{"data":{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39188714", "kind": "science-record", "revision": 4, "content_hash": "sha256:69a89bf041261a6d4f975d85ad3b99476832037318da30f214b212688eaeb3f5", "created_at": "2026-11-01T20:29:19Z", "updated_at": "2026-11-01T22:55:02Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of crosstalk genes and immune characteristics between Alzheimer's disease and atherosclerosis.", "doi": "10.3389/fimmu.2024.1443464", "pmid": "39188714", "authors": ["Wenhao An", "Jiajun Zhou", "Zhiqiang Qiu", "Peishen Wang", "Xinye Han", "Yanwen Cheng", "Zi He", "Yihua An", "Shouwei Li"], "journal": "Frontiers in immunology", "year": 2024, "abstract": "Background: Advancements in modern medicine have extended human lifespan, but they have also led to an increase in age-related diseases such as Alzheimer's disease (AD) and atherosclerosis (AS). Growing research evidence indicates a close connection between these two conditions.\nMethods: We downloaded four gene expression datasets related to AD and AS from the Gene Expression Omnibus (GEO) database (GSE33000, GSE100927, GSE44770, and GSE43292) and performed differential gene expression (DEGs) analysis using the R package \"limma\". Through Weighted gene correlation network analysis (WGCNA), we selected the gene modules most relevant to the diseases and intersected them with the DEGs to identify crosstalk genes (CGs) between AD and AS. Subsequently, we conducted functional enrichment analysis of the CGs using DAVID. To screen for potential diagnostic genes, we applied the least absolute shrinkage and selection operator (LASSO) regression and constructed a logistic regression model for disease prediction. We established a protein-protein interaction (PPI) network using STRING (https://cn.string-db.org/) and Cytoscape and analyzed immune cell infiltration using the CIBERSORT algorithm. Additionally, NetworkAnalyst (http://www.networkanalyst.ca) was utilized for gene regulation and interaction analysis, and consensus clustering was employed to determine disease subtypes. All statistical analyses and visualizations were performed using various R packages, with a significance level set at p<0.05.\nResults: Through intersection analysis of disease-associated gene modules identified by DEGs and WGCNA, we identified a total of 31 CGs co-existing between AD and AS, with their biological functions primarily associated with immune pathways. LASSO analysis helped us identify three genes (C1QA, MT1M, and RAMP1) as optimal diagnostic CGs for AD and AS. Based on this, we constructed predictive models for both diseases, whose accuracy was validated by external databases. By establishing a PPI network and employing four topological algorithms, we identified four hub genes (C1QB, CSF1R, TYROBP, and FCER1G) within the CGs, closely related to immune cell infiltration. NetworkAnalyst further revealed the regulatory networks of these hub genes. Finally, defining C1 and C2 subtypes for AD and AS respectively based on the expression profiles of CGs, we found the C2 subtype exhibited immune overactivation.\nConclusion: This study utilized gene expression matrices and various algorithms to explore the potential links between AD and AS. The identification of CGs revealed interactions between these two diseases, with immune and inflammatory imbalances playing crucial roles in their onset and progression. We hope these findings will provide valuable insights for future research on AD and AS.", "claims": [{"id": "claim-method", "statement": "We utilized the “limma” package in R software to perform differential gene expression analysis on the standardized GSE33000 and GSE100927 datasets.