A method and system for detection and screening of amnion epithelial stem cells

Abstract

The present application relates to the field of cell detection and screening, and particularly relates to a method and system for detection and screening of amniotic epithelial stem cells. The method comprises: generating a structured sample task sheet based on multi-source information; performing tissue digestion processing based on the task sheet to obtain a candidate cell group; then performing phenotype detection, molecular detection and quality detection to generate corresponding data packets and label data packets; based on the quality label data packets, performing gating, batch normalization, abnormality rejection, fusion scoring and consistency determination processing through a screening engine to generate a screening result packet; and finally generating a disposal work order and a traceability report based on the screening result packet. The present application realizes standardized and auditable automatic processing of the whole process of amniotic epithelial stem cells from receiving, detection to sorting and disposal, greatly improving the detection efficiency, result consistency and traceability.

Description

Technical Field

[0001] This invention relates to the field of cell detection and screening, and more particularly to a method and system for detecting and screening amniotic epithelial stem cells. Background Technology

[0002] In the field of cell detection and screening, existing solutions typically involve separate steps such as sample registration, tissue digestion and separation, surface marker immunostaining and flow cytometry or imaging acquisition, nucleic acid extraction and transcription marker detection, viability detection and microbial detection. These steps generate separate phenotypic detection data packages, molecular detection data packages, and quality label data packages. However, these solutions suffer from limitations such as inconsistent recording standards between sample task sheets and candidate cell population data packages, inconsistent judgment standards for gating and acquisition quality control across batches, and difficulty in linking screening result packages with treatment records. Existing methods often rely on threshold templates or control constraints at each step for judgment. The rule version numbers often exist only as record items and are not involved in the unified execution of gating, batch normalization, anomaly removal, fusion scoring, and consistency judgment. When sampling and transportation conditions, processing parameter templates, equipment identifiers, and control constraints change simultaneously, inconsistencies in the judgment paths and treatment strategy library calls for the same candidate cell population data package can easily occur, making it difficult to meet the requirement of a stable screening result package generation by the screening engine. For the joint processing of quality label data packages and rule version numbers, existing technologies generally lack unified constraints in aspects such as the scale mapping basis for batch normalization, the source judgment criteria for anomaly removal, and the conflict marker writing path for fusion scoring and consistency judgment. It is difficult to form a consistent process across sample task sheets, phenotypic detection data packages, molecular detection data packages, and quality label data packages. This leads to inconsistencies in the mapping and write-back records between screening result packages and sorting work orders, cryopreservation work orders, or elimination work orders. The summary link of traceability reports is prone to information fragmentation and inconsistencies in review criteria. Summary of the Invention

[0003] To address the aforementioned technical problems, this invention provides a method for detecting and screening amniotic epithelial stem cells, comprising:

[0004] S100. Based on the amniotic tissue source information, sampling and transportation conditions, processing parameter templates, equipment identification, and rule version number, perform access registration and uniqueness, temporal consistency, completeness, legality, and format verification to generate a sample task sheet. The amniotic tissue source information includes a combination of sample number, source code, and sampling timestamp. The sampling and transportation conditions include a combination of transportation temperature record, transportation duration, and preservation system identification. The processing parameter template includes a combination of digestion parameters, filtration parameters, and centrifugation parameters required for the tissue digestion and separation process. The equipment identification includes a combination of equipment number and reagent batch number used in subsequent testing. The rule version number includes a combination of gating rule version number, scoring rule version number, treatment mapping version number, and report template version number.

[0005] S200. Based on the sample task sheet, perform tissue digestion, filtration, and centrifugation to generate a candidate cell population data package;

[0006] S300: Based on the candidate cell population data package, perform surface marker immunostaining, flow cytometry / imaging acquisition and quality control processing to generate a phenotypic detection data package;

[0007] S400. Based on the phenotypic detection data package, perform nucleic acid extraction, transcriptional marker detection, internal reference correction and control constraint processing for the same batch of identifiers to generate a molecular detection data package;

[0008] S500: Based on the molecular detection data package, perform viability, proliferation, microbial and endotoxin detection and label according to threshold template to generate a quality label data package;

[0009] S600. Based on the quality label data package, perform gating, batch normalization, anomaly removal, fusion scoring and consistency determination processing to generate a screening result package;

[0010] S700: Based on the screening result package, perform disposal mapping, work order generation and report generation processing to generate a traceability report.

[0011] Further, the process of performing access registration and uniqueness, timing consistency, integrity, legality, and format verification to generate a sample task sheet includes:

[0012] The verification process includes performing uniqueness verification on the sample number and source code, temporal consistency verification on the sampling timestamp and transportation duration, integrity verification on the transportation temperature record, valid value verification on the storage system identifier, and format verification on the equipment number and reagent batch number. It also includes performing rule version number processing, which involves reading the rule entry index corresponding to the gating rule version number, scoring rule version number, disposal mapping version number, and report template version number from the rule base, locking the rule version number and writing it into the version identifier field of the sample task sheet to generate the sample task sheet.

[0013] Further, the process of tissue digestion, filtration, and centrifugation to generate candidate cell population data packages includes:

[0014] The tissue digestion, filtration, and centrifugation process includes a continuous processing chain of tissue washing, tissue mincing, digestion reaction, termination reaction, dispersion and resuspension, filtration separation, and centrifugation collection. It also performs batch identification and dispensing identification generation, which includes generating batch and dispensing identifiers, and performs cell counting and viability pre-detection, which includes generating cell count records and viability pre-detection records, and generating a candidate cell population data package. The candidate cell population data package includes the batch identifier, the dispensing identifier, the cell count record, the viability pre-detection record, process parameter records, and timestamp records.

[0015] Further, the process of performing surface marker immunostaining, flow cytometry / imaging acquisition, and quality control processing to generate phenotypic detection data packages includes:

[0016] The surface marker immunostaining includes antibody incubation, washing, and incubation for the target marker group and the exclusion marker group; the flow cytometry / imaging acquisition includes flow cytometry acquisition or imaging acquisition; the quality control process includes gating and acquisition quality control; the gating includes the execution of fragment rejection, two-cell event rejection, and dead cell rejection gates; the acquisition quality control includes the judgment of acquisition quality control indicators and the output of acquisition-available labels, generating a phenotypic detection data package; the phenotypic detection data package includes batch identifier, repackaging identifier, positive proportion of each surface marker, intensity statistics, gating boundaries, and acquisition quality control indicators.

[0017] Furthermore, the process of performing batch-labeled nucleic acid extraction, transcriptional marker detection, internal control calibration, and control constraint treatment includes:

[0018] The nucleic acid extraction includes a processing chain of lysis, separation, washing and elution; the transcription marker detection includes amplification curve acquisition; the internal reference calibration includes internal reference gene calibration; the control constraint treatment includes negative control determination, positive control determination and batch repeatability determination; and a molecular detection data package is generated. The molecular detection data package includes batch identifier, aliquot identifier, transcription marker expression characteristics, internal reference calibration results, curve quality indicators and control determination results.

[0019] Further, the process of performing viability, proliferation, microbial, and endotoxin detection, and labeling according to threshold templates to generate a quality label data package includes:

[0020] The viability detection includes sample homogenization, sampling and measurement, viability staining reaction, signal acquisition and threshold interpretation steps; the proliferation characterization includes proliferation observation under preset culture conditions, cell count retesting and proliferation trend interpretation; the microbial detection includes sampling and packaging, culture observation and result interpretation; the endotoxin detection includes performing detection according to preset detection reagent system and interpretation procedure; the threshold template includes a set of judgment thresholds for viability threshold, proliferation threshold, microbial threshold and endotoxin threshold and a fixed template of their label mapping rules, generating a quality label data package, which includes a viability label, proliferation label, microbial label, endotoxin label and retest label.

[0021] Furthermore, the process of gating, batch normalization, anomaly removal, fusion scoring, and consistency determination includes:

[0022] The gating includes determination of transport time window, determination of temperature record integrity, determination of process parameter deviation, determination of instrument calibration, and determination of control constraints. The batch normalization includes generating intensity mapping parameters and internal reference mapping parameters based on the intensity benchmark of control beads and the internal reference benchmark, and performing scale mapping on the intensity statistics of phenotypic detection data packets and the transcriptional marker expression characteristics of molecular detection data packets. The anomaly removal includes determination of background anomalies, determination of insufficient event quantity, and determination of amplification curve anomalies. The fusion score includes identity score, activity score, safety score, and consistency score. The consistency determination includes determining the consistency between the identity label of the phenotypic detection data packet and the identity label of the molecular detection data packet, and generating a conflict marker when they are inconsistent.

[0023] Furthermore, the screening result package includes:

[0024] The screening result package includes a transportation compliance label, a process compliance label, a calibration pass label, a control pass label, a collection usable label, a nucleic acid usable label, the identity score, the activity score, the safety score, the consistency score, the conflict marker, the anomaly marker, and the retest label.

[0025] Furthermore, the process of handling mapping, work order generation, and report generation, including generating a traceability report, includes:

[0026] The disposal mapping, based on the disposal mapping version number, maps the list of qualified batches and sorting thresholds in the screening result package to work order types and work order content elements. The work order generation includes outputting sorting work orders, frozen storage work orders, or elimination work orders. The report generation generates a traceability report based on the report template version number.

[0027] Furthermore, a detection and screening system for amniotic epithelial stem cells includes: a sample task sheet generation unit, a tissue digestion and separation unit, a phenotypic detection unit, a molecular detection unit, a quality label generation unit, a screening engine unit, a treatment strategy library, and a traceability report generation unit; the units are connected in sequence to implement the method described in any of the above embodiments.

