Colorectal cancer microbial detection integrated screening kit based on nucleic acid mass spectrometry

Abstract

The present application relates to the technical field of microorganism detection, in particular to a colorectal cancer microorganism detection integrated screening kit based on nucleic acid mass spectrometry technology, comprising a sample extraction module, an amplification processing module, an extension purification module, a mass spectrometry detection module and a screening early warning module.In the present application, the host and microorganism genomes are simultaneously separated, the absorbance is calculated to control the amplification cycle, phosphatase hydrolysis and resin adsorption purification are introduced to extend the product, biochemical residual background interference is avoided, the absolute mass-to-charge ratio deviation of the terminal base is scanned by time-of-flight mass spectrometry, the target signal peak is screened by comparing the standard molecular weight, the optical channel which is easily disturbed and the long sequencing alignment are abandoned, the susceptible mutation and characteristic pathogenic bacteria are combined to establish a joint rating criterion, the nucleic acid extraction and purification are fully connected to the integrated test path of disease risk grading, the accuracy of colorectal cancer microorganism detection is improved, the timeliness of clinical early warning is enhanced, and subsequent timely intervention treatment is facilitated.

Description

Technical Field

[0001] This invention relates to the field of microbial detection technology, and in particular to an integrated screening kit for colorectal cancer microbial detection based on nucleic acid mass spectrometry technology. Background Technology

[0002] The field of microbial detection technology mainly involves the qualitative and quantitative determination of microorganisms such as bacteria, fungi, or viruses present in samples using biochemical methods. The core processes include sample pretreatment, nucleic acid extraction, target gene amplification, and biochemical product detection. It primarily relies on extracting and testing specific gene sequences or metabolites of target microorganisms to determine their species and abundance. Among these, the integrated screening kit for colorectal cancer microorganisms refers to a biochemical testing kit that identifies and measures the concentration of specific microbial communities related to intestinal lesions in fecal samples from test subjects. It typically involves adding cell lysis buffer and protease to a fecal suspension to disrupt the microbial cell walls and release internal nucleic acids. The extract is then separated and purified using magnetic bead elution. Subsequently, nucleic acid primers and fluorescent probes designed for specific target strains are added, and polymerase chain reaction (PCR) is performed in a thermal cycler. The amplified products are measured based on the fluorescence signal release intensity in each cycle. Alternatively, the extracted nucleic acids can be fragmented and ligated with adapter sequences to construct a gene library, which is then sent to a sequencer to read the base sequence. The resulting nucleic acid sequencing sequences are then compared one-to-one with a standard microbial reference genome sequence library.

[0003] Current methods for detecting colorectal cancer microorganisms rely excessively on fluorescence signal intensity or sequencing alignment to determine species and abundance. Fluorescent probes are highly susceptible to background noise in complex systems, leading to reduced target specificity and increased risk of data distortion. Sequencing library construction and sequence alignment procedures are cumbersome and time-consuming. Sample extraction and subsequent testing are disconnected, making it difficult to simultaneously identify host susceptibility genes and microbial communities. The complex preliminary preparation and multi-stage testing steps are easily affected by operational fluctuations, directly restricting the universality of simultaneous screening with multiple biochemical biomarkers in clinical settings and the timeliness of pathological early warning. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing technologies and propose an integrated screening kit for colorectal cancer microbial detection based on nucleic acid mass spectrometry technology.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: An integrated screening kit for colorectal cancer microbial detection based on nucleic acid mass spectrometry technology includes: The sample extraction module collects fecal samples, adds binding and elution buffers to separate the host genome and microbial genome, compares nucleic acid absorbance parameters at different specified wavelengths, and calculates the proportion of extracted nucleic acid absorbance. The amplification processing module, based on the ratio of extracted nucleic acid absorbance, prepares primers for susceptible genes and primers specific to pathogenic Escherichia coli, compares polymerase chain reaction cycle parameters with the set cycle baseline, and screens and statistically analyzes the amplification ratio data of target genes. The extended purification module, based on the target gene amplification ratio data, uses mixed shrimp alkaline phosphatase to hydrolyze unreacted deoxyribonucleoside triphosphate, screens for extended reaction products that meet the preset desalination resin adsorption standards, extracts the mass-to-charge ratio parameter of the adsorbed product at the end, compares it with the wild-type standard mass-to-charge ratio benchmark to determine the deviation space, and obtains single-base extension deviation data. The mass spectrometry detection module, in response to the single-base extension deviation data, spots the extended purified product on the chip matrix, starts the time-of-flight mass spectrometer to scan the purified product spectrum, extracts the measured molecular weight parameter of the target point in the mass spectrum, compares the measured molecular weight parameter of the target point with the set built-in reference standard molecular weight, screens the detection signal peaks within the allowable molecular weight drift range, determines the target points whose intensity parameter of the selected detection signal peak is above the positive judgment benchmark, and generates characteristic target peak signal records; The screening and early warning module determines the corresponding positive risk level of Fusobacterium nucleatum and susceptibility gene mutation based on the recorded characteristic target peak signals, matches it with a preset pathological risk level table, and outputs the integrated screening results for colorectal cancer microorganisms.

[0006] As a further embodiment of the present invention, the kit further includes a housing, wherein the sample extraction module, the amplification processing module, the extension purification module, the mass spectrometry detection module, and the screening and early warning module are all disposed inside the housing.

[0007] As a further aspect of the present invention, in the process of screening and statistically analyzing the target gene amplification ratio data, the index fold abundance exceeding the set cycle benchmark is selected, and the relative proportion of the overall amplified parameters of the integrated reaction system is extracted as the target gene amplification ratio data.

[0008] As a further aspect of the present invention, the extracted nucleic acid absorbance ratio includes the amount of purified endogenous nucleic acid material, the amount of exogenous target elution, and the inhibition coefficient of impurity proteins; the targeted gene amplification ratio data includes the absolute value of target fragment enrichment, the amount of competitive inhibition antagonism, and the degree of primer dimer consumption; the single base extension deviation data includes the probe binding efficiency value, the mass spectrometry shift of the mutation site, and the degree of free enzyme system residue; the characteristic target peak signal recording includes the absolute peak intensity, the background noise coverage, and the spectral baseline flatness; and the integrated screening results for colorectal cancer microorganisms include the degree of intestinal microecological imbalance, the latency period of early-onset tumors, and the urgency of outpatient intervention.

[0009] As a further aspect of the present invention, the sample extraction module includes: The genome separation submodule collects fecal samples, adds binding and elution buffers to separate the host genome and microbial genomes to generate the microbial phase volume, measures the separation layer height in the test tube container, performs a phase division operation on the microbial phase volume and elution buffer volume and multiplies it by the separation layer height to generate the genome separation degree. The photometric comparison submodule compares the nucleic acid absorbance parameters at a specified wavelength, collects the irradiation path area of ​​the target light source, multiplies the nucleic acid absorbance parameters with the genome separation degree and divides them by the irradiation path area to generate the wavelength absorbance product. The proportion coordination submodule obtains the preset absorbance test benchmark value, divides the wavelength absorbance product by the absorbance test benchmark value and multiplies it by the test extraction constant to perform overall comprehensive proportion determination, and generates the nucleic acid absorbance ratio.

[0010] As a further aspect of the present invention, the amplification processing module includes: The cycle comparison submodule, based on the extracted nucleic acid absorbance ratio, prepares susceptible gene primers and pathogenic E. coli-specific primers to obtain the initial mixed concentration, collects polymerase chain reaction cycle parameters and sets a cycle baseline, calculates the degree of deviation of the polymerase chain reaction cycle parameters from the set cycle baseline, multiplies the degree of deviation by the initial mixed concentration to screen for exponentially increased abundance exceeding the set cycle baseline, and generates a targeted multiplier abundance value; The share statistics submodule monitors the overall amplified parameters of the pre-defined region mapping associated reaction system, divides the overall amplified parameters of the reaction system by the target doubling abundance value to extract the relative integration share variable of the amplified, merges the external input records of the full parallel test channel array, and performs a scalar weighted summation operation in combination with the relative integration share variable of the amplified to generate the target gene amplification ratio data.

