{"data":{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40470717", "kind": "science-record", "revision": 3, "content_hash": "sha256:0ec6a92f9fe6b0e3a53a8d15cd6ae0c42b9a491a53caece2cfaf637696fa8339", "created_at": "2026-11-01T20:30:35Z", "updated_at": "2026-11-01T22:14:09Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Tracing the molecular landscape of diabetic nephropathy: Insights from machine learning and experiment verification.", "doi": "10.1111/jdi.70026", "pmid": "40470717", "authors": ["Shahad W Kattan", "Ahmed M Basri", "Mohammad H Alhashmi", "Amany I Almars", "Reem Hasaballah Alhasani", "Ifat Alsharif", "Ikhlas A Sindi", "Ahmad H Mufti", "Iman S Abumansour", "Nasser A Elhawary", "Aishah Abdullah Qahtani", "Hailah M Almohaimeed"], "journal": "Journal of diabetes investigation", "year": 2025, "abstract": "OBJECTIVE: Diabetes is a chronic disease resulting from insufficient insulin secretion or impaired insulin function. Diabetic nephropathy (DN) is one of the most common complications of diabetes and a leading cause of end-stage renal disease. Early diagnosis of DN is crucial for timely intervention and effective disease management.\nMETHODS: Gene expression profiles GSE142025 and GSE220226 were retrieved from the GEO database and combined into a metadata cohort, while GSE189007 was obtained as an independent validation dataset. Differentially expressed genes (DEGs) were identified in 46 glomerular samples from DN patients and 31 control samples. Gene Ontology (GO) and Disease Ontology (DO) enrichment analyses, gene set enrichment analysis (GSEA), least absolute shrinkage and selection operator (LASSO) regression, support vector machine-recursive feature elimination (SVM-RFE) analysis, and area under the curve (AUC) calculations were performed.\nRESULTS: A total of 109 DEGs were identified. Among them, DUSP1, EGR1, FPR1, G6PC, GDF15, LOX, LPL, PRKAR2B, PTGDS, and TPPP3 were selected as potential diagnostic biomarkers for DN. These biomarkers exhibited a positive correlation with immune cell infiltration. Experimental validation identified LOX as the most promising novel diagnostic biomarker for DN. This study provides new insights into the early diagnosis, pathogenesis, and molecular mechanisms of DN.", "claims": [{"id": "claim-method", "statement": "DEGs were screened using the least absolute shrinkage and selection operator (LASSO) regression model for variable selection and regularization, reducing overfitting and improving predictive accuracy.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40470717", "locator": "sections[6] \"Selection of candidate biomarkers via machine learning\" paragraph 2", "quote": "DEGs were screened using the least absolute shrinkage and selection operator (LASSO) regression model for variable selection and regularization, reducing overfitting and improving predictive accuracy."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The DN Glomeruli and Control datasets, GSE142025 31 and GSE220226 32 , were downloaded from the Gene Expression Omnibus (GEO) database.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40470717", "locator": "sections[3] \"Acquisition of gene expression data\" paragraph 2", "quote": "The DN Glomeruli and Control datasets, GSE142025 31 and GSE220226 32 , were downloaded from the Gene Expression Omnibus (GEO) database."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Experimental validation identified LOX as the most promising novel diagnostic biomarker for DN.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:40470717", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Experimental validation identified LOX as the most promising novel diagnostic biomarker for DN."}, {"asset": "source:pmid:40470717", "locator": "sections[17] \"Diagnostic value of the identified biomarkers\" paragraph 2", "quote": "LOX (AUC = 0.926)"}, {"asset": "source:pmid:40470717", "locator": "sections[17] \"Diagnostic value of the identified biomarkers\" paragraph 2", "quote": "LOX (AUC = 1.000)"}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "Experimental validation identified LOX as the most promising novel diagnostic biomarker for DN.", "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:40470717"], "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:GSE142025"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "blocked", "blocked_on": "method:machine_learning_random_forest", "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:GSE142025"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:run_analysis blocked on method:machine_learning_random_forest"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:40470717", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1111/jdi.70026", "sha256": "sha256:dc3062aa298ebfe48da78c2c7a83aeecf568497bb2328c495157ea2fb9d0202d", "bytes": 41757, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1111/jdi.70026", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC12315251", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE142025", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE142025", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE142025", "version": null, "note": "The DN Glomeruli and Control datasets, GSE142025 31 and GSE220226 32 , were downloaded from the Gene Expression Omnibus (GEO) database.