{"data":{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36760248", "kind": "science-record", "revision": 4, "content_hash": "sha256:47c60412197fcde565837c676148d3f2c3324b9034f6911e32af6323d91d9cf1", "created_at": "2026-11-01T19:01:20Z", "updated_at": "2026-11-01T22:51:13Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Machine learning screening for Parkinson's disease-related cuproptosis-related typing development and validation and exploration of personalized drugs for cuproptosis genes.", "doi": "10.21037/atm-22-5756", "pmid": "36760248", "authors": ["Ji Wu", "Chengjian Qin", "Yuankun Cai", "Jiabin Zhou", "Dongyuan Xu", "Yu Lei", "Guoxing Fang", "Songshan Chai", "Nanxiang Xiong"], "journal": "Annals of translational medicine", "year": 2023, "abstract": "Background: Parkinson's disease (PD) is a common, degenerative disease of the nervous system that is characterized by the death of dopaminergic neurons in the substantia nigra densa (SNpc). There is growing evidence that copper (Cu) is involved in myelin formation and is involved in cell death through modulation of synaptic activity as well as neurotrophic factor-induced excitotoxicity.\nMethods: This study aimed to explore potential cuproptosis-related genes (CRGs) and immune infiltration patterns in PD and the development of Cu chelators relevant for PD treatment. The PD datasets GSE7621, GSE20141, and GSE49036 were downloaded from the Gene Expression Omnibus (GEO) database. The consensus clustering method was used to classify the specimens of PD. Using weighted gene co-expression network analysis (WGCNA) and random forest (RF) tree model, support vector machine (SVM) learning model, extreme gradient boosting (XGBoost) model, and general linear model (GLM) algorithms to screen disease progression-related models, the column charts were created to verify the accuracy of these CRGs in predicting PD progression. Single sample genomic enrichment analysis (ssGSEA) was used to estimate the correlation between genes associated with copper poisoning and genes associated with immune cells and immune function. Molecular docking was used to verify interactions with copper chelating agents associated with cuproptosis for PD treatment.\nResults: Through ssGSEA, we identified three copper poisoning related genes ATP7A, NFE2L2 and MTF1, which are related to immune cells in PD. We also verified that LAGASCATRIOL can bind to NFE2L2 through molecular docking. Consistent cluster analysis identified two subtypes, among which C2 subtype was just enriched in PD. And to more accurately diagnose PD progression, patients can benefit from a feature map based on these genes.\nConclusions: CRGs such as NFE2L2, MTF1, and ATP7B were identified to be associated with the pathogenesis of PD and provide a possible new direction for the treatment of PD, which needs further in-depth study.", "claims": [{"id": "claim-method", "statement": "To find differences in the expression of cuproptosis genes in the substantia nigra of PD patients compared to normal controls, CRGs were screened from the GSE49036 dataset using the limma package of R software (The R Foundation for Statistical Computing, Vienna, Austria) and CR-DEGs screening was performed on 20 PD samples and 8 control brain tissues.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36760248", "locator": "sections[5] \"Data processing and CR-DEGs screening\" paragraph 2", "quote": "To find differences in the expression of cuproptosis genes in the substantia nigra of PD patients compared to normal controls, CRGs were screened from the GSE49036 dataset using the limma package of R software (The R Foundation for Statistical Computing, Vienna, Austria) and CR-DEGs screening was performed on 20 PD samples and 8 control brain tissues."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Three tissue sequencing datasets of RNA microarray data on PD nigrostriatal samples were downloaded from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/): GSE7621 (9 control brain samples and 16 PD brain samples), GSE20141 (8 control brain samples and 10 PD brain samples), and GSE49036 (8 control brain samples and 20 PD brain samples).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36760248", "locator": "sections[4] \"Data download\" paragraph 2", "quote": "Three tissue sequencing datasets of RNA microarray data on PD nigrostriatal samples were downloaded from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/): GSE7621 (9 control brain samples and 16 PD brain samples), GSE20141 (8 control brain samples and 10 PD brain samples), and GSE49036 (8 control brain samples and 20 PD brain samples)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Using limma differential-expression analysis of GSE49036 nigrostriatal microarray data, the study identified three cuproptosis-related differentially expressed genes in Parkinson’s disease: ATP7B, NFE2L2, and MTF1.