[0028] The key innovations of this invention include:

[0029] (1) Input the quality label data package into the screening engine and perform gating, batch normalization, anomaly removal, fusion scoring and consistency judgment according to the rule version number to form a unified execution link driven by the rule version number and generate a screening result package. The gating covers transportation time window judgment, temperature record integrity judgment, process parameter deviation judgment, instrument calibration judgment and control constraint judgment, and the gating output is solidified into transportation compliance label, process compliance label, calibration pass label, control pass label, collection available label and nucleic acid available label.

[0030] (2) Obtain amniotic tissue source information, sampling and transportation conditions, processing parameter templates, equipment identification and rule version number to generate a sample task sheet. The sample task sheet runs through the links of tissue digestion and separation, surface marker immunostaining and flow cytometry or imaging acquisition, nucleic acid extraction bound to the same batch identifier, transcription marker detection and internal reference correction, viability detection, proliferation characterization, microbial detection and endotoxin detection, so that the processing parameter templates, equipment identification and rule version number are homologously bound to the timestamp and process parameters and are continuously called in subsequent steps.

[0031] (3) Within the screening engine, batch normalization is limited to performing scale mapping on the phenotypic detection data package and the molecular detection data package based on the control bead intensity benchmark and the internal reference benchmark. Anomaly removal is limited to background anomaly judgment, insufficient event quantity judgment, and amplification curve anomaly judgment. At the same time, the fusion score is split into identity score, activity score, safety score, and consistency score. The consistency judgment determines the consistency between the identity label of the phenotypic detection data package and the identity label of the molecular detection data package to generate a conflict mark. The conflict mark triggers the re-examination label to be written into the screening result package and linked with the treatment strategy library.

[0032] The following are its main beneficial effects:

[0033] (1) Compared with the existing schemes where the rule version number is mostly used as a record item and the gating scope is scattered, the present invention drives the unified execution of gating, batch normalization, anomaly removal, fusion scoring and consistency judgment through the rule version number, so that the processing path of the quality label data package of the screening engine is consistent under the same rule version number, and the transportation compliance label, process compliance label, calibration pass label, control pass label, collection available label and nucleic acid available label are included as the components of the screening result package and output with the results, reducing the dependence on manual interpretation of gating conclusions and facilitating the review of the screening process.

[0034] (2) Compared with the existing schemes where sample registration and subsequent testing are disconnected and process parameters and equipment information are difficult to connect, the present invention uses the sample task sheet to solidify the processing parameter template, equipment identification and rule version number with the amniotic tissue source information and sampling and transportation conditions. After the tissue digestion and separation record timestamp and process parameters are recorded, they are continuously transmitted to the phenotypic detection data package, molecular detection data package and quality label data package. This ensures that the input caliber of each step for the same task is consistent, reduces the difference between the screening result package and the treatment strategy library call caused by the inconsistency of cross-step references, and supports the summary association of the sample task sheet and the screening result package in the subsequent traceability report.

[0035] (3) Compared with the existing schemes that correct the phenotypic and molecular sides separately and have difficulty aligning across batch scales, distinguishing the source of anomalies, and lacking consistency constraints in fusion conclusions, this invention constrains the batch normalization by using the bead strength benchmark and internal reference benchmark together and performs scale mapping. It incorporates the background anomaly judgment, the event quantity insufficiency judgment, and the amplification curve anomaly judgment into the anomaly removal. Furthermore, it generates conflict markers and triggers re-examination labels by using the fusion scoring structure of identity score, activity score, safety score, and consistency score and consistency judgment. This enables the screening result package to have a traceable marking basis for anomalies and conflicts, and facilitates the disposal strategy library to generate sorting work orders, cryopreservation work orders, or elimination work orders and write back the disposal records. Attached Figure Description

[0036] Figure 1 A schematic flowchart illustrating a method for detecting and screening amniotic epithelial stem cells provided in this application embodiment;

[0037] Figure 2 This is a structural block diagram of a detection and screening system for amniotic epithelial stem cells provided in an embodiment of this application. Detailed Implementation

[0038] Example 1: Refer to Figure 1This is a schematic flowchart of a method for detecting and screening amniotic epithelial stem cells provided by an embodiment of the present invention. The process may include at least steps S100-S700:

[0039] S100: Based on the amniotic tissue source information, sampling and transportation conditions, processing parameter templates, equipment identification and rule version number, perform access registration and uniqueness, time sequence consistency, completeness, legality and format verification processing to generate a sample task sheet;

[0040] S200. Based on the sample task sheet, perform tissue digestion, filtration, and centrifugation to generate a candidate cell population data package;

[0041] S300: Based on the candidate cell population data package, perform surface marker immunostaining, flow cytometry / imaging acquisition and quality control processing to generate a phenotypic detection data package;

[0042] S400. Based on the phenotypic detection data package, perform nucleic acid extraction, transcriptional marker detection, internal reference correction and control constraint processing for the same batch of identifiers to generate a molecular detection data package;

[0043] S500: Based on the molecular detection data package, perform viability, proliferation, microbial and endotoxin detection and label according to threshold template to generate a quality label data package;

[0044] S600. Based on the quality label data package, perform gating, batch normalization, anomaly removal, fusion scoring and consistency determination processing to generate a screening result package;

[0045] S700: Based on the screening result package, perform disposal mapping, work order generation and report generation processing to generate a traceability report.

[0046] S100: Based on the amniotic tissue source information, sampling and transportation conditions, processing parameter templates, equipment identification and rule version number, perform access registration and uniqueness, time sequence consistency, completeness, legality and format verification processing to generate a sample task sheet;

[0047] Specifically, S100 is executed by the task order generation module, whose input sources include amniotic tissue source information, sampling and transportation conditions, processing parameter template, equipment identifier, and rule version number. The amniotic tissue source information represents the combination of the sample number, source code, and sampling timestamp corresponding to the amniotic tissue. The sampling and transportation conditions represent the combination of the transportation temperature record, transportation duration, and preservation system identifier. The processing parameter template represents the combination of the digestion parameters, filtration parameters, and centrifugation parameters required for the tissue digestion and separation process. The equipment identifier represents the combination of the equipment number and reagent batch number used in the subsequent detection chain. The rule version number represents the combination of the gating rule version number, scoring rule version number, treatment mapping version number, and report template version number. When the task order generation module registers the above inputs, it first performs a uniqueness check on the sample number and the source code, a time sequence consistency check on the sampling timestamp and the transportation duration, an integrity check on the transportation temperature record, a valid value check on the storage system identifier, and a format check on the device number and the reagent batch number. When a field is missing, a field conflict is found, or a check fails, the reason for the failure is written into the exception description field of the sample task order, and the task order status is marked as pending correction. After the correction is completed, the process proceeds to the next processing link.

[0048] During the processing of the rule version number, the task order generation module reads the rule entry indexes corresponding to the gating rule version number, the scoring rule version number, the disposal mapping version number, and the report template version number from the rule base, and locks the rule version number into the version identifier field of the sample task order, forming a rule reference benchmark consistent with the subsequent screening engine. When the rule base is updated, the rule base generates a new rule version number and retains the rule entry indexes corresponding to the old rule version number. The task order generation module performs reference binding based on the rule version number, preventing cross-version mixing, so that the same sample task order always references the same set of gating, scoring, disposal mapping, and report template rules in subsequent steps. Understandably, the rule version number belongs to the core parameter set of this invention and is directly related to the consistency of the rules called in subsequent S600; the device number and reagent batch number in the device identifier belong to the core parameter set of this invention and are directly related to the auditable records of subsequent detection data; the digestion parameters, filtration parameters, and centrifugation parameters in the processing parameter template belong to the core parameter set of this invention and are directly related to the tissue digestion and separation process in S200; the amniotic tissue source information and the sampling timestamp, transportation temperature record, transportation duration, and preservation system identifier in the sampling and transportation conditions belong to the core parameter set of this invention and are related to the transportation time window determination and temperature record integrity determination in the subsequent gated link. In contrast to the above core parameter set, the extended description field in the amniotic tissue source information, the extended event field in the sampling and transportation conditions, and the extended maintenance field in the device identifier are extended fields. The task order generation module synchronously writes the sample task order when the input contains such fields; the absence of such fields does not affect the generation of the sample task order.

[0049] In an engineering embodiment, after amniocentesis tissue sampling, the sampling end generates a sample number and records the sampling timestamp. During transportation, the transport end continuously records the transport temperature and forms a data entry associated with the sample number. After the sample arrives, the receiving end enters the transport duration and the preservation system identifier, and selects the digestion, filtration, and centrifugation parameters matching the batch from the preset processing parameter template. Simultaneously, it registers the equipment number and reagent batch number, and selects the rule version number to be used. After receiving the above input, the task sheet generation module generates the sample task sheet according to the aforementioned verification link, and writes the following fields into the sample task sheet: sample number, sampling timestamp, transport temperature record, transport duration, preservation system identifier, digestion parameters, filtration parameters, centrifugation parameters, equipment number, reagent batch number, gating rule version number, scoring rule version number, treatment mapping version number, and report template version number. It also writes the task sheet status and exception description fields, thus completing the structured solidification of the sample task sheet. The sample task sheet is transmitted to S200 as the output product of S100, and serves as the input for tissue digestion and separation in S200. S200 reads the digestion parameters, the filtration parameters, and the centrifugation parameters, and inherits the sample number, the sampling timestamp, the transport temperature record, the transport duration, the preservation system identifier, the equipment number, the reagent batch number, and the rule version number to form a data baseline that connects across steps.

[0050] In summary, the technical effects of this step are as follows: By structurally binding amniotic tissue source information, sampling and transportation conditions, processing parameter templates, equipment identification, and rule version numbers to the sample task sheet, subsequent steps can uniformly reference inputs from the same batch. By performing consistency checks and recording anomalies on key fields, a traceable input baseline is established for subsequent gating and auditing processes. By locking and referencing rule version numbers and retaining historical version indexes, auditable call relationships are achieved throughout the version evolution process of the detection, screening, and treatment links.

