{"data":{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38291374", "kind": "science-record", "revision": 4, "content_hash": "sha256:661526e123864cfe867c7e1bd87960aa690ce755bfd7864d0a13122b4e7d961d", "created_at": "2026-11-01T19:20:13Z", "updated_at": "2026-11-01T22:53:34Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "New insights into the role of mitochondrial metabolic dysregulation and immune infiltration in septic cardiomyopathy by integrated bioinformatics analysis and experimental validation.", "doi": "10.1186/s11658-024-00536-2", "pmid": "38291374", "authors": ["Yukun Li", "Jiachi Yu", "Ruibing Li", "Hao Zhou", "Xing Chang"], "journal": "Cellular & molecular biology letters", "year": 2024, "abstract": "BACKGROUND: Septic cardiomyopathy (SCM), a common cardiovascular comorbidity of sepsis, has emerged among the leading causes of death in patients with sepsis. SCM's pathogenesis is strongly affected by mitochondrial metabolic dysregulation and immune infiltration disorder. However, the specific mechanisms and their intricate interactions in SCM remain unclear. This study employed bioinformatics analysis and drug discovery approaches to identify the regulatory molecules, distinct functions, and underlying interactions of mitochondrial metabolism and immune microenvironment, along with potential interventional strategies in SCM.\nMETHODS: GSE79962, GSE171546, and GSE167363 datasets were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) and module genes were identified using Limma and Weighted Correlation Network Analysis (WGCNA), followed by functional enrichment analysis. Machine learning algorithms, including support vector machine-recursive feature elimination (SVM-RFE), least absolute shrinkage and selection operator (LASSO) regression, and random forest, were used to screen mitochondria-related hub genes for early diagnosis of SCM. Subsequently, a nomogram was developed based on six hub genes. The immunological landscape was evaluated by single-sample gene set enrichment analysis (ssGSEA). We also explored the expression pattern of hub genes and distribution of mitochondria/inflammation-related pathways in UMAP plots of single-cell dataset. Potential drugs were explored using the Drug Signatures Database (DSigDB). In vivo and in vitro experiments were performed to validate the pathogenetic mechanism of SCM and the therapeutic efficacy of candidate drugs.\nRESULTS: Six hub mitochondria-related DEGs [MitoDEGs; translocase of inner mitochondrial membrane domain-containing 1 (TIMMDC1), mitochondrial ribosomal protein S31 (MRPS31), F-box only protein 7 (FBXO7), phosphatidylglycerophosphate synthase 1 (PGS1), LYR motif containing 7 (LYRM7), and mitochondrial chaperone BCS1 (BCS1L)] were identified. The diagnostic nomogram model based on the six hub genes demonstrated high reliability and validity in both the training and validation sets. The immunological microenvironment differed between SCM and control groups. The Spearman correlation analysis revealed that hub MitoDEGs were significantly associated with the infiltration of immune cells. Upregulated hub genes showed remarkably high expression in the naive/memory B cell, CD14+ monocyte, and plasma cell subgroup, evidenced by the feature plot. The distribution of mitochondria/inflammation-related pathways varied across subgroups among control and SCM individuals. Metformin was predicted to be the most promising drug with the highest combined score. Its efficacy in restoring mitochondrial function and suppressing inflammatory responses has also been validated.\nCONCLUSIONS: This study presents a comprehensive mitochondrial metabolism and immune infiltration landscape in SCM, providing a potential novel direction for the pathogenesis and medical intervention of SCM.", "claims": [{"id": "claim-method", "statement": "DEG analysis was conducted using the “limma” package (version 3.54.0), a widely accepted method for analyzing data on gene expression [25].", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38291374", "locator": "sections[4] \"Acquisition of microarray data and identification of DEGs\" paragraph 2", "quote": "DEG analysis was conducted using the “limma” package (version 3.54.0), a widely accepted method for analyzing data on gene expression [25]."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Datasets supporting the conclusions of this article are available in the NCBI-GEO database (https://www.ncbi.nlm.nih.gov/geo/). All codes are available on GitHub (GitHub, Inc., San Francisco, California) at https://github.com/Lawrence-Albert/SCM_bioinform.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38291374", "locator": "sections[41] \"notes\" paragraph 1", "quote": "Datasets supporting the conclusions of this article are available in the NCBI-GEO database (https://www.ncbi.nlm.nih.gov/geo/). All codes are available on GitHub (GitHub, Inc., San Francisco, California) at https://github.com/Lawrence-Albert/SCM_bioinform."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The AUC of the nomogram model was 0.96 (95% CI: 0.91–1.00) in the training set and 0.94 (95% CI, 0.76–1.00) in the GSE171546 validation set.", "kind": "quantitative", "target": {"value": 95.0, "unit": "per cent", "tolerance": null, "source": "The AUC of the nomogram model was 0.96 (95% CI: 0.91–1.00) in the training set and 0.94 (95% CI, 0.76–1.00) in the GSE171546 validation set."}, "evidence": [{"asset": "source:pmid:38291374", "locator": "sections[24] \"Modeling and testing a diagnostic nomogram model for SCM\" paragraph 2", "quote": "The AUC of the nomogram model was 0.96 (95% CI: 0.91–1.00) in the training set and 0.94 (95% CI, 0.76–1.00) in the GSE171546 validation set."