{"data":{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38217541", "kind": "science-record", "revision": 4, "content_hash": "sha256:913a8cd9de380412e099eba4c5601359a93b26638b62dda81847fbbd19b069bf", "created_at": "2026-11-01T19:20:08Z", "updated_at": "2026-11-01T22:53:27Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Mechanism of lysine oxidase-like 1 promoting synovial inflammation mediating rheumatoid arthritis development.", "doi": "10.18632/aging.205429", "pmid": "38217541", "authors": ["Jiawei Hu", "Xuqiang Liu", "Qiang Xu", "Meisong Zhu", "Song Wang", "Kun Quan", "Min Dai", "Fengbo Mo", "Haibo Zhan"], "journal": "Aging", "year": 2024, "abstract": "Rheumatoid arthritis (RA) is a chronic inflammatory joint disease that causes great distress to patients and society. Early diagnosis is the key to the successful treatment of RA. The basement membrane, one of the oldest tissue structures, is localized under the epithelium. Its complex composition and rich biological functions have made it a focus of research in recent years, while basement membrane-associated genetic variants are involved in most human disease processes. The aim of this study is to find new diagnostic biomarkers for RA and explore their role and possible mechanism in rheumatoid arthritis. The GSE12021, GSE55235 and GSE55457 datasets were downloaded from the GEO database. Their fraction associated with basement membrane genes was analyzed and differentially expressed genes between the disease and normal groups were explored. We identified two basement membrane-associated genes, lysine oxidase-like 1 (LOXL1) and discoid peptide receptor 2 (DDR2). Focusing on the more interesting LOXL1, we found that LOXL1 expression was significantly elevated in the synovium of patients with rheumatoid arthritis, and LOXL1 mRNA and protein levels were elevated in tumor necrosis factor α-stimulated human synovial sarcoma cells (SW982). And LOXL1 knockdown inhibited tumor necrosis factor α-induced inhibition in SW982 cells expression of inducible nitric oxide synthase (INOS), cyclooxygenase-2 (COX2), and interleukin-6 (IL-6). Interestingly, knockdown of LOXL1 inhibited the phosphorylation of PI3K and AKT. In summary, LOXL1 may become a novel diagnostic gene for RA, and knockdown of LoxL1 may inhibit synovial inflammation by affecting PI3K/AKT pathway.", "claims": [{"id": "claim-method", "statement": "Basement membrane-associated differential genes between rheumatoid arthritis and healthy samples were identified using the Limma package in R. LASSO is a regression analysis algorithm that uses regularization to improve prediction accuracy by identifying genes with significant differences between basement membrane-associated RA and normal subjects through the “glmnet” package in R. Support Vector Machines (SVMs) are widely used for classification and regression analysis. We used recursive feature elimination (RFE) to circumvent overfitting. To obtain the most significant gene sets, SVM Recursive Feature Elimination (SVM-RFE) was used to screen appropriate features.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38217541", "locator": "sections[11] \"Analysis of basement membrane-associated differential genes and integrated microarray datasets\" paragraph 2", "quote": "Basement membrane-associated differential genes between rheumatoid arthritis and healthy samples were identified using the Limma package in R."}, {"asset": "source:pmid:38217541", "locator": "sections[13] \"Diagnostic gene selection\" paragraph 2", "quote": "LASSO is a regression analysis algorithm that uses regularization to improve prediction accuracy by identifying genes with significant differences between basement membrane-associated RA and normal subjects through the “glmnet” package in R."}, {"asset": "source:pmid:38217541", "locator": "sections[13] \"Diagnostic gene selection\" paragraph 2", "quote": "Support Vector Machines (SVMs) are widely used for classification and regression analysis."}, {"asset": "source:pmid:38217541", "locator": "sections[13] \"Diagnostic gene selection\" paragraph 2", "quote": "We used recursive feature elimination (RFE) to circumvent overfitting."}, {"asset": "source:pmid:38217541", "locator": "sections[13] \"Diagnostic gene selection\" paragraph 2", "quote": "To obtain the most significant gene sets, SVM Recursive Feature Elimination (SVM-RFE) was used to screen appropriate features."