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39188714", "locator": "sections[4] \"Differential gene expression analysis\" paragraph 2", "quote": "We utilized the “limma” package in R software to perform differential gene expression analysis on the standardized GSE33000 and GSE100927 datasets."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets analyzed for this study can be found in the GEO database; GSE33000, GSE100927, GSE44770, and GSE43292.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39188714", "locator": "sections[27] \"Data availability statement\" paragraph 2", "quote": "The datasets analyzed for this study can be found in the GEO database; GSE33000, GSE100927, GSE44770, and GSE43292."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Through intersection analysis of disease-associated gene modules identified by DEGs and WGCNA, we identified a total of 31 CGs co-existing between AD and AS, with their biological functions primarily associated with immune pathways.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39188714", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Through intersection analysis of disease-associated gene modules identified by DEGs and WGCNA, we identified a total of 31 CGs co-existing between AD and AS, with their biological functions primarily associated with immune pathways."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Through intersection analysis of disease-associated gene modules identified by DEGs and WGCNA, we identified a total of 31 CGs co-existing between AD and AS, with their biological functions primarily associated with immune pathways.", "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:39188714"], "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:GSE100927"], "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:GSE100927"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:39188714", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fimmu.2024.1443464", "sha256": "sha256:f0cd67062296a1b650f68e6d84f575fe856bede3037cec792649634ca222ec0e", "bytes": 46416, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fimmu.2024.1443464", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC11345154", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE100927", "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=GSE100927", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE100927", "version": null, "note": "The datasets analyzed for this study can be found in the GEO database; GSE33000, GSE100927, GSE44770, and GSE43292.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The datasets analyzed for this study can be found in the GEO database; GSE33000, GSE100927, GSE44770, and GSE43292.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM2696609", "GSM2696610", "GSM2696611", "GSM2696612", "GSM2696613", "GSM2696614", "GSM2696615", "GSM2696616", "GSM2696617", "GSM2696618", "GSM2696619", "GSM2696620", "GSM2696621", "GSM2696622", "GSM2696623", "GSM2696624", "GSM2696625", "GSM2696626", "GSM2696627", "GSM2696628", "GSM2696629", "GSM2696630", "GSM2696631", "GSM2696632", "GSM2696633", "GSM2696634", "GSM2696635", "GSM2696636", "GSM2696637", "GSM2696638", "GSM2696639", "GSM2696640", "GSM2696641", "GSM2696642", "GSM2696643", "GSM2696644", "GSM2696645", "GSM2696646", "GSM2696647", "GSM2696648", "GSM2696649", "GSM2696650", "GSM2696651", "GSM2696652", "GSM2696653", "GSM2696654", "GSM2696655", "GSM2696656", "GSM2696657", "GSM2696658", "GSM2696659", "GSM2696660", "GSM2696661", "GSM2696662", "GSM2696663", "GSM2696664", "GSM2696665