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The AUC of the nomogram model was 0.96 (95% CI: 0.91–1.00) in the training set and 0.94 (95% CI, 0.76–1.00) in the GSE171546 validation set.", "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:38291374"], "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:GSE167363"], "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:GSE167363"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:38291374", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s11658-024-00536-2", "sha256": "sha256:4a698944642a6fa214b13318ed3513fce6a391b82f11ebd1d4b57686f7f18803", "bytes": 71385, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1186/s11658-024-00536-2", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC10826082", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE167363", "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=GSE167363", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE167363", "version": null, "note": "Datasets supporting the conclusions of this article are available in the NCBI-GEO database (https://www.ncbi.nlm.nih.gov/geo/). 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In vivo and in vitro experiments were performed to v", "as the controls. For metformin intervention, mice were administered a single dose of metformin (250", "sis. Before conducting echocardiography, the mice were sufficiently anesthetized. Echocardiography", "mg/kg, Sigma) was intraperitoneally injected in mice for 48 h as described in previous studies [1"], "order_basis": "paper_text_order:1727", "id": "assay:1"}, {"role": "data_generation", "definition_id": "scrna_seq", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "unsupported", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["immune cell interactions in SCM. Analysis of single-cell RNA-seq data We used the single-cell transcriptome d", "for refined gene set enrichment analysis in single-cell transcriptomics, focusing on batch effect minimization. Indi"], "order_basis": "paper_text_order:4373", "id": "assay:2"}, {"role": "data_generation", "definition_id": "rna_seq", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["interactions in SCM. Analysis of single-cell RNA-seq data We used the single-cell transcriptome d", "le-cell RNA-seq data We used the single-cell transcriptome dataset (GSE167363) from the GEO database, c", "gene set enrichment analysis in single-cell transcriptomics, focusing on batch effect minimization. Indi"], "order_basis": "paper_text_order:4385", "id": "assay:3"}, {"role": "data_generation", "definition_id": "cell_culture", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.77, "evidence": ["lection (ATCC, Manassas, VA). The cells were cultured in Dulbecco’s modified Eagle’s medium (DMEM) su"], "order_basis": "paper_text_order:8248", "id": "assay:4"}, {"role": "data_generation", "definition_id": "clinical_trial", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.93, "evidence": ["n. To simulate SCM in vitro, HL-1 cells were treated with 10 mg/mL of LPS (Sigma) with or without 4 mM of metformin (Sigma) for 24 h, accordin"], "order_basis": "paper_text_order:8558", "id": "assay:5"}, {"role": "data_generation", "definition_id": "transfection", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["h the BCS1L knockdown model, HL-1 cells were transfected with siRNA targeting BCS1L (siBCS1L, obtaine", "hed studies [14, 34]. To establish the BCS1L knockdown model, HL-1 cells were transfected with siRN", "(Fig. 12B–D). We further constructed a BCS1L-knockdown cell model using siRNA to validate the inter", "on mitochondria were attenuated in the BCS1L-knockdown condition (n = 8 in each group, Fig. 12E–K)."], "order_basis": "paper_text_order:8722", "id": "assay:6"}, {"role": "data_generation", "definition_id": "cell_imaging", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["tes were stained with JC-1 dye and imaged by confocal microscopy. JC-1 fluorescence analysis revealed decreas", "stained with JC-1 dye and imaged by confocal microscopy. JC-1 fluorescence analysis revealed decreas"], "order_basis": "paper_text_order:14305", "id": "assay:7"}, {"role": "data_generation", "definition_id": "elisa", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["reatment (n = 8 in each group, Fig. 11E, F). ELISA was performed to assess whether metformin af", "under various conditions were observed using ELISA. Compared with the SCM + metformin group, a"], "order_basis": "paper_text_order:14508", "id": "assay:8"}, {"role": "data_generation", "definition_id": "qpcr", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.93, "evidence": ["(FBXO7, PGS1, BCS1L, MRPS31, and TIMMDC1) by qPCR in the control, SCM, and SCM + metformin gro"], "order_basis": "paper_text_order:16338", "id": "assay:9"}, {"role": "data_generation", "definition_id": "western_blot", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["wnregulated transcriptional levels of BCS1L. Western blot analysis confirmed that the interventional e", "levels of BCS1L and FBXO7 were determined by western blotting. E–K Analysis of HL-1 mitochondrial metaboli"], "order_basis": "paper_text_order:16588", "id": "assay:10"}, {"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": ["DEG analysis was conducted using the “limma” package (version 3.54.0), a widely accepted method for analyzing data on gene expression [25]."], "order_basis": "consumes_earlier_output", "id": "assay:11"}]},"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":"2026-11-04T02:14:00+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}}}