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38217541", "locator": "sections[16] \"Data availability statement\" paragraph 2", "quote": "The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The AUC value for DDR2 was 0.885, and the ROC value for LOXL1 was 0.836.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38217541", "locator": "sections[20] \"Identification of diagnostic markers and analysis of their diagnostic value\" paragraph 2", "quote": "The AUC value for DDR2 was 0.885, and the ROC value for LOXL1 was 0.836."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The AUC value for DDR2 was 0.885, and the ROC value for LOXL1 was 0.836.", "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:38217541"], "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:GSE12021"], "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:GSE12021"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:38217541", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.18632/aging.205429", "sha256": 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URL", "access_statement_verbatim": "The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM1332201", "GSM1332202", "GSM1332203", "GSM1332204", "GSM1332205", "GSM1332206", "GSM1332207", "GSM1332208", "GSM1332209", "GSM1332210", "GSM1332211", "GSM1332212", "GSM1332213", "GSM1332214", "GSM1332215", "GSM1332216", "GSM1332217", "GSM1332218", "GSM1332219", "GSM1332220", "GSM1332221", "GSM1332222", "GSM1332223", "GSM1332224", "GSM1332225", "GSM1332226", "GSM1332227", "GSM1332228", "GSM1332229", "GSM1332230"], "sample_count": 30, "group_sizes": null, "group_sizes_basis": null, "platform": "96", "taxon": "Homo sapiens", "title": "Identification of rheumatoid arthritis and osteoarthritis patients by transcriptome-based rule set generation", "resolution_checked_at": "2026-11-01T15:39:54Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}, {"id": "data:geo:GSE55457", "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=GSE55457", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE55457", "version": null, "note": "The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM1337304", "GSM1337305", "GSM1337306", "GSM1337307", "GSM1337308", "GSM1337309", "GSM1337310", 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LASSO is a regression analysis algorithm that uses regularization to improve prediction accuracy by identifying genes with significant differences between basement membrane-associated RA and normal subjects through the “glmnet” package in R. Support Vector Machines (SVMs) are widely used for classification and regression analysis. We used recursive feature elimination (RFE) to circumvent overfitting. To obtain the most significant gene sets, SVM Recursive Feature Elimination (SVM-RFE) was used to screen appropriate features."}}, {"id": "method-2", "family": "machine_learning", "specific": "support_vector_machine", "label": "Machine learning", "dsl_operations": ["train_classifier", "cross_validate", "roc_auc"], "missing_operations": [], "expressible": true, "parameters": {"feature_count": "ure 3A, 3B) A subset of 9 features in differentially expre"}, "data_shapes": ["labelled_feature_matrix"], "evidence": {"score": 0.8, "band": "high", "quote": "SVM"}}, {"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 analysis"}}], "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": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: limma_voom."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R1", "reachable": true, "accessions": ["GSE12021", "GSE55235", "GSE55457"], "resolved_accessions": ["GSE12021", "GSE55235", "GSE55457"], "statements": ["The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation."], "basis": "The paper names GSE12021, GSE55235, GSE55457 in GEO and the accession resolves in the repository. No dataset was downloaded and no licence was read."