", "GSM2696666", "GSM2696667", "GSM2696668", "GSM2696669", "GSM2696670", "GSM2696671", "GSM2696672", "GSM2696673", "GSM2696674", "GSM2696675", "GSM2696676", "GSM2696677", "GSM2696678", "GSM2696679", "GSM2696680", "GSM2696681", "GSM2696682", "GSM2696683", "GSM2696684", "GSM2696685", "GSM2696686", "GSM2696687", "GSM2696688", "GSM2696689", "GSM2696690", "GSM2696691", "GSM2696692", "GSM2696693", "GSM2696694", "GSM2696695", "GSM2696696", "GSM2696697", "GSM2696698", "GSM2696699", "GSM2696700", "GSM2696701", "GSM2696702", "GSM2696703", "GSM2696704", "GSM2696705", "GSM2696706", "GSM2696707", "GSM2696708", "GSM2696709", "GSM2696710", "GSM2696711", "GSM2696712"], "sample_count": 104, "group_sizes": null, "group_sizes_basis": null, "platform": "17077", "taxon": "Homo sapiens", "title": "Atherosclerotic and control peripheral artery gene expression", "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:GSE33000", "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=GSE33000", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE33000", "version": null, "note": "The datasets analyzed for this study can be found in the GEO database; GSE33000, GSE100927, GSE44770, and GSE43292.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The datasets analyzed for this study can be found in the GEO database; GSE33000, GSE100927, GSE44770, and GSE43292.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM1423780", "GSM1423781", "GSM1423782", "GSM1423783", "GSM1423784", "GSM1423785", "GSM1423786", "GSM1423787", "GSM1423788", "GSM1423789", "GSM1423790", "GSM1423791", "GSM1423792", "GSM1423793", "GSM1423794", "GSM1423795", "GSM1423796", "GSM1423797", "GSM1423798", "GSM1423799", "GSM1423800", "GSM1423801", "GSM1423802", "GSM1423803", "GSM1423804", "GSM1423805", "GSM1423806", "GSM1423807", "GSM1423808", "GSM1423809", "GSM1423810", "GSM1423811", "GSM1423812", "GSM1423813", "GSM1423814", "GSM1423815", "GSM1423816", "GSM1423817", "GSM1423818", "GSM1423819", "GSM1423820", "GSM1423821", "GSM1423822", "GSM1423823", "GSM1423824", "GSM1423825", "GSM1423826", "GSM1423827", "GSM1423828", "GSM1423829", "GSM1423830", "GSM1423831", "GSM1423832", "GSM1423833", "GSM1423834", "GSM1423835", "GSM1423836", "GSM1423837", "GSM1423838", "GSM1423839", "GSM1423840", "GSM1423841", "GSM1423842", "GSM1423843", "GSM1423844", "GSM1423845", "GSM1423846", "GSM1423847", "GSM1423848", "GSM1423849", "GSM1423850", "GSM1423851", "GSM1423852", "GSM1423853", "GSM1423854", "GSM1423855", "GSM1423856", "GSM1423857", "GSM1423858", "GSM1423859", "GSM1423860", "GSM1423861", "GSM1423862", "GSM1423863", "GSM1423864", "GSM1423865", "GSM1423866", "GSM1423867", "GSM1423868", "GSM1423869", "GSM1423870", "GSM1423871", "GSM1423872", "GSM1423873", "GSM1423874", "GSM1423875", "GSM1423876", "GSM1423877", "GSM1423878", "GSM1423879", "GSM1423880", "GSM1423881", "GSM1423882", "GSM1423883", "GSM1423884", "GSM1423885", "GSM1423886", "GSM1423887", "GSM1423888", "GSM1423889", "GSM1423890", "GSM1423891", "GSM1423892", "GSM1423893", "GSM1423894", "GSM1423895", "GSM1423896", "GSM1423897", "GSM1423898", "GSM1423899", "GSM1423900", "GSM1423901", "GSM1423902", "GSM1423903", "GSM1423904", "GSM1423905", "GSM1423906", "GSM1423907", "GSM1423908", "GSM1423909", "GSM1423910", "GSM1423911", "GSM1423912", "GSM1423913", "GSM1423914", "GSM1423915", "GSM1423916", "GSM1423917", "GSM1423918", "GSM1423919", "GSM1423920", "GSM1423921", "GSM1423922", "GSM1423923", "GSM1423924", "GSM1423925", "GSM1423926", "GSM1423927", "GSM1423928", "GSM1423929", "GSM1423930", "GSM1423931", "GSM1423932", "GSM1423933", "GSM1423934", "GSM1423935", "GSM1423936", "GSM1423937", "GSM1423938", "GSM1423939", "GSM1423940", "GSM1423941", "GSM1423942", "GSM1423943", "GSM1423944", "GSM1423945", "GSM1423946", "GSM1423947", "GSM1423948", "GSM1423949", "GSM1423950", "GSM1423951", "GSM1423952", "GSM1423953", "GSM1423954", "GSM1423955", "GSM1423956", "GSM1423957", "GSM1423958", "GSM1423959", "GSM1423960", "GSM1423961", "GSM1423962", "GSM1423963", "GSM1423964", "GSM1423965", "GSM1423966", "GSM1423967", "GSM1423968", "GSM1423969", "GSM1423970", "GSM1423971", "GSM1423972", "GSM1423973", "GSM1423974", "GSM1423975", "GSM1423976", "GSM1423977", "GSM1423978", "GSM1423979", "GSM1423980", "GSM1423981", "GSM1423982", "GSM1423983", "GSM1423984", "GSM1423985", "GSM1423986", "GSM1423987", "GSM1423988", "GSM1423989", "GSM1423990", "GSM1423991", "GSM1423992", "GSM1423993", "GSM1423994", "GSM1423995", "GSM1423996", "GSM1423997", "GSM1423998", "GSM1423999", "GSM1424000", "GSM1424001", "GSM1424002", "GSM1424003", "GSM1424004", "GSM1424005", "GSM1424006", "GSM1424007", "GSM1424008", "GSM1424009", "GSM1424010", "GSM1424011", "GSM1424012", "GSM1424013", "GSM1424014", "GSM1424015", "GSM1424016", "GSM1424017", "GSM1424018", "GSM1424019", "GSM1424020", "GSM1424021", "GSM1424022", "GSM1424023", "GSM1424024", "GSM1424025", "GSM1424026", "GSM1424027", "GSM1424028", "GSM1424029", "GSM1424030", "GSM1424031", "GSM1424032", "GSM1424033", "GSM1424034", "GSM1424035", "GSM1424036", "GSM1424037", "GSM1424038", "GSM1424039", "GSM1424040", "GSM1424041", "GSM1424042", "GSM1424043", "GSM1424044", "GSM1424045", "GSM1424046", "GSM1424047", "GSM1424048", "GSM1424049", "GSM1424050", "GSM1424051", "GSM1424052", "GSM1424053", "GSM1424054", "GSM1424055", "GSM1424056", "GSM1424057", "GSM1424058", "GSM1424059", "GSM1424060", "GSM1424061", "GSM1424062", "GSM1424063", "GSM1424064", "GSM1424065", "GSM1424066", "GSM1424067", "GSM1424068", "GSM1424069", "GSM1424070", "GSM1424071", "GSM1424072", "GSM1424073", "GSM1424074", "GSM1424075", "GSM1424076", "GSM1424077", "GSM1424078", "GSM1424079", "GSM1424080", "GSM1424081", "GSM1424082", "GSM1424083", "GSM1424084", "GSM1424085", "GSM1424086", "GSM1424087", "GSM1424088", "GSM1424089", "GSM1424090", "GSM1424091", "GSM1424092", "GSM1424093", "GSM1424094", "GSM1424095", "GSM1424096", "GSM1424097", "GSM1424098", "GSM1424099", "GSM1424100", "GSM1424101", "GSM1424102", "GSM1424103", "GSM1424104", "GSM1424105", "GSM1424106", "GSM1424107", "GSM1424108", "GSM1424109", "GSM1424110", "GSM1424111", "GSM1424112", "GSM1424113", "GSM1424114", "GSM1424115", "GSM1424116", "GSM1424117", "GSM1424118", "GSM1424119", "GSM1424120", "GSM1424121", "GSM1424122", "GSM1424123", "GSM1424124", "GSM1424125", "GSM1424126", "GSM1424127", "GSM1424128", "GSM1424129", "GSM1424130", "GSM1424131", "GSM1424132", "GSM1424133", "GSM1424134", "GSM1424135", "GSM1424136", "GSM1424137", "GSM1424138", "GSM1424139", "GSM1424140", "GSM1424141", "GSM1424142", "GSM1424143", "GSM1424144", "GSM1424145", "GSM1424146", "GSM1424147", "GSM1424148", "GSM1424149", "GSM1424150", "GSM1424151", "GSM1424152", "GSM1424153", "GSM1424154", "GSM1424155", "GSM1424156", "GSM1424157", "GSM1424158", "GSM1424159", "GSM1424160", "GSM1424161", "GSM1424162", "GSM1424163", "GSM1424164", "GSM1424165", "GSM1424166", "GSM1424167", "GSM1424168", "GSM1424169", "GSM1424170", "GSM1424171", "GSM1424172", "GSM1424173", "GSM1424174", "GSM1424175", "GSM1424176", "GSM1424177", "GSM1424178", "GSM1424179", "GSM1424180", "GSM1424181", "GSM1424182", "GSM1424183", "GSM1424184", "GSM1424185", "GSM1424186", "GSM1424187", "GSM1424188", "GSM1424189", "GSM1424190", "GSM1424191", "GSM1424192", "GSM1424193", "GSM1424194", "GSM1424195", "GSM1424196", "GSM1424197", "GSM1424198", "GSM1424199", "GSM1424200", "GSM1424201", "GSM1424202", "GSM1424203", "GSM1424204", "GSM1424205", "GSM1424206", "GSM1424207", "GSM1424208", "GSM1424209", "GSM1424210", "GSM1424211", "GSM1424212", "GSM1424213", "GSM1424214", "GSM1424215", "GSM1424216", "GSM1424217", "GSM1424218", "GSM1424219", "GSM1424220", "GSM1424221", "GSM1424222", "GSM1424223", "GSM1424224", "GSM1424225", "GSM1424226", "GSM1424227", "GSM1424228", "GSM1424229", "GSM1424230", "GSM1424231", "GSM1424232", "GSM1424233", "GSM1424234", "GSM1424235", "GSM1424236", "GSM1424237", "GSM1424238", "GSM1424239", "GSM1424240", "GSM1424241", "GSM1424242", "GSM1424243", "GSM1424244", "GSM1424245", "GSM1424246", "GSM1424247", "GSM1424248", "GSM1424249", "GSM1424250", "GSM1424251", "GSM1424252", "GSM1424253", "GSM1424254", "GSM1424255", "GSM1424256", "GSM1424257", "GSM1424258", "GSM1424259", "GSM1424260", "GSM1424261", "GSM1424262", "GSM1424263", "GSM1424264", "GSM1424265", "GSM1424266", "GSM1424267", "GSM1424268", "GSM1424269", "GSM1424270", "GSM1424271", "GSM1424272", "GSM1424273", "GSM1424274", "GSM1424275", "GSM1424276", "GSM1424277", "GSM1424278", "GSM1424279", "GSM1424280", "GSM1424281", "GSM1424282", "GSM1424283", "GSM1424284", "GSM1424285", "GSM1424286", "GSM1424287", "GSM1424288", "GSM1424289", "GSM1424290", "GSM1424291", "GSM1424292", "GSM1424293", "GSM1424294", "GSM1424295", "GSM1424296", "GSM1424297", "GSM1424298", "GSM1424299", "GSM1424300", "GSM1424301", "GSM1424302", "GSM1424303", "GSM1424304", "GSM1424305", "GSM1424306", "GSM1424307", "GSM1424308", "GSM1424309", "GSM1424310", "GSM1424311", "GSM1424312", "GSM1424313", "GSM1424314", "GSM1424315", "GSM1424316", "GSM1424317", "GSM1424318", "GSM1424319", "GSM1424320", "GSM1424321", "GSM1424322", "GSM1424323", "GSM1424324", "GSM1424325", "GSM1424326", "GSM1424327", "GSM1424328", "GSM1424329", "GSM1424330", "GSM1424331", "GSM1424332", "GSM1424333", "GSM1424334", "GSM1424335", "GSM1424336", "GSM1424337", "GSM1424338", "GSM1424339", "GSM1424340", "GSM1424341", "GSM1424342", "GSM1424343", "GSM1424344", "GSM1424345", "GSM1424346", "GSM1424347", "GSM1424348", "GSM1424349", "GSM1424350", "GSM1424351", "GSM1424352", "GSM1424353", "GSM1424354", "GSM1424355", "GSM1424356", "GSM1424357", "GSM1424358", "GSM1424359", "GSM1424360", "GSM1424361", "GSM1424362", "GSM1424363", "GSM1424364", "GSM1424365", "GSM1424366", "GSM1424367", "GSM1424368", "GSM1424369", "GSM1424370", "GSM1424371", "GSM1424372", "GSM1424373", "GSM1424374", "GSM1424375", "GSM1424376", "GSM1424377", "GSM1424378", "GSM1424379", "GSM1424380", "GSM1424381", "GSM1424382", "GSM1424383", "GSM1424384", "GSM1424385", "GSM1424386", "GSM1424387", "GSM1424388", "GSM1424389", "GSM1424390", "GSM1424391", "GSM1424392", "GSM1424393", "GSM1424394", "GSM1424395", "GSM1424396", "GSM1424397", "GSM1424398", "GSM1424399", "GSM1424400", "GSM1424401", "GSM1424402", "GSM1424403"], "sample_count": 624, "group_sizes": null, "group_sizes_basis": null, "platform": "4372", "taxon": "Homo sapiens", "title": "Gene expression profiles of human prefrontal cortex brain tissues", "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:GSE43292", "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=GSE43292", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE43292", "version": null, "note": "The datasets analyzed for this study can be found in the GEO database; GSE33000, GSE100927, GSE44770, and GSE43292.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The datasets analyzed for this study can be found in the GEO database; GSE33000, GSE100927, GSE44770, and GSE43292.