{"data":{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:41216601", "kind": "science-record", "revision": 4, "content_hash": "sha256:0403598f28f3e5fe918053af0e99b29bfe37011789b52cfc4282353c07b043dc", "created_at": "2026-11-01T20:31:15Z", "updated_at": "2026-11-01T22:57:26Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Shared Biomarkers and Potential Mechanisms Between Hashimoto's Thyroiditis and Recurrent Miscarriage Revealed by Transcriptomics Analysis.", "doi": "10.2147/IJGM.S552065", "pmid": "41216601", "authors": ["Zengmei Cheng", "Shuyun Zhao", "Lu Yang", "Yaqiong Xu", "Peiyu Zhang", "Sha Chen", "Hua Zhou"], "journal": "International journal of general medicine", "year": 2025, "abstract": "Background: More and more Research has shown that Hashimoto's thyroiditis (HT) is significantly associated with recurrent miscarriage (RM), but the specific mechanism is not yet clear. This study aimed to identify the key shared biomarkers between HT and RM using bioinformatics methods, reveal the potential molecular mechanisms they were involved in and the characteristics of the immune microenvironment, and provide new theoretical basis and potential diagnostic and therapeutic targets for the association between these two diseases.\nMethods: This study adopted an integrated transcriptomic analysis strategy. First, the HT thyroid tissue dataset (GSE138198) and RM endometrial tissue datasets (GSE165004 and GSE26787) were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were screened, and the intersection was taken to identify key genes. Further verification was conducted through the protein-protein interaction (PPI) network to screen candidate biomarkers. Subsequently, the final biomarkers were determined through consistency verification of expression levels. Gene Set Enrichment Analysis (GSEA) was used to reveal biomarker-related pathways, and the ssGSEA algorithm was applied to quantify immune cell infiltration for analyzing its association with the immune microenvironment. Finally, targeted drugs were predicted via molecular docking, and experimental verification was performed using an HT animal model.\nResults: CFL1 and TRAPPC1 were identified as biomarkers, and their expression levels were up-regulated in disease groups. A nomogram with superior diagnostic performance was constructed to predict the occurrence of RM. In the GSE138198 dataset, biomarkers CFL1 and TRAPPC1 were found to be enriched in multiple pathways, like \"graft-versus-host disease\", \"autoimmune thyroid disease\", and \"antigen processing and presentation\". In the GSE165004 dataset, biomarkers were enriched in multiple pathways, like \"ribosome\", \"Huntington's disease\", and \"cell adhesion molecules (CAMs)\". Additionally, the abundance of infiltration of monocytes and eosinophils infiltration showed significant differences between HT and RM patients (p < 0.05). Biomarkers showed significant positive correlations with monocytes and eosinophils in HT and RM, respectively. Moreover, artenimol and S-palmitoyl-L-cysteine might be potential therapeutic drugs for HT and RM.\nConclusion: CFL1 and TRAPPC1 were found to be common biomarkers for HT and RM in this study. These genes were thoroughly investigated and analyzed, yielding novel insights for both fundamental experimental research and early clinical diagnosis and treatment of disease.", "claims": [{"id": "claim-method", "statement": "In the GSE138198 dataset, to identify differentially expressed genes in HT (HT-DEGs), the limma package (v 3.54.0)21 was used to perform differential expression analysis between HT and control groups (|log2Fold Change (FC)| > 0.5, adjusted p-value (adj.p) < 0.05).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41216601", "locator": "sections[4] \"Differential Expression Analysis\" paragraph 2", "quote": "In the GSE138198 dataset, to identify differentially expressed genes in HT (HT-DEGs), the limma package (v 3.54.0)21 was used to perform differential expression analysis between HT and control groups (|log2Fold Change (FC)| > 0.5, adjusted p-value (adj.p) < 0.05)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets analyzed in this study (GSE138198, GSE165004, GSE26787) were publicly available in the Gene Expression Omnibus (GEO) repository (https://www.ncbi.nlm.nih.gov/gds).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41216601", "locator": "sections[39] \"Data Sharing Statement\" paragraph 2", "quote": "The datasets analyzed in this study (GSE138198, GSE165004, GSE26787) were publicly available in the Gene Expression Omnibus (GEO) repository (https://www.ncbi.nlm.nih.gov/gds)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "CFL1 and TRAPPC1 were identified as shared biomarkers for HT and RM, and a nomogram based on them achieved an AUC of 0.927 for RM diagnosis.", "kind": "quantitative", "target": {"value": 0.927, "unit": "auc", "tolerance": null, "source": "The ROC curve emphasized the excellent diagnostic value of the nomogram, with an AUC of 0.927 (Figure 3c)."}, "evidence": [{"asset": "source:pmid:41216601", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "CFL1 and TRAPPC1 were identified as biomarkers, and their expression levels were up-regulated in disease groups."}, {"asset": "source:pmid:41216601", "locator": "sections[27] \"Nomogram with Excellent Performance for Diagnosis of HT\" paragraph 2", "quote": "The ROC curve emphasized the excellent diagnostic value of the nomogram, with an AUC of 0.927 (Figure 3c)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "CFL1 and TRAPPC1 were identified as shared biomarkers for HT and RM, and a nomogram based on them achieved an AUC of 0.927 for RM diagnosis.", "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:41216601"], "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:GSE138198"], "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:GSE138198"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:41216601", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.2147/IJGM.S552065", "sha256": 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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":"2026-11-04T03:39:59+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}}}