[0051] S200. Based on the sample task sheet, perform tissue digestion, filtration, and centrifugation to generate a candidate cell population data package;

[0052] Specifically, step S200 is executed by the sample processing station, and its input source is the sample task sheet output by step S100. In this step, the sample task sheet serves as the carrier of process drive and parameter constraints. The key fields read include sample number, sampling timestamp, transport temperature record, transport duration, preservation system identifier, digestion parameters, filtration parameters, centrifugation parameters, equipment number, reagent batch number, and rule version number. The tissue digestion and separation refers to a continuous processing chain for amniotic tissue, including tissue cleaning, tissue mincing, digestion reaction, termination reaction, dispersion and resuspension, filtration separation, and centrifugation collection. The digestion parameters represent the combination of digestive enzyme preparation type, preparation batch number, digestion incubation conditions, and mixing strategy; the filtration parameters represent the combination of filter media specifications, pre-wetting method, and clogging treatment strategy; and the centrifugation parameters represent the combination of centrifugation program setting, runtime, braking strategy, and resuspension volume. After receiving the amniotic tissue, the sample processing station first binds the sample number and the preservation system identifier into the receiving record, and then writes the transport temperature record and the transport duration into the process parameter record, forming a transport link trace before the start of this step. When the transport temperature record is missing, the transport duration is abnormal, or the preservation system identifier is mismatched, the reason for the abnormality is written into the process parameter record and the status of this step is marked as a deviation status. The deviation status does not block the subsequent processing link, but the deviation mark is retained in the subsequent data packet for S600 gating call.

[0053] In terms of the processing chain, the tissue digestion and separation preferably includes a three-stage action sequence. The first stage is tissue pretreatment and cleaning separation. The sample processing station performs surface rinsing and mincing operations on the amniotic membrane tissue according to the digestion parameters, and writes the mincing completion time into a timestamp record. In automated implementation, the mincing action is completed by a shearing fixture with a limiting groove. The fixture number is associated with the equipment number and written into the process parameter record. After the operator triggers the start button, the processing station automatically generates two timestamp records: mincing start time and mincing end time. The second stage is the digestion reaction and termination reaction. The sample processing station puts the minced tissue into a digestion container and adds digestive enzyme preparation, and performs incubation and mixing according to the digestion parameters. In engineering implementation, the mixing strategy is completed by a timed flipping mechanism or an orbital oscillation mechanism. The operating status of the mechanism and the digestion container number are written into the process parameter record. When abnormal liquid level, abnormal clump residue, or alarm of the mixing mechanism occur during incubation, the processing station writes the alarm type into the process parameter record and triggers a manual review action. The manual review conclusion is written into the review field of the process parameter record. The third stage involves dispersion-resuspending, filtration-separation, and centrifugation-collection. After the reaction is terminated, the sample processing station mechanically disperses the digestion products to form a cell suspension. Then, according to the filtration parameters, impurities are removed through the filter medium. If clogging occurs during filtration, the processing station performs clogging disposal strategy according to the filtration parameters, replacing the filter medium or performing segmented filtration, and recording the number of replacements and replacement times in a timestamp record. After filtration, the processing station centrifuges and resuspends the cell suspension according to the centrifugation parameters. The centrifugation start time, centrifugation end time, and resuspending completion time are recorded in a timestamp record, and the centrifugation program setting and braking strategy are recorded in the process parameter record.

[0054] Furthermore, to establish a batch binding relationship with subsequent detection links, the sample processing station generates a batch identifier and a dispensing identifier simultaneously within this step. The batch identifier is formed by combining the sample number with the batch serial number generated in this step, and is written into the process parameter record before the digestion reaction begins. The dispensing identifier is generated after cell resuspension and is used to identify different dispensing containers under the same batch identifier; the dispensing container number and dispensing time are written into the timestamp record. The batch identifier and the dispensing identifier belong to the core field set of the candidate cell population data package, which is read by S300 and extends to the batch identifier binding link of S400. Understandably, if the sample processing station detects insufficient dispensing containers or container identifier conflicts during the dispensing stage, it writes the reason for the conflict into the process parameter record and pauses the dispensing action. Execution resumes after the conflict is resolved, and the pause start time and resumption time are written into the timestamp record upon resumption, thus preserving a complete operational trajectory.

[0055] After the candidate cell population is generated, the sample processing station performs cell counting and viability pre-detection on the cell suspension and generates records. The cell counting record represents a combination of information including the counting method, counting time, counting result, and operator identification. The viability pre-detection record represents a combination of information including the viability detection method, detection time, viability interpretation result, and anomaly description. When the viability pre-detection record indicates the presence of significant fragmentation, clumping, or background interference in the sample, the sample processing station writes this description into the process parameter record and triggers a re-filtering or re-dispersion action. The trigger time and completion time of the re-filtering or re-dispersion action are written into the timestamp record. The cell counting record and the viability pre-detection record belong to the core field set of the candidate cell population data package, which is called by S300 before surface marker immunostaining, serving as the input basis for sample loading volume and staining ratio.

[0056] The output of this step is a candidate cell population data package. This data package is generated by the sample processing station after resuspending and written to the data storage terminal. The candidate cell population data package includes a batch identifier, aliquot identifier, cell count record, viability pre-detection record, process parameter record, and timestamp record. The process parameter record covers transport link access record, digestion parameter execution record, filtration parameter execution record, and centrifugation parameter execution record. The timestamp record covers receiving time, shearing start time, shearing end time, digestion start time, digestion end time, filtration start time, filtration end time, centrifugation start time, centrifugation end time, and resuspending completion time. After generation, the candidate cell population data package is transmitted to S300 as input for S300's surface marker immunostaining and flow cytometry or imaging acquisition, and for gating and acquisition quality control. S300 reads the batch identifier, the aliquot identifier, the cell count record, the viability pre-detection record, and inherits the process parameter record and the timestamp record, forming a data foundation for cross-step connections.

[0057] Summary of the technical effects of this step: This step drives tissue digestion and separation through a sample task sheet, simultaneously recording timestamps and process parameters, thus providing structured traceability of the candidate cell population's origin and processing. The generation and binding of batch and dispensing identifiers ensures consistent reference for subsequent phenotypic and molecular detection data packages. The formation of cell count and viability pre-detection records enables the S300 to achieve a stable baseline for sample loading and staining input.

[0058] S300: Based on the candidate cell population data package, perform surface marker immunostaining, flow cytometry / imaging acquisition and quality control processing to generate a phenotypic detection data package;

[0059] Specifically, step S300 is executed by the phenotypic detection station, and its input source is the candidate cell population data packet output by step S200. After receiving the candidate cell population data packet, the phenotypic detection station first parses the batch identifier and the packaging identifier, reads the cell count record and the viability pre-detection record, and retrieves the process parameter record and the timestamp record as reference information for the sample source link and the preprocessing link; at the same time, the phenotypic detection station retrieves the surface marker panel version number, control protocol version number, equipment number and reagent batch number from the sample task sheet based on the batch identifier association, and completes the panel selection, control configuration and equipment matching for this step. The surface marker immunostaining refers to the processing chain of antibody incubation, washing to remove free antibodies, staining termination, and instrument preparation for the cell suspension corresponding to the candidate cell population data package. The surface marker immunostaining includes a target marker group and an exclusion marker group. The target marker group represents antibody combinations used to characterize the surface features related to amniotic epithelial cells, and the exclusion marker group represents antibody combinations used to exclude non-target cell populations or abnormal events. The control protocol version number represents the combination constraints of the control samples required for negative control determination, the control samples required for positive control determination, and the repeatability determination required for batch consistency. The surface marker panel version number, the control protocol version number, the device number, and the reagent batch number are the core parameter set of this step. The phenotypic detection station writes this core parameter set into the acquisition quality control indicators of this step as the source of fields for subsequent traceability reports.

[0060] During the surface marker immunostaining process, the phenotypic detection station determines the sample loading range according to the cell count record and determines whether to trigger a pretreatment action based on the viability pre-detection record. When the viability pre-detection record indicates fragment aggregation or background interference, the phenotypic detection station triggers a redispersion or re-filtration operation and records the triggering reason. The trigger start time and trigger end time are written into the supplementary entries of the timestamp record. The surface marker immunostaining is performed by a staining unit, which includes a sample loading module, a mixing module, an incubation module, and a washing module. The sample loading module adds the corresponding antibody reagents according to the configuration of the target marker group and the exclusion marker group and binds the reagent batch number. The mixing module performs mixing according to a preset mixing rhythm and outputs a mixing status record. The incubation module performs timed incubation according to the incubation conditions corresponding to the panel version and outputs the incubation start time and incubation end time. The washing module performs centrifugation resuspension or buffer replacement according to a preset washing cycle and outputs a washing count record. The mixing status record, the incubation start time, the incubation end time, and the washing count record are all written into the process parameter record. If the staining unit detects insufficient sample addition, abnormal incubation temperature control, or abnormal washing and centrifugation, the phenotypic detection station will write the abnormality type into the process parameter record and mark the staining as pending verification. The pending verification status will trigger repeated staining or repeated washing actions, and the number of repeated actions will be written into the timestamp record synchronously with the timestamp.

