Construction of lactylation (LA) risk signature in prostate cancer based on 4D fast DIA L-lactated quantitative genomics.

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"readers": ["xml"], "capabilities": [], "data_gaps": ["source:pmid:40877920"]}, "gaps": ["corpus_record_absent"]}, "review": {"state": "partial", "machine_critique": {"status": "blocked", "providers": [{"provider": "minimax", "model": "MiniMax-M3.1-Flash", "prompt_version": "full-text-reading-v1", "prompt_hash": "7e61bf48e5d50213f766140eecab3bc32f5d257c6be4cdbcc4d4de846797429f", "source_hash": "48d6310e590bcbf746861e05977ec4e1b26c75656365075305bcda316e89eb7a", "raw_response_sha256": "sha256:d918a6922daf0a409e75dd81502db288a8c2c0c603e58cd0ab6eb4167d59cd84", "raw_response_chars": 5606}, {"provider": "minimax_alt", "model": "MiniMax-M3", "prompt_version": "full-text-reading-v1", "prompt_hash": "bc1081ff8c55ec291d74e2845b784e6338515491829dedbfca789c8537e5d2c7", "source_hash": "48d6310e590bcbf746861e05977ec4e1b26c75656365075305bcda316e89eb7a", "raw_response_sha256": "sha256:ad1ab1ccc1b9a69211d02cc71bb49cb0a1e5c9153b3eb91bf1f21df28ac00d77", "raw_response_chars": 55587}], "target_providers": [{"provider": "minimax", "model": "MiniMax-M3.1-Flash", "prompt_version": "replication-target-v2", "prompt_hash": "2838d66eafd37a0b9e0d17e2a8280f34ac976b724e492ed13710a4df7910b5ba", "source_hash": "48d6310e590bcbf746861e05977ec4e1b26c75656365075305bcda316e89eb7a", "raw_response": "{\"parent_value\":122,\"unit\":\"DEGs\",\"evidence\":\"A total of 122 DEGs were screened to construct a gene risk model.\",\"target_result\":\"The paper states that 122 differentially expressed genes were screened to construct the lactylation-related gene risk model.\",\"number_stated\":true}", "reading": {"parent_value": 122, "unit": "DEGs", "evidence": "A total of 122 DEGs were screened to construct a gene risk model.", "target_result": "The paper states that 122 differentially expressed genes were screened to construct the lactylation-related gene risk model.", "number_stated": true, "source_locator": "chars 1420-1485 of source sha256 48d6310e590bcbf746861e05977ec4e1b26c75656365075305bcda316e89eb7a"}, "recorded_at": "2026-10-31T15:33:14.635153+00:00"}, {"provider": "minimax_alt", "model": "MiniMax-M3", "prompt_version": "replication-target-v2", "prompt_hash": "f673567c716ea00045ec3d1e7c1f20d57c5524dcfb6513e2c0e6688386376804", "source_hash": "48d6310e590bcbf746861e05977ec4e1b26c75656365075305bcda316e89eb7a", "raw_response": "<think>We need answer JSON only. Need identify single numerical result headline main finding registered target. Source is abstract/results. Numerical candidates: total 122 DEGs screened to construct gene risk model; K-M p values test 0.025 training &lt;0.001. Main headline perhaps 122 DEGs? User wording \"headline numerical result the paper itself states for its main finding.\" Need infer target likely asks replication of registered target headline numerical result. Need report value paper states. The abstract's main finding includes \"A total of 122 DEGs were screened to construct a gene risk model.\" Is that headline number? Could be. Need exact contiguous quote containing number and attribution whole clause. Unit perhaps \"DEGs\" or \"differentially expressed genes\" or empty? Number 122 and unit \"DEGs\". Evidence exact quote: \"A total of 122 DEGs were screened to construct a gene risk model.\" This states numerical result. Target main finding is LA risk model. Maybe p values are validation statistical results, but \"headline numerical result\" perhaps 122 DEGs. Need understand instruction likely benchmark extraction. Registered target generic. They expect numerical result in abstract. Could select 122 DEGs because prominent and tied to model. Let's parse.\n\nQuestion: \"Read the supplied paper text and find the single numerical result that a replication of the registered target has to reproduce. Report the headline number only: the value the paper itself states for the registered target, not a value you calculated, converted, averaged or reconstructed. The unit must be the unit the paper prints. The evidence must be an exact, contiguous quotation from SOURCE TEXT that contains the number; do not shorten it, do not add an ellipsis and do not paraphrase it. Attribution may be carried by the text's own wording: a number may be attached to the target by a parenthetical list, by the word 'respectively', or by words that name the target directly. When that is how the text links them, quote the whole clause so a reader can check the attribution for themselves. If the text states no number for the registered target, set number_stated to false, leave parent_value at 0, leave unit and evidence empty, and say in target_result what the text does state instead.