PGT-A workflow and laboratory process data management system

Abstract

The application provides a PGT-A process and laboratory process data management system, and relates to the technical field of medical test process data management.The system comprises a sample information management module, a whole-process node control module, a laboratory data automatic acquisition module, a quality control management module and a report and traceability module, associates whole-process data by generating a unique identification mark for each embryo sample, controls detection node flow according to the PGT-A standard operation process, automatically connects experimental equipment to acquire process data, and synchronously realizes real-time quality control verification and whole-link data traceability.The system can realize standardized and digital closed-loop management of the whole PGT-A detection process, effectively reduces manual input errors, standardizes experimental operation processes, improves detection quality and operation efficiency, and meets the compliance quality control and data traceability requirements of medical test laboratories.

Description

Technical Field

[0001] This invention relates to the field of medical testing process data management technology, and in particular to a PGT-A process and laboratory process data management system. Background Technology

[0002] PGT-A (Preimplantation Genetic Testing for Aneuploidies) is a core testing technology in the field of assisted reproduction. Its testing process covers multiple stages, including embryo biopsy sample reception, sample preprocessing, whole-genome amplification, library construction, high-throughput sequencing, bioinformatics analysis, result review, and report generation. The process is lengthy and involves many steps, demanding extremely high levels of operational standardization and data accuracy.

[0003] Currently, some PGT-A laboratories still have the following shortcomings in process and data management: First, although some laboratories use general LIMS systems, these systems are not deeply customized for PGT-A processes. Data is stored in a scattered manner at each stage, making it difficult to form a complete data chain from clinical information to genetic counseling around a single sample. Second, the process control functions of general LIMS are not deeply coupled with the quality control rules of each PGT-A testing node. The flow between nodes lacks mandatory verification bound to PGT-A specific QC thresholds, occasionally resulting in skipped or missed steps. Third, although some experimental equipment has data export capabilities, general LIMS does not have corresponding device interfaces, requiring raw data to still be manually exported and manually entered, posing a risk of transcriptional errors. Fourth, quality control verification is mostly done centrally after each node is completed, lacking real-time quality control interception capabilities during data writing. Fifth, when sample anomalies require full-process traceability, the data is scattered across different systems or documents, making it time-consuming to reconstruct the complete testing chain and difficult to meet the compliance management requirements of medical testing. Summary of the Invention

[0004] This invention provides a PGT-A process and laboratory process data management system, which solves the technical problems of non-standardized PGT-A laboratory process management, scattered data, lagging quality control, and difficult traceability through modular full-process control, automatic equipment data collection, real-time quality control verification, and full-link data traceability.

[0005] This invention provides a PGT-A process and laboratory process data management system, comprising: The sample information management module is used to input and store the clinical information of the examinee and the basic information of the embryo sample, generate a globally unique identification identifier for each embryo sample, and associate all data generated throughout the process with the unique identification identifier of the corresponding sample. The full-process node control module is used to configure sequentially connected testing nodes according to the standard operating procedure of PGT-A testing. It verifies and records the operation permissions, operators, operation time and operation content of each node, and drives the embryo samples to flow sequentially according to the established process. The laboratory data automatic acquisition module is used to connect with various testing equipment in the PGT-A experimental process. It automatically captures the raw experimental data and process operation parameters output by each device through the device communication interface and synchronously associates them with the unique identification identifier of the corresponding sample. The quality control management module is used to preset the quality control thresholds and rules for each testing node, and to perform real-time verification of the process data and experimental results collected at each node. When the data exceeds the quality control threshold, it triggers an anomaly warning and intercepts the process flow. The reporting and traceability module is used to automatically generate standardized PGT-A test reports based on data collected throughout the entire process. The report generation includes adaptive format adjustments based on test complexity and data quality, and supports tracing back the operation records, original equipment data, and quality control results of the entire process through unique sample identification.

[0006] Preferably, the detection nodes configured in the full-process node control module include, in sequence, a sample receiving node, a sample preprocessing node, a whole genome amplification node, a library construction node, a high-throughput sequencing node, a bioinformatics analysis node, and a result review node; a mandatory transfer verification mechanism is set between each node, and the sample enters the next detection node after the quality control judgment of the previous node is passed.

[0007] Preferably, the detection equipment connected to the automatic laboratory data acquisition module includes a nucleic acid amplification instrument, a capillary electrophoresis instrument, a high-throughput sequencer, and a nucleic acid quantitative detection instrument; the automatically acquired data includes amplification curve data, electrophoresis peak data, effective sequencing data volume, sequencing Q30 percentage, and nucleic acid concentration and purity parameters.

[0008] Preferably, the quality control management module also includes an inter-laboratory quality assessment and internal quality control management unit, which automatically calculates the quality control pass rate for each testing cycle, generates quality control trend analysis charts, and retains all quality control records and anomaly handling logs.

[0009] Preferably, the report and traceability module supports multi-dimensional reverse traceability of related samples by operator, operation time, equipment number, and test batch. At the same time, it sets multi-level electronic signature review permissions for test reports, and reports without complete signatures cannot be released to the public.