[0061] Regarding the acquisition link, the streaming acquisition or imaging acquisition is performed by an acquisition unit, which includes either a streaming acquisition subunit or an imaging acquisition subunit. The selection of either subunit is triggered by the device type indicated by the device number in the sample task sheet. Technical solution one is streaming acquisition. Before starting acquisition, the streaming acquisition subunit performs a device self-check and reads the quality control record corresponding to the device number. If the quality control record does not meet the self-check conditions, a quality control conclusion is generated indicating that the acquisition is unavailable and written into the acquisition quality control indicators. After passing the self-check, the streaming acquisition subunit loads the channel configuration according to the surface label panel version number and loads the control acquisition sequence corresponding to the control scheme version number, sequentially acquiring control samples and test samples. During the acquisition process, it records the event quantity statistics required for determining insufficient event quantity, the background statistics required for determining background anomalies, and the drift statistics required for determining channel drift, forming the acquisition quality control indicators. Technical Solution Two involves image acquisition. Before starting acquisition, the image acquisition subunit performs an optical path self-test and reads the imaging calibration record corresponding to the device number. If the imaging calibration record does not meet the self-test conditions, a quality control conclusion is generated indicating that the acquisition is unavailable and written into the acquisition quality control indicators. After passing the self-test, the image acquisition subunit loads the imaging channel and exposure range according to the version number of the surface marking panel, acquires the control image and the image to be tested according to the control acquisition sequence corresponding to the control scheme version number, and records the sharpness statistics required for defocus determination, the grayscale statistics required for exposure anomaly determination, and the field of view statistics required for field of view coverage determination, forming the acquisition quality control indicators. Both technical solutions bind and store the original acquisition file with the batch identifier and the repackaging identifier, and write the binding index into the acquisition quality control indicators for reference in subsequent traceability reports.

[0062] The gating and acquisition quality control are executed in parallel within this step. The gating object is either a set of flow cytometry events or a set of imaging cell objects. The gating includes a fragmentation rejection gate, a two-cell event rejection gate, and a dead cell rejection gate. Specifically, the fragmentation rejection gate sets the gating boundary based on size-related features and granularity-related features and rejects fragmented events; the two-cell event rejection gate sets the gating boundary based on pulse morphology-related features or cell overlap-related features and rejects two-cell events; and the dead cell rejection gate sets the gating boundary based on dead cell indicator staining signals or morphological abnormalities and rejects dead cell events. The gating boundaries and gating execution logs are written to the gating boundary field of the phenotypic detection data packet. The acquisition quality control is performed by the quality control unit. The quality control unit judges the acquisition quality control indicators and outputs acquisition usable labels. When an acquisition usable label is unavailable, the phenotypic detection station triggers a re-acquisition action or a re-staining action. The trigger type is determined by the abnormality type in the acquisition quality control indicators, and the trigger type and trigger time are written into the process parameter record. When an acquisition usable label is available, the phenotypic detection station extracts the positive proportion and intensity statistics of each surface marker from the gated event set or cell object set. The positive proportion of each surface marker represents the proportion of positive events in each channel of the target marker group and the exclusion marker group. The intensity statistics represent the statistical characteristics of the signal intensity of each channel and are recorded synchronously with the control sample statistics, thus providing a basis for reference in subsequent steps. Understandably, the positive proportion of each surface marker and the intensity statistics belong to the core field set output by this step, while the gating boundary and the acquisition quality control indicators belong to the audit field set output by this step. Together, they constitute the phenotypic evidence source for the subsequent screening engine.

[0063] The output of this step is a phenotypic detection data package. This phenotypic detection data package is generated by the phenotypic detection station after data collection and written to the data storage terminal. The phenotypic detection data package includes the batch identifier, the packaging identifier, the positive rate of each surface marker, intensity statistics, gating boundaries, and collection quality control indicators, and retains the association index with the candidate cell population data package, thus enabling the phenotypic detection data package to have a source tracing path. After generation, the phenotypic detection data package is transmitted to S400 as input for S400's batch identifier-bound nucleic acid extraction, transcription marker detection, and internal reference calibration, and for executing control constraints. S400 reads the batch identifier and inherits the collection quality control indicators and the gating boundaries as reference information for control constraints and batch repeatability determination.

[0064] Summary of the technical effects of this step: This step uses surface marker immunostaining of the target and exclusion marker groups to create a structured output of phenotypic evidence within the same batch of markers. Through the linkage of acquisition quality control and gating between flow cytometry and imaging acquisition, the gating boundaries and acquisition quality control indicators are synchronously deposited into the phenotypic detection data package. By associating the version numbers of the same batch of markers and control protocols within the phenotypic detection data package, the S400 obtains an inheritable phenotypic input baseline.

[0065] S400. Based on the phenotypic detection data package, perform nucleic acid extraction, transcriptional marker detection, internal reference correction and control constraint processing for the same batch of identifiers to generate a molecular detection data package;

[0066] Specifically, step S400 is executed by the molecular detection station, and its input source is the phenotypic detection data packet output by step S300. After receiving the phenotypic detection data packet, the molecular detection station parses the batch identifier and the repackaging identifier, and reads the positive proportion of each surface marker, the intensity statistics, the gating boundary, and the collection quality control indicators to complete the identification binding and collection availability reference of the batch samples; at the same time, the molecular detection station retrieves the transcription marker panel version number, control protocol version number, equipment number, and reagent batch number from the sample task sheet based on the batch identifier association to form the molecular detection configuration for this step. The batch identification binding refers to the homologous binding of the nucleic acid extraction record, transcription marker detection record, and internal reference calibration record generated in this step to the batch identification, and establishing a one-to-one correspondence with the dispensing identification, so that there is a traceable mapping link between the phenotypic detection data package and the molecular detection data package of the same batch. The dispensing identification refers to the identification of the dispensing unit of the same batch of cells. The dispensing unit inherits the dispensing identification of the candidate cell population data package formed in the S200 stage. When receiving samples, the molecular detection station performs barcode verification or manual verification on the dispensing unit and writes the verification action into the process record of this step. The transcription marker panel version number is a core parameter of this step, representing a fixed combination of the transcription marker set and the internal reference gene set. The control protocol version number is a core parameter of this step, representing the constraints of negative control determination, positive control determination, and batch repeatability determination. The equipment number and reagent batch number are audit parameters of this step. The molecular detection station writes them into the curve quality index of this step and into the associated field of the control determination result.

[0067] During the nucleic acid extraction and transcription marker detection process, the molecular detection station performs nucleic acid extraction on samples from the same batch corresponding to the phenotypic detection data package. Nucleic acid extraction refers to the process of releasing intracellular nucleic acids under preset lysis conditions and performing separation and purification. Nucleic acid extraction is completed by a nucleic acid extraction unit, which consists of a lysis module, a separation module, a washing module, and an elution module. The lysis module loads the lysis formula according to the extraction protocol corresponding to the transcription marker panel version number and records the lysis start and end times. The separation module uses centrifugation or solid-phase carrier separation to complete nucleic acid capture and records the separation method identifier. The washing module performs a washing cycle and records the washing status. The elution module outputs the nucleic acid product and records the elution volume identifier. Nucleic acid extraction includes nucleic acid quality determination, which is performed by a quality determination unit. The quality determination unit generates a usable nucleic acid label based on nucleic acid concentration, nucleic acid integrity, and inhibition risk indicators. The usable nucleic acid label is written into the control determination result and simultaneously written into the curve quality index. When the usable nucleic acid label is unusable, the molecular detection station triggers a re-extraction process and writes the triggering reason into the process record. The number of re-extractions and the corresponding timestamps are written into the time record for this step. The transcriptional marker detection refers to the processing chain of amplifying nucleic acid products and acquiring amplification curves. Transcriptional marker detection is performed by the amplification detection unit. This unit loads primer and probe configurations according to the transcriptional marker panel version number and control well configurations according to the control protocol version number. It first acquires amplification curves for negative and positive controls, then acquires amplification curves for the same batch of samples and generates amplification curve quality assessment results. The amplification curve quality assessment determines the stability of the curve baseline, the identifiability of curve inflection points, the consistency of duplicate wells, and the risk of non-specific signals. The assessment output is written into the curve quality index. The internal reference calibration includes internal reference gene calibration, performed by the calibration unit. This unit loads the internal reference gene set according to the transcriptional marker panel version number, aligns the target transcriptional marker signals of the same batch of samples with the internal reference gene signals for homology, and generates internal reference calibration results. When the internal reference gene signal does not meet the internal reference effectiveness constraints, the calibration unit writes the internal reference abnormality marker into the control assessment results and triggers a retest process. The retest process is performed by the amplification detection unit and records the retest timestamp. In an engineering embodiment, the molecular detection station is deployed in the nucleic acid detection area of ​​the cell preparation laboratory. The nucleic acid extraction unit is composed of an automated solid-phase carrier extraction device. The device number is fixed by the sample task sheet and written into the log when the device is used. The amplification detection unit is composed of an amplification platform with curve acquisition function. Before each batch of detection begins, the platform runs the control sample according to the control protocol version number. When the negative control or positive control fails the judgment, the amplification detection unit outputs a control failure mark and stops the effective interpretation of the same batch of samples. The control failure mark is written into the control judgment result and simultaneously written into the curve quality index.

