical parameters recited in the following specification and claims are approximations that may vary depending upon the desired properties sought to be obtained by the present technology. At the very least, and not as an attempt to limit the application of the doctrine of equivalents to the scope of the claims, each numerical parameter should at least be construed in light of the number of reported significant digits and by applying ordinary rounding techniques. In addition, all ranges disclosed herein should be understood to include any and all subranges subsumed therein. For example, a range of "1 to 10" includes all subranges between (and including) the minimum value 1 and the maximum value 10, that is, all subranges having a minimum value equal to or greater than 1 and a maximum value equal to or less than 10, such as 5.5 to 10.

[0130] The foregoing disclosure should not be construed as requiring that any claim have more features than are expressly recited in that claim. On the contrary, as reflected in the following claims, the inventive step lies in a combination of fewer features than all the features of any single foregoing disclosed embodiment. Accordingly, the claims following this detailed description are hereby expressly incorporated into this detailed description, each claim standing on its own as a separate embodiment. The present disclosure includes all permutations of the independent claims and their dependent claims.

[0131] For convenience, aspects of the technology are illustrated, for example, as numbered clauses (1, 2, 3, etc.) according to the following description. These are provided as examples and do not limit the technology. It should be noted that any dependent clauses can be combined arbitrarily and placed into the corresponding independent clauses. Other clauses can be presented in a similar manner.

[0132] 1. A method for analyzing red blood cells in a patient's blood sample, the method comprising:

[0133] transferring red blood cells through a microchannel sized to compress the red blood cells;

[0134] analyzing the movement of individual red blood cells moving along and compressed by the microchannel using one or more sensor circuits disposed along the microchannel;

[0135] determining a ratio between a first movement parameter and a second movement parameter of an individual red blood cell based on the analyzed movement of the red blood cell; and

[0136] determining an analyte characteristic of the red blood cell based on the determined ratio.

[0137] 2. The method according to any clause herein, wherein the first movement parameter includes a first velocity of the red blood cell in a first region of the microchannel, and wherein the second movement parameter includes a second velocity of the red blood cell in a second region of the microchannel.

[0138] 3. The method according to any clause herein, wherein the first movement parameter includes the velocity of the red blood cell in a region of the microchannel, and wherein the second movement parameter includes the average velocity of the red blood cell along the microchannel.

[0139] 4. The method according to any clause herein, wherein analyzing the movement of an individual red blood cell includes obtaining a voltage signal, wherein the first movement parameter includes the width of a peak of the voltage signal, and wherein the second movement parameter includes the width of a valley of the voltage signal.

[0140] 5. The method according to any clause herein, wherein the ratio between the first movement parameter and the second movement parameter indicates the degree of symmetry of the red blood cell shape.

[0141] 6. The method according to any clause herein, further comprising:

[0142] Determine the distribution of the determined ratios of red blood cells, wherein based on this distribution, the analyte characteristics of red blood cells are further determined.

[0143] 7. The method according to any clause herein, wherein the determined distribution includes the determined ratios of at least 150 red blood cells.

[0144] 8. The method according to any clause herein, wherein the analyte characteristics indicate the glycated hemoglobin levels of one or more red blood cells.

[0145] 9. The method according to any clause herein, wherein transferring red blood cells includes passively transferring red blood cells through a microchannel by capillary action.

[0146] 10. The method according to any clause herein, wherein transferring red blood cells includes passively transferring red blood cells through a microchannel by evaporation at the microchannel outlet.

[0147] 11. A cartridge for analyzing red blood cells in a patient's blood sample, the cartridge comprising:

[0148] A cartridge body including a blood cell compression microchannel; and

[0149] A blood cell analyzer coupled to the cartridge body for analyzing individual red blood cells compressed by and moving along the blood cell compression microchannel, wherein the blood cell analyzer is configured to output a signal indicating one or more movement parameters of the compressed red blood cells at multiple positions along the blood cell compression microchannel to determine the analyte characteristics of red blood cells.