}}, "can_run": {"dsl_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "assets": ["source:pmid:38217541", "data:geo:GSE12021"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE12021"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T19:05:13Z", "independence": "single_reader"}, "evidence_check": {"method": "reordered", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 14148, "completion_tokens": 1646, "cached_tokens": 256}}, "human_critique": null, "stages": [{"id": "extraction", "state": "complete", "reader": "MiniMax-M3.1-Flash", "provider": "minimax"}], "gaps": ["review.independent_reader", "review.machine_critique", "review.human_critique"]}, "execution": {"state": "absent", "status": "not_run", "mode": null, "runs": [], "gaps": ["execution.not_run"]}, "provenance": {"entities": [{"id": "pmid:38217541", "type": "paper", "label": "pmid:38217541"}, {"id": "source:pmid:38217541", "type": "manuscript", "label": "PMID 38217541 full text"}, {"id": "data:geo:GSE12021", "type": "dataset", "label": "GEO GSE12021"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:38217541", "type": "paper", "label": "pmid:38217541"}, {"id": "source:pmid:38217541", "type": "manuscript", "label": "PMID 38217541 full text"}, {"id": "data:geo:GSE12021", "type": "dataset", "label": "GEO GSE12021"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-38217541", "type": "extraction", "used": ["source:pmid:38217541"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE12021", "type": "accession_resolution", "used": ["data:geo:GSE12021"], "agent": "agent:corpus-assay-engine"}], "agents": [{"id": "agent:corpus-assay-engine", "type": "software", "label": "Corpus assay engine"}, {"id": "agent:minimax-reader", "type": "software", "label": "MiniMax literature reader"}], "source_run_id": "reading-sci-2026-11-01", "dsl_catalogue_fingerprint": "343fb30756ba6ec3", "capability_table_fingerprint": "2cb3e42f047d2ecf", "snapshot_id": "ba34b49490d9bfaf"}, "lineage": {"parent_id": "pmid:38217541", "parent_hash": "sha256:d1914748f5ac41b897a511e884ea7f9d683155e12ad811404d1b2577049cb0fb", "fork_of": null, "generation": 3}, "gaps": ["asset:data:geo:GSE12021 licence not read", "asset:data:geo:GSE12021 not downloaded", "asset:data:geo:GSE55235 licence not read", "asset:data:geo:GSE55235 not downloaded", "asset:data:geo:GSE55457 licence not read", "asset:data:geo:GSE55457 not downloaded", "execution.not_run", "review.human_critique", "review.independent_reader", "review.machine_critique"], "assays": [{"role": "data_generation", "definition_id": "transfection", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["an synovial sarcoma cells (SW982). And LOXL1 knockdown inhibited tumor necrosis factor α-induced in", "2), and interleukin-6 (IL-6). Interestingly, knockdown of LOXL1 inhibited the phosphorylation of PI", "y become a novel diagnostic gene for RA, and knockdown of LoxL1 may inhibit synovial inflammation b"], "order_basis": "paper_text_order:1330", "id": "assay:1"}, {"role": "data_generation", "definition_id": "cell_culture", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["from Pricella (Wuhan, China). The cells were cultured in DMEM containing 10% extra fetal bovine serum", "nged once every 2–3 days, and the cells were passaged when the cell fusion reached 90% or more. Tu"], "order_basis": "paper_text_order:2665", "id": "assay:2"}, {"role": "data_generation", "definition_id": "microarray", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.93, "evidence": ["associated differential genes and integrated microarray datasets Three data sets were merged into on"], "order_basis": "paper_text_order:3231", "id": "assay:3"}, {"role": "analysis", "definition_id": "differential_expression", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["Basement membrane-associated differential genes between rheumatoid arthritis and healthy samples were identified using the Limma package in R. LASSO is a regression analysis algorithm that uses regularization to improve prediction accuracy by identifying genes with significant differences between basement membrane-associated RA and normal subjects through the “glmnet” package in R. Support Vector Machines (SVMs) are widely used for classification and regression analysis. We used recursive feature elimination (RFE) to circumvent overfitting. To obtain the most significant gene sets, SVM Recursive Feature Elimination (SVM-RFE) was used to screen appropriate features."], "order_basis": "consumes_earlier_output", "id": "assay:4"}]},"meta":{"api_version":"v1","automation_ladder_snapshot":"ba34b49490d9bfaf","automation_ladder_caveats":["Method extraction is rule-based and has no hand-judged accuracy sample. Evidence bands order textual support; method agreement intervals are null (n=0), never probabilities. Work-type validation does not validate methods.","Operation coverage describes only explicitly named methods. 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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}}}