[0068] The control constraints are performed in parallel with transcription marker detection in this step. These control constraints include negative control determination, positive control determination, and batch repeatability determination. The negative control determination unit performs non-specific amplification constraint interpretation on the negative control amplification curve and outputs the negative control determination result. The positive control determination unit performs effective amplification constraint interpretation on the positive control amplification curve and outputs the positive control determination result. The batch repeatability determination unit performs difference measurement on the repeatability curves of the same batch and outputs the repeatability determination result. If any control determination result is unsuccessful, the control determination unit generates a control failure marker and writes it into the control determination result, while simultaneously associating the control failure marker with the batch identifier for reference in subsequent steps. The molecular detection data package is generated by the molecular detection station after completing nucleic acid extraction, transcription marker detection, internal reference gene correction, and control determination. The molecular detection data package includes the batch identifier, the packaging identifier, transcription marker expression characteristics, internal reference correction results, curve quality indicators, and control determination results. The transcription marker expression characteristics are output from the amplification detection unit and aligned to the internal reference correction results by the correction unit. The curve quality indicators include reference fields for amplification curve quality determination and nucleic acid quality determination. The control determination results include conclusion fields for negative control determination, positive control determination, and batch repeatability determination. After generation, the molecular detection data package is written to the data storage terminal and transmitted to S500, serving as the input basis for S500 to perform viability detection, proliferation characterization, microbial detection, and endotoxin detection, and to annotate according to threshold templates. Simultaneously, the control determination results and the curve quality indicators serve as references for subsequent S600 gating and anomaly removal, with consistent binding achieved through the batch identifier during cross-step transitions. In summary, this step, through batch identifier binding, solidifies nucleic acid extraction, transcription marker detection, and internal reference gene correction within the same batch link, forming a traceable association. By using control constraints, the determination of negative control, positive control, and batch repeatability are written into the control determination results and simultaneously deposited into the molecular detection data package. Through the field-based output of the molecular detection data package, S500 and S600 obtain stable molecular evidence entry and quality determination entry points.

[0069] S500: Based on the molecular detection data package, perform viability, proliferation, microbial and endotoxin detection and label according to threshold template to generate a quality label data package;

[0070] Specifically, this step is executed by the quality inspection station, whose input source is the molecular detection data packet output by S400. Upon access, it parses the batch identifier and the repackaging identifier, reads the transcriptional marker expression characteristics, the internal reference correction results, the curve quality indicators, and the control judgment results, forming a detection payload consistent with the batch of samples. The quality inspection station further retrieves the device number and reagent batch number associated with the batch identifier from the sample task sheet as audit fields for the detection process record of this step, and obtains the threshold template by indexing the scoring rule version number. The threshold template refers to a fixed template of the judgment threshold set and its label mapping rules for viability detection, proliferation characterization, microbial detection, and endotoxin detection. It includes at least the viability threshold, proliferation threshold, microbial threshold, and endotoxin threshold, and is the minimum set of core parameters for quality label generation in this step. If any threshold is missing, the quality inspection station outputs a configuration missing marker and writes the configuration missing marker into the process parameter record, while triggering the manual review entry and pausing the label generation link of the repackaging identifier in this step. Before starting the testing, the quality inspection station generates a batch context and writes the batch identifier, the packaging identifier, the equipment number, and the reagent batch number into the timestamp record of this step. The timestamp record includes the testing start timetamp and the testing end timetamp, which are used for subsequent S600 execution process parameter deviation judgment and traceability reference when batch normalization.

[0071] During the viability detection and proliferation characterization process, the quality inspection station performs viability detection on the aliquoted samples corresponding to the molecular detection data package. Viability detection refers to a detection chain that quantifies cell viability and generates viability labels. This detection chain includes sample homogenization, sampling and measurement, viability staining reaction, signal acquisition, and threshold interpretation. Sample homogenization is achieved through gentle mixing; sampling and measurement are achieved through quantitative sampling equipment; the viability staining reaction is completed according to a preset reaction time and the reaction conditions are recorded; signal acquisition is completed by a cell counting and viability interpretation device, and the device number is recorded. The quality inspection station writes the raw viability detection readings and interpretation conclusions into the process parameter record and writes the viability labels output according to the viability threshold into the quality label data package. When the curve quality index or the control judgment result has a control failure mark or a nucleic acid quality judgment abnormality mark, the quality inspection station adds a consistency verification action to the viability detection results. If the verification fails, a retesting process is triggered and a retesting label is written. The triggering conditions and the number of retests for the retesting process are written into the timestamp record. The proliferation characterization refers to a detection chain that quantifies cell proliferation status and generates proliferation tags. The quality inspection station performs proliferation characterization on the packaged samples under the same batch identifier binding. Proliferation characterization includes at least proliferation observation under preset culture conditions, cell count retesting, and proliferation trend interpretation, and the observation window, culture condition elements, and count retest records are written into the process parameter record. The quality inspection station performs tag mapping on the proliferation trend interpretation results according to the proliferation threshold, generates proliferation tags, and writes them into the quality tag data package. Understandably, the tag generation steps for viability detection and proliferation characterization use the same threshold template. The tag mapping rules are fixed by the scoring rule version number. When versions do not match, a version conflict flag is output and a retest tag writing is triggered. The version conflict flag is also written into the process parameter record for reference by S600 when performing anomaly removal and consistency determination.

[0072] During the operation of microbial and endotoxin detection, the quality inspection station performs microbial detection on the packaged samples. Microbial detection refers to a detection chain that determines the risk of contamination by bacteria and fungi and generates microbial tags. This chain consists of sampling and packaging, culture and observation, and result interpretation. Sampling and packaging are completed under aseptic conditions, and the operation timestamp and operator identification are recorded. Culture and observation are completed under preset culture conditions, and the culture start and end timestamps are recorded. Result interpretation outputs the microbial detection conclusion and writes it into the process parameter record. In an engineering embodiment, the quality inspection station is deployed in the aseptic testing area of ​​the cell preparation laboratory. Microbial detection employs a dual-path strategy with batch-specific tag binding. Technical solution one uses culture and observation to form the interpretation conclusion; technical solution two adds rapid contamination indication detection on top of culture and observation and records the two types of conclusions in parallel; technical solution three triggers a tightened retesting process and writes a retesting tag when a suspicious signal is detected in culture and observation. All three technical solutions use the microbial threshold to complete tag mapping and generate microbial tags. The differences lie only in the triggering conditions and the retesting chain. Both the triggering conditions and the retesting action are written into the process parameter record. The endotoxin detection refers to a detection chain that determines the risk of bacterial endotoxin contamination and generates endotoxin tags. The quality inspection station performs endotoxin detection according to a preset test reagent system and interpretation procedure, and records the reagent batch number, reaction conditions, and reading acquisition conditions. The endotoxin detection conclusion is tagged according to the endotoxin threshold, generating endotoxin tags and writing them into the quality tag data package. When endotoxin detection shows background anomalies or reading anomalies, the quality inspection station writes an anomaly marker and triggers the writing of a re-inspection tag. At the same time, the anomaly marker is associated with the same batch identifier, which can be directly called by S600 when performing background anomaly judgment and anomaly removal. The quality label data package is generated and stored solidified upon completion of this step. The quality label data package includes a viability label, a proliferation label, a microbial label, an endotoxin label, and a retest label, and includes reference fields for process parameter records and timestamp records corresponding to the same batch identifier and the repackaging identifier. The viability label, proliferation label, microbial label, endotoxin label, and retest label are written into the quality label data package as output field names, and serve as direct inputs for S600 to input the quality label data package into the screening engine and perform gating, batch normalization, anomaly removal, fusion scoring, and consistency determination during cross-step transitions. The process parameter records and the timestamp records serve as traceability input sources for S600 to perform process parameter deviation determination and transportation time window determination.

[0073] Summary of the technical effects of this step: Under the same batch identifier binding, this step forms a consistent quality testing chain for viability detection, proliferation characterization, microbial detection, and endotoxin detection, and synchronously stores the detection process parameters and timestamp records. A unified label mapping is performed on multiple types of test results using threshold templates, generating a field-based quality label data package and establishing an auditable association with the rule version number. Through the triggering and writing back of re-inspection labels, the S600 system subsequently has directly referenceable quality judgment and anomaly handling entry points.

[0074] S600. Based on the quality label data package, perform gating, batch normalization, anomaly removal, fusion scoring and consistency determination processing to generate a screening result package;

[0075] Specifically, this step is executed by the screening engine. The screening engine receives the quality label data packet output from S500 as input, and during the access phase, parses the same batch identifier and the repackaging identifier, reads the viability label, the proliferation label, the microbial label, the endotoxin label, and the re-inspection label, and simultaneously reads the reference fields of the process parameter record and the timestamp record to construct a batch-level screening context. The screening engine further obtains the rule version number from the version field associated with the sample task sheet. The rule version number is used to solidify the gating rule version number and the scoring rule version number to be called in this step, and writes the rule version number into the screening audit record. When the version field in the quality label data packet is inconsistent with the version field of the sample task sheet, the screening engine writes a version conflict flag and triggers the re-inspection label to be set. The version conflict flag, along with the re-inspection label, is incorporated into the subsequently output screening result packet for synchronous reference by the S700 treatment strategy library when calling the treatment mapping version number.

[0076] During the gating process, the screening engine performs gating processing on the quality label data package. Gating refers to the rule execution chain that determines the compliance of transportation and processes, instrument calibration status, and control constraint status based on the gating rule version number. Specifically, the screening engine calls the transportation time window judgment to verify the consistency of the sampling timestamp, transportation duration, and transportation temperature record, and writes a failure status to the transportation compliance label when records are missing, time is inverted, or temperature records are discontinuous. The screening engine calls the temperature record integrity judgment to check the sampling interval and gaps in the transportation temperature record, and synchronously writes a failure status to the acquisition usable label when an anomaly occurs, as a priori condition for subsequent anomaly removal. Further, the screening engine calls the process parameter deviation judgment to compare the process parameter record with the digestion, filtering, and centrifugation parameters in the processing parameter template. When the deviation exceeds the template's allowable range or key process records are missing, a failure status is written to the process compliance label, and the deviation entry is written to the screening audit record. The screening engine invokes instrument calibration judgment, generating a calibration pass label based on the calibration status record corresponding to the device number. When the calibration status is unavailable or calibration fails, a failure status is simultaneously written to the acquisition available label. The screening engine also invokes control constraint judgment, reads the control judgment result from the molecular detection data package and forms a control pass label, while simultaneously associating the nucleic acid available label with the curve quality index. When the negative control judgment or positive control judgment fails, or the same batch repeatability consistency judgment fails, the control pass label is set to a failure status and the retest label is set. After the gating link is completed, the transportation compliance label, process compliance label, calibration pass label, control pass label, acquisition available label, and nucleic acid available label output by the screening engine are incorporated into the screening result package as intermediate quality labels, and also serve as input conditions for subsequent batch normalization and anomaly removal in this step. Samples that fail the gating enter the retest channel and retain the original record for traceability.