[0010] Preferably, the reporting and traceability module includes: The completion submodule is used to divide the PGT-A end-to-end data collected throughout the entire process into a sample basic information domain, a sequencing quality control parameter domain, a chromosome copy number detection result domain, and a clinical genetic counseling domain. For each data domain, field-level, logic-level, and cross-domain semantic-level validations are performed sequentially to calculate the comprehensive standardized completion degree within the domain. The formula for calculating the intra-domain comprehensive standardization completion degree of the i-th data domain is: ,in, For field completeness rate, To ensure format compliance, a basic verification layer is formed; For the pass rate of intra-domain logic verification, The cross-domain semantic matching rate constitutes a semantic verification layer; the cross-domain semantic matching rate The calculation method is as follows: ,in, This represents the total number of items for cross-domain semantic verification. The number of items that pass the verification includes: the compatibility verification between the chromosomal abnormality conclusion and the clinical indication, the matching verification between the sequencing depth and the CNV detection threshold, and the correspondence verification between the consultation advice and the abnormality level. Each item that passes the verification is recorded as 1, and that item that fails is recorded as 0. , The weight coefficients for the basic validation layer and the semantic validation layer are respectively, satisfying... ; , These are the sub-item weights for field completeness rate and format compliance rate within the basic validation layer, respectively. , These are the sub-item weights for the logical validation pass rate within the semantic validation layer and the cross-domain semantic matching rate, respectively. The score submodule is used to calculate the PGT-A specific detection complexity coefficient K based on the full data of the chromosome copy number detection result domain. Simultaneously, it calculates the standardized comprehensive fit score Z by combining the intra-domain comprehensive standardization completion degree of each data domain. The formula for calculating the detection complexity coefficient K is as follows: ,in, This represents the total number of copy number variations detected. For the reference value of chromosome logarithm, take =23; Normalized variant detection rate; The maximum chimerism rate of the mosaic variant; P is the pathogenicity level assignment; , , The adjustment coefficient of the corresponding factor satisfies The value of K ranges from [0,1]. The formula for calculating the standardized comprehensive fit score Z is: ,in, The clinical priority weight for the i-th data domain satisfies ; Z is the anomaly correction coefficient; the value range of Z is [0,1], which represents the standardization qualification of the current full data under the corresponding detection complexity. The adjustment submodule is used to calculate the target space entropy using the standardized comprehensive fit score Z and the detection complexity coefficient K as inputs. Adjust the data block area allocation to make the actual typesetting entropy SS approach the target spatial entropy. ; If the page space overflows after iteration, a collapse compression is triggered; if there is space redundancy, the size of the collapse display block is adaptively adjusted.

[0011] Preferably, the adjustment submodule includes: The first computing unit is used to calculate the target space entropy. : ,in, K is the baseline spatial entropy of the standard normal sample template; Z is the detection complexity coefficient and Z is the standardized comprehensive fit score. The value is dimensionless and its range is [ , +0.4]; The second calculation unit is used to calculate the actual typesetting entropy value S: , Let A be the layout area occupied by the j-th report data block, and let A be the total available layout area per page. The area percentage of this data block satisfies S represents the spatial information entropy of the current layout, with a value range of... This is used to quantify the uniformity of the area distribution of data blocks; The update unit is used for adaptive updating of the data block area in the (t+1)th iteration: ,in, and Let be the areas of the j-th data block in the t-th and t+1-th iterations, respectively. This is the iteration step size coefficient; The visual weight score for the j-th data block; The arithmetic mean of the visual weight scores for all data blocks; The maximum visual weight score across all data blocks; To normalize visual weight bias; After each iteration, the area of ​​all data blocks is normalized to maintain the total area as A. ; When S> When S < 0, the area of ​​data blocks with visual weights above the average is increased, and the area of ​​data blocks with visual weights below the average is decreased; when S < 0. Then, the adjustment is reversed to make the area distribution more uniform; The third calculation unit is used to calculate the visual weight score of the j-th data block. : ,in, The basic clinical priority for data blocks; Assign a value to the pathogenicity of the highest-level variant contained in this data block; r is the anomalous gain coefficient; The value range is [1, 16]; The layout iteration unit is used to calculate the page space overflow rate after the maximum iteration is reached. ,when When the score is greater than 0, the score is determined by visual weight. Non-required fields will be collapsed and moved to the report appendix in ascending order of importance, and the field completeness rate of the corresponding data fields will be updated synchronously. Comprehensive standardization completion within the region Recalculate the standardized overall fit score ZZ and re-execute the typesetting iteration until... ; Explicit units are used after the maximum number of iterations has been reached. And page space redundancy rate At that time, the score was determined by visual weight. From high to low, restore the folded fields in the appendix to explicit data blocks, establish a planar layout coordinate system with the upper left corner of the target area of ​​the block as the origin, and perform adaptive size adjustment of the data blocks.

[0012] Preferably, the visual weight score Positively correlated with the area ratio of data blocks and page hierarchy: the top 30% of data blocks in terms of visual weight score are automatically placed in the core area of ​​the report's homepage, and their border line width is increased. Increment of title font size According to , pt is calculated linearly, where, This represents the maximum visual weight score across all data blocks.

