{"data":{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40927188", "kind": "science-record", "revision": 3, "content_hash": "sha256:e15c3854ad09e701785af6c37945708651504360da9d3aa891ed85550ab0d018", "created_at": "2026-11-01T20:31:01Z", "updated_at": "2026-11-01T22:14:41Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification and validation for biomarkers associated with mitochondrial metabolism in chronic obstructive pulmonary disease.", "doi": "10.3389/fmed.2025.1612390", "pmid": "40927188", "authors": ["Wenjun Wang", "Lanxiang Wu", "Chao Ouyang", "Tao Huang"], "journal": "Frontiers in medicine", "year": 2025, "abstract": "Background: Chronic obstructive pulmonary disease (COPD) is a chronic respiratory disease. However, the biological role of mitochondrial metabolism (MM) in COPD remains poorly understood. This study aimed to explore the underlying mechanisms of MM in COPD using bioinformatics methods.\nMethods: The datasets GSE57148 and GSE8581 were downloaded from Gene Expression Omnibus (GEO), and 1,234 mitochondrial metabolism-related genes (MM-RGs) were downloaded from the literature. In GSE57148 dataset, differentially expressed genes (DEGs) were determined. The intersection of DEGs and MM-RGs was taken to obtain candidate genes. Protein-protein interaction (PPI) network was used to obtained candidate key genes. Machine learning was employed to detect key genes. The biomarkers were identified through expression validation and receiver operating characteristic (ROC) curves. Subsequently, a nomogram was developed to forecast the likelihood of developing COPD. In addition, functional enrichment analysis, immune infiltration, molecular regulatory network, and drug prediction were carried out. Finally, reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and immunohistochemistry analysis were used to verify DEGs of lung tissues of COPD patients and controls.\nResults: Adenine phosphoribosyltransferase (APRT) and lecithin-cholesterol acyltransferase (LCAT) were identified as potential biomarkers. Subsequently, a nomogram was formulated based on these two biomarkers, revealing their significant diagnostic potential. Pathways co-enriched by two biomarkers included ribosome, among others. Immune infiltration analysis showed that 15 types of immune cells were differential immune cells. APRT predicted a total of 30 miRNAs and LCAT predicted a total of 17 miRNAs. APRT was predicted to be targeted by 30 microRNAs (miRNAs), while LCAT was associated with 17 miRNAs. Additionally, 178 transcription factors (TFs) were predicted to regulate APRT, and 86 TFs were predicted for LCAT. TFs shared by both biomarkers include SPI1, CTCF and BCL3, etc. Finally, drug prediction analysis found a total of 114 target drugs for APRT and 156 target drugs for LCAT. The mRNA and protein expression of APRT and LCAT were significantly decreased in COPD patients' lung tissues.\nConclusion: APRT and LCAT were identified as biomarkers for COPD, and this provides deeper understanding into the mechanisms behind COPD and identifies potential markers for early diagnosis and therapeutic intervention.", "claims": [{"id": "claim-method", "statement": "Statistical analyses were conducted using R software (v 4.2.2), with the Wilcoxon employed to assess differences among groups.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40927188", "locator": "sections[12] \"Statistical analysis\" paragraph 2", "quote": "Statistical analyses were conducted using R software (v 4.2.2), with the Wilcoxon employed to assess differences among groups."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary material.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40927188", "locator": "sections[25] \"Data availability statement\" paragraph 2", "quote": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary material."}], "state": "complete"}, {"id": "claim-target-result", "statement": "APRT and LCAT were identified as COPD biomarkers, and a nomogram based on them achieved an AUC of 0.841.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:40927188", "locator": "sections[16] \"Constructing a model with exceptional diagnostic capability for COPD\" paragraph 2", "quote": "In ROC curve, the AUC value was 0.841; thus, the ROC curve indicated that the nomogram performed well clinical utility (Figure 4C)."}, {"asset": "source:pmid:40927188", "locator": "sections[15] \"APRT and LCAT as biomarkers\" paragraph 2", "quote": "Therefore, the APRT and LCAT were selected as biomarkers for this study (Figure 3E)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "APRT and LCAT were identified as COPD biomarkers, and a nomogram based on them achieved an AUC of 0.841.", "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:40927188"], "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:GSE57148"], "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:group_comparison_wilcoxon", "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:GSE57148"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:run_analysis blocked on method:group_comparison_wilcoxon"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:40927188", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fmed.2025.1612390", "sha256": "sha256:4de37ea95d2cf2ba31f3a32d9546765db4dbbaba3ade5c19fd5169407558592b", "bytes": 64976, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fmed.2025.1612390", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC12414765", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE57148", "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=GSE57148", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE57148", "version": null, "note": "The datasets presented in this study can be found in online repositories. 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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}}}