[0077] During batch normalization and anomaly removal, the screening engine performs batch normalization on gated aliquots. Batch normalization refers to a processing chain that maps and corrects the scale differences between the phenotypic detection data package and the molecular detection data package based on the control bead intensity benchmark and the internal reference benchmark. Specifically, the screening engine reads the intensity statistics from the phenotypic detection data package and extracts the benchmark intensity item corresponding to the control bead intensity benchmark, generates intensity mapping parameters, and writes them into the screening audit record. Simultaneously, it reads the internal reference correction results from the molecular detection data package and extracts the benchmark item corresponding to the internal reference benchmark, generates internal reference mapping parameters, and writes them into the screening audit record. The screening engine performs scale mapping on the intensity statistics of the phenotypic detection data package based on the intensity mapping parameters, performs scale mapping on the transcriptional marker expression features of the molecular detection data package based on the internal reference mapping parameters, and uses the mapped data as the input payload for subsequent fusion scoring. Furthermore, the screening engine performs anomaly removal, which refers to the processing chain that determines background anomalies, insufficient event volume, and amplification curve anomalies, and removes or downweights abnormal samples. Specifically, background anomaly determination generates a background anomaly label by reading the gating boundary and control correlation statistics of the phenotypic detection data packet; insufficient event volume determination generates an insufficient event volume label by reading the event volume correlation records of the streaming acquisition; and amplification curve anomaly determination generates an amplification curve anomaly label by reading the curve quality index. When any anomaly label appears, the screening engine sets the re-examination label and writes the anomaly label into the screening result packet, while retaining the corresponding original judgment basis in the screening audit record. The anomaly label also serves as an input item for the consistency score in subsequent calculations. In the engineering implementation, in the batch screening scenario of the cell preparation laboratory, the screening engine is deployed on the laboratory information processing terminal and establishes a data access relationship with the flow cytometry equipment and nucleic acid detection equipment. After the sample is packaged and tested, the data packet output by the equipment enters the screening engine. When the transport temperature record is missing or the calibration status is unavailable, the screening engine automatically triggers the retest label to be set and includes the corresponding packaged sample in the pending processing queue. After manual review, the screening engine re-executes this step. During the rerun, the original rule version number is still used and the rerun timestamp is written, forming an auditable running trajectory.

[0078] During the fusion scoring and consistency determination process, the screening engine performs fusion scoring on samples that have completed batch normalization and have not triggered rejection. The fusion score includes an identity score, activity score, safety score, and consistency score. The identity score is generated based on the identity tags of the phenotypic detection data package and the molecular detection data package. The activity score is generated based on the viability tag and the proliferation tag. The safety score is generated based on the microbial tag and the endotoxin tag. The consistency score is generated by combining the gated output quality tag and the anomaly marker. During the consistency determination phase, the screening engine performs consistency determination on the identity tags of the phenotypic detection data package and the molecular detection data package. When inconsistencies are found, a conflict marker is generated, and the re-examination tag is written into the screening result package. The conflict marker is associated with the same batch identifier and the repackaging identifier and written into the screening audit record. The screening engine generates the screening result package in the output stage. The screening result package includes sample number, task order number, rule version number, transportation compliance label, process compliance label, calibration passed label, control passed label, collection available label, nucleic acid available label, identity score, activity score, safety score, consistency score, conflict marker, anomaly marker, and retest label. Based on the scoring rule version number, it generates a qualified batch list and sorting threshold as output field names and writes them into the screening result package. The qualified batch list and sorting threshold are used as direct inputs in the cross-step connection, where the screening result package is input into the disposal strategy library by S700 to generate sorting work order, cryopreservation work order, or elimination work order. The screening result package summary is synchronously written into the screening result package and used for subsequent traceability report summary.

[0079] In summary, this step integrates the quality label data package into the screening engine and binds it to the rule version number to complete a continuous processing chain of gating, batch normalization, anomaly removal, fusion scoring, and consistency determination. By linking the gating output of quality labels, anomaly markers, conflict markers, and re-inspection labels, a traceable record loop for samples in the same batch during the screening stage is achieved. By generating the screening result package containing a list of qualified batches and sorting thresholds, the S700 has direct input for processing.

[0080] S700: Based on the screening result package, perform disposal mapping, work order generation and report generation processing to generate a traceability report;

[0081] Specifically, this step is triggered after the screening engine completes S600 and generates the screening result package. The triggering condition is that the screening result package has been written and contains a list of qualified batches and sorting thresholds that can be used for disposal mapping. After receiving the screening result package as input, the disposal strategy library first parses the sample number, task order number, batch identifier, repackaging identifier, and rule version number in the screening result package, and reads the comparison judgment result, quality label data package summary, and screening result package summary as reference content for subsequent traceability and summary. At the same time, the disposal strategy library uses the task order number to look up the disposal mapping version number and report template version number related to disposal in the sample task order, thereby forming a disposal context and writing the rule version number, the disposal mapping version number, and the report template version number into the disposal audit record of this step. Furthermore, when there is a version inconsistency between the rule version number in the screening result package and the gating rule version number and scoring rule version number in the sample task sheet, the disposal strategy library uses the re-inspection tag as the basis for determining the disposal priority and retains the version relationship in the disposal record. The re-inspection tag is used as a constraint input in the subsequent work order generation to participate in the disposal type selection.

[0082] The disposal strategy library is a version-managed disposal mapping data set, which at least contains mapping entries corresponding one-to-one with the disposal mapping version number and report templates corresponding one-to-one with the report template version number. The mapping entries are used to map the qualified batch list and sorting threshold within the screening result package to work order types and work order content elements. Specifically, the disposal strategy library locates mapping entries based on the disposal mapping version number and performs disposal routing on the qualified batch list. The minimum input set for disposal routing is limited to the qualified batch list, the sorting threshold, the re-inspection label, and conflict and anomaly markers related to anomalies. Based on this, identity scoring, activity scoring, security scoring, and consistency scoring are understood as preferred inputs used to refine boundary case handling of the disposal mapping, but their absence does not affect the determination of the work order type using the minimum input set. Further, the disposal strategy library performs re-inspection priority routing on repackaged samples with the re-inspection label set, and performs anomaly priority routing on repackaged samples with conflict or anomaly markers. The routing determination process is written into the disposal audit record, thereby maintaining a traceable connection between the disposal mapping process and the screening result package.

[0083] During the work order generation process, the disposal strategy library outputs one or a combination of sorting work orders, freezing work orders, or elimination work orders based on the work order type of the mapped entries. The sorting work order carries the reference relationship between the sorted object and the sorting threshold; the freezing work order carries the reference relationship between the qualified batch list and the frozen object; and the elimination work order carries the reference relationship between unqualified or eliminated objects. Specifically, the disposal strategy library generates corresponding work order content for each packaged sample and writes the sample number, task order number, batch identifier, packaging identifier, rule version number, disposal mapping version number, and references to the qualified batch list and sorting threshold into the work order content. It also writes the re-inspection label, conflict marker, and anomaly marker as work order constraint fields, enabling subsequent execution stages to directly identify re-inspection and anomaly-related disposal restrictions when reading the work order. Furthermore, in an engineering embodiment of the cell preparation laboratory, the disposal strategy library is deployed in a storage medium within the same information processing device or local area network as the detection data processing terminal. After the screening result package is generated, it automatically enters the mapping entry of the disposal strategy library. When the qualified batch list exists and the sorting threshold meets the sorting conditions of the mapping entry, the sorting work order is generated and associated with the corresponding repackaging identifier. When the qualified batch list exists but the repackaged sample has a re-inspection label, the cryopreservation work order is generated and the re-inspection label is retained. When the safety-related label corresponding to the repackaged sample is in a failed state or the disposal mapping entry points to elimination and diversion, the elimination work order is generated. The above generation process writes the trigger timestamp into the disposal audit record and establishes an association with the timestamp record of the screening result package.

[0084] During the write-back process of the disposal record, the disposal strategy library generates the disposal record and performs a write-back after the work order is generated. The write-back refers to the process of writing the disposal generation information and execution receipt information into a traceable carrier. Specifically, the disposal record includes at least the sample number, task order number, rule version number, disposal mapping version number, work order type identifier, and timestamp record, and further includes the generation receipt information of the sorting work order, the frozen work order, or the elimination work order. The disposal strategy library writes the disposal record back to the disposal record storage location bound to the batch identifier of the screening result package, and synchronously writes the disposal record summary into the disposal record reference field of the screening result package, thereby forming a bidirectional association between the screening result package and the disposal record at the data level. Furthermore, when a disposal status change occurs during the execution of the work order, the disposal strategy library receives the execution receipt and writes the receipt timestamp into the disposal record, and simultaneously writes the disposal status summary back to the disposal status segment of the screening result package summary, so that the final disposal status can be directly referenced when the subsequent traceability report is generated without re-parsing the work order execution details.

[0085] During the traceability report generation process, the disposal strategy library aggregates the screening result package and the disposal records to generate the traceability report. The traceability report selects the corresponding report template based on the report template version number and performs field mapping and filling. Specifically, the disposal strategy library extracts the sample number, task order number, and rule version number from the screening result package, and reads the comparison judgment result, quality label data package summary, and screening result package summary as the main content of the report. At the same time, it reads the work order type identifier and disposal status summary from the disposal records and writes them into the disposal record segment, ultimately forming the traceability report. The output field names of the traceability report include at least the sample number, task order number, rule version number, comparison judgment result, quality label data package summary, screening result package summary, and disposal record within the report. The traceability report and the sample task order are associated and stored according to the task order number, serving as the final output of this method. Understandably, the associated storage of the traceability report allows subsequent sample task orders corresponding to the same batch identifier to retrieve and reference existing rule version numbers and disposal records when generating sample task orders in S100, achieving auditable connection between versions across batches without introducing new disposal logic branches.

