Meta_B cells: a computationally identified candidate immunosuppressive driver of gastric cancer metastasis revealed by single-cell analysis and machine learning.

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"94bd62140fdd588908e9de72ee75bdff139d425e9c5c9a870b4015169178aa61", "raw_response_sha256": "sha256:722b7ea5a9eb7cd8da0a03669882281b9e3e5d8737b5c815706406388c314745", "raw_response_chars": 3537}, {"provider": "minimax_alt", "model": "MiniMax-M3", "prompt_version": "full-text-reading-v1", "prompt_hash": "77c69961bbd8acea5002a4d3621a88a526d7a3603f5fb8e05f3b0668de2c3d9a", "source_hash": "94bd62140fdd588908e9de72ee75bdff139d425e9c5c9a870b4015169178aa61", "raw_response_sha256": "sha256:9d69ee42006943c6e955817ca437185b3f42cc17ebba8596fe7a767c1213f54f", "raw_response_chars": 48751}], "target_providers": [{"provider": "minimax", "model": "MiniMax-M3.1-Flash", "prompt_version": "replication-target-v2", "prompt_hash": "f8bcd95c6b06a742d9eb0c508cdfa74613e074290d9abfc0086f821fb08d42b0", "source_hash": "94bd62140fdd588908e9de72ee75bdff139d425e9c5c9a870b4015169178aa61", "raw_response": "{\"parent_value\":10,\"unit\":\"genes\",\"evidence\":\"A machine learning-derived 10-gene prognostic model effectively stratified high-risk patients with stromal-rich tumor microenvironments and predicted potential enhanced chemosensitivity to axitinib, dasatinib, olaparib, rapamycin, and ribociclib.\",\"target_result\":\"The paper states a 10-gene prognostic model as the numerical result for its main finding.\",\"number_stated\":true}", "reading": {"parent_value": 10, "unit": "genes", "evidence": "A machine learning-derived 10-gene prognostic model effectively stratified high-risk patients with stromal-rich tumor microenvironments and predicted potential enhanced chemosensitivity to axitinib, dasatinib, olaparib, rapamycin, and ribociclib.", "target_result": "The paper states a 10-gene prognostic model as the numerical result for its main finding.", "number_stated": true, "source_locator": "chars 1193-1439 of source sha256 94bd62140fdd588908e9de72ee75bdff139d425e9c5c9a870b4015169178aa61"}, "recorded_at": "2026-10-31T15:28:21.105876+00:00"}, {"provider": "minimax_alt", "model": "MiniMax-M3", "prompt_version": "replication-target-v2", "prompt_hash": "7e6dbbe5bd9a2a4b3df3778accfa33161b3a0d1268038e34b4084c35ff074b44", "source_hash": "94bd62140fdd588908e9de72ee75bdff139d425e9c5c9a870b4015169178aa61", "raw_response": "<think>We need answer JSON only. Need identify headline numerical result paper states for main finding. Source abstract. Candidates: 10-gene prognostic model? Meta_B cells maybe no numeric headline. User asks single numerical result replication registered target, headline numerical result stated for main finding. Likely \"10-gene prognostic model\". Need exact contiguous quote containing number, whole clause attribution. Unit paper prints perhaps \"10-gene\". parent_value 10, unit \"gene\" maybe \"genes\"? They demand unit must be unit paper prints. Exact quote: \"A machine learning-derived 10-gene prognostic model effectively stratified high-risk patients with stromal-rich tumor microenvironments and predicted potential enhanced chemosensitivity to axitinib, dasatinib, olaparib, rapamycin, and ribociclib.\" This contains 10 and unit embedded as \"10-gene\". Could unit be \"gene\" or \"genes\"? Parent result perhaps 10 genes. Headline number is 10? Registered target \"headline numerical result paper states for its main finding.\" Main finding Meta_B cells perhaps no numerical result for meta_B cell itself. The only numerical result abstract is 10-gene model. They explicitly instruct if text states no number for registered target, false, and say what does state instead. Is \"registered target\" broad enough to headline numerical result, likely expected extraction from paper. The prompt is templated, asks find single numerical result. We need choose. Could perhaps numerical results are absent because source excerpt lacks detailed figures, and 10-gene is model architecture rather than headline result. But likely answer is 10 genes.