{"data":{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35713363", "kind": "science-record", "revision": 5, "content_hash": "sha256:f4699ed96d17be6ea5c89b4476f5efd49f9b11be6a5b78b44d0a827dbe395589", "created_at": "2026-11-01T18:14:42Z", "updated_at": "2026-11-01T22:49:20Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Renal tubular gen e biomarkers identification based on immune infiltrates in focal segmental glomerulosclerosis.", "doi": "10.1080/0886022X.2022.2081579", "pmid": "35713363", "authors": ["JunYuan Bai", "XiaoWei Pu", "YunXia Zhang", "Enlai Dai"], "journal": "Renal failure", "year": 2022, "abstract": "OBJECTIVE: The present study identified novel renal tubular biomarkers that may influence the diagnosis and treatment of focal segmental glomerulosclerosis (FSGS) based on immune infiltration.\nMETHODS: Three FSGS microarray datasets, GSE108112, GSE133288 and GSE121211, were downloaded from the Gene Expression Omnibus (GEO) database. The R statistical software limma package and the combat function of the sva package were applied for preprocessing and to remove the batch effects. Differentially expressed genes (DEGs) between 120 FSGS and 15 control samples were identified with the limma package. Disease Ontology (DO) pathway enrichment analysis was conducted with statistical R software to search for related diseases. Gene set enrichment analysis (GSEA) was used to interpret the gene expression data and it revealed many common biological pathways. A protein-protein interaction (PPI) network was built using the Search Tool for the Retrieval of Interacting Genes (STRING) database, and hub genes were identified by the Cytoscape (version 3.7.2) plug-in CytoHubba. The plug-in Molecular Complex Detection (MCODE) was used to screen hub modules of the PPI network in Cytoscape, while functional analysis of the hub genes and hub nodes involved in the submodule was performed by ClusterProfiler. The least absolute shrinkage and selection operator (LASSO) regression and support vector machine recursive feature elimination (SVM-RFE) analysis were used to screen characteristic genes and build a logistic regression model. Receiver operating characteristic (ROC) curve analyses were used to investigate the logistic regression model and it was then validated by an external dataset GSE125779, which contained 8 FSGS samples and 8 healthy subjects. Cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) was used to calculate the immune infiltration of FSGS samples.\nRESULTS: We acquired 179 DEGs, 79 genes with downregulated expression (44.1%) and 100 genes with upregulated expression (55.9%), in the FSGS samples. The DEGs were significantly associated with arteriosclerosis, kidney disease and arteriosclerotic cardiovascular disease. GSEA revealed that these gene sets were significantly enriched in allograft rejection signaling pathways and activation of immune response in biological processes. Fifteen genes were demonstrated to be hub genes by PPI, and three submodules were screened by MCODE linked with FSGS. Analysis by machine learning methodologies identified nuclear receptor subfamily 4 group A member 1 (NR4A1) and dual specificity phosphatase 1 (DUSP1) as sensitive tubular renal biomarkers in the diagnosis of FSGS, and they were selected as hub genes, as well as hub nodes which were enriched in the MAPK signaling pathway. Immune cell infiltration analysis revealed that the genetic biomarkers were both correlated with activated mast cells, which may amplify FSGS biological processes.\nCONCLUSION: DUSP1 and NR4A1 were identified as sensitive potential biomarkers in the diagnosis of FSGS. Activated mast cells have a decisive effect on the occurrence and development of FSGS through tubular lesions and tubulointerstitial inflammation, and they are expected to become therapeutic targets in FSGS.", "claims": [{"id": "claim-method", "statement": "The least absolute shrinkage and selection operator (LASSO) regression and support vector machine recursive feature elimination (SVM-RFE) analysis were used to screen characteristic genes and build a logistic regression model.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35713363", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "The least absolute shrinkage and selection operator (LASSO) regression and support vector machine recursive feature elimination (SVM-RFE) analysis were used to screen characteristic genes and build a logistic regression model."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Publicly available datasets were analyzed in this study. All raw data are available in GEO datasets (GSE108112, GSE133288, GSE121211 and GSE125779).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35713363", "locator": "sections[25] \"Data availability statement\" paragraph 2", "quote": "Publicly available datasets were analyzed in this study. All raw data are available in GEO datasets (GSE108112, GSE133288, GSE121211 and GSE125779)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The study identified DUSP1 and NR4A1 as sensitive potential renal tubular biomarkers for diagnosing FSGS.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35713363", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "DUSP1 and NR4A1 were identified as sensitive potential biomarkers in the diagnosis of FSGS."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The study identified DUSP1 and NR4A1 as sensitive potential renal tubular biomarkers for diagnosing FSGS.", "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:35713363"], "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:GSE108112"], "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:GSE108112"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:35713363", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1080/0886022X.2022.2081579", "sha256": "sha256:c511bd6ec9c19c373832b00a65b78583399be57966e00e3e3123b7ffbcadbb5a", "bytes": 39807, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1080/0886022X.2022.2081579", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9225740", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE108112", "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=GSE108112", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE108112", "version": null, "note": "Publicly available datasets were analyzed in this study. 