[0086] In summary, this step binds the screening result package to the disposal mapping version number and report template version number of the disposal strategy library, achieving a closed-loop operation chain from screening results to work order generation and write-back of disposal records. Through versioned mapping of sorting work orders, frozen storage work orders, or obsolescence work orders and write-back of disposal records, the screening output and disposal execution maintain a traceable association under the same task order number and rule version number. By summarizing the screening result package and the disposal records to generate the traceability report, the final output possesses a unified traceability carrier for batches and packaged samples.

[0087] Example 2: Figure 2 A structural block diagram of a system for detecting and screening amniotic epithelial stem cells according to an embodiment of the present invention is shown. Figure 2 As shown, the structure may include:

[0088] The sample task sheet generation unit 01 receives amniotic tissue source information, sampling and transportation conditions, processing parameter templates, equipment identification, and rule version number, and writes in the sample number, sampling timestamp, transportation temperature record, transportation duration, preservation system identification, digestion parameters, filtration parameters, centrifugation parameters, equipment number, reagent batch number, surface marker panel version number, transcription marker panel version number, control protocol version number, gating rule version number, scoring rule version number, treatment mapping version number, and report template version number, generating a sample task sheet and transmitting it to the tissue digestion and separation unit. Specifically, when receiving amniotic tissue source information and sampling and transportation conditions, the sample task sheet generation unit binds the equipment identification and rule version number into the sample task sheet, and expands the digestion parameters, filtration parameters, and centrifugation parameters according to the processing parameter template. The parameters of the heart parameter form a callable processing parameter template instance; the sample task sheet generation unit performs a record integrity check on the sampling timestamp, transportation temperature record, transportation duration and preservation system identifier, retains the original blank for missing items and writes the judgment required fields corresponding to the gating rule version number and scoring rule version number, and writes the reagent batch number and equipment number into the sample task sheet to complete the generation and registration of the sample number; the sample task sheet, as an output product, contains the correspondence between the sample number and the rule version number, and provides the sample task sheet to the tissue digestion and separation unit as the input object for tissue digestion and separation. The surface marker panel version number, transcription marker panel version number and control protocol version number in the sample task sheet are kept consistent with the calling relationship of the subsequent phenotypic detection unit and molecular detection unit.

[0089] The tissue digestion and separation unit 02 is used to receive the sample task order and perform tissue digestion and separation, record timestamps and process parameters, generate a candidate cell population data package containing batch identifier, dispensing identifier, cell count record, viability pre-detection record, process parameter record, and timestamp record, and transmit it to the phenotypic detection unit; specifically, after receiving the sample task order, the tissue digestion and separation unit reads the sample number, saves the system identifier and digestion parameters, and triggers the tissue digestion and separation process, writes timestamp records at the start and end points of the tissue digestion and separation, and records the temperature control conditions, reaction time, mixing action, and other parameters. Termination conditions are written into the process parameter record; after separation, the tissue digestion and separation unit performs aliquoting on the obtained cell suspension and generates aliquoting identifiers, then forms cell count records and viability pre-detection records and writes them into the candidate cell population data package, and at the same time associates the sampling and transportation conditions in the sample task sheet with the process parameter record to the same batch identifier; the candidate cell population data package is transmitted to the phenotypic detection unit as an output product for surface marker immunostaining and flow cytometry or imaging acquisition, wherein the same batch identifier is used for the same batch identifier binding of the molecular detection unit and the batch normalization reference of the screening engine unit.

[0090] Phenotypic detection unit 03 is used to receive the candidate cell population data package and perform surface marker immunostaining and flow cytometry or imaging acquisition, and to perform gating and acquisition quality control. The gating includes a fragmentation rejection gate, a two-cell event rejection gate, and a dead cell rejection gate. It generates a phenotypic detection data package containing the positive proportion of each surface marker, intensity statistics, gating boundaries, and acquisition quality control indicators, and transmits it to the molecular detection unit and the screening engine unit. Specifically, after receiving the candidate cell population data package, the phenotypic detection unit reads the surface marker panel version number in the sample task sheet based on the batch identifier and calls the corresponding surface marker immunostaining process to complete the staining reaction on the cell suspension corresponding to the dispensing identifier and record the key timestamp. Then, it performs flow cytometry or imaging acquisition and generates data synchronously during the acquisition process. The system collects quality control indicators, including event quantity statistics and signal stability records. The phenotypic detection unit executes a gating chain on the collected events, first applying a fragment rejection gate to remove fragmented events, then a two-cell event rejection gate to remove two-cell events, and finally a dead cell rejection gate to remove dead cell events. The gating boundaries of each gate are written into the phenotypic detection data packet. After gating, the phenotypic detection unit calculates the positive proportion and intensity statistics of each surface marker and writes them into the phenotypic detection data packet. This phenotypic detection data packet, as an output product, provides the molecular detection unit with batch identification and identity tag related fields, and provides the screening engine unit with the positive proportion, intensity statistics, gating boundaries, and collection quality control indicators of each surface marker for use in fusion scoring and consistency determination.

[0091] Molecular detection unit 04 is used to receive the phenotypic detection data package and perform nucleic acid extraction, transcriptional marker detection, and internal reference correction according to the batch identifier. It also performs control constraints, including negative control determination, positive control determination, and batch repeatability determination. It generates a molecular detection data package containing transcriptional marker expression characteristics, internal reference correction results, curve quality indicators, and control determination results, and transmits it to the quality label generation unit and the screening engine unit. Specifically, after receiving the phenotypic detection data package, the molecular detection unit reads the batch identifier and completes the batch identifier binding. It includes the cell samples corresponding to the aliquot identifier in the candidate cell population data package into the nucleic acid extraction process. During nucleic acid extraction, it records the extraction batch, key timestamps, and forms nucleic acid quality determination results. The transcriptional marker detection process corresponding to the transcriptional marker panel version number is then invoked to generate an amplification curve and output curve quality indicators. Simultaneously, an internal reference correction process is applied to the transcriptional marker detection output to form an internal reference correction result, which is then written into the molecular detection data package. The molecular detection unit executes control constraints according to the control protocol version number, first completing the negative control and positive control determination, and then performing batch-wide consistency determination on the same batch of duplicate samples, writing the control determination results into the molecular detection data package. The molecular detection data package, as an output product, provides the quality label generation unit with transcriptional marker expression characteristics, internal reference correction results, curve quality indicators, and control determination results, and provides the screening engine unit with control determination results and identity label-related fields for gating and consistency determination.

[0092] The quality label generation unit 05 is used to receive the molecular detection data package and perform viability detection, proliferation characterization, microbial detection, and endotoxin detection. It labels the viability threshold, proliferation threshold, microbial threshold, and endotoxin threshold according to a threshold template, generating a quality label data package containing viability labels, proliferation labels, microbial labels, endotoxin labels, and retest labels, and then transmits it to the screening engine unit. Specifically, after receiving the molecular detection data package, the quality label generation unit retrieves the viability pre-detection record of the candidate cell population data package according to the same batch identifier and completes the viability detection process. It then compares the detection results with the viability threshold to generate a viability label. The quality label generation unit compares the same batch data package with the viability threshold to generate a viability label. Cell samples corresponding to the batch identifier undergo proliferation characterization and are compared with the proliferation threshold to generate proliferation tags. Simultaneously, microbial and endotoxin detection are performed on the cell samples, and microbial and endotoxin tags are generated respectively compared with the microbial and endotoxin thresholds. The quality tag generation unit incorporates the curve quality indicators and control judgment results in the molecular detection data package into the retest tag judgment conditions, and writes a retest tag when an amplification curve abnormality judgment or control constraint failure occurs. The quality tag data package is transmitted to the screening engine unit as an output product, and the field reference relationship of batch normalization, abnormal rejection and fusion score of the same batch identifier and the screening engine unit is kept consistent.

[0093] The screening engine unit 06 is used to receive the quality label data package and perform gating, batch normalization, anomaly removal, fusion scoring, and consistency determination according to the rule version number. The gating includes transportation time window determination, temperature record integrity determination, process parameter deviation determination, instrument calibration determination, and control constraint determination, and outputs transportation compliance labels, process compliance labels, calibration pass labels, control pass labels, collection usable labels, and nucleic acid usable labels. Batch normalization performs scale mapping on the phenotypic detection data package and the molecular detection data package based on the control bead intensity benchmark and internal reference benchmark. Anomaly removal includes background anomaly determination, insufficient event quantity determination, and amplification curve anomaly determination. The fusion scoring includes identity scoring, activity scoring, safety scoring, and consistency scoring. The consistency determination checks the consistency of the identity label of the phenotypic detection data package and the identity label of the molecular detection data package and generates a conflict marker. The conflict marker triggers the re-examination label to be written into the screening result package, generating the screening result package and transmitting it to the disposal strategy library and traceability report generation unit. Specifically, after receiving the quality label data package, the screening engine unit simultaneously receives the phenotypic detection data package and the molecular detection data package, and associates the samples according to the same batch identifier. The sampling timestamp, transport temperature record, transport duration, equipment number, and reagent batch number in the task order are used to invoke the gating process corresponding to the gating rule version number. First, transport time window determination and temperature record integrity determination are performed. Then, the process parameter records output by the tissue digestion and separation unit are included in the process parameter deviation determination, and the equipment number is combined with the instrument calibration determination. Simultaneously, the control determination results in the molecular detection data package are read to complete the control constraint determination. The gating process writes transport compliance label, process compliance label, calibration pass label, control pass label, collection usable label, and nucleic acid usable label and associates them with the quality label data. The screening engine unit triggers a batch normalization process according to the rule version number after gating, reads the intensity statistics in the phenotypic detection data package and completes scale mapping by referencing the intensity benchmark of the control beads, and simultaneously reads the internal reference correction results in the molecular detection data package and completes scale mapping by referencing the internal reference benchmark. The scale mapping results are written back to the input field of the fusion score. The screening engine unit calls the anomaly removal process, performs background anomaly judgment and insufficient event quantity judgment on the acquisition quality control indicators of the phenotypic detection data package, performs amplification curve anomaly judgment on the curve quality indicators of the molecular detection data package, and writes the anomaly removal conclusion into the record field of the screening result package.After anomaly removal, the screening engine unit invokes the fusion scoring process corresponding to the scoring rule version number. It generates identity scores, activity scores, safety scores, and consistency scores based on identity tags and the positive proportions of each surface marker, transcriptional marker expression characteristics, viability tags, microbial tags, and endotoxin tags. It then performs a consistency check between the identity tags of the phenotypic detection data package and the identity tags of the molecular detection data package to generate conflict markers. These conflict markers are written into the screening result package, triggering the writing of retest tags into the screening result package. The screening result package, as an output product, is transmitted to the treatment strategy library and the traceability report generation unit, maintaining the corresponding link relationship of the same batch identifiers.