\n\nNeed output fields:\nparent_value: 10\nunit: \"gene\" perhaps exact paper's printed unit is \"10-gene\" where unit notion could be \"genes\". They say unit must be unit paper prints. Usually extraction asks parent value and unit. For count, unit \"genes\" although source says \"10-gene prognostic model\" singular attributive. Could use \"genes\" as normalized? They emphasize printed unit. \"gene\" perhaps printed exactly. Yet parent_value numerical and unit string. Evidence exact quote. target_result likely perhaps \"10-gene prognostic model\". number_stated true.\n\nNeed ensure valid JSON no markdown. Could unit be \"gene\" or \"genes\"? Quote says \"10-gene\". In natural numerical unit, \"gene\". They may expect \"genes\". The schema maybe parent_value refers \"parent value\" target and unit. Asked \"Report headline number only: the value the paper itself states...\" Unit must be unit paper prints. Likely answer:\n{\"parent_value\":10,\"unit\":\"genes\",\"evidence\":\"A machine learning-derived 10-gene prognostic model effectively stratified high-risk patients with stromal-rich tumor microenvironments and predicted potential enhanced chemosensitivity to axitinib, dasatinib, olaparib, rapamycin, and ribociclib.\",\"target_result\":\"10-gene prognostic model\",\"number_stated\":true}\n\nBut target_result perhaps should name main finding, maybe \"The machine-learning-derived prognostic model contains 10 genes.\" Does that violate headline number only? They ask report headline number only, but schema target_result. We can state concise. Evidence cannot shorten, exact contiguous quote. Whole clause to check attribution. We have complete sentence exact. They say \"do not shorten it\" perhaps evidence should quote whole clause, which we do. \"headline number only\" maybe target_result should be \"10 genes\" rather than elaborate. Unit maybe \"genes\". Yet \"unit the paper prints\" source prints \"10-gene\", not \"genes\". Could unit field be \"gene\" exactly. Usually expected unit strings e.g. \"years\", \"%\", etc. For \"10-gene prognostic model\", unit = \"genes\". But singular attributive suggests number of genes and unit \"gene\". Let's inspect wording: \"Report the headline number only: the value the paper itself states ... The unit must be the unit the paper prints.\" They perhaps expect unit from phrase. \"10-gene\" means 10 genes, unit \"genes.\" However, \"paper prints\" could insist \"gene\". Hmm.\n\nCould main finding instead be CLEC2B/YBX3 overexpression (2 genes) no explicit number 2. No.\n\nMaybe there is no \"registered target\" tied to numeric model? Registered target is \"the headline numerical result the paper states for its main finding\", and source has \"10-gene prognostic model\" as only number. So yes.\n\nNeed JSON only.</think>\n\n{\"parent_value\":10,\"unit\":\"genes\",\"evidence\":\"A machine learning-derived 10-gene prognostic model effectively stratified high-risk patients with stromal-rich tumor microenvironments and predicted potential enhanced chemosensitivity to axitinib, dasatinib, olaparib, rapamycin, and ribociclib.