[0150] 12. The cartridge according to any clause herein, wherein the blood cell compression channel is configured to interact with individual blood cells to generate a first movement parameter for each blood cell and a second movement parameter that is sufficiently different from the first movement parameter to determine the movement parameter ratio of the corresponding blood cell.

[0151] 13. The cartridge according to any clause herein, wherein the compression channel has an inlet portion, an outlet portion, and an observation window extending between the inlet portion and the outlet portion, wherein a first portion of the observation window near the inlet portion is configured to allow red blood cells to flow in an uncompressed state, and a second portion of the observation window near the outlet portion is configured to allow red blood cells to flow in a compressed state.

[0152] 14. A system for analyzing red blood cells in a patient's blood sample, the system comprising:

[0153] A cartridge, comprising:

[0154] A cartridge body including a blood cell compression microchannel sized to compress red blood cells;

[0155] A blood cell analyzer for analyzing a single compressed red blood cell located in a blood cell compression microchannel; and

[0156] An analysis device configured to be operably coupled to a cartridge to communicate with the blood cell analyzer, wherein the analysis device is programmed to cause the blood cell analyzer to analyze the compressed red blood cells to determine movement parameters associated with the respective compressed red blood cells at multiple locations along the blood cell compression microchannel.

[0157] 15. The system according to any clause herein, wherein the analysis device is programmed to determine an analyte characteristic of the red blood cells based on a comparison of the movement parameters.

[0158] 16. The system according to any clause herein, wherein the compression channel is configured to interact with a single blood cell to generate a first movement parameter for each blood cell and a second movement parameter that is sufficiently different from the first movement parameter to determine a ratio of the movement parameters, wherein the analysis device includes one or more sensor circuits that can be used to measure signals of the respective red blood cells to determine the ratio of the movement parameters of the respective red blood cells.

[0159] 17. A method of analyzing cells in a patient body fluid sample, the method comprising:

[0160] Transferring the cells through a microchannel sized to compress the cells;

[0161] Analyzing the movement of a single cell moving along and compressed by the microchannel using one or more sensor circuits disposed along the microchannel; and

[0162] Sending the analyzed movement of the single cell to a machine learning model that is trained to identify a medical condition based on the analyzed movement, wherein the machine learning model is trained on a cell movement data training set to identify a medical condition based on one or more movement parameters.

[0163] 18. The method according to any clause herein, wherein the analyzed movement includes the wavelength of a signal received by one or more sensor circuits.

[0164] 19. The method according to any clause herein, wherein the analyzed movement includes the amplitude of a signal received by one or more sensor circuits.

[0165] 20. The method according to any clause herein, wherein the analyzed movement includes the symmetry of a signal received by one or more sensor circuits.

[0166] 21. The method according to any clause herein, wherein the analyzed movement includes the count of cells during a predetermined measurement period.

[0167] 22. A method according to any clause herein, wherein the machine learning model is trained to recognize a medical condition including at least one of the following: anemia, sickle cell disease, thalassemia, polycythemia vera, G6PD deficiency, autoimmune hemolytic anemia, hereditary spherocytosis, paroxysmal nocturnal hemoglobinuria, hemolytic uremic syndrome, malaria, leukemia, lymphoma, myelodysplastic syndrome, HIV / AIDS, neutropenia, mononucleosis, chronic granulomatous disease, hematological cancer, autoimmune neutropenia, rheumatoid arthritis, or systemic lupus erythematosus.

[0168] 23. A computer-readable medium including processor instructions that, when executed by one or more processors, cause the one or more processors to:

[0169] Receive a set of sensor outputs representing the movement of each red blood cell through a microchannel sized to compress red blood cells;

[0170] Based on the analyzed movement of the red blood cells, determine a comparative measurement between the set of sensor outputs for each red blood cell; and

[0171] Determine an analyte characteristic of the red blood cells based on the comparative measurement.

[0172] 24. A non-transitory computer-readable medium according to any clause herein, wherein:

[0173] The received set of sensor outputs includes at least: (1) a first movement parameter representing the passage of the corresponding red blood cell through a first sensing region in the microchannel; (2) a second movement parameter representing the passage of the corresponding red blood cell through a second sensing region in the microchannel; and

[0174] The comparative measurement represents a change in the movement of each red blood cell between the first and second sensing regions.