[0013] Compared with the prior art, the beneficial effects of this application are as follows: 1. Achieve standardized management and control of the entire PGT-A process. By verifying node permissions and enforcing flow rules, regulate the behavior of operators, eliminate skipping or missing steps, and ensure the consistency and standardization of the testing process. 2. Automatic data collection and aggregation from experimental equipment: By configuring dedicated communication interfaces for nucleic acid amplification instruments, capillary electrophoresis instruments, high-throughput sequencers, and nucleic acid quantitative detection instruments, manual data entry is replaced, significantly reducing human input errors, improving data accuracy and entry efficiency, and simultaneously achieving automatic correlation between process data and samples. 3. Real-time quality control and verification across all nodes, with immediate warnings and process interception for abnormal situations, enabling timely detection and handling of quality issues and ensuring the reliability of test results from the process end; 4. End-to-end data closed-loop traceability allows for forward viewing of the entire process information for a single sample, as well as reverse traceability by batch, equipment, and personnel, meeting the needs of laboratory compliance management and quality verification. 5. An adaptive report layout adjustment mechanism based on the detection complexity coefficient K and the standardized comprehensive fit score Z dynamically matches the information density of the test report with the complexity of the case and the level of data quality. Under the premise of ensuring clinical compliance, complex case reports can highlight key variation information, while simple case reports maintain a balanced layout, thereby improving the clinical readability and information transmission efficiency of the report.

[0014] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings.

[0015] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0016] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings: Figure 1 This is a structural diagram of a PGT-A process and laboratory process data management system according to an embodiment of the present invention. Detailed Implementation

[0017] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0018] This embodiment specifically illustrates the use of the invented PGT-A workflow and laboratory process data management system to achieve complete workflow management of the PGT-A testing process, from patient information entry to report delivery.

[0019] This invention provides a PGT-A process and laboratory process data management system, such as Figure 1 As shown, it includes: The sample information management module is used to input and store the clinical information of the examinee and the basic information of the embryo sample, generate a globally unique identification identifier for each embryo sample, and associate all data generated throughout the process with the unique identification identifier of the corresponding sample. The full-process node control module is used to configure sequentially connected testing nodes according to the standard operating procedure of PGT-A testing. It verifies and records the operation permissions, operators, operation time and operation content of each node, and drives the embryo samples to flow sequentially according to the established process. The laboratory data automatic acquisition module is used to connect with various testing equipment in the PGT-A experimental process. It automatically captures the raw experimental data and process operation parameters output by each device through the device communication interface and synchronously associates them with the unique identification identifier of the corresponding sample. The quality control management module is used to preset the quality control thresholds and rules for each testing node, and to perform real-time verification of the process data and experimental results collected at each node. When the data exceeds the quality control threshold, it triggers an anomaly warning and intercepts the process flow. The reporting and traceability module is used to automatically generate standardized PGT-A test reports based on data collected throughout the entire process. The report generation includes adaptive format adjustments based on test complexity and data quality, and supports tracing back the operation records, original equipment data, and quality control results of the entire process through unique sample identification.

[0020] Preferably, the detection nodes configured in the full-process node control module include, in sequence, a sample receiving node, a sample preprocessing node, a whole genome amplification node, a library construction node, a high-throughput sequencing node, a bioinformatics analysis node, and a result review node; a mandatory transfer verification mechanism is set between each node, and the sample enters the next detection node after the quality control judgment of the previous node is passed.

[0021] Preferably, the detection equipment connected to the automatic laboratory data acquisition module includes a nucleic acid amplification instrument, a capillary electrophoresis instrument, a high-throughput sequencer, and a nucleic acid quantitative detection instrument; the automatically acquired data includes amplification curve data, electrophoresis peak data, effective sequencing data volume, sequencing Q30 percentage, and nucleic acid concentration and purity parameters.

[0022] Preferably, the quality control management module also includes an inter-laboratory quality assessment and internal quality control management unit, which automatically calculates the quality control pass rate for each testing cycle, generates quality control trend analysis charts, and retains all quality control records and anomaly handling logs.

[0023] Preferably, the report and traceability module supports multi-dimensional reverse traceability of related samples by operator, operation time, equipment number, and test batch. At the same time, it sets multi-level electronic signature review permissions for test reports, and reports without complete signatures cannot be released to the public.

