Immune status changing helps diagnose osteoarticular tuberculosis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:34129634", "kind": "science-record", "revision": 6, "content_hash": "sha256:1aeb6ba5505e29a809ee03b960f70bd77092451940d2184edb65ad2f1e5e90ac", "created_at": "2026-11-01T15:41:39Z", "updated_at": "2026-11-01T21:25:31Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Immune status changing helps diagnose osteoarticular tuberculosis.", "doi": "10.1371/journal.pone.0252875", "pmid": "34129634", "authors": ["Tuo Liang", "Jiarui Chen", "GuoYong Xu", "Zide Zhang", "Jiang Xue", "Haopeng Zeng", "Jie Jiang", "Tianyou Chen", "Zhaojie Qin", "Hao Li", "Zhen Ye", "Yunfeng Nie", "Chong Liu", "Xinli Zhan"], "journal": "PloS one", "year": 2021, "abstract": "OBJECTIVE: This study is aimed to develop a new nomogram for the clinical diagnosis of osteoarticular tuberculosis (TB).\nMETHODS: xCell score estimation to obtained the immune cell type abundance scores. We downloaded the expression profile of GSE83456 from GEO and proceed xCell score estimation. The routine blood examinations of 326 patients were collected for further validation. We analyzed univariate and multivariate logistic regression to identified independent predicted factor for developing the nomogram. The performance of the nomogram was assessed using the receiver operating characteristic (ROC) curves. The correlation of ESR with lymphocytes, monocytes, and ML ratio was performed and visualized in osteoarticular TB patients.\nRESULTS: Compared with the healthy control group in the dataset GSE83456, the xCell score of basophils, monocytes, neutrophils, and platelets was higher, while lymphoid was lower in the EPTB group. The clinical data showed that the cell count of monocytes were much higher, while the cell counts of lymphocytes were lower in the osteoarticular TB group. AUCs of the nomogram was 0.798 for the dataset GSE83456, and 0.737 for the clinical data. We identified the ML ratio, BMI, and ESR as the independent predictive factors for osteoarticular TB diagnosis and constructed a nomogram for the clinical diagnosis of osteoarticular TB. AUCs of this nomogram was 0.843.\nCONCLUSIONS: We demonstrated a significant change between the ML ratio of the EPTB and non-TB patients. Moreover, we constructed a nomogram for the clinical diagnosis of the osteoarticular TB diagnosis, which works satisfactorily.", "claims": [{"id": "claim-method", "statement": "To identify the independent risk factors in the dataset GSE83456 and the clinical data, we performed the univariate and multivariate logistics regression analyses using the R package ‘rms’ (https://cran.rstudio.com/bin/windows/contrib/4.0/rms_6.1-0.zip).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34129634", "locator": "sections[5] \"Statistical analysis\" paragraph 2", "quote": "To identify the independent risk factors in the dataset GSE83456 and the clinical data, we performed the univariate and multivariate logistics regression analyses using the R package ‘rms’ (https://cran.rstudio.com/bin/windows/contrib/4.0/rms_6.1-0.zip)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The GEO database dataset (GEO ID: GSE83456) generated and/or analyzed during the current study are available in the GEO database (www.ncbi.nlm.nih.gov/gds).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34129634", "locator": "sections[17] \"Data Availability\" paragraph 2", "quote": "The GEO database dataset (GEO ID: GSE83456) generated and/or analyzed during the current study are available in the GEO database (www.ncbi.nlm.nih.gov/gds)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": null, "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:34129634"], "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:GSE83456"], "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:GSE83456"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:34129634", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1371/journal.pone.0252875", "sha256": 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"quote": "To identify the independent risk factors in the dataset GSE83456 and the clinical data, we performed the univariate and multivariate logistics regression analyses using the R package ‘rms’ (https://cran.rstudio.com/bin/windows/contrib/4.0/rms_6.1-0.zip)."}}], "targets": [], "gaps": []}, "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": true, "required_operations": ["glm_logit_fit"], 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