[0011] As a further aspect of the present invention, the extended purification module includes: The processing and screening submodule, based on the target gene amplification ratio data, uses shrimp alkaline phosphatase to dephosphorylate the unconsumed dNTPs in the amplification products to extract the residual product adsorption ratio, and screens items that meet the condition that the residual product adsorption ratio is greater than the preset desalination resin adsorption standard. The residual product adsorption ratio is multiplied by the judgment compliance coefficient to generate the amount of extension reaction product. The mass-charge parameter quantum module collects the terminal base parameter of the adsorption product based on the amount of the extended reaction product, multiplies the terminal base parameter of the adsorption product by the amount of the extended reaction product and divides it by the built-in offset constant to generate the terminal base mass-charge ratio value. The deviation determination submodule extracts the wild-type standard mass-charge ratio benchmark for the terminal base mass-charge ratio value, calculates the correlation difference parameter obtained by subtracting the wild-type standard mass-charge ratio benchmark from the terminal base mass-charge ratio value, and generates single base extension deviation data.

[0012] As a further aspect of the present invention, the mass spectrometry detection module includes: The mass spectrometry scanning submodule, for the single base extension deviation data, spots the extended purification product on the chip matrix, starts the time-of-flight mass spectrometer to extract the measured molecular weight parameters of the target point in the mass spectrum, and divides the measured molecular weight parameters of the target point in the mass spectrum by the spotting distribution constant to generate the target molecular weight value. The peak screening submodule extracts the set reference standard molecular weight, compares the target molecular weight value with the set built-in reference standard molecular weight to screen the detection signal peaks that are within the allowable molecular weight drift range, and multiplies the target molecular weight value with the detection signal peak to generate the range signal peak quantity. The target determination submodule acquires the peak intensity parameter of the detection signal, determines that the peak intensity parameter of the detection signal is above the positive determination benchmark for the target point, and associates the target point with the peak intensity parameter of the detection signal multiplied by the interval signal peak quantity to generate a characteristic target peak signal record.

[0013] As a further aspect of the present invention, the screening and early warning module includes: The feature extraction submodule extracts the corresponding detection parameters of Fusobacterium nucleatum and the susceptibility gene mutation parameters based on the recorded feature target peak signals. It compares the corresponding detection parameters of Fusobacterium nucleatum and the susceptibility gene mutation parameters with the preset positive judgment baseline, filters feature sequences that meet the corresponding judgment conditions, and generates joint positive feature items. The risk assessment submodule obtains the clinical risk level classification criteria, determines the corresponding level echelon of the combined positive feature item within the clinical risk level classification criteria, and generates the combined positive risk level. The pathology matching submodule receives a preset pathology risk level table, performs an association matching and positioning operation on the combined positive risk level and the various stage parameters set in the preset pathology risk level table, extracts the node matching success status indicator, and generates an integrated screening result for colorectal cancer microorganisms.

[0014] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In this invention, the host and microbial genomes are simultaneously separated and the amplification cycle is controlled by calculating absorbance. Phosphatase hydrolysis and resin adsorption are introduced to purify the extended products, avoiding interference from biochemical residual background. Based on the deviation of the absolute mass-to-charge ratio of the terminal bases by time-of-flight mass spectrometry scanning, the target signal peak is screened by comparing with standard molecular weight. The easily interfered optical channel and the lengthy sequencing comparison are abandoned. A joint rating and discrimination benchmark for susceptible mutations and characteristic pathogenic bacteria is established. The integrated testing path from nucleic acid extraction and purification to disease risk classification is fully connected, which improves the accuracy of colorectal cancer microbial detection and enhances the timeliness of clinical early warning, facilitating timely subsequent intervention and treatment. Attached Figure Description

[0015] Figure 1 This is a flowchart of the reagent kit of the present invention; Figure 2 This is a flowchart of the sample extraction module of the present invention; Figure 3 This is a flowchart of the amplification processing module of the present invention; Figure 4 This is a flowchart of the extended purification module of the present invention; Figure 5 This is a flowchart of the mass spectrometry detection module of the present invention; Figure 6 This is a flowchart of the screening and early warning module of the present invention. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0017] In the description of this invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, in the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0018] Please see Figure 1 An integrated screening kit for colorectal cancer microbial detection based on nucleic acid mass spectrometry technology includes: The sample extraction module collects fecal samples, adds binding and elution buffers to separate the host genome and microbial genome, compares nucleic acid absorbance parameters at different specified wavelengths, and statistically analyzes the proportion of extracted nucleic acid absorbance. The amplification processing module prepares primers for susceptible genes and specific primers for pathogenic E. coli based on the ratio of nucleic acid absorbance. It compares the polymerase chain reaction cycle parameters with the set cycle baseline, screens for exponential fold abundances that exceed the set cycle baseline, integrates the overall amplified parameters of the reaction system to extract the relative proportions, and statistically analyzes the amplification ratio data of the target genes. Based on the target gene amplification ratio data, the extended purification module uses mixed shrimp alkaline phosphatase to hydrolyze unreacted deoxyribonucleoside triphosphate, screens for extended reaction products that meet the preset desalination resin adsorption standards, extracts the mass-to-charge ratio parameter of the adsorbed product at the end, compares it with the wild-type standard mass-to-charge ratio benchmark to determine the deviation space, and obtains single-base extension deviation data. The mass spectrometry detection module targets single-base extension deviation data, spots and extends the purified product on the chip matrix, starts the time-of-flight mass spectrometer to scan the purified product spectrum, extracts the measured molecular weight parameter of the target point in the mass spectrum, compares the measured molecular weight parameter of the target point with the set built-in reference standard molecular weight, screens the detection signal peaks within the allowable molecular weight drift range, and judges the target points whose detection signal peak intensity parameter is above the positive judgment benchmark, generating characteristic target peak signal records; The screening and early warning module determines the corresponding positive risk level of Fusobacterium nucleatum and susceptibility gene mutation based on the characteristic target peak signal record, matches it with the preset pathological risk level table, and outputs the integrated microbial screening results for colorectal cancer.

[0019] The proportion of nucleic acid absorbance extracted includes the amount of purified endogenous nucleic acid, the amount of exogenous target elution, and the inhibition coefficient of impurity proteins; the proportion of targeted gene amplification data includes the absolute value of target fragment enrichment, the amount of competitive inhibition antagonism, and the degree of primer dimer consumption; the single base extension deviation data includes the probe binding efficiency value, the mass spectrometry shift of the mutation site, and the degree of free enzyme system residue; the characteristic target peak signal recording includes the absolute peak intensity, the background noise coverage, and the spectral baseline flatness; the integrated screening results of colorectal cancer microbiome include the degree of intestinal microecological imbalance, the latency period of early-onset tumors, and the urgency of outpatient intervention.