[0024] Preferably, the reporting and traceability module includes: The completion submodule is used to divide the PGT-A end-to-end data collected throughout the entire process into a sample basic information domain, a sequencing quality control parameter domain, a chromosome copy number detection result domain, and a clinical genetic counseling domain. For each data domain, field-level, logic-level, and cross-domain semantic-level validations are performed sequentially to calculate the comprehensive standardized completion degree within the domain. The formula for calculating the intra-domain comprehensive standardization completion degree of the i-th data domain is: ,in, For field completeness rate, The format compliance rate has a value range of [0,1] and is a dimensionless ratio, forming the basic verification layer. For the pass rate of intra-domain logic verification, The cross-domain semantic matching rate, with values ​​ranging from [0,1], is a dimensionless ratio and constitutes the semantic verification layer; the cross-domain semantic matching rate The calculation method is as follows: ,in, This represents the total number of items for cross-domain semantic verification. The number of items that pass the verification includes: the compatibility verification between the chromosomal abnormality conclusion and the clinical indication, the matching verification between the sequencing depth and the CNV detection threshold, and the correspondence verification between the consultation advice and the abnormality level. Each item that passes the verification is recorded as 1, and that item that fails is recorded as 0. , The weight coefficients for the basic validation layer and the semantic validation layer are respectively, satisfying... Furthermore, the result domain for chromosome copy number detection is taken. , The remaining data fields are taken 6. All of them are dimensionless coefficients; , These are the sub-item weights for field completeness rate and format compliance rate within the basic validation layer, respectively. , ; , These are the sub-item weights of the logical validation pass rate within the semantic validation layer and the cross-domain semantic matching rate, respectively. , The weights of each group are all 1, and all are dimensionless coefficients. The score submodule is used to calculate the PGT-A specific detection complexity coefficient K based on the full data of the chromosome copy number detection result domain. Simultaneously, it calculates the standardized comprehensive fit score Z by combining the intra-domain comprehensive standardization completion degree of each data domain. The formula for calculating the detection complexity coefficient K is as follows: ,in, The total number of copy number variations detected is a dimensionless positive integer. For the reference value of chromosome logarithm, take =23, dimensionless; The normalized variation detection rate, with a value range of [0,1], is dimensionless. is the maximum chimerism rate of the chimeric variant, with a value range of [0,1], dimensionless; P is the pathogenicity level assignment, with 0 for normal or benign, 0.5 for undetermined significance, and 1 for pathogenicity, dimensionless; , , The adjustment coefficient for the corresponding factor is taken as... , , ,satisfy All are dimensionless coefficients; the value range of K is [0,1], and it is dimensionless. The formula for calculating the standardized comprehensive fit score Z is: ,in, The clinical priority weight for the i-th data domain satisfies The specific value is: Sample basic information domain =0.15, Sequencing quality control parameter domain =0.25, Chromosome copy number detection result field =0.45, Clinical Genetic Counseling Domain =0.15, all are dimensionless coefficients; This is the anomaly correction coefficient, and its value is determined by the following rule: when there are sequencing quality control non-compliance items, it is set to [value missing]. =0.1, taken only when there are variants of unclear significance. =0.05, other normal scenarios take =0, is a dimensionless coefficient with a value range of [0, 0.1]; Z has a value range of [0, 1], and is a dimensionless score that represents the standardization qualification of the current full data under the corresponding detection complexity. The adjustment submodule is used to calculate the target space entropy using the standardized comprehensive fit score Z and the detection complexity coefficient K as inputs. Adjust the data block area allocation to make the actual typesetting entropy SS approach the target spatial entropy. ; If the page space overflows after iteration, a collapse compression is triggered; if there is space redundancy, the size of the collapse display block is adaptively adjusted.

[0025] Preferably, the adjustment submodule includes: The first computing unit is used to calculate the target space entropy. : ,in, The baseline spatial entropy for the standard normal sample template is dimensionless. The calibration method is as follows: using a standard clinical report template of a normal karyotype sample (46,XY or 46,XX), the fixed area of ​​each data block is configured according to clinical compliance requirements. The entropy value calculated using the actual layout entropy formula is the baseline spatial entropy. , During calibration, the following must be met: ,in, The total number of data blocks reported; Let be the entropy adjustment coefficient, and take . =0.4, dimensionless; K is the detection complexity coefficient, and Z is the standardized comprehensive fit score; The value is dimensionless and its range is [ , +0.4]; The second calculation unit is used to calculate the actual typesetting entropy value S: , Let A be the layout area occupied by the j-th report data block, and let A be the total available layout area per page. The area percentage of this data block is dimensionless and satisfies... S represents the spatial information entropy of the current layout, which is dimensionless and has a range of values. This is used to quantify the uniformity of the area distribution of data blocks; The update unit is used for adaptive updating of the data block area in the (t+1)th iteration: ,in, and Let be the areas of the j-th data block in the t-th and t+1-th iterations, respectively. Let the iteration step size coefficient be taken as... =0.05, dimensionless; Let be the visual weight score of the j-th data block; The arithmetic mean of the visual weight scores for all data blocks; The maximum visual weight score across all data blocks; To normalize the visual weight bias, the value range is [ [1,1], dimensionless; After each iteration, the area of ​​all data blocks is normalized to maintain the total area as A. ; When S> When S < 0, the area of ​​data blocks with visual weights above the average increases, and the area of ​​data blocks with visual weights below the average decreases, thus differentiating the area distribution and reducing the entropy value; when S < 0. In the opposite direction, the area distribution is adjusted to become more uniform in order to increase the entropy value; The third calculation unit is used to calculate the visual weight score of the jj-th data block. : ,in, The basic clinical priority of the data block, with a value range of [1, 10], is dimensionless; Assign a value to the pathogenicity of the highest-level variant contained in the data block: 0 for normal or benign, 0.5 for undetermined significance, and 1 for pathogenicity. The value is dimensionless. r is the anomalous gain coefficient, which is set to r=0.6. The value range is [1, 16], and it is dimensionless. The layout iteration unit is used to calculate the page space overflow rate after the maximum iteration is reached. ,when When the score is greater than 0, it is determined by visual weight. Non-required fields will be collapsed and moved to the report appendix in ascending order of importance, and the field completeness rate of the corresponding data fields will be updated synchronously. Comprehensive standardization completion within the region Recalculate the standardized overall fit score ZZ and re-execute the typesetting iteration until... ; Explicit units are used after the maximum number of iterations has been reached. And page space redundancy rate At that time, the score was determined by visual weight. From high to low, restore the folded fields in the appendix to explicit data blocks, establish a planar layout coordinate system with the upper left corner of the target area of ​​the block as the origin, and perform adaptive size adjustment of the data blocks.

