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pmid:40735323
Lactylation prognostic signature identifies DHCR7 as a modulator of chemoresistance and immunotherapy efficacy in bladder cancer.
Frontiers in immunology · 2025 doi:10.3389/fimmu.2025.1585727
Paper
partial
Experiment
partial
Data
partial
Analysis
absent
Execution
absent
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Record details sci_version · id · kind · revision · content_hash · schema · created_at · updated_at · generator
generator object · 3 fields
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reagents array · 0 items
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outputs array · 1 item
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materials array · 0 items
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conditions object · 0 fields
gaps array · 3 items
data object · 3 fields
assets array · 1 item
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structure object · 1 field
gaps array · 1 item
capability object · 3 fields
readiness object · 8 fields
axes object · 0 fields
can_run object · 3 fields
dsl_operations array · 0 items
assets array · 1 item
steps array · 1 item
must_build object · 5 fields
dsl_operations array · 0 items
converters array · 0 items
readers array · 1 item
capabilities array · 0 items
data_gaps array · 1 item
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review object · 5 fields
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target_providers array · 2 items
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reading object · 6 fields
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lineage object · 4 fields
gaps array · 13 items
Complete file
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"target_providers": [{"provider": "minimax", "model": "MiniMax-M3.1-Flash", "prompt_version": "replication-target-v2", "prompt_hash": "a6f73910a475c3ee29686575ecba4a97d40cae255b39ecaf1102c42ab7a75336", "source_hash": "34568557d3d0155d08144ee5cbe9e571bdb09c92dc86135bdb00fd22f71c9ee0", "raw_response": "{\"parent_value\":9,\"unit\":\"genes\",\"evidence\":\"DHCR7 emerged as a pivotal prognostic gene from the nine gene model, prompting subsequent focused analyses.\",\"target_result\":\"The lactylation prognostic signature was based on a nine-gene model, with DHCR7 identified as a pivotal prognostic gene.\",\"number_stated\":true}", "reading": {"parent_value": 9, "unit": "genes", "evidence": "DHCR7 emerged as a pivotal prognostic gene from the nine gene model, prompting subsequent focused analyses.", "target_result": "The lactylation prognostic signature was based on a nine-gene model, with DHCR7 identified as a pivotal prognostic gene.", "number_stated": true, "source_locator": "chars 1686-1793 of source sha256 34568557d3d0155d08144ee5cbe9e571bdb09c92dc86135bdb00fd22f71c9ee0"}, "recorded_at": "2026-10-31T15:23:35.595331+00:00"}, {"provider": "minimax_alt", "model": "MiniMax-M3", "prompt_version": "replication-target-v2", "prompt_hash": "b4c93deff8b4b2803eb42a23d8003e1d85272771ae348f2b3e1a14b4dd1e9cce", "source_hash": "34568557d3d0155d08144ee5cbe9e571bdb09c92dc86135bdb00fd22f71c9ee0", "raw_response": "<think>We need answer JSON only. Need infer registered target \"headline numerical result the paper states for its main finding\". Source text seems no headline numerical result. It has 10th most common, 22 genes, two clusters, nine gene model, etc. User asks single numerical result replication target has to reproduce. Likely no headline number in supplied source. Need set false, parent 0, unit evidence empty, target_result say what text states instead. Need exact JSON. Need carefully identify perhaps key finding DHCR7 enhances cisplatin and immunotherapy, no number. The text does not state effect sizes, AUC, survival etc. So number_stated false. target_result perhaps \"The text states that knockdown of DHCR7 enhances the efficacy of both cisplatin chemotherapy and immunotherapy in bladder cancer.\" They ask if no number, say what text does state instead. Could include 10th most common? But target is headline numerical result paper states main finding. The headline finding is DHCR7, no numerical result. So target_result concise.\n\nNeed ensure unit empty and evidence \"\". parent_value 0. JSON valid.</think>\n\n{\"parent_value\":0,\"unit\":\"\",\"evidence\":\"\",\"target_result\":\"Knockdown of DHCR7 enhances the efficacy of both cisplatin chemotherapy and immunotherapy in bladder cancer; no numerical result for this main finding is stated.\",\"number_stated\":false}", "reading": {"parent_value": 0, "unit": "", "evidence": "", "target_result": "Knockdown of DHCR7 enhances the efficacy of both cisplatin chemotherapy and immunotherapy in bladder cancer; no numerical result for this main finding is stated.", "number_stated": false, "source_locator": null}, "recorded_at": "2026-10-31T15:23:35.403903+00:00"}], "agreement": false, "disagreements": {"target_result": {"minimax": "A nine-gene lactylation risk prediction model for forecasting bladder cancer prognosis, with DHCR7 identified as a pivotal biomarker involved in cisplatin resistance and influencing immunotherapy efficacy in bladder cancer.", "minimax_alt": "unknown", "decisive": false}, "physical_methods": {"minimax": ["in vivo experiments", "in vitro experiments", "DHCR7 knockdown", "cisplatin chemotherapy", "immunotherapy efficacy experiments"], "minimax_alt": ["in vivo experiments", "in vitro experiments", "DHCR7 knockdown", "cisplatin chemotherapy", "immunotherapy"], "decisive": false}, "analysis_methods": {"minimax": ["unsupervised clustering analysis", "survival analysis using Kaplan-Meier curves with log-rank tests", "ROC curve analysis using the timeROC R package", "CIBERSORT", "single-sample gene set enrichment analysis (ssGSEA)", "Gene Set Enrichment Analysis (GSEA)", "Gene Set Variance Analysis (GSVA)", "WGCNA", "Spearman correlation analysis", "two-tailed Wilcoxon test", "Student’s two-tailed t-test", "single-cell RNA sequencing analysis using Seurat", "Harmony batch-effect integration", "CellCall intercellular communication analysis", "tumor mutational burden analysis using maftools"], "minimax_alt": ["unsupervised clustering analysis", "Kaplan-Meier survival analysis", "log-rank test", "ROC analysis using the timeROC R package", "CIBERSORT", "single-sample Gene Set Enrichment Analysis (ssGSEA)", "ESTIMATE", "single-cell RNA-seq analysis using the Seurat R package", "Harmony data integration", "AUCell", "UCell", "singscore", "ssgsea", "AddModuleScore", "CellCall", "functional enrichment analysis using clusterProfiler", "Gene Ontology (GO) enrichment analysis", "Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis", "GSVA", "GSEA", "WGCNA", "two-tailed Wilcoxon test", "Student’s two-tailed t-test", "Spearman correlation analysis"], "decisive": false}, "statistical_method": {"minimax": "Survival differences were assessed using K-M survival curves with log-rank tests.", "minimax_alt": "", "decisive": true}, "number_stated": {"minimax": true, "minimax_alt": false, "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; 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