{"data":{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39054612", "kind": "science-record", "revision": 4, "content_hash": "sha256:b7f715cfbbbdfbe5b21fa235fba9ecc94cafc3aa5443b41894df9c5f4757535e", "created_at": "2026-11-01T20:29:07Z", "updated_at": "2026-11-01T22:54:46Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of MORN3 and LLGL2 as novel diagnostic biomarkers for latent tuberculosis infection using machine learning strategies and experimental verification.", "doi": "10.1080/07853890.2024.2380797", "pmid": "39054612", "authors": ["Longxiang Xie", "Gaoya Zhu", "Sibo Long", "Mengna Wang", "Xinxin Cheng", "Yuzhe Dong", "Chaoyang Wang", "Guirong Wang"], "journal": "Annals of medicine", "year": 2024, "abstract": "BACKGROUND: Current diagnostic methods cannot effectively distinguish between latent tuberculosis infection (LTBI) and active tuberculosis (ATB). This study aims to explore novel non-invasive diagnostic biomarkers for LTBI and to elucidate possible molecular mechanisms of LTBI pathogenesis.\nMETHODS: Three GEO datasets (GSE19439, GSE19444, and GSE62525) were utilized to analyze the differentially expressed genes (DEGs). Functional enrichment studies were then performed on these DEGs. To ascertain potential diagnostic biomarkers, we utilized two different machine learning techniques: LASSO and RF. ROC curves were constructed in both the training and validation datasets to assess the diagnostic efficacy. The expression of identified biomarkers was verified by RT-qPCR in our own Chinese cohort. Using CIBERSORT, we estimated the abundances of 22 immune cell types in LTBI group, and subsequently analyzed the relationship between biomarker expression and immune cell infiltration.\nRESULTS: 166 DEGs were identified between ATB and LTBI groups, which are primarily associated with immune responses, inflammatory signaling pathways, and infection factors. Following that, 22 candidate diagnostic biomarkers for LTBI were selected in the machine learning process. Three up-regulated genes, MORN3, LLGL2, and IFT140, whose expression levels were not previously reported in TB, were validated using the training and validation cohort datasets. In our own Chinese cohort, we also found that MORN3 and LLGL2 showed good diagnostic effect using RT-qPCR method. Finally, we revealed the specific infiltration features of immune cells in LTBI and observed a notable correlation between potential marker expression and immune cells.\nCONCLUSIONS: MORN3 and LLGL2 emerged as candidate diagnostic biomarkers for LTBI, following the elucidation of the key immune cell types involved. Our findings will contribute to providing a potential target for early noninvasive diagnosis of LTBI patients.", "claims": [{"id": "claim-method", "statement": "To select the candidate biomarkers in the DEGs of training set, LASSO regression model and random forest were performed.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39054612", "locator": "sections[6] \"Identification of candidate biomarkers in LTBI using machine learning methods\" paragraph 2", "quote": "To select the candidate biomarkers in the DEGs of training set, LASSO regression model and random forest were performed."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The GEO database provides access to the datasets examined in this study (http://www.ncbi.nlm.nih.gov/geo/). Please contact the corresponding author if you have any more questions.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39054612", "locator": "sections[26] \"Data availability statement\" paragraph 2", "quote": "The GEO database provides access to the datasets examined in this study (http://www.ncbi.nlm.nih.gov/geo/). Please contact the corresponding author if you have any more questions."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The combined two-gene approach further enhanced diagnostic capabilities, achieving AUCs of 0.994 (LTBI vs ATB), 0.782 (LTBI vs HC), and 0.914 (ATB vs HC).", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39054612", "locator": "sections[17] \"Validation of potential biomarkers in our Chinese samples\" paragraph 4", "quote": "The combined two-gene approach further enhanced diagnostic capabilities, achieving AUCs of 0.994 (LTBI vs ATB), 0.782 (LTBI vs HC), and 0.914 (ATB vs HC)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "The combined two-gene approach further enhanced diagnostic capabilities, achieving AUCs of 0.994 (LTBI vs ATB), 0.782 (LTBI vs HC), and 0.914 (ATB vs HC).", "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:39054612"], "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:GSE19439"], "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:GSE19439"], "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:39054612", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1080/07853890.2024.2380797", "sha256": "sha256:87f6af666e812a307ffa9357164d6735cf495a97536af9bfc12cc4481c5beab5", "bytes": 32044, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1080/07853890.2024.2380797", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC11285216", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE19439", "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=GSE19439", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE19439", "version": null, "note": "The GEO database provides access to the datasets examined in this study (http://www.ncbi.nlm.nih.gov/geo/). Please contact the corresponding author if you have any more questions.