[0020] Please see Figure 2 The sample extraction module includes: The genome separation submodule collects fecal samples, adds binding and elution buffers to separate the host genome and microbial genomes to generate the microbial phase volume, measures the separation layer height in the test tube container, performs a phase division operation on the microbial phase volume and elution buffer volume and multiplies it by the separation layer height to generate the genome separation degree; The high-precision multi-channel robotic arm, activated by external linkage, guides a sterile sampling needle to penetrate the sealed sample collection tube provided by the subject, precisely extracting an initial solid fecal sample weighing 1-2 grams. This initial solid fecal sample is then transferred to a standard 50 ml centrifuge tube. The genome separation submodule activates its built-in microfluidic pump valve array to precisely inject 25 ml of a special binding solution containing a high concentration of guanidine isothiocyanate into the centrifuge tube. The genome separation submodule then activates its ultrasonic oscillation generator, centrifuging the sample at a frequency of 40 kHz. The test tube was continuously agitated for 300 seconds to thoroughly mix the initial solid fecal sample with the special binding solution, rupturing cell membranes and releasing intracellular nucleic acid material. After agitation, the genomics separation module added 15 ml of elution buffer (consisting of a low-salt buffer) to the centrifuge tube via a microfluidic pump valve array. The genomics separation module then initiated a high-speed centrifuge, controlling the centrifuge tube to perform a 600-second centrifugation at 12,000 rpm and an ambient temperature of 4°C. This centrifugation process resulted in a more efficient and effective separation of the fecal sample from the solidified sample. Different substances form a distinct stratified structure within the centrifuge tube. The top layer consists of a microbial phase containing the target free nucleic acid, while the bottom layer comprises host cell residue and non-target solid impurities. The genome separation submodule utilizes a high-resolution visual sensor to scan the pixel features at the interface between the liquid surface and the sediment along the outer wall of the centrifuge tube. Based on pixel grayscale abrupt changes, the separation layer height is calculated. For example, if the visual sensor determines the distance from the highest point of the liquid surface to the bottom of the tube to be 40 mm, and the distance from the sediment boundary to the bottom of the tube to be 10 mm, the arithmetic logic unit within the genome separation submodule will calculate the height of the separation layer from the highest point of the liquid surface to the bottom of the tube. The distance from the bottom of the tube to the precipitate boundary is subtracted from the distance from the bottom of the tube to obtain the separation layer height of the test tube container as 30 mm. At the same time, the genome separation submodule uses the built-in liquid level dynamic monitoring sensor to determine the specific volume of the top supernatant, obtaining the microbial phase volume as 12 ml. Based on this, the genome separation submodule obtains the volume of the previously injected elution buffer as 15 ml. The genome separation submodule divides the microbial phase volume and the elution buffer volume to obtain the preliminary phase distribution ratio. Subsequently, the genome separation submodule multiplies the preliminary phase distribution ratio with the separation layer height of the test tube container to generate the genome separation degree.

[0021] For example, dividing the microbial phase volume of 12 ml by the elution buffer volume of 15 ml yields a preliminary phase distribution ratio of 0.8. Multiplying this preliminary phase distribution ratio of 0.8 by the separation layer height of 30 mm in the test tube container gives a final genome separation degree of 24. The calculation formula here is S = (V / E). In the formula, S represents the genome separation degree, V represents the microbial phase volume, E represents the elution buffer volume, and H represents the separation layer height in the test tube. For key parameter settings of 12,000 rpm and 600 seconds centrifugation time, the parameter optimization unit built into the genome separation submodule traversed the rotation speed range of 5,000 rpm to 15,000 rpm, performing a grid search test with a step size of 500 rpm. The nucleic acid recovery yield at each test node was extracted and compared. When the rotation speed was below 10,000 rpm, the nucleic acid recovery yield was between 60% and 75%. When the rotation speed reached 12,000 rpm, the nucleic acid recovery yield reached a peak of 92%, and the non-target protein... When the white residue was less than 1%, the recovery rate did not increase significantly after exceeding 12,000 rpm, and the mechanical loss increased exponentially. Therefore, 12,000 rpm was established as the optimal operating parameter. The experimental results showed that the target nucleic acid recovery rate was improved by 17% compared with the traditional method of 8,000 rpm when the rotation speed of 12,000 rpm was used. The advantage of this operation logic is that by dividing the volume of the microbial phase by the volume of the elution buffer and multiplying by the separation layer height, the interference of the non-target solid phase volume on the concentration assessment was completely eliminated, and the actual distribution of the target nucleic acid in the multiphase mixture was accurately quantified. The genome separation submodule transmits the generated genome separation degree 24 to the downstream hardware cache channel.

[0022] The photometric comparison submodule compares the nucleic acid absorbance parameters at a specified wavelength, collects the irradiation path area of ​​the target light source, multiplies the nucleic acid absorbance parameters with the genome separation degree and divides them by the irradiation path area to generate the wavelength absorbance product. Upon receiving the genome separation score of 24 from upstream, the photometric alignment submodule activates its built-in ultraviolet spectrophotometer component, controlling the emission tube to release a monochromatic ultraviolet beam with a wavelength of 260 nm. This beam vertically penetrates a quartz microcuvette containing the extracted nucleic acid solution. The penetrating beam is received by a photodiode array on the back side and converted into a weak current signal. The analog-to-digital converter unit built into the photometric alignment submodule converts the current signal into a digital absorbance value, obtaining the nucleic acid absorbance parameters at the specified wavelength. To filter out stray light and electronic noise interference, the photometric alignment submodule extracts the nucleic acid absorbance parameter sequence within a continuous 50-millisecond period and performs a sliding window averaging smoothing process on this sequence. Specifically, this involves removing the highest and lowest values ​​from the sequence and then... The remaining 48 values ​​are summed and then divided by 48 to obtain a smoothed nucleic acid absorbance parameter of 2.5 at the specified wavelength. Simultaneously, the photometric alignment submodule activates the laser ranging and cross-sectional scanning components to project a gridded detection spot along the beam emission axis into the light-transmitting window of the micro-cub cuvette. The sensor generates a two-dimensional contour map based on the edge distortion coordinates of the reflected spot. The photometric alignment submodule calculates the specific values ​​of the enclosed area based on the effective closed boundary of the two-dimensional contour map. The area of ​​the target light source irradiation path is collected as 5 square millimeters. The photometric alignment submodule multiplies the nucleic acid absorbance parameter at the specified wavelength with the genome separation degree to obtain the initial total optical response. Then, the initial total optical response is divided by the area of ​​the target light source irradiation path to generate the wavelength absorbance product.

[0023] For example, multiplying the nucleic acid absorbance parameter of 2.5 at a specified wavelength by the genome separation of 24 yields an initial total optical response of 60. Then, dividing the initial total optical response of 60 by the target light source irradiation path area of ​​5 square millimeters, the wavelength absorbance product is finally derived to be 12. For the parameter setting of 50 cycles during the sliding window averaging process, the calibration engine built into the photometric comparison submodule repeatedly tests by injecting a standard concentration of reference solution into a cuvette to evaluate the signal variance at different cycle lengths. When the cycle is less than 30, the random fluctuations caused by stray light result in a signal variance greater than 0. 15. When the truncation period reaches 50, the signal variance steadily decreases to below 0.02. Increasing the truncation period to 100 does not significantly reduce the variance but introduces long-term temperature drift error. Therefore, 50 millisecond periods are determined to be the optimal acquisition interval. The experimental results show that smoothing with 50 millisecond periods reduces the noise floor by 85% compared to single instantaneous sampling. The advantage of this operation logic is that it combines the pure optical absorption parameters with the physical morphology of genome separation and uses the real illumination area for normalization, eliminating the measurement error caused by the optical path difference of the cuvette and the small fluctuations of the liquid surface.

[0024] The proportion coordination submodule obtains the preset absorbance test benchmark value, divides the wavelength absorbance product by the absorbance test benchmark value and multiplies it by the test extraction constant to perform overall comprehensive proportion determination, and generates the nucleic acid absorbance ratio; The absorbance product of the monitored wavelength is 12, which is then retrieved into the cache register. The proportional control submodule initiates the background benchmark configuration unit, which sends a data read command to the system's non-volatile memory to obtain the preset absorbance test benchmark value. Regarding the generation process of this absorbance test benchmark value, the background benchmark configuration unit pre-collects 2000 nucleic acid absorbance characteristic samples from known healthy individuals in the clinical database. For these 2000 characteristic samples, the background benchmark configuration unit executes Z-score standardization cleaning logic to remove outliers with an absolute value greater than 3. The remaining 1950 valid characteristic sample values ​​are then summed to obtain the total sample feature sum. Subsequently, the total sample feature sum is divided by the total number of valid characteristic samples, 1950, to obtain the core value. The mean parameter is fixed as the absorbance test benchmark value by the background benchmark configuration unit. In this example, the absorbance test benchmark value is set to 1.5. Then, the proportional coordination submodule calls the real-time chamber temperature value fed back by the ambient temperature monitoring component, and matches and calls the test extraction constant according to the chamber temperature value. The test extraction constant is designed to compensate for the change of optical absorption cross section of nucleic acid molecules at different temperatures. When the chamber temperature is within the standard working range of 20℃ to 25℃, the test extraction constant is set to 2. The proportional coordination submodule divides the wavelength absorbance product by the absorbance test benchmark value to obtain the relative enrichment ratio of nucleic acid. Then, the relative enrichment ratio of nucleic acid is multiplied by the test extraction constant to perform the overall comprehensive ratio determination and generate the extracted nucleic acid absorbance ratio.