[0026] Preferably, the visual weight score Positively correlated with the area ratio of data blocks and page hierarchy: the top 30% of data blocks in terms of visual weight score are automatically placed in the core area of ​​the report's homepage, and their border line width is increased. Increment of title font size According to , pt linear calculation, where, This represents the maximum visual weight score across all data blocks.

[0027] The PGT-A workflow and laboratory process data management system includes full-chain node management, from patient information entry, embryo biopsy sample reception, single-cell genome amplification, next-generation sequencing library construction, library sequencing, data analysis after sequencing, report issuance, report review, and report delivery.

[0028] The patient information entry function records all clinical information required for the PGT-A process, including patient ID, patient name, patient age, the time the patient entered the PGT-A process, and any special patient instructions. The system provides a structured form interface. The unique patient ID is automatically generated using a hospital code-date-serial number rule. Unique identification identifiers for embryo samples are generated using a patient ID-embryo number rule, such as P000123-EMB01, ensuring global uniqueness. Sensitive information (such as ID card numbers) is encrypted using the AES-256 algorithm at the application layer and stored; the key is managed by a separate Hardware Security Module (HSM). All data changes are dual-recorded through a MySQL binary log and a self-built audit table, ensuring traceability. After form submission, the system writes the data to the patient master table via database transactions, simultaneously creates a record in the process instance table, and asynchronously sends task notifications to the next stage leader via a message queue. The interface uses a token mechanism to prevent duplicate submissions and duplicate data entry.

[0029] Embryo biopsies are performed in the IVF laboratory, and the samples are then transported to the PGT-A testing laboratory via cold chain. Personnel responsible for receiving samples will see their tasks in the system, including all patient information, the number of biopsied embryos, and embryo quality ratings. Upon sample receipt, the system generates an electronic tag containing a sample number, patient information, and a QR code. Row-level locking and optimistic locking mechanisms in MySQL ensure that the same sample is not received repeatedly. Receiving personnel scan the QR code using a handheld terminal, and the system automatically associates the sample with the patient's workflow instance. The sample's storage location information is written to the database's monitoring record table in real time. The system sets a status flag for each sample, updating it from "pending receipt" to "already in the database," while simultaneously updating the workflow instance status. This process ensures data integrity through foreign key constraints in the database and encapsulates complex business logic through stored procedures. After completing sample receipt, clicking the "Complete" button automatically redirects the family to the single-cell genome amplification task node.

[0030] Personnel responsible for single-cell genome amplification will see the family pedigree received in the previous step in the system. They will then retrieve embryo biopsy samples from the sample storage location to perform the single-cell amplification task. Researchers select a preset amplification program (such as MALBAC, DOP-PCR, or SurePlex) on the front-end interface, and the system binds the parameter combinations to the sample information. During the experiment, the nucleic acid amplification instrument automatically outputs amplification curve data via RS-232 serial port or Ethernet interface, which the system reads and stores in real time through a background acquisition service. Amplification yield and concentration data are automatically read by the nucleic acid quantification instrument via USB or network interface. The system performs real-time verification according to a preset QC rule engine. If the amplification yield is lower than the threshold or the concentration is outside the expected range, an alarm is triggered, preventing progress to the next stage. All raw data and metadata are stored in structured form in relevant MySQL tables. Upon task completion, the system updates the task status table and inserts process logs, driving the state machine to jump to the next-generation sequencing library construction node.

[0031] Personnel responsible for next-generation sequencing library construction will see the sample that underwent single-cell amplification in the previous step within the system. They will then retrieve the amplified DNA at a designated location and perform next-generation sequencing library construction. The system provides step-by-step guidance for the library construction process, with each step (fragmentation, end repair, adapter ligation, PCR amplification, etc.) having independent completion confirmation and parameter recording points. Post-construction QC data, including the peak plot file path, concentration values, and fragment distribution from the fragment analyzer, is automatically obtained by the capillary electrophoresis system via a file-sharing protocol or the equipment manufacturer's SDK interface. After parsing, the data is stored in a MySQL JSON field in structured JSON format. Upon completion, the system automatically creates a library inventory record via a database trigger and initiates the next task assignment, translating the family to the library sequencing node.

[0032] The personnel responsible for sequencing the library will see the samples that have completed library preparation in the previous step in the system. They will then retrieve the library from the sample at the designated location and sequence the library. The system records the path of the raw data (BCL or FastQ file) generated by sequencing. The system subscribes to the sequencer's status notification interface through a separate background service. Once sequencing is complete, the system immediately updates the workflow status and automatically triggers the downstream data analysis workflow, redirecting the family to the offline data analysis node.