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM1060117", "GSM1060118", "GSM1060119", "GSM1060120", "GSM1060121", "GSM1060122", "GSM1060123", "GSM1060124", "GSM1060125", "GSM1060126", "GSM1060127", "GSM1060128", "GSM1060129", "GSM1060130", "GSM1060131", "GSM1060132", "GSM1060133", "GSM1060134", "GSM1060135", "GSM1060136", "GSM1060137", "GSM1060138", "GSM1060139", "GSM1060140", "GSM1060141", "GSM1060142", "GSM1060143", "GSM1060144", "GSM1060145", "GSM1060146", "GSM1060147", "GSM1060148", "GSM1060149", "GSM1060150", "GSM1060151", "GSM1060152", "GSM1060153", "GSM1060154", "GSM1060155", "GSM1060156", "GSM1060157", "GSM1060158", "GSM1060159", "GSM1060160", "GSM1060161", "GSM1060162", "GSM1060163", "GSM1060164", "GSM1060165", "GSM1060166", "GSM1060167", "GSM1060168", "GSM1060169", "GSM1060170", "GSM1060171", "GSM1060172", "GSM1060173", "GSM1060174", "GSM1060175", "GSM1060176", "GSM1060177", "GSM1060178", "GSM1060179", "GSM1060180"], "sample_count": 64, "group_sizes": null, "group_sizes_basis": null, "platform": "6244", "taxon": "Homo sapiens", "title": "Genome-wide expression study of human carotid atheroma", "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:GSE44770", "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=GSE44770", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE44770", "version": null, "note": "The datasets analyzed for this study can be found in the GEO database; GSE33000, GSE100927, GSE44770, and GSE43292.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The datasets analyzed for this study can be found in the GEO database; GSE33000, GSE100927, GSE44770, and GSE43292.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM1090501", "GSM1090502", "GSM1090503", "GSM1090504", "GSM1090505", "GSM1090506", "GSM1090507", "GSM1090508", "GSM1090509", "GSM1090510", "GSM1090511", "GSM1090512", "GSM1090513", "GSM1090514", "GSM1090515", "GSM1090516", "GSM1090517", "GSM1090518", "GSM1090519", "GSM1090520", "GSM1090521", "GSM1090522", "GSM1090523", "GSM1090524", "GSM1090525", "GSM1090526", "GSM1090527", "GSM1090528", "GSM1090529", "GSM1090530", "GSM1090531", "GSM1090532", "GSM1090533", "GSM1090534", "GSM1090535", "GSM1090536", "GSM1090537", "GSM1090538", "GSM1090539", "GSM1090540", "GSM1090541", "GSM1090542", "GSM1090543", "GSM1090544", "GSM1090545", "GSM1090546", "GSM1090547", "GSM1090548", "GSM1090549", "GSM1090550", "GSM1090551", "GSM1090552", "GSM1090553", "GSM1090554", "GSM1090555", "GSM1090556", "GSM1090557", "GSM1090558", "GSM1090559", "GSM1090560", "GSM1090561", "GSM1090562", "GSM1090563", "GSM1090564", "GSM1090565", "GSM1090566", "GSM1090567", "GSM1090568", "GSM1090569", "GSM1090570", "GSM1090571", "GSM1090572", "GSM1090573", "GSM1090574", "GSM1090575", "GSM1090576", "GSM1090577", "GSM1090578", "GSM1090579", "GSM1090580", "GSM1090581", "GSM1090582", "GSM1090583", "GSM1090584", "GSM1090585", "GSM1090586", "GSM1090587", "GSM1090588", "GSM1090589", "GSM1090590", "GSM1090591", "GSM1090592", "GSM1090593", "GSM1090594", "GSM1090595", "GSM1090596", "GSM1090597", "GSM1090598", "GSM1090599", "GSM1090600", "GSM1090601", "GSM1090602", "GSM1090603", "GSM1090604", "GSM1090605", "GSM1090606", "GSM1090607", "GSM1090608", "GSM1090609", "GSM1090610", "GSM1090611", "GSM1090612", "GSM1090613", "GSM1090614", "GSM1090615", "GSM1090616", "GSM1090617", "GSM1090618", "GSM1090619", "GSM1090620", "GSM1090621", "GSM1090622", "GSM1090623", "GSM1090624", "GSM1090625", "GSM1090626", "GSM1090627", "GSM1090628", "GSM1090629", "GSM1090630", "GSM1090631", "GSM1090632", "GSM1090633", "GSM1090634", "GSM1090635", "GSM1090636", "GSM1090637", "GSM1090638", "GSM1090639", "GSM1090640", "GSM1090641", "GSM1090642", "GSM1090643", "