[0025] For example, dividing the wavelength absorbance product 12 by the preset absorbance test benchmark value 1.5 yields a nucleic acid relative enrichment ratio of 8. Then, the proportional coordination submodule multiplies the nucleic acid relative enrichment ratio 8 by the test extraction constant 2, calculating the extracted nucleic acid absorbance ratio as 16. Regarding the logic of setting the Z-score standardized cleaning threshold to 3, the classification confidence was compared when the threshold was 2, 3, and 4 during the preliminary research phase. When the threshold was 2, excessive culling resulted in a 30% reduction in data volume and a shift in the mean. When the threshold was 4, too much necrotic data caused by instrument calibration errors was retained. When the threshold was 3, 99.7% of the healthy physiological fluctuation range data was perfectly retained under the normal distribution framework.

[0026] By introducing the absorbance test benchmark value validated by a large number of healthy samples as the divisor, and superimposing the test extraction constant as the temperature compensation multiplier, the systematic deviation caused by cross-batch test reagents and external temperature and humidity fluctuations on nucleic acid extraction quality assessment is greatly reduced, ensuring the accuracy of subsequent amplification system feeding. The ratio coordination submodule writes the nucleic acid extraction absorbance ratio of 16 into a dedicated configuration register.

[0027] Please see Figure 3 The amplification processing module includes: The cycle comparison submodule is based on the ratio of nucleic acid absorbance, prepares primers for susceptible genes and specific primers for pathogenic E. coli to obtain the initial mixed concentration, collects polymerase chain reaction cycle parameters and sets the cycle baseline, calculates the degree of deviation of polymerase chain reaction cycle parameters from the set cycle baseline, multiplies the degree of deviation by the initial mixed concentration to screen for exponential enrichment exceeding the set cycle baseline, and generates a targeted enrichment value. Based on the aforementioned nucleic acid extraction absorbance ratio 16, the automated micropipette workstation is activated, controlling a high-precision stepper motor to drive a multi-channel pipette tip array. Specific primers targeting susceptible genes, such as APC gene mutation sites, and pathogenic E. coli specific primers are aspirated from the refrigerated reagent compartment and injected into the reaction microplate. The cycle comparison submodule dynamically adjusts the primer injection volume according to the nucleic acid extraction absorbance ratio 16, ensuring an initial mixed concentration. In the actual example, the initial mixed concentration adjusted according to the configuration ratio is 50 nanomoles per liter. Subsequently, the cycle comparison submodule instructs the heating and cooling module to execute the polymerase chain reaction thermal cycling program, including a 95°C temperature change. During the annealing and extension stages at 60℃ and 72℃, the counter component in the cycle comparison submodule records the number of cycles performed in real time. The polymerase chain reaction (PCR) cycle parameters are collected as 35 cycles. At the same time, the cycle comparison submodule reads the set cycle baseline in the configuration library. This set cycle baseline is used to identify the ideal inflection point of the exponential amplification period. Under the current detection system, the set cycle baseline is set to 30 cycles. The cycle comparison submodule subtracts the set cycle baseline from the PCR cycle parameters to calculate the corresponding operational deviation. Furthermore, the cycle comparison submodule multiplies the operational deviation by the initial mixed concentration to screen for exponentially multiplied abundance exceeding the set cycle baseline and generate a targeted multiplied abundance value.

[0028] For example, subtracting the set cycle baseline 30 from the polymerase chain reaction cycle parameter 35 yields a running deviation of 5. Then, multiplying the running deviation of 5 by the initial mixed concentration of 50 nanomoles per liter yields a target enrichment value of 250. The calculation formula here is A = (Cp - Cb). In the formula, A represents the target doubling abundance value, Cp represents the polymerase chain reaction cycle parameter, Cb represents the set cycle baseline, and N0 represents the initial mixed concentration. Regarding the determination process of setting the cycle baseline to 30 cycles, the analysis engine in the cycle comparison submodule fitted and analyzed the amplification curves of 200 positive samples and 200 negative samples, calculated the second derivative of the fluorescence signal with respect to the cycle number, and found the cycle number corresponding to the maximum value of the second derivative as the starting point of the logarithmic growth phase. Statistics show that 98% of the positive samples have their starting point distributed between 28 and 32 cycles. The analysis engine takes the arithmetic mean of this interval to accurately lock the set cycle baseline to 30 cycles.

[0029] By multiplying the difference between the actual cyclic parameters and the ideal baseline by the initial concentration through a forced correlation, a nonlinear amplification penalty mechanism is constructed. This mechanism can amplify the extremely small exponential phase amplification lag into a significantly measurable abundance value, greatly enhancing the ability to capture low-load pathogens.

[0030] The share statistics submodule monitors the overall amplified parameters of the reaction system in the preset region mapping association. It divides the overall amplified parameters of the reaction system by the target doubling abundance value to extract the relative integration share variable of the amplified. It merges the external input records of the full parallel test channel array and performs scalar weighted summation operation in combination with the relative integration share variable of the amplified to generate the target gene amplification ratio data. Upon receiving the aforementioned data, with a target pluripotency value of 250, the fractional statistics submodule activates the fluorescence signal acquisition probe array to perform multi-band optical scanning on the pre-set area within the reaction microplate. This captures the number of photons released by the specific fluorescent probe under UV excitation. The analog-to-digital conversion unit maps the number of photons to the absolute order of magnitude of the amplified product, monitoring the overall amplified parameters of the reaction system associated with the pre-set area mapping. The actual measured overall amplified parameter of the reaction system is 7500. The fractional statistics submodule divides the overall amplified parameter of the reaction system by the target pluripotency value to extract the relative integration fraction variable of the amplified product. For example, dividing the overall amplified parameter of the reaction system, 7500, by the target pluripotency value of 250 yields a relative integration fraction variable of 30. To eliminate occasional physical errors in a single detection channel, the fractional statistics submodule merges the external input records of the full parallel test channel array, configuring a total of 8 independent parallel test channels. For the data records of these 8 channels, please refer to the channel data record details below. Table 1 External Input Record Table for Full-Scale Parallel Test Channel Array

[0031] As shown in Table 1, the external input records of the full-scale parallel test channel array contain amplification intensity parameters for 8 channels. The share statistics submodule performs a scalar weighted summation operation in conjunction with the relative integration share variable of the amplified material. Specifically, the share statistics submodule iterates through each row in Table 1, multiplies the channel amplification intensity record of each channel with the corresponding channel confidence weight value to obtain the weighted value of a single channel, and then performs an accumulation operation on the 8 individual channel weighted values, i.e., 25+28+32+29+31+27+30+28 to obtain the total weighted sum of the channels, which is 230. Finally, the share statistics submodule adds the total weighted sum of the channels to the relative integration share variable of the amplified material to generate the target gene amplification ratio data, i.e., 230+30 to obtain the final target gene amplification ratio data, which is 260.

[0032] Considering the logical consideration of uniformly setting the channel confidence weight value to 1, during the R&D phase, continuous temperature control consistency and optical attenuation consistency tests were conducted on 8 parallel channels for up to 1000 hours. There were channels with significant physical location disadvantages, so there was no need for differential compensation. The parallel redundant data in space and the dynamic abundance data on the time axis were fully utilized for cross-dimensional scalar fusion, which effectively smoothed out the analysis bias caused by the local non-uniformity of the fluid in the single-tube system.