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36760248", "locator": "sections[16] \"Identification of CR-DEGs in PD and correlation analysis between genes\" paragraph 2", "quote": "The GSE49036 data were normalized and CRGs were extracted using the limma package of R software and screened for DEG analysis."}, {"asset": "source:pmid:36760248", "locator": "sections[16] \"Identification of CR-DEGs in PD and correlation analysis between genes\" paragraph 3", "quote": "CR-DEGs in PD. (A) Heat maps of the 3 CR-DEGs in PD; the color of the columns represents the gene expression of the tissue samples, with red indicating high expression and green indicating low expression."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Using limma differential-expression analysis of GSE49036 nigrostriatal microarray data, the study identified three cuproptosis-related differentially expressed genes in Parkinson’s disease: ATP7B, NFE2L2, and MTF1.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:36760248"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE20141"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE20141"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:36760248", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.21037/atm-22-5756", "sha256": 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Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/): GSE7621 (9 control brain samples and 16 PD brain samples), GSE20141 (8 control brain samples and 10 PD brain samples), and GSE49036 (8 control brain samples and 20 PD brain samples).", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "Three tissue sequencing datasets of RNA microarray data on PD nigrostriatal samples were downloaded from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/): GSE7621 (9 control brain samples and 16 PD brain samples), GSE20141 (8 control brain samples and 10 PD brain samples), and GSE49036 (8 control brain samples and 20 PD brain samples).", "access_statement_kind": "repository_route_statement", "sample_accessions": ["GSM503950", "GSM503951", "GSM503952", "GSM503953", "GSM503954", "GSM503955", "GSM503956", "GSM503957", "GSM503958", "GSM503959", "GSM503960", "GSM503961", "GSM503962", "GSM503963", "GSM503964", 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16 PD brain samples), GSE20141 (8 control brain samples and 10 PD brain samples), and GSE49036 (8 control brain samples and 20 PD brain samples).", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "Three tissue sequencing datasets of RNA microarray data on PD nigrostriatal samples were downloaded from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/): GSE7621 (9 control brain samples and 16 PD brain samples), GSE20141 (8 control brain samples and 10 PD brain samples), and GSE49036 (8 control brain samples and 20 PD brain samples).", "access_statement_kind": "repository_route_statement", "sample_accessions": ["GSM1192691", "GSM1192692", "GSM1192693", "GSM1192694", "GSM1192695", "GSM1192696", "GSM1192697", "GSM1192698", "GSM1192699", "GSM1192700", "GSM1192701", "GSM1192702", "GSM1192703", "GSM1192704", "GSM1192705", "GSM1192706", "GSM1192707", "GSM1192708", "GSM1192709", "GSM1192710", "GSM1192711", 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No dataset was downloaded and no licence was read."}}, "can_run": {"dsl_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "assets": ["source:pmid:36760248", "data:geo:GSE20141"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE20141"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T18:26:24Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": null}, "calls": 5, "usage": {"prompt_tokens": 12006, "completion_tokens": 1307, "cached_tokens": 264}}, "human_critique": null, "stages": [{"id": "extraction", "state": "complete", "reader": "MiniMax-M3.1-Flash", "provider": "minimax"}], "gaps": ["review.independent_reader", "review.machine_critique", "review.human_critique"]}, "execution": {"state": "absent", "status": "not_run", "mode": null, "runs": [], "gaps": ["execution.not_run"]}, "provenance": {"entities": [{"id": "pmid:36760248", "type": "paper", "label": "pmid:36760248"}, {"id": "source:pmid:36760248", "type": "manuscript", "label": "PMID 36760248 full text"}, {"id": "data:geo:GSE20141", "type": "dataset", "label": "GEO GSE20141"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:36760248", "type": "paper", "label": "pmid:36760248"}, {"id": "source:pmid:36760248", "type": "manuscript", "label": "PMID 36760248 full text"}, {"id": "data:geo:GSE20141", "type": "dataset", "label": "GEO GSE20141"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-36760248", "type": "extraction", "used": ["source:pmid:36760248"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE20141", "type": "accession_resolution", "used": ["data:geo:GSE20141"], "agent": "agent:corpus-assay-engine"}], "agents": [{"id": "agent:corpus-assay-engine", "type": "software", "label": "Corpus