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The GEO database provides access to the datasets examined in this study (http://www.ncbi.nlm.nih.gov/geo/). 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Please contact the corresponding author if you have any more questions.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The GEO database provides access to the datasets examined in this study (http://www.ncbi.nlm.nih.gov/geo/). Please contact the corresponding author if you have any more questions.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM1528336", "GSM1528337", "GSM1528338", "GSM1528339", "GSM1528340", "GSM1528341", "GSM1528342", "GSM1528343", "GSM1528344", "GSM1528345", "GSM1528346", "GSM1528347", "GSM1528348", "GSM1528349", "GSM1528350", "GSM1528351", "GSM1528352", "GSM1528353", "GSM1528354", "GSM1528355", "GSM1528356", "GSM1528357", "GSM1528358", "GSM1528359", "GSM1528360", "GSM1528361", "GSM1528362", "GSM1528363", "GSM1528364", "GSM1528365", "GSM1528366", "GSM1528367", "GSM1528368", "GSM1528369", "GSM1528370", "GSM1528371", "GSM1528372", "GSM1528373", "GSM1528374", "GSM1528375", "GSM1528376", "GSM1528377"], "sample_count": 42, "group_sizes": null, "group_sizes_basis": null, "platform": "16951", "taxon": "Homo sapiens", "title": "Gene expression profiling identifies candidate biomarkers for active and latent tuberculosis", "resolution_checked_at": "2026-11-01T15:39:55Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}], "gaps": ["asset:data:geo:GSE19439 not downloaded", "asset:data:geo:GSE19439 licence not read", "asset:data:geo:GSE19444 not downloaded", "asset:data:geo:GSE19444 licence not read", "asset:data:geo:GSE62525 not downloaded", "asset:data:geo:GSE62525 licence not read"]}, "analysis": {"state": "partial", "methods": [{"id": "method-1", "family": "machine_learning", "specific": "random_forest", "label": "Machine learning", "dsl_operations": [], "missing_operations": ["machine_learning_random_forest"], "expressible": false, "parameters": {}, "data_shapes": ["labelled_feature_matrix"], "evidence": {"score": 1.0, "band": "verbatim", "quote": "To select the candidate biomarkers in the DEGs of training set, LASSO regression model and random forest were performed."}}, {"id": "method-2", "family": "regression_modelling", "specific": "logistic", "label": "Regression modelling", "dsl_operations": ["glm_logit_fit"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["predictors_and_outcome"], "evidence": {"score": 0.8, "band": "high", "quote": "logistic regression"}}, {"id": "method-3", "family": "diagnostic_evaluation", "specific": "roc", "label": "Diagnostic test evaluation", "dsl_operations": ["confusion_counts", "roc_curve", "sensitivity_specificity", "solve_confusion_matrix", "classification_metrics", "published_precision_envelope"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["reference_standard_and_test"], "evidence": {"score": 0.8, "band": "high", "quote": "ROC curve"}}], "targets": [], "gaps": ["method.machine_learning_random_forest"]}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["meta_analysis", "machine_learning", "survival_analysis", "statistical_regression", "pca", "molecular_docking", "network_analysis", "differential_expression", "gwas"], "availability_state": "owned", "tier_unlocked": "R1", "candidate": "Automated Assays Laravel application and simulated adapters"}], "gaps": []}, "readiness": {"state": "complete", "tier": "R1", "blocking_axis": "none", "blocked_on": "nothing:replicable_now", "axes": {"method": {"tier": "R1", "expressible": false, "required_operations": [], "missing_operations": ["machine_learning_random_forest"], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: random_forest."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. 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Using CIBERSORT,", "nd LLGL2 showed good diagnostic effect using RT-qPCR method. Finally, we revealed the specific in", "of identified biomarkers was verified by RT-qPCR in our own Chinese cohort. Using CIBERSORT,", "LLGL2 showed good diagnostic effect using RT-qPCR method. 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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. 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. 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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}}}