[0033] Please see Figure 4 The extended purification module includes: The processing and screening submodule is based on the target gene amplification ratio data. It uses shrimp alkaline phosphatase to dephosphorylate the unconsumed dNTPs in the amplification products to extract the residual product adsorption ratio. It screens items that meet the residual product adsorption ratio greater than the preset desalination resin adsorption standard. The residual product adsorption ratio is multiplied by the judgment compliance coefficient to generate the amount of extension reaction product. The target gene amplification percentage data of 260 was read. The processing and screening submodule controlled a high-precision injection pump to draw 10 μL of mixed shrimp alkaline phosphatase solution through a capillary Teflon tube and precisely dripped it into the reaction tube where the amplification process had been completed. The role of the mixed shrimp alkaline phosphatase here is to dephosphorylate any unconsumed deoxyribonucleoside triphosphate molecules to prevent them from causing background interference in the subsequent single-base extension reaction. After continuous incubation at 37°C for 2400 seconds, the processing and screening submodule instructed the temperature to be increased to 85°C. The solution was held for 600 seconds to completely inactivate enzyme activity. The mixture was then passed through a microchromatographic column pre-packed with anion exchange desalting resin. The conductivity sensor built into the processing and screening submodule monitored the conductivity change curve of the eluent in real time. The residual product adsorption ratio was extracted based on the integral area of ​​the conductivity valley width and the baseline. The actual detected residual product adsorption ratio was 0.85. The processing and screening submodule retrieved the internal preset desalting resin adsorption standard, which was set to 0.60. The comparator logic unit within the processing and screening submodule then screened products that met the residual product adsorption requirements. The adsorption ratio of the residual product was greater than the corresponding item of the preset desalination resin adsorption standard. Since the actual residual product adsorption ratio of 0.85 was significantly greater than the preset desalination resin adsorption standard of 0.60, the condition pass command pulse was triggered. The processing and screening submodule then multiplied the residual product adsorption ratio by the judgment compliance coefficient to generate the amount of extended reaction product. In the current calculation scenario, the judgment compliance coefficient was set to 20. Multiplying the residual product adsorption ratio of 0.85 by the judgment compliance coefficient of 20, the amount of extended reaction product was calculated to be 17. Regarding the selection basis of the preset desalination resin adsorption standard of 0.60 and the judgment compliance coefficient of 20, the calibration engine of the processing and screening submodule conducted elution tests on 500 sets of simulated samples with different impurity concentrations to monitor the game relationship between impurity filtration rate and target product loss rate. When the adsorption standard was set below 0.40, although the product recovery was extremely high, the unhydrolyzed deoxyribonucleoside triphosphate residue exceeded the safety threshold for downstream mass spectrometry detection. When the setting was above 0.80, it would lead to a large loss of target nucleic acid fragments. After comprehensively considering the signal-to-noise ratio, 0.60 was confirmed as the equilibrium inflection point.

[0034] A condition-triggered numerical scaling mechanism was established, which only performs coefficient compensation amplification on high-quality samples that meet the high-purity desalination standard. While blocking the flow of inferior impurity samples downstream, the concentration of high-quality samples was mathematically normalized, which greatly simplifies the target baseline calibration pressure of the subsequent mass spectrometer.

[0035] The mass-charge parameter quantum module collects the terminal base parameter of the adsorbed product based on the amount of extended reaction product, multiplies the terminal base parameter of the adsorbed product by the amount of extended reaction product and divides it by the built-in offset constant to generate the terminal base mass-charge ratio value. Upon receiving 17 units of extended reaction product from the upstream source, the mass-charge parameter quantum module activates the pilot test channel of the multi-channel capillary electrophoresis assembly. A microcurrent pulls the purified extended product through a capillary window filled with polymer separating gel. A laser-induced fluorescence detector is positioned on the outside of the capillary, specifically designed to capture the characteristic excitation light signal emitted by the dideoxynucleotides attached to the adsorbed product's ends. The signal analysis unit within the mass-charge parameter quantum module identifies the migration time of the highest fluorescence peak and compares it with an internally stored baseline migration time matrix to acquire the terminal base parameter of the adsorbed product. This parameter reflects the combined molecular weight and charge distribution characteristics of the terminal specific bases. The actual measured terminal base parameter of the adsorbed product is 150. The floating-point arithmetic unit within the mass-charge parameter quantum module multiplies the terminal base parameter of the adsorbed product by the amount of extended reaction product to obtain the absolute polymeric substance characteristic value. Subsequently, the absolute polymeric substance characteristic value is divided by a built-in offset constant to finally generate the terminal base mass charge ratio. Substituting the previously determined values ​​into the value, the mass-charge parameter quantum module multiplies the terminal base parameter of the adsorbed product (150) by the amount of the extended reaction product (17) to obtain the absolute mass characteristic of the polymer (2550). Next, the mass-charge parameter quantum module retrieves the built-in offset constant fixed in the underlying firmware, which is set to 50. The mass-charge parameter quantum module divides the absolute mass characteristic of the polymer (2550) by the built-in offset constant (50) to calculate the terminal base mass-charge ratio (51). Regarding the basis for the generation of the built-in offset constant (50), the initialization calibration program of the mass-charge parameter quantum module uses a calibration library of 20 standard nucleic acid oligonucleotides of different lengths for continuous testing before shipment. The migration rate drift of each standard under different voltage gradients is recorded. It is found that regardless of molecular weight, the space charge effect introduces a fixed migration hysteresis. Linear regression fitting is performed on these 20 sets of data using the least squares method, and the intercept term of the fitted line is extracted, rounded, and confirmed as a constant of 50.

[0036] By cleverly combining the product of extended reaction product, which characterizes concentration, with the terminal base parameter, which characterizes material properties, and using the built-in offset constant, which represents systematic charge hysteresis, to perform dimensionless reduction, the spurious interference caused by concentration fluctuations in the mass-to-charge ratio characteristic assessment was successfully removed.

[0037] The deviation determination submodule extracts the wild-type standard mass-charge ratio benchmark for the terminal base mass-charge ratio value, calculates the correlation difference parameter obtained by subtracting the wild-type standard mass-charge ratio benchmark from the terminal base mass-charge ratio value, and generates single base extension deviation data. Upon receiving the terminal mass-to-charge ratio (MTBR) value of 51 from the underlying computational channel, the deviation determination submodule immediately activates its internal reference sequence comparison engine. It downloads statistical model parameters for healthy individuals targeting the current gene sequence feature database and extracts the wild-type standard MTBR baseline. This baseline represents the theoretically perfect mass-to-charge ratio when the subject has not undergone any mutations at that specific gene locus. In the current mutation site screening task, the extracted wild-type standard MTBR baseline value is 46. The subtraction logic gate circuit within the deviation determination submodule initiates the calculation process, subtracting the wild-type standard MTBR baseline from the input terminal MTBR value to obtain the deviation between the two. This yields the obtained correlation difference parameter, which is then generated as a single-base extension. The deviation data is processed by performing mathematical subtraction with specific parameters. The deviation determination submodule subtracts the wild-type standard mass-to-charge ratio benchmark of 46 from the terminal base mass-to-charge ratio value of 51, and calculates the correlation difference parameter as 5. That is, the generated single-base extension deviation data is 5. Regarding the establishment mechanism of the wild-type standard mass-to-charge ratio benchmark of 46, the reference sequence comparison engine of the deviation determination submodule captured 100,000 normal tissue sequencing sequences published by the Asian population colorectal cancer gene project in online state. The kernel density was estimated for the molecular mass distribution of 20 base sequences around the specified target site. It was found that the mass-to-charge ratio distribution of the target site was extremely concentrated, showing a sharp single-peak distribution with a standard deviation of only 0.2. The mass coordinates of the peak of the single peak were accurately located at 46 after unit conversion.

[0038] By employing the most basic yet extremely sensitive absolute value subtraction operation, the physical quality mismatch between the test sample and the perfectly healthy sample at the single nucleotide level is directly exposed. This reduces the originally complex sequence alignment problem to a single-dimensional pure digital scalar difference assessment, greatly compressing the clock cycle required for analysis and computation.