assay engine"}, {"id": "agent:minimax-reader", "type": "software", "label": "MiniMax literature reader"}], "source_run_id": "reading-sci-2026-11-01", "dsl_catalogue_fingerprint": "343fb30756ba6ec3", "capability_table_fingerprint": "2cb3e42f047d2ecf", "snapshot_id": "ba34b49490d9bfaf"}, "lineage": {"parent_id": "pmid:36760248", "parent_hash": "sha256:53c342a283e13690a86bc2b05b17edd1fbf41c8a66c80c64ec2ea78626341674", "fork_of": null, "generation": 3}, "gaps": ["asset:data:geo:GSE20141 licence not read", "asset:data:geo:GSE20141 not downloaded", "asset:data:geo:GSE49036 licence not read", "asset:data:geo:GSE49036 not downloaded", "asset:data:geo:GSE7621 licence not read", "asset:data:geo:GSE7621 not downloaded", "execution.not_run", "method.reader_differs_from_extractor", "review.human_critique", "review.independent_reader", "review.machine_critique"], "assays": [{"role": "analysis", "definition_id": "differential_expression", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["To find differences in the expression of cuproptosis genes in the substantia nigra of PD patients compared to normal controls, CRGs were screened from the GSE49036 dataset using the limma package of R software (The R Foundation for Statistical Computing, Vienna, Austria) and CR-DEGs screening was performed on 20 PD samples and 8 control brain tissues."], "order_basis": "analysis_only", "id": "assay:1"}]},"meta":{"api_version":"v1","automation_ladder_snapshot":"ba34b49490d9bfaf","automation_ladder_caveats":["Method extraction is rule-based and has no hand-judged accuracy sample. 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Records without an assay match currently receive a zero-hour model value.","status":"unverified","severity":"high"},{"code":"signal_score_without_citations","basis":["5,529 of 5,529 records (100.0%) have no citation count from the source metadata."],"claim":"Signal score","gloss":"The signal score uses publication-type weight because the mirror supplied zero citation counts.","detail":"The source supplied no citation counts for any record in this run, so the signal score is publication-type weight only. The citation term of the ranking is inert here, and the shortlist is ordered by publication type alone.","status":"unverified","severity":"high"},{"code":"abstract_absent","basis":["1,667 of 5,529 records (30.2%) carry no abstract at all."],"claim":"Evidence base for classification","gloss":"Some records contain a title and publication type only, defining the available classification evidence.","detail":"Where the mirror contains a title and publication types only, those fields define the classifier evidence. A methods section is required for fuller automated and human review.","status":"unverified","severity":"medium"},{"code":"classifier_agreement_measured","basis":["Published two-way decision agreement 77.7% (310 of 399 decidable records) from a stratified hand-check of 438 real records.","Three-way diagnostic agreement 73.9% (288 of 390 decidable records).","data_only agreement 75.2% (100 of 133 decidable).","48 record(s) could not be decided from title and abstract and were excluded from the denominator.","56 analysis-only records were published under another work type, so the data_only share is not an upper bound."],"claim":"Published automatable decision","gloss":"This figure has been checked against a human reading the same papers, so we know how often it is right.","detail":"The published automatable / not-automatable distribution comes from a rule-based classifier that was hand-checked against a fixed ground-truth definition. The agreement rates below are measured, not estimated. The three-way work type remains a diagnostic and is reported separately.","status":"measured","severity":"high"},{"code":"llm_comparison_not_measured","basis":["rule_based_v1+lex99e1195c7d6f produced 5,529 of 5,529 classifications.","Measured on 438 labelled records; these are RULE-BASED figures, not model results."],"claim":"Rule-based versus LLM agreement","gloss":"Every number here is the rule engine’s own. Model comparison begins when a working API credential is connected and measured.","detail":"The published two-way decision agreed with a human reading on 310 of 399 decidable records (77.7%). The diagnostic three-way label agreed on 288 of 390 decidable records (73.9%). Every percentage in this run belongs to the rule engine. Model comparison begins when a working API credential is connected and measured.","status":"not_measured","severity":"high"},{"code":"confidence_is_a_band","basis":["low band: 135 of 210 hand-judged records were correct, 64.3% [57.6, 70.5] (95% CI).","medium band: 80 of 104 hand-judged records were correct, 76.9% [68.0, 84.0] (95% CI).","high