GSM1090644", "GSM1090645", "GSM1090646", "GSM1090647", "GSM1090648", "GSM1090649", "GSM1090650", "GSM1090651", "GSM1090652", "GSM1090653", "GSM1090654", "GSM1090655", "GSM1090656", "GSM1090657", "GSM1090658", "GSM1090659", "GSM1090660", "GSM1090661", "GSM1090662", "GSM1090663", "GSM1090664", "GSM1090665", "GSM1090666", "GSM1090667", "GSM1090668", "GSM1090669", "GSM1090670", "GSM1090671", "GSM1090672", "GSM1090673", "GSM1090674", "GSM1090675", "GSM1090676", "GSM1090677", "GSM1090678", "GSM1090679", "GSM1090680", "GSM1090681", "GSM1090682", "GSM1090683", "GSM1090684", "GSM1090685", "GSM1090686", "GSM1090687", "GSM1090688", "GSM1090689", "GSM1090690", "GSM1090691", "GSM1090692", "GSM1090693", "GSM1090694", "GSM1090695", "GSM1090696", "GSM1090697", "GSM1090698", "GSM1090699", "GSM1090700", "GSM1090701", "GSM1090702", "GSM1090703", "GSM1090704", "GSM1090705", "GSM1090706", "GSM1090707", "GSM1090708", "GSM1090709", "GSM1090710", "GSM1090711", "GSM1090712", "GSM1090713", "GSM1090714", "GSM1090715", "GSM1090716", "GSM1090717", "GSM1090718", "GSM1090719", "GSM1090720", "GSM1090721", "GSM1090722", "GSM1090723", "GSM1090724", "GSM1090725", "GSM1090726", "GSM1090727", "GSM1090728", "GSM1090729", "GSM1090730"], "sample_count": 230, "group_sizes": null, "group_sizes_basis": null, "platform": "4372", "taxon": "Homo sapiens", "title": "Multi-tissue gene expression profiles of human brain (PFC)", "resolution_checked_at": "2026-11-01T18:46:34Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}], "gaps": ["asset:data:geo:GSE100927 not downloaded", "asset:data:geo:GSE100927 licence not read", "asset:data:geo:GSE33000 not downloaded", "asset:data:geo:GSE33000 licence not read", "asset:data:geo:GSE43292 not downloaded", "asset:data:geo:GSE43292 licence not read", "asset:data:geo:GSE44770 not downloaded", "asset:data:geo:GSE44770 licence not read"]}, "analysis": {"state": "partial", "methods": [{"id": "method-1", "family": "differential_expression", "specific": "limma_voom", "label": "Differential expression analysis", "dsl_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["count_matrix_with_condition"], "evidence": {"score": 1.0, "band": "verbatim", "quote": "We utilized the “limma” package in R software to perform differential gene expression analysis on the standardized GSE33000 and GSE100927 datasets."}}, {"id": "method-2", "family": "group_comparison", "specific": "wilcoxon", "label": "Group comparison", "dsl_operations": [], "missing_operations": ["group_comparison_wilcoxon"], "expressible": false, "parameters": {}, "data_shapes": ["two_or_more_groups_continuous"], "evidence": {"score": 0.8, "band": "high", "quote": "Wilcoxon"}}, {"id": "method-3", "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": 0.7, "band": "medium", "quote": "logistic regression"}}, {"id": "method-4", "family": "correlation_analysis", "specific": "pearson", "label": "Correlation analysis", "dsl_operations": ["pearson_r"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["paired_numeric_columns"], "evidence": {"score": 0.8, "band": "high", "quote": "Pearson correlation"}}, {"id": "method-5", "family": "network_analysis", "specific": "ppi_network", "label": "Network / pathway analysis", "dsl_operations": ["build_graph", "centrality_scores", "pathway_enrich"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["edge_list"], "evidence": {"score": 0.7, "band": "medium", "quote": "protein-protein interaction"}}, {"id": "method-6", "family": "network_analysis", "specific": "pathway_enrichment", "label": "Network / pathway analysis", "dsl_operations": ["pathway_enrich"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["edge_list"], "evidence": {"score": 