{"data":{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:41102315", "kind": "science-record", "revision": 4, "content_hash": "sha256:85153c5824e613d67e944cee1d4da3c7ba72ea021251155db4bc558fc6525c22", "created_at": "2026-11-01T20:31:09Z", "updated_at": "2026-11-01T22:57:19Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Machine learning identifies MiRNA biomarkers and immune mechanisms in active tuberculosis.", "doi": "10.1038/s41598-025-20112-8", "pmid": "41102315", "authors": ["Zihan Cai", "Chunxiao Huang", "Yuyang Zhou", "Lixian Wu", "Shoupeng Ding"], "journal": "Scientific reports", "year": 2025, "abstract": "Tuberculosis (TB), caused by Mycobacterium tuberculosis (Mtb), remains a major global public health threat. The rising prevalence of HIV/TB co-infection and multidrug-resistant tuberculosis (MDR-TB) has further intensified this challenge. This study aims to explore the role of microRNAs (miRNAs) in the immune response to Mtb infection and to identify potential miRNA biomarkers for active TB diagnosis using machine learning techniques. miRNA expression profiles were retrieved from the Gene Expression Omnibus (GEO) database (accession number: GSE70425). Differential expression analysis between active TB and latent TB infection (LTBI) patients was conducted using the \"limma\" package, with a screening threshold of |logFC| > 0.25 and p-value < 0.05. Key differentially expressed miRNAs (DE-miRNAs) were further refined using machine learning algorithms, including least absolute shrinkage and selection operator (LASSO) regression, support vector machine-recursive feature elimination (SVM-RFE), and Boruta. TargetScan was employed to predict miRNA target genes, and a regulatory network was visualized using Cytoscape. Subsequently, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted to elucidate the functional roles of target mRNAs. Nine machine learning models were developed based on the selected miRNAs, and their predictive performance was assessed using metrics including AUROC, accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). In vitro experiments were performed using THP-1 macrophages to establish an Mtb infection model. Cells were transfected with miR-3607-3p mimics and negative controls, followed by flow cytometry for apoptosis detection, Western blot for protein expression analysis, and Quantitative Real-Time PCR (qRT-PCR) for validation of apoptosis-related gene expression. A total of 72 differentially expressed miRNAs were identified. Key miRNAs, including hsa-miR-3607-3p, hsa-miR-148b, and hsa-miR-519e, were identified through multiple machine learning methods, with hsa-miR-3607-3p emerging as the primary candidate miRNA. The nine machine learning models exhibited robust predictive performance in both the training and test sets. PPI network and functional enrichment analyses indicated that the target genes of hsa-miR-3607-3p are primarily associated with cell growth and apoptosis-related pathways. In vitro experiments further suggested that hsa-miR-3607-3p may modulate the apoptotic response of THP-1 cells to Mtb infection via a caspase-dependent mechanism. miR-3607-3p was upregulated in active TB patients and may modulate the apoptosis of THP-1 cells during Mtb infection via a caspase-dependent pathway. The mechanism of this miRNA offers preliminary insights into the immune regulation of tuberculosis. miR-3607-3p may serve as a potential biomarker for the early diagnosis and intervention of active TB; however, its clinical applicability necessitates further validation through larger sample sizes and multicenter studies.", "claims": [{"id": "claim-method", "statement": "To investigate the differences in miRNA expression levels between ATB and LTBI patients, differential expression analysis was performed using the “limma” package in R.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41102315", "locator": "sections[4] \"Differential expression analysis of MiRNAs\" paragraph 2", "quote": "To investigate the differences in miRNA expression levels between ATB and LTBI patients, differential expression analysis was performed using the “limma” package in R."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets generated and/or analyzed during this study are available in the GEO (Gene Expression Omnibus) repository. The direct link to access the gene expression and clinical pathology dataset is: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE70425. This link provides access to the original dataset used in our analysis. The raw data supporting the findings of this study are available upon reasonable request. Researchers interested in accessing the data should contact the corresponding author, at [zihancai001@163.com]. Requests will be reviewed to ensure compliance with ethical and privacy standards.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41102315", "locator": "sections[30] \"Data availability\" paragraph 2", "quote": "The datasets generated and/or analyzed during this study are available in the GEO (Gene Expression Omnibus) repository. The direct link to access the gene expression and clinical pathology dataset is: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE70425. This link provides access to the original dataset used in our analysis. The raw data supporting the findings of this study are available upon reasonable request. Researchers interested in accessing the data should contact the corresponding author, at [zihancai001@163.com]. Requests will be reviewed to ensure compliance with ethical and privacy standards."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Using limma differential expression analysis on the GSE70425 dataset, the study identified 72 differentially expressed miRNAs and identified hsa-miR-3607-3p as the primary candidate biomarker for distinguishing active TB from latent TB infection.", "kind": "quantitative", "target": {"value": 0.98, "unit": "auc", "tolerance": null, "source": "ROC curve analysis revealed that hsa-miR-3607-3p alone achieved an AUC of 0.980 in distinguishing between ATB and LTBI (Fig. 4D), indicating its exceptional diagnostic performance."}, "evidence": [{"asset": "source:pmid:41102315", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "Differential expression analysis between active TB and latent TB infection (LTBI) patients was conducted using the “limma” package, with a screening threshold of |logFC| > 0.25 and p-value < 0.05."}, {"asset": "source:pmid:41102315", "locator": "sections[3] \"Collection and processing of expression data\" paragraph 2", "quote": "miRNA expression profiles associated with Mtb infection were retrieved from the GEO database (accession number: GSE70425)."}, {"asset": "source:pmid:41102315", "locator": "sections[19] \"Differential expression analysis\" paragraph 2", "quote": "Using stringent criteria of p-value < 0.05 and False Discovery Rate (FDR) < 0.05, 72 miRNAs were found to be differentially expressed."}, {"asset": "source:pmid:41102315", "locator": "sections[21] \"Selection and diagnostic performance of target MiRNAs\" paragraph 2", "quote": "ROC curve analysis revealed that hsa-miR-3607-3p alone achieved an AUC of 0.980 in distinguishing between ATB and LTBI (Fig. 4D), indicating its exceptional diagnostic performance."}, {"asset": "source:pmid:41102315", "locator": "sections[21] \"Selection and diagnostic performance of target MiRNAs\" paragraph 2", "quote": "Consequently, hsa-miR-3607-3p was identified as the key candidate miRNA in this study."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Using limma differential expression analysis on the GSE70425 dataset, the study identified 72 differentially expressed miRNAs and identified hsa-miR-3607-3p as the primary candidate biomarker for distinguishing active TB from latent TB infection.", "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:41102315"], "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. 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The direct link to access the gene expression and clinical pathology dataset is: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE70425. This link provides access to the original dataset used in our analysis. The raw data supporting the findings of this study are available upon reasonable request. Researchers interested in accessing the data should contact the corresponding author, at [zihancai001@163.com]. Requests will be reviewed to ensure compliance with ethical and privacy standards.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The datasets generated and/or analyzed during this study are available in the GEO (Gene Expression Omnibus) repository. The direct link to access the gene expression and clinical pathology dataset is: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE70425. This link provides access to the original dataset used in our analysis. The raw data supporting the findings of this study are available upon reasonable request. Researchers interested in accessing the data should contact the corresponding author, at [zihancai001@163.com]. 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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}}}