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The DN Glomeruli and Control datasets, GSE142025 31 and GSE220226 32 , were downloaded from the Gene Expression Omnibus (GEO) database.", "access_statement_kind": "repository_route_statement", "sample_accessions": ["GSM4217781", "GSM4217782", "GSM4217783", "GSM4217784", "GSM4217785", "GSM4217786", "GSM4217787", "GSM4217788", "GSM4217789", "GSM4217790", "GSM4217791", "GSM4217792", "GSM4217793", "GSM4217794", "GSM4217795", "GSM4217796", "GSM4217797", "GSM4217798", "GSM4217799", "GSM4217800", "GSM4217801", "GSM4217802", "GSM4217803", "GSM4217804", "GSM4217805", "GSM4217806", "GSM4217807", "GSM4217808", "GSM4217809", "GSM4217810", "GSM4217811", "GSM4217812", "GSM4217813", "GSM4217814", "GSM4217815", "GSM4217816"], "sample_count": 36, "group_sizes": null, "group_sizes_basis": null, "platform": "20301", "taxon": "Homo sapiens", "title": "Comparison of Kidney Transcriptomic Profiles of Early and Advanced Diabetic Nephropathy Reveals Potential New Mechanisms for Disease Progression", "resolution_checked_at": "2026-11-01T18:06:43Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}, {"id": "data:geo:GSE189007", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE189007", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE189007", "version": null, "note": "The DN Glomeruli and Control datasets, GSE142025 31 and GSE220226 32 , were downloaded from the Gene Expression Omnibus (GEO) database.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The DN Glomeruli and Control datasets, GSE142025 31 and 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130, "group_sizes": null, "group_sizes_basis": null, "platform": "23126;5082;19117", "taxon": "synthetic construct; Homo sapiens", "title": "Gene-expression profiles of whole blood cells from a Han Chinese population with or without Type-2 Diabetes Mellitus or/and its complications in nephropathy and retinopathy", "resolution_checked_at": "2026-11-01T20:16:08Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}, {"id": "data:geo:GSE220226", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE220226", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE220226", "version": null, "note": "The DN Glomeruli and Control datasets, GSE142025 31 and GSE220226 32 , were downloaded from the Gene Expression Omnibus (GEO) database.", 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No dataset was downloaded and no licence was read."}}, "can_run": {"dsl_operations": [], "assets": ["source:pmid:40470717", "data:geo:GSE142025"], "steps": []}, "must_build": {"dsl_operations": [{"operation": "machine_learning_random_forest", "unlocks_papers": null, "priority": null}], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE142025"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T19:53:51Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": null}, "calls": 5, "usage": {"prompt_tokens": 12152, "completion_tokens": 1668, "cached_tokens": 0}}, "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:40470717", "type": "paper", "label": "pmid:40470717"}, {"id": "source:pmid:40470717", "type": "manuscript", "label": "PMID 40470717 full text"}, {"id": "data:geo:GSE142025", "type": "dataset", "label": "GEO GSE142025"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:40470717", "type": "paper", "label": "pmid:40470717"}, {"id": "source:pmid:40470717", "type": "manuscript", "label": "PMID 40470717 full text"}, {"id": "data:geo:GSE142025", "type": "dataset", "label": "GEO GSE142025"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-40470717", "type": "extraction", "used": ["source:pmid:40470717"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE142025", "type": "accession_resolution", "used": ["data:geo:GSE142025"], "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:40470717", "parent_hash": "sha256:060d42aa21f1e2ab2c28940bb186aa6c9d13c2fa3258d83b5ce3659ae0e62cd2", "fork_of": null, "generation": 2}, "gaps": ["asset:data:geo:GSE142025 licence not read", "asset:data:geo:GSE142025 not downloaded", "asset:data:geo:GSE189007 licence not read", "asset:data:geo:GSE189007 not downloaded", "asset:data:geo:GSE220226 licence not read", "asset:data:geo:GSE220226 not downloaded", "execution.not_run", "method.machine_learning_random_forest", "method.reader_named_unmapped", "review.human_critique", "review.independent_reader", "review.machine_critique", "step:run_analysis blocked on method:machine_learning_random_forest"], "assays": [{"role": "data_generation", "definition_id": "western_blot", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.93, "evidence": ["upplementary Materials File S1 | Unprocessed Western blot images for Figure 7b. 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At the evidence floor the work type comes from title framing and a fixed prior, not from anything in the paper, so the published work-type split should be read as a rough triage signal rather than a measured distribution. The impact is bounded but not removed: since 2026-10-23 the engine excludes floor-confidence records from the shortlist entirely and publishes them separately as held out, so the recommendation is unaffected even though the census is.","status":"unverified","severity":"high"},{"code":"runtime_is_modelled","basis":["Runtime is summed from published per-assay hour figures. 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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}}}