0.8, "band": "high", "quote": "pathway enrichment"}}], "targets": [], "gaps": ["method.reader_differs_from_extractor"]}, "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": ["GSE33000", "GSE100927", "GSE44770", "GSE43292"], "resolved_accessions": ["GSE33000", "GSE100927", "GSE44770", "GSE43292"], "statements": ["The datasets analyzed for this study can be found in the GEO database; GSE33000, GSE100927, GSE44770, and GSE43292."], "basis": "The paper names GSE33000, GSE100927, GSE44770, GSE43292 in Gene Expression Omnibus (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:39188714", "data:geo:GSE100927"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE100927"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T19:29:35Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 11718, "completion_tokens": 1675, "cached_tokens": 264}}, "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:39188714", "type": "paper", "label": "pmid:39188714"}, {"id": "source:pmid:39188714", "type": "manuscript", "label": "PMID 39188714 full text"}, {"id": "data:geo:GSE100927", "type": "dataset", "label": "GEO GSE100927"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:39188714", "type": "paper", "label": "pmid:39188714"}, {"id": "source:pmid:39188714", "type": "manuscript", "label": "PMID 39188714 full text"}, {"id": "data:geo:GSE100927", "type": "dataset", "label": "GEO GSE100927"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-39188714", "type": "extraction", "used": ["source:pmid:39188714"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE100927", "type": "accession_resolution", "used": ["data:geo:GSE100927"], "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:39188714", "parent_hash": "sha256:e3224ca0ae7463c3a4d9e78e0b3aaf6a72aee3554f69b7129e5b5cd38459eac8", "fork_of": null, "generation": 3}, "gaps": ["asset:data:geo:GSE100927 licence not read", "asset:data:geo:GSE100927 not downloaded", "asset:data:geo:GSE33000 licence not read", "asset:data:geo:GSE33000 not downloaded", "asset:data:geo:GSE43292 licence not read", "asset:data:geo:GSE43292 not downloaded", "asset:data:geo:GSE44770 licence not read", "asset:data:geo:GSE44770 not downloaded", "execution.not_run", "method.reader_differs_from_extractor", "review.human_critique", "review.independent_reader", "review.machine_critique"], "assays": [{"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": ["We utilized the “limma” package in R software to perform differential gene expression analysis on the standardized GSE33000 and GSE100927 datasets."], "order_basis": "analysis_only", "id": "assay:1"}]},"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. It does not prove a complete protocol, compatible parameters, successful compilation or re-execution. Unrecognised or ambiguous methods require review.","Data reachability is a textual accession or public-data statement. No dataset was downloaded or checked for access, completeness or licence.","Capabilities are planning states seeded from the dated equipment register. No bench ownership, completed quote or partner booking is inferred. Procurement counts are dependencies, not guaranteed replications or financial returns.","Tier and blocker counts are deterministic assessments of this corpus, not measured agreement. R3 is reserved for procurement; unresolved methods and data require human work (R4). The most restrictive axis wins, with capability, method, data as tie-breaks. 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":"2026-11-04T02:48:35+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}}}