[0033] Personnel responsible for analyzing the sequencing data will see the samples that have completed sequencing in the previous step in the system, and will perform corresponding data analysis based on the family's case information in the system. The system uses the Nextflow workflow scheduling engine to manage the analysis process. Upon receiving the sequencing completion message, the scheduling engine reads the task configuration, sample information, and data path from MySQL, and then submits the analysis job to the high-performance computing cluster (HPC). The analysis process includes read alignment, deduplication, GC bias correction, binning, and CNV calls. The final analysis results (such as copy number variation maps) are converted into a format and stored in a dedicated results table, closely linked to the original workflow instance. The system uses MD5 checksums to ensure the consistency of input and output data. After completion, the system redirects to the report generation node.

[0034] The personnel responsible for issuing the report will see the family and sample information from the previous data analysis step in the system, and will generate a clinical report based on the analysis results. The system has a built-in intelligent report generation engine, which includes three stages: completion calculation, comprehensive fit scoring, and adaptive layout adjustment, as detailed below: The system divides the end-to-end data into four data domains, each containing the following fields: Sample basic information fields: subject ID, name, age, embryo number, biopsy date, clinical indications (advanced age, recurrent miscarriage, male factors, etc.), and previous PGT-A test results.

[0035] Sequencing quality control parameters include: amplification yield, amplification concentration, library fragment size, library concentration, sequencing data volume (number of reads), Q30 ratio, alignment rate, and coverage depth.

[0036] Chromosome copy number detection result fields: chromosome number, variant type (euploidy / aneuploidy / mosaic), mosaic ratio, CNV location and size, and variant pathogenicity classification.

[0037] Clinical genetic counseling domain: genetic counseling recommendations, embryo transfer recommendations, risk assessment conclusions, and follow-up recommendations.

[0038] Perform three layers of validation sequentially on each data field: Field-level validation: Checks if required fields are empty and if field value formats conform to specifications. Field completeness rate. The calculation method is as follows: =Number of required fields filled / Total number of required fields. Format compliance rate. The calculation method is as follows: =Number of correctly formatted fields / Total number of fields filled. Both values ​​are in the range [0,1].

[0039] Logical layer validation: Checks whether field values ​​are within a reasonable range and whether logical relationships between fields are valid. For example, patient age should be 18-55 years, Q30 ratio should be no less than 0.75, coverage depth should be no less than 0.1×, and chimerism ratio should be within the range [0,1]. Intra-domain logical validation pass rate. The calculation method is as follows: =Number of logical items that passed the verification / Total number of logical items verified, with a value range of [0,1].

[0040] Cross-domain semantic layer verification: Verifies the clinical logical consistency between the current data domain and other related domains. Specific verification items include: If trisomy 21 or other chromosomal abnormalities are detected, record 1 if the clinical indications include advanced age (≥38 years) or related risk factors, and 0 if not. If the sequencing depth is not lower than the minimum depth threshold required for CNV detection (e.g., 0.1×), record 1 if yes and 0 if no. If the percentage of Q30 is not lower than the minimum requirement for result reliability (e.g., 0.75), record 1 if yes and 0 if no. If a pathogenic variant is detected, record 1 if the consultation domain contains corresponding genetic counseling advice and 0 if not. If an unidentified variant is detected, check whether the consultation domain contains a VUS (Variant of Uncertain Significance) description. Record 1 if yes, and 0 if no. If a chimeric variant is detected, is the chimerism rate not lower than the minimum detection rate corresponding to the reporting sensitivity? If yes, record 1; otherwise, record 0.

[0041] The complexity coefficient K is set based on the following: the number of variants detected N directly affects the complexity and length of the report results paragraph, and is given the highest weight of 0.4; chimeric variants require more detailed descriptions and interpretations of the detection threshold, and are given a weight of 0.35; pathogenicity level affects the weight of clinical decision-making but has a relatively indirect impact on the page complexity, and is given a weight of 0.25.

[0042] Using multiplication correction method Instead of using an additive correction method, this ensures that the value of Z always stays within the range [0,1]. When =0, Z=0; when all When Z = 1 and δ = 0, Z = 1, avoiding the negative value problem that may be caused by additive correction.

[0043] Target space entropy The physical meaning is that when the detection complexity K is high and the data quality Z is low, the target entropy... An increase in size means the report needs to accommodate more differentiated information blocks, and the information density on the page needs to be allocated more flexibly; when K is low or Z is high, Approaching the benchmark The report maintains the standard format. K and (1 The product term of Z) ensures that the layout is only significantly adjusted in complex and incomplete data scenarios, avoiding unnecessary layout changes for simple cases.

[0044] A larger S value indicates that the area of ​​each data block is more uniform, while a smaller S value indicates that the area distribution is more concentrated in a few large data blocks.

[0045] For example, 8 data blocks and The calculation results are shown in Table 1. At this point, the average visual weight is 5.4125. =11.7.

[0046] Table 1 Results After multiple iterations (maximum 50 rounds), when Convergence is determined at that time.

[0047] Because the area change directly alters This changes S, and the direction is always adjusted so that S tends towards... The system converges within a finite number of rounds.

[0048] After the report is confirmed, the family will be redirected to the report review stage.