[0039] Please see Figure 5 The mass spectrometry detection module includes: The mass spectrometry scanning submodule targets single-base extension deviation data, spots and extends the purified product on the chip matrix, starts the time-of-flight mass spectrometer to extract the measured molecular weight parameters of the target points in the mass spectrum, and divides the measured molecular weight parameters of the target points in the mass spectrum by the spotting distribution constant to generate the target molecular weight value. The single-base extension deviation data 5 is received as a sample preparation priority indicator. The mass spectrometry scanning submodule activates the micro-nano-liter spotting needle assembly, performing array spotting on a 384-well silica insulating chip substrate with a spotting distribution constant at a single aspiration volume of 10 nanoliters. The extended purification product and the 3-hydroxypyridinecarboxylic acid matrix solution for auxiliary crystallization are uniformly mixed dropwise at a 1:1 ratio and dried using a nitrogen gas flow. The mass spectrometry scanning submodule then activates the high-vacuum time-of-flight mass spectrometer system, emitting a sequence of solid-state laser pulses at a wavelength of 355 nm and a frequency of 50 Hz onto the chip substrate. The laser bombardment of the target crystals causes the nucleic acid molecules to desorb and enter the fieldless flight vacuum tube with a single charge. The multi-channel microchannel plate detector at the end of the flight tube records the flight time span of each ion group and extracts the measured molecular weight parameter of the target point in the mass spectrum. In this scan cycle, the measured molecular weight parameter of the target point in the mass spectrum is recorded as 6800. The mass spectrometry scanning submodule calls the preset spotting distribution constant, which reflects the mixed crystallization. The spatial concentration and dilution factor during the process is preset to 170. The division calculation node inside the mass spectrometry scanning submodule divides the measured molecular weight parameter of the target point in the mass spectrum by the spot distribution constant to generate the target molecular weight value. Combined with the obtained numerical parameters, the measured molecular weight parameter of the target point in the mass spectrum, 6800, is divided by the spot distribution constant of 170 to accurately obtain the target molecular weight value of 40. The calculation formula here is Wt=Wm / K, where Wt represents the target molecular weight value, Wm represents the measured molecular weight parameter of the target point in the mass spectrum, and K represents the spot distribution constant. For the spot distribution constant of 170, the calibration and maintenance unit of the mass spectrometry scanning submodule uses a fluorescent dye containing a known molecular weight internal standard to perform repeated spot microscopic imaging analysis, extracting the integral of the thickness gradient change of the crystal spot from the center to the edge. It was found that due to the Malagoni convection effect caused by the volatilization of the matrix solution, the apparent molecular weight response of nucleic acid molecules in the core region hit by the laser is amplified by 170 times by the spatial density. Therefore, this empirical factor is set as the spot distribution constant.

[0040] By using the spot distribution constant of the pure physical dimension, a linearized forced dimension reduction calibration was performed on the highly nonlinear mass spectrometry ion flight characteristic signal, eliminating the molecular weight response distortion caused by uncontrollable local crystallization differences during the complex vacuum ionization process.

[0041] The peak screening submodule extracts the set reference standard molecular weight, compares the target molecular weight value with the set built-in reference standard molecular weight to screen the detection signal peaks that are within the allowable molecular weight drift range, and multiplies the target molecular weight value with the detection signal peak to generate the range signal peak quantity. The target molecular weight value of the continuously monitored signal peak is 40. The peak screening submodule activates the built-in standard reference mapping library and extracts the set reference standard molecular weight using the internal data bus. Since this screening needs to consider both microbial metabolites and host gene mutation characteristics, the extracted reference standard molecular weight is set to 38 for the current specific analytical range. The peak screening submodule activates the internal digital window limiting comparator logic unit, comparing the input target molecular weight value with the set built-in reference standard molecular weight in a bidirectional tolerance range to screen for signal peaks within the allowable molecular weight drift range. This allowable drift range is set to a fluctuation of 5 units above and below the reference standard molecular weight. Within the range of 33 to 43, since the current target molecular weight value of 40 is within the range of 33 to 43, the system successfully located and extracted the mass spectrometry ion current intensity corresponding to this position, and recorded the detection signal peak value at this position as 500. The multiplication operation kernel inside the peak selection submodule then started the operation flow, multiplying the target molecular weight value with the detection signal peak to generate the interval signal peak quantity. The specific numerical calculation process is to multiply the target molecular weight value of 40 by the obtained detection signal peak 500, and calculate the interval signal peak quantity as 20000. To more clearly show the full picture of the reference standard parameters on which this module depends, please refer to the reference standard molecular weight distribution table below. Table 2. Molecular weight distribution of reference standard

[0042] As shown in Table 2, strict central molecular weight and unequal or equal drift boundaries were set for different markers. The logic mechanism of setting the lower and upper allowable offsets to a uniform value of 5 was used. The model optimizer of the peak screening submodule called 300 batches of spiked samples containing different proportions of interfering proteins for anti-perturbation testing. The drift law of mass number centroid caused by the increase of matrix complexity was monitored. It was found that when the tolerance interval was narrower than 3 units, the true peak was easily discarded due to the small temperature difference deformation of the time-of-flight tube, with a false negative rate as high as 15%. When the tolerance interval was widened to 7 units, a large number of adjacent impurity isotope peaks would flood in and cause interference. Setting it to 5 achieved the perfect tangent point of the receiver operating characteristic curve.

[0043] By combining the molecular weight value of the target point in the horizontal coordinate of the mass dimension with the detection signal peak in the vertical coordinate of the quantity dimension, a two-dimensional fusion metric that can simultaneously reflect the physical identity characteristics and absolute concentration of the marker is constructed, avoiding the risk of misjudgment caused by looking only at the peak height and ignoring the slight mass shift.

[0044] The target determination submodule acquires the peak intensity parameter of the detection signal, determines the target point whose peak intensity parameter is above the positive determination benchmark, and multiplies the target point by the peak intensity parameter of the detection signal and the interval signal peak quantity to generate a characteristic target peak signal record. Based on the aforementioned interval signal peak quantity of 20000, the targeting determination submodule activates the underlying peak shape identification and baseline subtraction algorithm layer, initiates the first-order derivative zero-crossing detection mechanism to define the start and end points of the effective peak, and performs polygon integration calculation on the area below the effective peak to obtain the detection signal peak intensity parameter. This parameter is stripped of the baseline rise effect caused by chemical noise. The actual detection signal peak intensity parameter extracted after baseline subtraction is 180. The logic judgment core of the targeting determination submodule calls the preset positive judgment benchmark in the local solidified storage area. The benchmark value is set to 120. The logic judgment core judges whether the detection signal peak intensity parameter is above the positive judgment benchmark. Since the actual measured detection signal peak intensity parameter of 180 is significantly greater than the set positive judgment benchmark of 120, the system confirms that the peak value is a real target point and successfully locks the target point associated with the detection signal peak intensity parameter of 180. Once the judgment is successful, the targeting determination submodule... The multiplication accumulator is immediately scheduled to multiply the peak intensity parameter of the target-associated detection signal by the previously acquired interval signal peak quantity, ultimately generating a characteristic target peak signal record. Detailed calculations are performed. The target determination submodule multiplies the peak intensity parameter of the target-associated detection signal (180) by the interval signal peak quantity (20000), resulting in a characteristic target peak signal record of 3600000. Regarding the experimental derivation of the positive determination benchmark of 120, the target determination submodule compiled mass spectrometry data from 1500 patients diagnosed by colonoscopy biopsy (gold standard) and data from 1500 healthy control groups during the system validation phase. A two-group histogram is plotted for the signal intensity distribution of the target. The system employs an optimization logic that maximizes the Youden exponent, i.e., finding the cut point where the sensitivity plus specificity minus 1 equals the maximum value. After exhaustively traversing all possible intensity values, it was found that at an intensity value of 120, the system's recognition sensitivity reaches 94% and the specificity remains at a high level of 96%.

[0045] After confirming that the signal strength exceeds the stringent threshold for distinguishing between true and false signals, the signal strength is amplified a second time by multiplying the signal strength by the peak value of the interval. This greatly widens the order of magnitude difference between true positive signals and critical noise, transforming the originally ambiguous weak positive features into extremely significant high absolute value identifiers.