\"\n\nRegistered target: \"the headline numerical result the paper itself states for its main finding\". We need identify. Abstract result says total 122 DEGs, p-values, etc. Which is \"headline numerical result\"? Probably 122 DEGs. Could also be P values of KM: test group 0.025 and training group &lt;0.001 (two numbers, contrary \"single numerical result\"). \"Single\" may point to 122. The paper title 4D fast DIA L-lactated quantitative genomics; maybe headline result is \"A total of 122 DEGs...\" in results. Need report unit paper prints: DEGs. Could unit perhaps \"DEGs\" exactly. Parent_value 122. Evidence exact sentence. target_result perhaps description. number_stated true.\n\nNeed JSON valid no extra. What should target_result say? Probably \"122 DEGs were screened to construct the LA risk model.\" Since headline number. We can put that. Evidence exact contiguous. Ensure quote not add. Unit \"DEGs\". Is \"unit\" count of DEGs. Could use \"DEGs\". Let's ensure no ambiguity.\n\nMaybe main numerical result is P = 0.025 test group / P &lt; 0.001 training cohort, but asks single numerical result perhaps registered target itself could have one headline metric such as AUC usually. Here no AUC number. There are many method parameters. Only headline model-related count is 122 DEGs. Yes.\n\nCould unit be \"DEGs\" or \"genes\"? Paper prints \"122 DEGs\". User says unit must be unit paper prints. \"DEGs\" is printed. Use \"DEGs\". Evidence exact.\n\ntarget_result maybe \"A total of 122 DEGs were screened to construct a gene risk model.\" Exact? Could repeat quote. Fine.\n\nLet's answer only JSON object.</think>\n\n{\"parent_value\":122,\"unit\":\"DEGs\",\"evidence\":\"A total of 122 DEGs were screened to construct a gene risk model.\",\"target_result\":\"A total of 122 DEGs were screened to construct the LA risk model.\",\"number_stated\":true}", "reading": {"parent_value": 122, "unit": "DEGs", "evidence": "A total of 122 DEGs were screened to construct a gene risk model.", "target_result": "A total of 122 DEGs were screened to construct the LA risk model.", "number_stated": true, "source_locator": "chars 1420-1485 of source sha256 48d6310e590bcbf746861e05977ec4e1b26c75656365075305bcda316e89eb7a"}, "recorded_at": "2026-10-31T15:33:19.257584+00:00"}], "agreement": false, "disagreements": {"target_result": {"minimax": "A total of 122 DEGs were screened to construct a gene risk model.", "minimax_alt": "The K-M curves verified the differences between the high - and low - risk groups of the model in the test group and the training cohort (test group: P = 0.025; training group: P < 0.001).", "decisive": false}, "physical_methods": {"minimax": ["quantitative lactylation proteomics", "LC-MS/MS analysis", "Western blot analysis", "in vitro experiments", "animal experiments"], "minimax_alt": ["quantitative lactylation proteomics of human prostate cancer tissues", "LC-MS/MS analysis using timsTOF Pro2 mass spectrometry in dia-PASEF mode", "Western blot analysis", "in vitro prostate cancer cell experiments", "animal tumor experiments"], "decisive": false}, "analysis_methods": {"minimax": ["ConsensusCluster Plus consensus unsupervised clustering analysis", "Principal component analysis (PCA)", "differential expression analysis", "Lasso-Cox regression analysis", "K-M curves", "receiver operating characteristic (ROC) curves", "nomograms", "chi-square test", "stratification analysis", "Gene Ontology enrichment analysis", "KEGG pathway enrichment analysis", "ESTIMATE", "CIBERSORT", "IOBR"], "minimax_alt": ["consensus unsupervised clustering analysis using ConsensusClusterPlus", "Principal component analysis (PCA)", "identification of differentially expressed genes (DEGs)", "Lasso-Cox regression analysis", "K-M curves", "receiver operating characteristic (ROC) curves", "nomograms", "chi-square test", "ESTIMATE", "CIBERSORT", "IOBR", "gene set enrichment analysis", "GO and KEGG pathway enrichment analysis", "one-way ANOVA", "scRNA-seq analysis using TISCH2"], "decisive": false}, "statistical_method": {"minimax": "Lasso-Cox regression analysis", "minimax_alt": "K-M curves", "decisive": true}}, "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": "blocked", "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.blocked"]}, "execution": {"state": "absent", "status": "not_run", "mode": null, "runs": [], "gaps": ["execution.not_run"]}, "provenance": {"entities": [{"id": "package:096a4d403b5619732ca04b48dae71bf9ef8da39550467fff216438f582202e82", "type": "sep_package", "label": "SEP v1 package for pmid:40877920"}], "activities": [{"id": "activity:conversion:pmid:40877920", "type": "conversion", "used": ["package:096a4d403b5619732ca04b48dae71bf9ef8da39550467fff216438f582202e82"], "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": "pmid:40877920", "parent_hash": "sha256:36fd99e6a4e53eaa3adef6d121d8e64631bd77cbc35f45034bbc677705b6dbe6", "fork_of": null, "generation": 2}, "gaps": ["abstract", "asset:source:pmid:40877920 not verified", "authors", "capability", "corpus_record_absent", "execution.not_run", "method", "review.human_critique", "review.machine_critique.blocked", "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:40877920' does not resolve to a declared asset", "targets"]}