{"data":{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39834440", "kind": "science-record", "revision": 4, "content_hash": "sha256:3d7e1e652d6643c5529f8d7d731aeafbbbde69634434b6766b414a40f3b0447c", "created_at": "2026-11-01T20:30:08Z", "updated_at": "2026-11-01T22:56:01Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Untangling the complex mechanisms associated with Alzheimer's disease in elderly patients using high-throughput RNA sequencing data and next-generation knowledge discovery methods: Focus on potential gene signatures and drugs for dementia.", "doi": "10.1016/j.heliyon.2024.e41266", "pmid": "39834440", "authors": ["Hind A Alkhatabi", "Peter Natesan Pushparaj"], "journal": "Heliyon", "year": 2025, "abstract": "Objectives: Alzheimer's disease (AD) is a complex neurodegenerative disorder that primarily affects elderly individuals. This study aimed to elucidate the intricate mechanisms underlying AD in elderly patients compared with healthy aged individuals using high-throughput RNA sequencing (RNA-seq) data and next-generation knowledge discovery methods (NGKD), with a focus on identifying potential therapeutic agents.\nMethods: High-throughput RNA-seq data were obtained from the Gene Expression Omnibus (GEO) database (accession number: GSE104704). These data were derived from healthy and diseased human brains (eight young healthy brains [young], 10 aged healthy brains [Old], and 12 aged diseased brains [AD]). We used NGKD tools such as GEO RNA-seq Experiments Interactive Navigator (GREIN) to obtain differentially expressed genes (DEGs) by comparing the AD versus Old RNA-seq data and further filtered and normalized to obtain differentially regulated Kyoto Encyclopedia of Genes and Genomes (KEGG), Reactome and Panther pathways using ExpressAnalyst tool. Besides, WebGestalt was used to identify differentially regulated Gene Ontologies (GO) and the pre-ranked Gene Set Enrichment Analysis (GSEA) was performed using GSEA software. The X2K web tool was used to infer upstream regulator networks and X2K Appyter tool for obtaining transcription factors (TFs) and kinase network information. LFW1000 and L1000CDS2 tools were used to identify specific drugs that reverse AD-associated gene signatures in elderly patients.\nResults: Our study revealed significant downregulation of pathways related to neuroactive receptor-ligand interaction, synaptic vesicle cycle, and neuronal system in elderly individuals with AD. GO analysis showed negative enrichment of functions related to cognition, potassium ion transport, receptor-ligand activity, SNARE binding, and primary lysosomes. The transcription factors SUZ12 and REST, along with increased MAPK signaling, were identified as key regulators of downregulated genes. Several drugs and natural products, including dihydroergocristine, mepacrine, gedunin, amlodipine, and disulfiram have been identified as potential therapeutic agents for reversing AD-associated gene signatures.\nConclusions: This comprehensive analysis of AD in elderly individuals using RNA-seq data and NGKD tools revealed multiple differentially regulated pathways, gene signatures, and potential drugs for dementia treatment. These findings highlight the complex molecular mechanisms underlying AD and provide insights into potential therapeutic strategies. Further research is needed to validate these findings and to develop personalized treatment approaches for AD in elderly patients.", "claims": [{"id": "claim-method", "statement": "The normalized data were subsequently used as inputs for the limma-based differential expression analysis method, which employs a log2-counts per million (log CPM) transformation and applied filters, such as the low abundance (cutoff value set at 4) and variance filters, as well as a filter for unannotated genes (cutoff value set at 15) [19,20] (Access Date: Jan 28, 2024).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39834440", "locator": "sections[6] \"ExpressAnalyst analysis\" paragraph 2", "quote": "The normalized data were subsequently used as inputs for the limma-based differential expression analysis method, which employs a log2-counts per million (log CPM) transformation and applied filters, such as the low abundance (cutoff value set at 4) and variance filters, as well as a filter for unannotated genes (cutoff value set at 15) [19,20] (Access Date: Jan 28, 2024)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The RNAseq dataset used in this study (accession number GSE104704) is openly available in the Gene Expression Omnibus (GEO) for download and reuse.