\",\"target_result\":\"10-gene prognostic model\",\"number_stated\":true}", "reading": {"parent_value": 10, "unit": "genes", "evidence": "A machine learning-derived 10-gene prognostic model effectively stratified high-risk patients with stromal-rich tumor microenvironments and predicted potential enhanced chemosensitivity to axitinib, dasatinib, olaparib, rapamycin, and ribociclib.", "target_result": "10-gene prognostic model", "number_stated": true, "source_locator": "chars 1193-1439 of source sha256 94bd62140fdd588908e9de72ee75bdff139d425e9c5c9a870b4015169178aa61"}, "recorded_at": "2026-10-31T15:28:26.219302+00:00"}], "agreement": true, "disagreements": {"analysis_methods": {"minimax": ["Seurat clustering", "hdWGCNA", "LASSO-Cox regression", "GSEA", "Monocle2 pseudotime trajectory analysis", "CellChat", "Wilcoxon-Mann-Whitney tests", "DESeq2", "Pearson correlation analysis", "one-way analysis of variance with Tukey’s tests", "two-tailed unpaired student t-tests", "CIBERSORT", "TIDE scoring", "oncoPredict"], "minimax_alt": ["Seurat clustering", "Wilcoxon-Mann-Whitney differential-expression testing", "DESeq2 differential-expression analysis", "hdWGCNA", "LASSO-Cox regression", "GSEA", "Monocle2 pseudotime trajectory analysis", "CellChat cell-cell communication analysis", "Pearson correlation", "one-way analysis of variance with Tukey’s tests", "two-tailed unpaired student t-tests", "CIBERSORT", "TIDE score prediction", "oncoPredict drug-sensitivity prediction"], "decisive": false}}, "agreement_basis": {"policy": "decisive_fields", "decisive_fields": ["work_type", "number_stated", "parent_value", "unit"]}, "blinded": true}, "human_critique": {"status": "not_run", "reviewer": null, "signed_at": null, "conflict_checked": false, "commentary": null}, "stages": [{"key": "target_materials", "status": "pending", "reason": null, "evidence": null}, {"key": "independent_readings", "status": "passed", "reason": "The independent readings are incomplete or disagree; the source evidence must resolve the difference.", "evidence": {"provider_errors": []}}, {"key": "source_resolution", "status": "pending", "reason": null, "evidence": null}, {"key": "protocol_reconstruction", "status": "pending", "reason": null, "evidence": null}, {"key": "control_execution", "status": "pending", "reason": null, "evidence": null}, {"key": "control_comparison", "status": "pending", "reason": null, "evidence": null}, {"key": "publication", "status": "pending", "reason": null, "evidence": null}], "gaps": ["review.human_critique", "review.machine_critique.passed"]}, "execution": {"state": "absent", "status": "not_run", "mode": null, "runs": [], "gaps": ["execution.not_run"]}, "provenance": {"entities": [{"id": "package:6a7c614f1a8b59f7f08c6d9feecf249afa07467437e7e68392bb31784fe083bd", "type": "sep_package", "label": "SEP v1 package for pmid:40770460"}], "activities": [{"id": "activity:conversion:pmid:40770460", "type": "conversion", "used": ["package:6a7c614f1a8b59f7f08c6d9feecf249afa07467437e7e68392bb31784fe083bd"], "agent": "agent:corpus-assay-engine"}], "agents": [{"id": "agent:minimax", "type": "model", "label": "minimax"}, {"id": "agent:minimax_alt", "type": "model", "label": "minimax_alt"}, {"id": "agent:corpus-assay-engine", "type": "software", "label": "Corpus assay engine"}], "source_run_id": "run-20261023T122449232913", "dsl_catalogue_fingerprint": "343fb30756ba6ec3", "capability_table_fingerprint": "2cb3e42f047d2ecf", "snapshot_id": "ba34b49490d9bfaf"}, "lineage": {"parent_id": "64eb0c66-0d67-4eca-923e-956880ba7448", "parent_hash": "6a7c614f1a8b59f7f08c6d9feecf249afa07467437e7e68392bb31784fe083bd", "fork_of": null, "generation": 1}, "gaps": ["abstract", "asset:source:pmid:40770460 not verified", "authors", "capability", "corpus_record_absent", "execution.not_run", "method", "review.human_critique", "review.machine_critique.passed", "step:obtain_source_data blocked on data:not_downloaded", "step:run_analysis blocked on no owned computational capability is bound to this experiment", "step:run_analysis input 'materials:the source data behind the published result in pmid:40770460' does not resolve to a declared asset", "targets"]}