[0094] The disposal strategy library and traceability report generation unit 07 is used to receive the screening result package and call the disposal strategy library to map the qualified batch list and sorting threshold of the screening result package to generate sorting work orders, freezing work orders or elimination work orders, and write back the disposal records. It also summarizes the screening result package and the disposal records to generate a traceability report. The traceability report includes sample number, task order number, rule version number, comparison judgment result, quality label data package summary, screening result package summary and disposal record. Specifically, after receiving the screening result package, the disposal strategy library and traceability report generation unit reads the qualified batch list, sorting threshold, and rule version number, and calls the disposal strategy library mapping relationship corresponding to the disposal mapping version number. It maps the qualified batch list to the sorting threshold to generate a sorting work order, a frozen storage work order, or an elimination work order. Simultaneously, it associates the work order generation action with the sample number and writes it into the disposal record. When writing back the disposal record, the disposal strategy library and traceability report generation unit retains the comparison judgment result, re-inspection label, and conflict marker from the screening result package and incorporates them into the comparison judgment result and disposal record fields of the traceability report. The disposal strategy library and traceability report generation unit summarizes the screening result package summary and the quality label data package summary, and binds the sample number, task number, and report template version number to generate a traceability report. The traceability report, as an output product, maintains the same rule version number association relationship with the sorting work order, frozen storage work order, or elimination work order.

Claims

1. A method for detecting and screening amniotic epithelial stem cells, characterized in that, include: S100. Based on the amniotic tissue source information, sampling and transportation conditions, processing parameter templates, equipment identification, and rule version number, perform access registration and uniqueness, temporal consistency, completeness, legality, and format verification to generate a sample task sheet. The amniotic tissue source information includes a combination of sample number, source code, and sampling timestamp. The sampling and transportation conditions include a combination of transportation temperature record, transportation duration, and preservation system identification. The processing parameter template includes a combination of digestion parameters, filtration parameters, and centrifugation parameters required for the tissue digestion and separation process. The equipment identification includes a combination of equipment number and reagent batch number used in subsequent testing. The rule version number includes a combination of gating rule version number, scoring rule version number, treatment mapping version number, and report template version number. S200. Based on the sample task sheet, perform tissue digestion, filtration, and centrifugation to generate a candidate cell population data package; S300: Based on the candidate cell population data package, perform surface marker immunostaining, flow cytometry / imaging acquisition and quality control processing to generate a phenotypic detection data package; S400. Based on the phenotypic detection data package, perform nucleic acid extraction, transcriptional marker detection, internal reference correction and control constraint processing for the same batch of identifiers to generate a molecular detection data package; S500: Based on the molecular detection data package, perform viability, proliferation, microbial and endotoxin detection and label according to threshold template to generate a quality label data package; S600. Based on the quality label data package, perform gating, batch normalization, anomaly removal, fusion scoring and consistency determination processing to generate a screening result package; S700: Based on the screening result package, perform disposal mapping, work order generation and report generation processing to generate a traceability report.

2. The method according to claim 1, characterized in that, The process of performing access registration and uniqueness, timing consistency, integrity, legality, and format verification to generate a sample task sheet includes: The verification process includes performing uniqueness verification on the sample number and source code, temporal consistency verification on the sampling timestamp and transportation duration, integrity verification on the transportation temperature record, valid value verification on the storage system identifier, and format verification on the equipment number and reagent batch number. It also includes performing rule version number processing, which involves reading the rule entry index corresponding to the gating rule version number, scoring rule version number, disposal mapping version number, and report template version number from the rule base, locking the rule version number and writing it into the version identifier field of the sample task sheet to generate the sample task sheet.

3. The method according to claim 1, characterized in that, The process of digesting, filtering, and centrifuging tissues to generate candidate cell population data packages includes: The tissue digestion, filtration, and centrifugation process includes a continuous processing chain of tissue washing, tissue mincing, digestion reaction, termination reaction, dispersion and resuspension, filtration separation, and centrifugation collection. It also performs batch identification and dispensing identification generation, which includes generating batch and dispensing identifiers, and performs cell counting and viability pre-detection, which includes generating cell count records and viability pre-detection records, and generating a candidate cell population data package. The candidate cell population data package includes the batch identifier, the dispensing identifier, the cell count record, the viability pre-detection record, process parameter records, and timestamp records.

4. The method according to claim 1, characterized in that, The process of performing surface marker immunostaining, flow cytometry / imaging acquisition, and quality control processing to generate phenotypic detection data packages includes: The surface marker immunostaining includes antibody incubation, washing, and incubation for the target marker group and the exclusion marker group; the flow cytometry / imaging acquisition includes flow cytometry acquisition or imaging acquisition; the quality control process includes gating and acquisition quality control; the gating includes the execution of fragment rejection, two-cell event rejection, and dead cell rejection gates; the acquisition quality control includes the judgment of acquisition quality control indicators and the output of acquisition-available labels, generating a phenotypic detection data package; the phenotypic detection data package includes batch identifier, repackaging identifier, positive proportion of each surface marker, intensity statistics, gating boundaries, and acquisition quality control indicators.

5. The method according to claim 1, characterized in that, The process of performing batch-labeled nucleic acid extraction, transcriptional marker detection, internal control calibration, and control constraint treatment includes: The nucleic acid extraction includes a processing chain of lysis, separation, washing and elution; the transcription marker detection includes amplification curve acquisition; the internal reference calibration includes internal reference gene calibration; the control constraint treatment includes negative control determination, positive control determination and batch repeatability determination; and a molecular detection data package is generated. The molecular detection data package includes batch identifier, aliquot identifier, transcription marker expression characteristics, internal reference calibration results, curve quality indicators and control determination results.

6. The method according to claim 1, characterized in that, The process of performing viability, proliferation, microbial, and endotoxin detection, and then labeling the data according to threshold templates to generate a quality label data package includes: The viability detection includes sample homogenization, sampling and measurement, viability staining reaction, signal acquisition and threshold interpretation steps; the proliferation characterization includes proliferation observation under preset culture conditions, cell count retesting and proliferation trend interpretation; the microbial detection includes sampling and packaging, culture observation and result interpretation; the endotoxin detection includes performing detection according to preset detection reagent system and interpretation procedure; the threshold template includes a set of judgment thresholds for viability threshold, proliferation threshold, microbial threshold and endotoxin threshold and a fixed template of their label mapping rules, generating a quality label data package, which includes a viability label, proliferation label, microbial label, endotoxin label and retest label.

7. The method according to claim 1, characterized in that, The process of gating, batch normalization, anomaly removal, fusion scoring, and consistency determination includes: The gating includes determination of transport time window, determination of temperature record integrity, determination of process parameter deviation, determination of instrument calibration, and determination of control constraints. The batch normalization includes generating intensity mapping parameters and internal reference mapping parameters based on the intensity benchmark of control beads and the internal reference benchmark, and performing scale mapping on the intensity statistics of phenotypic detection data packets and the transcriptional marker expression characteristics of molecular detection data packets. The anomaly removal includes determination of background anomalies, determination of insufficient event quantity, and determination of amplification curve anomalies. The fusion score includes identity score, activity score, safety score, and consistency score. The consistency determination includes determining the consistency between the identity label of the phenotypic detection data packet and the identity label of the molecular detection data packet, and generating a conflict marker when they are inconsistent.

8. The method according to claim 1, characterized in that, The screening results package includes: The screening result package includes a transportation compliance label, a process compliance label, a calibration pass label, a control pass label, a collection usable label, a nucleic acid usable label, the identity score, the activity score, the safety score, the consistency score, the conflict marker, the anomaly marker, and the retest label.

9. The method according to claim 1, characterized in that, The process of handling disposal mapping, work order generation, and report generation, and generating a traceability report includes: The disposal mapping, based on the disposal mapping version number, maps the list of qualified batches and sorting thresholds in the screening result package to work order types and work order content elements. The work order generation includes outputting sorting work orders, frozen storage work orders, or elimination work orders. The report generation generates a traceability report based on the report template version number.

10. A system for detecting and screening amniotic epithelial stem cells, characterized in that, include: The sample task sheet generation unit, tissue digestion and separation unit, phenotypic detection unit, molecular detection unit, quality label generation unit, screening engine unit, treatment strategy library and traceability report generation unit are connected in sequence to implement the method described in any one of claims 1-9.

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