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39834440", "locator": "sections[15] \"Data availability statement\" paragraph 2", "quote": "The RNAseq dataset used in this study (accession number GSE104704) is openly available in the Gene Expression Omnibus (GEO) for download and reuse."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Utilizing the ExpressAnalyst tool and filtered metadata and raw counts from GREIN, we identified 1255 DEGs between the two groups (aged, deceased vs. aged, healthy), as depicted in the volcano plot (Fig. 1A) (Supplementary File 1).", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39834440", "locator": "sections[11] \"Results\" paragraph 2", "quote": "Utilizing the ExpressAnalyst tool and filtered metadata and raw counts from GREIN, we identified 1255 DEGs between the two groups (aged, deceased vs. aged, healthy), as depicted in the volcano plot (Fig. 1A) (Supplementary File 1)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Utilizing the ExpressAnalyst tool and filtered metadata and raw counts from GREIN, we identified 1255 DEGs between the two groups (aged, deceased vs. aged, healthy), as depicted in the volcano plot (Fig. 1A) (Supplementary File 1).", "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:39834440"], "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:GSE104704"], "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:GSE104704"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:39834440", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1016/j.heliyon.2024.e41266", "sha256": "sha256:79c562a68fc61ae5d66d00be6b8eacabdfb1d35bde46e6f0be3664c6f52d20a9", "bytes": 56007, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1016/j.heliyon.2024.e41266", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC11743088", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE104704", "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=GSE104704", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE104704", "version": null, "note": "The RNAseq dataset used in this study (accession number GSE104704) is openly available in the Gene Expression Omnibus (GEO) for download and reuse.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The RNAseq dataset used in this study (accession number GSE104704) is openly available in the Gene Expression Omnibus (GEO) for download and reuse.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM2806214", "GSM2806215", "GSM2806216", "GSM2806217", "GSM2806218", "GSM2806219", "GSM2806220", "GSM2806221", "GSM2806222", "GSM2806223", "GSM2806224", "GSM2806225", "GSM2806226", "GSM2806227", "GSM2806228", "GSM2806229", "GSM2806230", "GSM2806231", "GSM2806232", "GSM2806233", "GSM2806234", "GSM2806235", "GSM2806236", "GSM2806237", "GSM2806238", "GSM2806239", "GSM2806240", "GSM2806241", "GSM2806242", "GSM2806243"], "sample_count": 30, "group_sizes": null, "group_sizes_basis": null, "platform": "18573", "taxon": "Homo sapiens", "title": "Dysregulation of the epigenetic landscape of normal aging in Alzheimer's disease [RNA-Seq]", "resolution_checked_at": "2026-11-01T20:16:08Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}], "gaps": ["asset:data:geo:GSE104704 not downloaded", "asset:data:geo:GSE104704 licence not read"]}, "analysis": {"state": "complete", "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": "The normalized data were subsequently used as inputs for the limma-based differential expression analysis method, which employs a log2-counts per million (log CPM) transformation and applied filters, such as the low abundance (cutoff value set at 4) and variance filters, as well as a filter for unannotated genes (cutoff value set at 15) [19,20] (Access Date: Jan 28, 2024)."}}], "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. 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Methods: High-throughput RNA-seq data were obtained from the Gene Expression", "brains [AD]). We used NGKD tools such as GEO RNA-seq Experiments Interactive Navigator (GREIN) to"], "order_basis": "paper_text_order:67", "id": "assay:1"}, {"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": ["The normalized data were subsequently used as inputs for the limma-based differential expression analysis method, which employs a log2-counts per million (log CPM) transformation and applied filters, such as the low abundance (cutoff value set at 4) and variance filters, as well as a filter for unannotated genes (cutoff value set at 15) [19,20] (Access Date: Jan 28, 2024)."], "order_basis": "consumes_earlier_output", "id": "assay:2"}]},"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":"2026-11-04T01:50:18+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}}}