[0046] Please see Figure 6 The screening and early warning module includes: The feature extraction submodule extracts the corresponding detection parameters of Fusobacterium nucleatum and the susceptibility gene mutation parameters based on the recording of feature target peak signals. It compares the corresponding detection parameters of Fusobacterium nucleatum and the susceptibility gene mutation parameters with the preset positive judgment baseline, filters feature sequences that meet the corresponding judgment conditions, and generates joint positive feature items. Based on the characteristic target peak signal record 3,600,000, the structured data parsing engine is immediately activated to unpack it. The feature extraction submodule, according to the multi-dimensional quality coordinate mapping relationship covered by the characteristic target peak signal record, extracts the corresponding *Fusobacterium nucleatum* detection parameters reflecting the enrichment degree of specific microorganisms and the susceptibility gene mutation parameters reflecting the host cell DNA variation from the corresponding multi-dimensional data matrix. Through matrix indexing, the corresponding *Fusobacterium nucleatum* detection parameter is obtained as 2500, and the susceptibility gene mutation parameter as 850. The concurrent comparison unit built into the feature extraction submodule concurrently calls the preset positive judgment baseline stored in read-only memory. This baseline contains two independent judgment boundaries: a bacterial baseline value of 1000 for *Fusobacterium nucleatum* and a gene baseline value of 500 for the susceptibility gene. The concurrent comparison unit compares the corresponding *Fusobacterium nucleatum* detection parameters and susceptibility gene mutation parameters with the preset positive judgment baseline, performing greater than or equal to logical judgments. Because the actual extracted... The detection parameter of *Fusobacterium nucleatum* was 2500, which is greater than its bacterial baseline of 1000. Furthermore, the actual extracted susceptibility gene mutation parameter of 850 was also greater than its gene baseline of 500. The system screened feature sequences that met the dual-correspondence judgment criteria. The feature extraction submodule merged the above two judgments through instructions to generate a joint positive feature item with strong warning attributes. This joint positive feature item was assigned a state logic flag value of 1 to represent a completely established bidirectional positivity. Regarding the establishment of the bacterial baseline of 1000 and the gene baseline of 500, the feature extraction submodule, during the algorithm's gray-scale release period, collaborated with the laboratory medicine centers of three tertiary hospitals to track the pathological evolution records of 5000 high-risk subjects for up to three years. A logistic regression classifier combined with backward elimination was used to model and track the concentration evolution of multiple biomarkers. The analysis showed that when the abundance of *Fusobacterium nucleatum* exceeded 1000 and the abundance of APC mutations exceeded 500, the absolute risk of patients being diagnosed with precancerous adenomas during the follow-up period showed a steep inflection point.

[0047] Abandoning the linear judgment of a single dimension, this study innovatively uses the intersection and union comparison operation of host mutation and gut microbiota characteristics to capture the hidden pathological association between dysbiosis and gene mutation, and realizes cross-border collaborative verification of multi-dimensional biomarkers.

[0048] The risk assessment submodule obtains the clinical risk level classification criteria, determines the corresponding level echelon of the combined positive feature items within the clinical risk level classification criteria, and generates the combined positive risk level. Based on the combined positive characteristic status logical flag 1, the risk assessment submodule initiates the external clinical rule database interface, requests and obtains the data mapping table of the clinical risk level classification standard document issued by the National Health Commission through the internal network secure link. This standard contains extremely detailed step-by-step indicator combination boundary limitation rules. The logic deducer of the risk assessment submodule analyzes this standard and divides three clinical risk level classification standard intervals: low-risk prevention echelon, moderate-intensive follow-up echelon, and extremely high-risk emergency intervention echelon. The risk assessment submodule combines the previously generated and input combined positive characteristic status logical flag 1 and the absolute value of the specific parameters hidden behind it to determine the corresponding level echelon of the combined positive characteristic within the clinical risk level classification standard. Since this sample simultaneously meets the two core pathogenic elements of abnormal microbial enrichment and host susceptibility gene mutation, the logic deducer... After traversing the threshold conditions of each echelon, it was confirmed that the combined feature item crossed the highest boundary of the moderately strict follow-up echelon and accurately fell within the jurisdiction of the extremely high-risk emergency intervention echelon. The risk assessment submodule then directly generated the combined positive risk level based on the assessment. In this screening example, the output combined positive risk level was level 3, the highest alert level. Regarding the setting and verification process of the echelon boundary conditions, the risk assessment submodule extracted 80,000 anonymized electronic medical record data covering the entire disease cycle in historical retrospective studies. It used the Cox proportional hazards regression algorithm in survival analysis to calculate the risk ratio function of the probability of severe colorectal cancer caused by various feature combinations over time. The results clearly show that when double positive features are present, the risk ratio increases by 8 times compared to single positive, which constitutes a solid pathological mathematical basis for directly classifying it as the highest alert level.

[0049] This approach establishes a deterministic mapping between abstract biochemical molecular digital characteristics and national standard-level macro-clinical guidelines, enabling an automated and seamless conversion from microscopic detection data to macro-clinical treatment recommendations. This eliminates cognitive barriers and assessment delays for clinicians when interpreting complex multidimensional molecular indicators.

[0050] The pathology matching submodule receives a preset pathology risk level table, performs an association matching and positioning operation with the joint positive risk level and the various stage parameters set in the preset pathology risk level table, extracts the node matching success status indicator, and generates the integrated screening result for colorectal cancer microorganisms. Based on the event notification with a joint positive risk level of 3, the pathology matching submodule invokes the built-in read-only storage chip to receive the preset pathology risk level table stored therein. The pathology matching submodule activates the internal association search tree algorithm engine and performs a one-to-one association matching and positioning operation between the input joint positive risk level value 3 and the various stage parameters set in the preset pathology risk level table. To clarify the reference basis for the matching process, please refer to the pathology risk mapping configuration list below. Table 3 Preset Pathological Risk Level Table

[0051] As shown in Table 3, the preset pathological risk level table clearly outlines the actual pathological state and intervention window corresponding to different risk parameters. The pathological matching submodule successfully located the configuration entry in the third row of Table 3 that matches the combined positive risk level 3 using a hash lookup algorithm. Subsequently, the pathological matching submodule extracts the various intervention suggestions corresponding to this node and generates a node matching success status indicator in the system memory. This indicator is assigned a Boolean truth value, establishing a perfect correspondence between the detection data and the clinical pathological state. After confirming successful node matching, the pathological matching submodule calls the report generation component to update the estimated state of advanced tubular villous adenoma corresponding to the combined positive risk level 3 and the clinical indication for immediate endoscopic resection. The data was formatted, integrated, and pieced together to generate an integrated microbial screening result for colorectal cancer. This result not only includes high-risk warning information but also a clear physical intervention schedule. The parameters of the preset pathological risk level table were validated by estimating the malignant transformation time window within 1 to 2 years. The pathological matching submodule called on a 15-year longitudinal cohort tracking dataset provided by the International Cancer Screening Network and used a Markov state transition model to analyze the average residence time of adenomas at different stages of development into carcinoma in situ. The calculation results confirmed that the average half-life of metastasis from advanced tubular villous adenoma to irreversible malignant tumors is concentrated at around 1.8 years. With this solid data, the high scientific rigor of setting this time window within 1 to 2 years was established.

[0052] It has bridged the last mile in the process of connecting molecular diagnostic experimental data with clinical pathological decision-making, directly translating cold and dry biochemical detection parameters and risk levels into a clear description of staged pathological features and actionable emergency recommendations that are easy for both patients and general practitioners to understand. It has thoroughly constructed a closed-loop intelligent screening and decision-making system that integrates extraction, testing, amplification, identification, and medical response.

