Placenta inflammation is closely associated with gestational diabetes mellitus.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:34149999", "kind": "science-record", "revision": 4, "content_hash": "sha256:6f2b4bf4e137c2100b4e02008310fb50309fc137cf10ed47bb50714afd510fa4", "created_at": "2026-11-01T17:35:29Z", "updated_at": "2026-11-01T22:04:00Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "partial", "title": "Placenta inflammation is closely associated with gestational diabetes mellitus.", "doi": null, "pmid": "34149999", "authors": ["Xue Pan", "Xin Jin", "Jun Wang", "Qing Hu", "Bing Dai"], "journal": "American journal of translational research", "year": 2021, "abstract": "OBJECTIVE: To investigate the potential role of placenta inflammation in gestational diabetes mellitus (GDM) and construct a model for the diagnosis of GDM.\nMETHODS: In this study, transcriptome-wide profiling datasets, GSE70493 and GSE128381 were downloaded from Gene Expression Omnibus (GEO) database. Significant immune-related genes were identified separately to be the biomarkers for the diagnosis of GDM by using random forest model (RF), support vector machine model (SVM), and generalized linear model (GLM).\nRESULTS: RF was the best model and was used to select the four key immune-related genes (FABP4, DKK1, CXCL10, and IL1RL1) to diagnose GDM. A nomogram model was constructed to predict GDM based on the four key immune-related genes by using \"rms\" package. The relative proportion of 22 immune cell types were calculated by using CIBERSORT algorithm. Higher M1 macrophage ratio and lower M2 macrophage ratio in GDM placenta compared to normal patients were observed.\nCONCLUSIONS: This study provides clues that inflammation was correlated with GDM and suggests inflammation may be the cause and also the potential targets of GDM.", "claims": [{"id": "claim-method", "statement": "Significant immune-related genes were identified separately to be the biomarkers for the diagnosis of GDM by using random forest model (RF), support vector machine model (SVM), and generalized linear model (GLM).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34149999", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "Significant immune-related genes were identified separately to be the biomarkers for the diagnosis of GDM by using random forest model (RF), support vector machine model (SVM), and generalized linear model (GLM)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "In this study, transcriptome-wide profiling datasets, GSE70493 and GSE128381 were downloaded from Gene Expression Omnibus (GEO) database.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34149999", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "In this study, transcriptome-wide profiling datasets, GSE70493 and GSE128381 were downloaded from Gene Expression Omnibus (GEO) database."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The random forest model was the best model and was used to select four key immune-related genes to diagnose GDM.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:34149999", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "RF was the best model and was used to select the four key immune-related genes (FABP4, DKK1, CXCL10, and IL1RL1) to diagnose GDM."}], "state": "complete"}], "gaps": ["doi"]}, "experiment": {"state": "partial", "objective": "The random forest model was the best model and was used to select four key immune-related genes to diagnose GDM.", "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:34149999"], "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:GSE128381"], "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:GSE128381"], "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:34149999", "role": "manuscript", "format": "xml", "media_type": "application/xml", 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