band: 73 of 76 hand-judged records were correct, 96.0% [89.0, 98.7] (95% CI).","A fitted calibrated probability was built, measured and withdrawn: it overstated held-out accuracy and reorders nothing."],"claim":"Confidence band and evidence score","gloss":"The confidence band carries a measured accuracy interval; 0.85 represents relative rank strength.","detail":"Confidence on this run is a BAND plus its measured accuracy interval. The number stored alongside each record is a monotone evidence score that orders records; 0.85 represents relative rank strength. A fitted probability was withdrawn after it overstated held-out accuracy. Threshold on the band.","status":"measured","severity":"medium"},{"code":"three_way_split_is_diagnostic","basis":["The corpus is published at the two-way decision: automatable or not-automatable. The three-way split (data_only / physical / mixed) is published as a diagnostic with its measured reliability attached, not as a claim, because the rule engine's `mixed` call cannot be defended.","The engine called 22 record(s) mixed; 10 matched the human judgement (45.5%, 95% CI 26.9-65.3).","The engine's `mixed` call is right on 10/22 hand-judged records (45.5%, 95% CI 26.9%-65.3%); a constant `physical` guess scores 36.4%.","The engine's `mixed` call does not reliably beat a constant `physical` guess on the hand-judged records it labels mixed.","The sign of that difference flips between two independent samples, so the three-way refinement is not demonstrably informative.","The mixed stratum contributes a disproportionate share of errors relative to its size.","At title-only depth the physical/mixed split collapses: a title says that work was done, not whether it was also analysed."],"claim":"The published decision is two-way","gloss":"The three-way split is shown for diagnosis only. The published split is two ways.","detail":"Count and filter the corpus on automatable versus not-automatable. The data_only / physical / mixed split remains an internal engine diagnostic because the mixed call has lower measured reliability.","status":"measured","severity":"medium"},{"code":"automatable_flag_unproven","basis":["6 of 29 assay families are marked automatable now: meta_analysis, machine_learning, survival_analysis, pca, molecular_docking, differential_expression.","The flag is a rule over assay family and work type; validation begins with the first executed replication.","It inherits the 75.2% data_only agreement rate, because the flag requires the record to be analysis-only."],"claim":"Automatable now","gloss":"This flag marks assay families the rules classify as data-only. The first executed replication will validate it.","detail":"This flag marks families the rules consider replicable from data alone. Its validity depends on the analysis-only classification and will be measured through an executed replication.","status":"unverified","severity":"medium"}],"confidence_policy":"band_with_measured_interval","confidence_contract":{"policy":"band_with_measured_interval","summary":"Confidence is reported as a BAND with a measured accuracy interval. The raw evidence score supplies monotone ordering. A fitted point estimate was withdrawn after it overstated held-out accuracy.","evidence_score":"A monotone evidence score in [0,1] built from cue mass, axis separation and text coverage. Use it to order records. Interpret 0.85 as relative rank strength.","confidence_band":"low | medium | high. Consumers threshold on this value.","band_accuracy":"Wilson 95% interval on the measured agreement of the hand-judged records in this band.","calibrated_confidence":"WITHDRAWN. Reintroduction requires held-out evidence demonstrating reliable estimates across every bin."},"band_accuracy":{"low":{"n":210,"low":0.576,"band":"low","high":0.7046,"agree":135,"accuracy":0.6429},"high":{"n":76,"low":0.8903,"band":"high","high":0.9865,"agree":73,"accuracy":0.9605},"medium":{"n":104,"low":0.6795,"band":"medium","high":0.8397,"agree":80,"accuracy":0.7692}},"provider":"rule_based_v1+lex99e1195c7d6f","lexicon_id":"99e1195c7d6f","contract_published_at":"2026-10-23T12:25:29.022232+00:00","confidence_is_band":true,"confidence_is_probability":false,"decision_taxonomy":"two_way","decidable_fraction_two_way":0.8854,"decidable_fraction_two_way_low":0.8565,"decidable_fraction_two_way_high":0.9143,"decidable_fraction_three_way":0.8586,"decidable_fraction_three_way_low":0.8287,"decidable_fraction_three_way_high":0.8884,"decidable_population":5529,"validation_sample_size":438,"three_way_cost_fraction":0.0268,"three_way_cost_records":148,"decision_counts":{"automatable":230,"not_automatable":5299},"decision_split":{"automatable":{"count":230,"share":0.04159884246699222},"not_automatable":{"count":5299,"share":0.9584011575330078}},"diagnostic_work_type_counts":{"mixed":24,"physical":5275,"data_only":230}}}