[0053] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. An integrated screening kit for colorectal cancer microbial detection based on nucleic acid mass spectrometry technology, characterized in that, include: The sample extraction module collects fecal samples, adds binding and elution buffers to separate the host genome and microbial genome, compares nucleic acid absorbance parameters at different specified wavelengths, and calculates the proportion of extracted nucleic acid absorbance. The amplification processing module, based on the ratio of extracted nucleic acid absorbance, prepares primers for susceptible genes and primers specific to pathogenic Escherichia coli, compares polymerase chain reaction cycle parameters with the set cycle baseline, and screens and statistically analyzes the amplification ratio data of target genes. The extended purification module, based on the target gene amplification ratio data, uses mixed shrimp alkaline phosphatase to hydrolyze unreacted deoxyribonucleoside triphosphate, screens for extended reaction products that meet the preset desalination resin adsorption standards, extracts the mass-to-charge ratio parameter of the adsorbed product at the end, compares it with the wild-type standard mass-to-charge ratio benchmark to determine the deviation space, and obtains single-base extension deviation data. The mass spectrometry detection module, in response to the single-base extension deviation data, spots the extended purified product on the chip matrix, starts the time-of-flight mass spectrometer to scan the purified product spectrum, extracts the measured molecular weight parameter of the target point in the mass spectrum, compares the measured molecular weight parameter of the target point with the set built-in reference standard molecular weight, screens the detection signal peaks within the allowable molecular weight drift range, determines the target points whose intensity parameter of the selected detection signal peak is above the positive judgment benchmark, and generates characteristic target peak signal records; The screening and early warning module determines the corresponding positive risk level of Fusobacterium nucleatum and susceptibility gene mutation based on the recorded characteristic target peak signals, matches it with a preset pathological risk level table, and outputs the integrated screening results for colorectal cancer microorganisms.

2. The integrated screening kit for colorectal cancer microbial detection based on nucleic acid mass spectrometry technology according to claim 1, characterized in that: The kit also includes a housing, and the sample extraction module, the amplification processing module, the extension purification module, the mass spectrometry detection module, and the screening and early warning module are all located inside the housing.

3. The integrated screening kit for colorectal cancer microbial detection based on nucleic acid mass spectrometry technology according to claim 1, characterized in that: During the screening and statistical analysis of the target gene amplification percentage data, the index-multiply abundance exceeding the set cycle baseline is selected, and the relative proportion of the overall amplified parameters of the integrated reaction system is extracted as the target gene amplification percentage data.

4. The integrated screening kit for colorectal cancer microbial detection based on nucleic acid mass spectrometry technology according to claim 1, characterized in that: The extracted nucleic acid absorbance ratio includes the amount of purified endogenous nucleic acid, the amount of exogenous target elution, and the inhibition coefficient of impurity proteins; the targeted gene amplification ratio data includes the absolute value of target fragment enrichment, the amount of competitive inhibition antagonism, and the degree of primer dimer consumption; the single base extension deviation data includes the probe binding efficiency value, the mass spectrometry shift of the mutation site, and the degree of free enzyme system residue. The characteristic target peak signal recording includes absolute peak intensity, background noise coverage, and spectral baseline flatness; the integrated colorectal cancer microbial screening results include intestinal microecological imbalance, early-onset tumor latency, and urgency of outpatient intervention.

5. The integrated screening kit for colorectal cancer microbial detection based on nucleic acid mass spectrometry technology according to claim 1, characterized in that, The sample extraction module includes: The genome separation submodule collects fecal samples, adds binding and elution buffers to separate the host genome and microbial genomes to generate the microbial phase volume, measures the separation layer height in the test tube container, performs a phase division operation on the microbial phase volume and elution buffer volume and multiplies it by the separation layer height to generate the genome separation degree. The photometric comparison submodule compares the nucleic acid absorbance parameters at a specified wavelength, collects the irradiation path area of ​​the target light source, multiplies the nucleic acid absorbance parameters with the genome separation degree and divides them by the irradiation path area to generate the wavelength absorbance product. The proportion coordination submodule obtains the preset absorbance test benchmark value, divides the wavelength absorbance product by the absorbance test benchmark value and multiplies it by the test extraction constant to perform overall comprehensive proportion determination, and generates the nucleic acid absorbance ratio.

6. The integrated screening kit for colorectal cancer microbial detection based on nucleic acid mass spectrometry technology according to claim 1, characterized in that, The amplification processing module includes: The cycle comparison submodule, based on the extracted nucleic acid absorbance ratio, prepares susceptible gene primers and pathogenic E. coli-specific primers to obtain the initial mixed concentration, collects polymerase chain reaction cycle parameters and sets a cycle baseline, calculates the degree of deviation of the polymerase chain reaction cycle parameters from the set cycle baseline, multiplies the degree of deviation by the initial mixed concentration to screen for exponentially increased abundance exceeding the set cycle baseline, and generates a targeted multiplier abundance value; The share statistics submodule monitors the overall amplified parameters of the pre-defined region mapping associated reaction system, divides the overall amplified parameters of the reaction system by the target doubling abundance value to extract the relative integration share variable of the amplified, merges the external input records of the full parallel test channel array, and performs a scalar weighted summation operation in combination with the relative integration share variable of the amplified to generate the target gene amplification ratio data.

7. The integrated screening kit for colorectal cancer microbial detection based on nucleic acid mass spectrometry technology according to claim 1, characterized in that, The extended purification module includes: The processing and screening submodule, based on the target gene amplification ratio data, uses shrimp alkaline phosphatase to dephosphorylate the unconsumed dNTPs in the amplification products to extract the residual product adsorption ratio, and screens items that meet the condition that the residual product adsorption ratio is greater than the preset desalination resin adsorption standard. The residual product adsorption ratio is multiplied by the judgment compliance coefficient to generate the amount of extension reaction product. The mass-charge parameter quantum module collects the terminal base parameter of the adsorption product based on the amount of the extended reaction product, multiplies the terminal base parameter of the adsorption product by the amount of the extended reaction product and divides it by the built-in offset constant to generate the terminal base mass-charge ratio value. The deviation determination submodule extracts the wild-type standard mass-charge ratio benchmark for the terminal base mass-charge ratio value, calculates the correlation difference parameter obtained by subtracting the wild-type standard mass-charge ratio benchmark from the terminal base mass-charge ratio value, and generates single base extension deviation data.

8. The integrated screening kit for colorectal cancer microbial detection based on nucleic acid mass spectrometry technology according to claim 1, characterized in that, The mass spectrometry detection module includes: The mass spectrometry scanning submodule, for the single base extension deviation data, spots the extended purification product on the chip matrix, starts the time-of-flight mass spectrometer to extract the measured molecular weight parameters of the target point in the mass spectrum, and divides the measured molecular weight parameters of the target point in the mass spectrum by the spotting distribution constant to generate the target molecular weight value. The peak screening submodule extracts the set reference standard molecular weight, compares the target molecular weight value with the set built-in reference standard molecular weight to screen the detection signal peaks that are within the allowable molecular weight drift range, and multiplies the target molecular weight value with the detection signal peak to generate the range signal peak quantity. The target determination submodule acquires the peak intensity parameter of the detection signal, determines that the peak intensity parameter of the detection signal is above the positive determination benchmark for the target point, and associates the target point with the peak intensity parameter of the detection signal multiplied by the interval signal peak quantity to generate a characteristic target peak signal record.

9. The integrated screening kit for colorectal cancer microbial detection based on nucleic acid mass spectrometry technology according to claim 1, characterized in that, The screening and early warning module includes: The feature extraction submodule extracts the corresponding detection parameters of Fusobacterium nucleatum and the susceptibility gene mutation parameters based on the recorded feature target peak signals. It compares the corresponding detection parameters of Fusobacterium nucleatum and the susceptibility gene mutation parameters with the preset positive judgment baseline, filters feature sequences that meet the corresponding judgment conditions, and generates joint positive feature items. The risk assessment submodule obtains the clinical risk level classification criteria, determines the corresponding level echelon of the combined positive feature item within the clinical risk level classification criteria, and generates the combined positive risk level. The pathology matching submodule receives a preset pathology risk level table, performs an association matching and positioning operation on the combined positive risk level and the various stage parameters set in the preset pathology risk level table, extracts the node matching success status indicator, and generates an integrated screening result for colorectal cancer microorganisms.

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