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The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R1", "reachable": true, "accessions": ["GSE108112", "GSE133288", "GSE121211", "GSE125779"], "resolved_accessions": ["GSE108112", "GSE133288", "GSE121211", "GSE125779"], "statements": ["Publicly available datasets were analyzed in this study. All raw data are available in GEO datasets (GSE108112, GSE133288, GSE121211 and GSE125779)."], "basis": "The paper names GSE108112, GSE133288, GSE121211, GSE125779 in Gene Expression Omnibus (GEO) and the accession resolves in the repository. No dataset was downloaded and no licence was read."}}, "can_run": {"dsl_operations": ["train_classifier", "cross_validate", "roc_auc"], "assets": ["source:pmid:35713363", "data:geo:GSE108112"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE108112"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T17:51:00Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 10664, "completion_tokens": 1414, "cached_tokens": 256}}, "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:35713363", "type": "paper", "label": "pmid:35713363"}, {"id": "source:pmid:35713363", "type": "manuscript", "label": "PMID 35713363 full text"}, {"id": "data:geo:GSE108112", "type": "dataset", "label": "GEO GSE108112"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:35713363", "type": "paper", "label": "pmid:35713363"}, {"id": "source:pmid:35713363", "type": "manuscript", "label": "PMID 35713363 full text"}, {"id": "data:geo:GSE108112", "type": "dataset", "label": "GEO GSE108112"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-35713363", "type": "extraction", "used": ["source:pmid:35713363"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE108112", "type": "accession_resolution", "used": ["data:geo:GSE108112"], "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:35713363", "parent_hash": "sha256:166db5da6ffce47a77d73fc859e4d04442fd1518fbd394fd28a619a765071734", "fork_of": null, "generation": 4}, "gaps": ["asset:data:geo:GSE108112 licence not read", "asset:data:geo:GSE108112 not downloaded", "asset:data:geo:GSE121211 licence not read", "asset:data:geo:GSE121211 not downloaded", "asset:data:geo:GSE125779 licence not read", "asset:data:geo:GSE125779 not downloaded", "asset:data:geo:GSE133288 licence not read", "asset:data:geo:GSE133288 not downloaded", "execution.not_run", "method.reader_differs_from_extractor", "review.human_critique", "review.independent_reader", "review.machine_critique"], "assays": [{"role": "data_generation", "definition_id": "microarray", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.93, "evidence": ["on immune infiltration. METHODS: Three FSGS microarray datasets, GSE108112, GSE133288 and GSE121211"], "order_basis": "paper_text_order:282", "id": "assay:1"}, {"role": "analysis", "definition_id": "machine_learning", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["train_classifier", "cross_validate", "roc_auc"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["The least absolute shrinkage and selection operator (LASSO) regression and support vector machine recursive feature elimination (SVM-RFE) analysis were used to screen characteristic genes and build a logistic regression model."], "order_basis": "consumes_earlier_output", "id": "assay:2"}]},"meta":{"api_version":"v1","automation_ladder_snapshot":"ba34b49490d9bfaf","automation_ladder_caveats":["Method extraction is rule-based and has no hand-judged accuracy sample. Evidence bands order textual support; method agreement intervals are null (n=0), never probabilities. Work-type validation does not validate methods.","Operation coverage describes only explicitly named methods. It does not prove a complete protocol, compatible parameters, successful compilation or re-execution. Unrecognised or ambiguous methods require review.","Data reachability is a textual accession or public-data statement. No dataset was downloaded or checked for access, completeness or licence.","Capabilities are planning states seeded from the dated equipment register. No bench ownership, completed quote or partner booking is inferred. Procurement counts are dependencies, not guaranteed replications or financial returns.","Tier and blocker counts are deterministic assessments of this corpus, not measured agreement. R3 is reserved for procurement; unresolved methods and data require human work (R4). The most restrictive axis wins, with capability, method, data as tie-breaks. Resolving one blocker can leave another at the same tier."],"engine":"corpus_engine","engine_provider":"rule_based_v1+lex99e1195c7d6f","run_id":"run-20261023T122449232913","source":"esearch","source_detail":"PubMed mirror biomed_pubmed (read-only)","run_status":"complete","imported_at":"2026-11-03T20:16:52+00:00","generated_at":"2026-11-04T01:50:34+00:00","total_papers":5529,"total_assays_tracked":29,"automatable_assay_families":6,"validated":true,"caveat_count":9,"blocking_caveats":5,"caveats":[{"code":"low_evidence_dominates","basis":["4,152 of 5,529 classifications (75.1%) matched no cue evidence and sit at the 0.05 floor.","34 of 230 data_only calls (14.8%) are in that group."],"claim":"Work type and confidence bands","gloss":"Most records were classified from their title and framing alone, with assay-cue coverage at zero.","detail":"Most classifications in this run rest on no matched evidence at all. 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. Timing awaits the first executed replication.","4,407 of 5,529 records (79.7%) model to 0.00 hours because no assay was matched, which the ranking then reads as \"cheapest\".","0 of 10 shortlisted records carry a 0.00-hour estimate."],"claim":"Estimated replication runtime","gloss":"The hour figures are estimates built from assay mentions; timing awaits the first executed replication.","detail":"The hour figures are a cost model derived from the bench steps implied by each matched assay family. The first executed replication will supply measured timing. 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}}}