[0175] 25. A non-transitory computer-readable medium according to any clause herein, wherein:

[0176] The first movement parameter represents a first rate at which the corresponding red blood cell traverses the first sensing region;

[0177] The second movement parameter represents a second rate at which the corresponding red blood cell traverses the second sensing region;

[0178] The comparative measurement includes a ratio between the first and second movement parameters; and

[0179] The analyte characteristic indicates a glycated hemoglobin level representing the stiffness of one or more red blood cells.

[0180] 26. A non-transitory computer-readable medium according to any clause herein, wherein:

[0181] The output of the set of sensors includes the difference between (1) an initial signal from an initial electrode located along the microchannel before the reference electrode and (2) a subsequent signal from a subsequent electrode located after the reference electrode,

[0182] wherein both the initial signal and the subsequent signal are either a reference signal or a derivative signal thereof transmitted from the reference electrode and received through a corresponding portion of the microchannel,

[0183] wherein the difference represents a level change corresponding to a voltage change and / or a phase change in the reference signal caused by the proximity or overlap between the corresponding red blood cell and the reference electrode, the initial electrode, and the subsequent electrode,

[0184] wherein the difference includes: (1) an initial change above or below an initial state, representing partial overlap of the corresponding red blood cell passing through the initial electrode with the reference electrode; (2) a midpoint matching the initial state; and then (3) a subsequent change opposite in polarity or direction to the initial change, representing the corresponding red blood cell passing through the reference electrode and past the subsequent electrode;

[0185] The comparison measurement represents a comparison of the initial change and the subsequent change in terms of amplitude, shape, width, or a combination thereof; and

[0186] The analyte characteristic represents a glycated hemoglobin level, an evaluated blood disorder, or a combination thereof based on the comparison measurement of red blood cells.

Claims

1. A method for analyzing red blood cells in a patient's blood sample, the method comprising: Transferring the red blood cells through a microchannel sized to compress the red blood cells; Using one or more sensor circuits disposed along the microchannel to analyze the movement of a single one of the red blood cells moving along and compressed by the microchannel; Determining a ratio between a first movement parameter and a second movement parameter of a single one of the red blood cells based on the analyzed movement of the red blood cell; And Determining an analyte characteristic of the red blood cell based on the determined ratio.

2. The method according to claim 1, wherein the first movement parameter includes a first velocity of the red blood cell in a first region of the microchannel, and wherein the second movement parameter includes a second velocity of the red blood cell in a second region of the microchannel.

3. The method according to claim 1, wherein the first movement parameter includes a velocity of the red blood cell in a region of the microchannel, and the second movement parameter includes an average velocity of the red blood cell along the microchannel.

4. The method according to claim 1, wherein analyzing the movement of a single one of the red blood cells includes obtaining a voltage signal, wherein the first movement parameter includes a width of a peak of the voltage signal, and wherein the second movement parameter includes a width of a valley of the voltage signal.

5. The method according to claim 1, wherein the ratio between the first movement parameter and the second movement parameter indicates a degree of symmetry of the shape of the red blood cell.

6. The method according to claim 1, further comprising: Determining a distribution of the determined ratios of the red blood cells, wherein the analyte characteristic of the red blood cells is further determined based on the distribution.

7. The method according to claim 1, wherein transferring the red blood cells includes passively transferring the red blood cells through the microchannel by capillary action.

8. The method according to claim 1, wherein transferring the red blood cells includes passively transferring the red blood cells through the microchannel by evaporation at an outlet of the microchannel.

9. A cartridge for analyzing red blood cells in a patient's blood sample, the cartridge comprising: A cartridge body including a blood cell compression microchannel; And A blood cell analyzer coupled to the cartridge body for analyzing a single red blood cell compressed by and moving along the blood cell compression microchannel, wherein the blood cell analyzer is configured to output a signal indicative of one or more movement parameters of the compressed red blood cell at a plurality of positions along the blood cell compression microchannel to determine an analyte characteristic of the red blood cell.