[0049] Personnel responsible for report review will see the families requiring review in the system and review the issued reports. The review process employs a double-blind or cross-checking electronic workflow with three levels of electronic signature permissions: primary review (signature of the testing personnel), intermediate review (signature of the reporting physician), and final review (signature of the authorized signatory). Electronic signatures are based on the PKI / CA certificate system, with each level of signature accompanied by digital certificate verification and a trusted timestamp to ensure the legal validity and immutability of the signatures. The report status transition from pending review to approved or returned for modification is ensured atomicity through database transaction control. Review comments and modification history are recorded in the associated review log table. After the review is completed and the "Complete" button is clicked, the family will proceed to the report distribution node.

[0050] The personnel responsible for sending the report will see the family members requiring the report in the system. The system will then send the report to the clinical electronic medical record system via Secure File Transfer Protocol (SFTP), recording the sending time, recipient, and file SHA-256 hash value as evidence. Simultaneously, a notification will be sent to the requesting doctor confirming the report's delivery via the hospital's messaging platform API. At this point, the test report is complete.

[0051] At the core of this system is a lightweight state machine engine implemented in MySQL. Each stage of the process is defined as a state of the state machine, and the transition rules between stages are predefined in a configuration table. When a task is completed, the application service layer calls the state machine engine's transition function, which performs the following operations within a database transaction: 1) Verify the current state and operator permissions; 2) Mark the current task as completed; 3) Calculate and create the task record for the next state according to the rules; 4) Update the current state pointer of the main process instance. This ensures the atomicity, consistency, and traceability of the process flow. Automatic transitions between stages are typically completed within seconds, significantly improving efficiency compared to the approximately 10 minutes per stage and 90 minutes for the entire process using conventional manual intervention.

[0052] Utilizing MySQL's Recursive Query (CTE) feature, the system can query the complete lifecycle record of any sample, from patient entry to final report, forming a clear data chain. All critical operations, including each sample handover and each data analysis, are recorded in an audit log table with timestamps and operator information. Through multi-table joins in the database, the complete testing process can be reconstructed within seconds, meeting the stringent traceability requirements of the quality management system.

[0053] This system leverages the transaction control, data modeling, query optimization, and scalability features of MySQL databases, combined with technologies such as message queues, workflow engines, and device communication interfaces, to construct a data-driven, automatically flowing, and fully traceable PGT-A workflow management system. By deeply coupling the adaptive layout adjustment mechanism with the complexity of PGT-A testing and data quality, the information density of the test report dynamically matches the actual complexity of the case, improving the clinical readability and information delivery efficiency of the report while ensuring clinical compliance.

[0054] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A PGT-A process and laboratory process data management system, characterized in that, include: The sample information management module is used to input and store the clinical information of the examinee and the basic information of the embryo sample, generate a globally unique identification identifier for each embryo sample, and associate all data generated throughout the process with the unique identification identifier of the corresponding sample. The full-process node control module is used to configure sequentially connected testing nodes according to the standard operating procedure of PGT-A testing. It verifies and records the operation permissions, operators, operation time and operation content of each node, and drives the embryo samples to flow sequentially according to the established process. The laboratory data automatic acquisition module is used to connect with various testing equipment in the PGT-A experimental process. It automatically captures the raw experimental data and process operation parameters output by each device through the device communication interface and synchronously associates them with the unique identification identifier of the corresponding sample. The quality control management module is used to preset the quality control thresholds and rules for each testing node, and to perform real-time verification of the process data and experimental results collected at each node. When the data exceeds the quality control threshold, it triggers an anomaly warning and intercepts the process flow. The reporting and traceability module is used to automatically generate standardized PGT-A test reports based on data collected throughout the entire process. The report generation includes adaptive format adjustments based on test complexity and data quality, and supports tracing back the operation records, original equipment data, and quality control results of the entire process through unique sample identification.

2. The system according to claim 1, characterized in that, The detection nodes configured in the full-process node control module include, in sequence, a sample receiving node, a sample preprocessing node, a whole genome amplification node, a library construction node, a high-throughput sequencing node, a bioinformatics analysis node, and a result review node; a mandatory transfer verification mechanism is set between each node, and the sample enters the next detection node after the quality control judgment of the previous node is passed.

3. The system according to claim 1, characterized in that, The laboratory data automatic acquisition module is connected to detection equipment including nucleic acid amplification instruments, capillary electrophoresis instruments, high-throughput sequencers, and nucleic acid quantitative detection instruments; the automatically acquired data includes amplification curve data, electrophoresis peak data, effective sequencing data volume, sequencing Q30 percentage, and nucleic acid concentration and purity parameters.

4. The system according to claim 1, characterized in that, The quality control management module also includes an inter-laboratory quality assessment and internal quality control management unit, which automatically calculates the quality control pass rate for each testing cycle, generates quality control trend analysis charts, and retains all quality control records and anomaly handling logs.

5. The system according to claim 1, characterized in that, The reporting and traceability module supports multi-dimensional reverse traceability of related samples by operator, operation time, equipment number, and test batch. It also sets multi-level electronic signature review permissions for test reports, and reports without a complete signature cannot be released to the public.