10. The cartridge according to claim 9, wherein the blood cell compression channel is configured to interact with the single blood cell to produce a first movement parameter of each blood cell and a second movement parameter sufficiently different from the first movement parameter for determining a ratio of movement parameters of the corresponding blood cell.

11. The cartridge according to claim 9, wherein the compression channel has an inlet portion, an outlet portion, and a viewing window extending between the inlet portion and the outlet portion, wherein a first portion of the viewing window near the inlet portion is configured to allow red blood cells to flow in an uncompressed state, and wherein a second portion of the viewing window near the outlet portion is configured to allow red blood cells to flow in a compressed state.

12. A method of analyzing cells in a patient's body fluid sample, the method comprising: transferring the cells through a microchannel sized to compress the cells; analyzing the movement of a single one of the cells moving along and compressed by the microchannel using one or more sensor circuits disposed along the microchannel; and sending the analyzed movement of the single cell to a machine learning model trained to identify a medical condition based on the analyzed movement, wherein the machine learning model is trained on a cell movement data training set to identify a medical condition based on one or more movement parameters.

13. The method according to claim 12, wherein the analyzed movement includes the wavelength of a signal received by the one or more sensor circuits.

14. The method according to claim 12, wherein the analyzed movement includes the amplitude of a signal received by the one or more sensor circuits.

15. The method according to claim 12, wherein the analyzed movement includes the symmetry of a signal received by the one or more sensor circuits.

16. The method according to claim 12, wherein the analyzed movement includes the count of cells during a predetermined measurement period.

17. A computer-readable medium comprising processor instructions that, when executed by one or more processors, cause the one or more processors to: receive a set of sensor outputs representative of the movement of each red blood cell through a microchannel sized to compress the red blood cell; determine a comparative measurement between the set of sensor outputs for each red blood cell based on the analyzed movement of the red blood cell; and determine an analyte characteristic of the red blood cell based on the comparative measurement.

18. The non-transitory computer-readable medium according to claim 17, wherein: the received set of sensor outputs includes at least: (1) a first movement parameter representative of a respective red blood cell passing through a first sensing region in the microchannel; (2) a second movement parameter representative of the respective red blood cell passing through a second sensing region in the microchannel; and the comparative measurement represents a change in the movement of each red blood cell between the first and second sensing regions.

19. The non-transitory computer-readable medium according to claim 18, wherein the first movement parameter represents a first rate at which a respective red blood cell traverses the first sensing region; the second movement parameter represents a second rate at which the respective red blood cell traverses the second sensing region; the comparative measurement includes a ratio between the first movement parameter and the second movement parameter; and the analyte characteristic indicates a glycated hemoglobin level representative of the stiffness of one or more red blood cells.

20. The non-transitory computer-readable medium according to claim 17, wherein: the set of sensor outputs includes a difference between (1) an initial signal emitted by an initial electrode located along the microchannel before a reference electrode and (2) a subsequent signal emitted by a subsequent electrode located after the reference electrode, wherein the initial signal and the subsequent signal are each a reference signal or a derivative signal thereof transmitted from the reference electrode and received across a corresponding portion of the microchannel, wherein the difference represents a level change corresponding to a voltage change and / or a phase change in the reference signal caused by the proximity or overlap between a corresponding red blood cell and the reference electrode, the initial electrode, and the subsequent electrode, wherein the difference includes: (1) an initial change above or below an initial state, representing a partial overlap of a corresponding red blood cell passing through the initial electrode with the reference electrode; (2) a midpoint matching the initial state; and (3) a subsequent change opposite in polarity or direction to the initial change, representing a corresponding red blood cell passing through the reference electrode and past the subsequent electrode; comparative measurement represents a comparison of the initial change and the subsequent change in terms of amplitude, shape, width, or a combination thereof; and analyte characteristic represents a glycated hemoglobin level, an evaluated blood disease, or a combination thereof based on the comparative measurement of the red blood cells.

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