6. The system according to claim 1, characterized in that, The reporting and traceability module includes: The completion submodule is used to divide the PGT-A end-to-end data collected throughout the entire process into a sample basic information domain, a sequencing quality control parameter domain, a chromosome copy number detection result domain, and a clinical genetic counseling domain. For each data domain, field-level, logic-level, and cross-domain semantic-level validations are performed sequentially to calculate the comprehensive standardized completion degree within the domain. The formula for calculating the intra-domain comprehensive standardization completion degree of the i-th data domain is: ,in, For field completeness rate, To ensure format compliance, a basic verification layer is formed; For the pass rate of intra-domain logic verification, The cross-domain semantic matching rate constitutes a semantic verification layer; the cross-domain semantic matching rate The calculation method is as follows: ,in, This represents the total number of items for cross-domain semantic verification. The number of items that pass the verification includes: the compatibility verification between the chromosomal abnormality conclusion and the clinical indication, the matching verification between the sequencing depth and the CNV detection threshold, and the correspondence verification between the consultation advice and the abnormality level. Each item that passes the verification is recorded as 1, and that item that fails is recorded as 0. , The weight coefficients for the basic validation layer and the semantic validation layer are respectively, satisfying... ; , These are the sub-item weights for field completeness rate and format compliance rate within the basic validation layer, respectively. , These are the sub-item weights for the logical validation pass rate within the semantic validation layer and the cross-domain semantic matching rate, respectively. The score submodule is used to calculate the PGT-A specific detection complexity coefficient K based on the full data of the chromosome copy number detection result domain. Simultaneously, it calculates the standardized comprehensive fit score Z by combining the intra-domain comprehensive standardization completion degree of each data domain. The formula for calculating the detection complexity coefficient K is as follows: ,in, This represents the total number of copy number variations detected. For the reference value of chromosome logarithm, take =23; Normalized variant detection rate; The maximum chimerism rate of the mosaic variant; P is the pathogenicity level assignment; , , The adjustment coefficient for the corresponding factor satisfies... The value of K ranges from [0,1]. The formula for calculating the standardized comprehensive fit score Z is: ,in, The clinical priority weight for the i-th data domain satisfies ; Z is the anomaly correction coefficient; the value range of Z is [0,1], which represents the standardization qualification of the current full data under the corresponding detection complexity. The adjustment submodule is used to calculate the target space entropy using the standardized comprehensive fit score Z and the detection complexity coefficient K as inputs. Adjust the data block area allocation to make the actual typesetting entropy SS approach the target spatial entropy. ; If the page space overflows after iteration, a collapse compression is triggered; if there is space redundancy, the size of the collapse display block is adaptively adjusted.

7. The system according to claim 6, characterized in that, The adjustment submodule includes: The first computing unit is used to calculate the target space entropy. : ,in, K is the baseline spatial entropy of the standard normal sample template; Z is the detection complexity coefficient and Z is the standardized comprehensive fit score. The value is dimensionless and its range is [ , +0.4]; The second calculation unit is used to calculate the actual typesetting entropy value S: , Let A be the layout area occupied by the j-th report data block, and let A be the total available layout area per page. The area percentage of this data block satisfies S represents the spatial information entropy of the current layout, with a value range of... This is used to quantify the uniformity of the area distribution of data blocks; The update unit is used for adaptive updating of the data block area in the (t+1)th iteration: ,in, and Let be the areas of the j-th data block in the t-th and t+1-th iterations, respectively. This is the iteration step size coefficient; The visual weight score for the j-th data block; The arithmetic mean of the visual weight scores for all data blocks; The maximum visual weight score across all data blocks; To normalize visual weight bias; After each iteration, the area of ​​all data blocks is normalized to maintain the total area as A. ； When S> When S < 0, the area of ​​data blocks with visual weights above the average is increased, and the area of ​​data blocks with visual weights below the average is decreased; when S < 0. Then, the adjustment is reversed to make the area distribution more uniform; The third calculation unit is used to calculate the visual weight score of the j-th data block. : ,in, Prioritize the basic clinical aspects of the data blocks; Assign a value to the pathogenicity of the highest-level variant contained in this data block; r is the anomalous gain coefficient; The value range is [1, 16]; The layout iteration unit is used to calculate the page space overflow rate after the maximum iteration is reached. ,when When the score is greater than 0, it is determined by visual weight. Non-required fields will be collapsed and moved to the report appendix in ascending order of importance, and the field completeness rate of the corresponding data fields will be updated synchronously. Comprehensive standardization completion within the region Recalculate the standardized overall fit score ZZ and re-execute the typesetting iteration until... ; Explicit units are used after the maximum number of iterations has been reached. And page space redundancy rate At that time, the score was determined by visual weight. From high to low, restore the folded fields in the appendix to explicit data blocks, establish a planar layout coordinate system with the upper left corner of the target area of ​​the block as the origin, and perform adaptive size adjustment of the data blocks.

8. The system according to claim 7, characterized in that, The visual weight score Positively correlated with the area ratio of data blocks and page hierarchy: the top 30% of data blocks in terms of visual weight score are automatically placed in the core area of ​​the report's homepage, and their border line width is increased. Increment of title font size According to , pt linear calculation, where, This represents the maximum visual weight score across all data blocks.

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