Deciphering hypoxia's role in hepatocellular carcinoma prognosis with single-cell approaches.

{"sci_version": "1.0", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40715889", "kind": "science-record", "revision": 1, "content_hash": "sha256:597398030f91da265c1d005db90e138908c6972f0cc214a92e835819fd5e6e16", "created_at": "2026-11-01T10:25:52Z", "updated_at": "2026-11-01T10:25:52Z", "generator": {"name": "corpus-assay-engine", "converter": "sep_to_sci_v1", "source_package_schema": "urn:bntrg:sep:1.0.0"}, "paper": {"state": "partial", "title": "Deciphering hypoxia's role in hepatocellular carcinoma prognosis with single-cell approaches.", "doi": "10.1007/s12672-025-03201-y", "pmid": "40715889", "authors": null, "journal": "Discover oncology", "year": 2025, "abstract": null, "claims": [{"id": "claim-1", "statement": "the headline numerical result the paper states for its main finding", "kind": "qualitative", "state": "blocked", "target": {"value": null, "unit": null, "tolerance": null, "source": null}, "evidence": []}], "gaps": ["abstract", "authors"]}, 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"capabilities": [], "data_gaps": ["source:pmid:40715889"]}, "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": "ac027c73b093169dcd9dba9890e115aafe2351699c54481c632e0285cd0df287", "source_hash": "bd8a0ee5b9e5dbdf6e5bb5d8be09afec44db9ab7370956585bf82f6936569875", "raw_response_sha256": "sha256:10f5a4675b1a7e9ac29851ac9e80a647d870242a7e872a883b1621cf3cde0499", "raw_response_chars": 4676}, {"provider": "minimax_alt", "model": "MiniMax-M3", "prompt_version": "full-text-reading-v1", "prompt_hash": "e30b78dc826daed711d2c144d5cf4dbe8309d034bd499cb72a9fb489aa52e432", "source_hash": "bd8a0ee5b9e5dbdf6e5bb5d8be09afec44db9ab7370956585bf82f6936569875", "raw_response_sha256": "sha256:f4b3c8d6932ded033d18a96aba0c5b8b7691de8a753edf615bb3161b2157314c", "raw_response_chars": 27255}], "target_providers": [{"provider": "minimax", "model": "MiniMax-M3.1-Flash", "prompt_version": "replication-target-v2", "prompt_hash": "4d3701935dced922ac910634d59070aa65ac0c7f77863f1eebc4c2082512934f", "source_hash": "bd8a0ee5b9e5dbdf6e5bb5d8be09afec44db9ab7370956585bf82f6936569875", "raw_response": "{\"parent_value\":119,\"unit\":\"key genes\",\"evidence\":\"A cumulative total of 119 key genes were pinpointed within the H2 subset, while 70 were discovered in the N2 subset.\",\"target_result\":\"119 key genes were pinpointed within the H2 subset.\",\"number_stated\":true}", "reading": {"parent_value": 119, "unit": "key genes", "evidence": "A cumulative total of 119 key genes were pinpointed within the H2 subset, while 70 were discovered in the N2 subset.", "target_result": "119 key genes were pinpointed within the H2 subset.", "number_stated": true, "source_locator": "chars 12523-12639 of source sha256 bd8a0ee5b9e5dbdf6e5bb5d8be09afec44db9ab7370956585bf82f6936569875"}, "recorded_at": "2026-10-31T15:26:43.820845+00:00"}, {"provider": "minimax_alt", "model": "MiniMax-M3", "prompt_version": "replication-target-v2", "prompt_hash": "e4e12f6dafa7d6b1a56abcff7f2fe9fb265448137df1c35ab1594318316afa8b", "source_hash": "bd8a0ee5b9e5dbdf6e5bb5d8be09afec44db9ab7370956585bf82f6936569875", "raw_response": "<think>We need answer JSON object only. Need identify single numerical result headline numerical result paper states for main finding. Source text appears no headline numerical result. It has various numbers: clusters H1-H4/N1-N3, top 1%, 119 genes H2, 70 N2, 5 compounds totaling 44, correlation |Cor| >0.3, P<0.05, versions 2.22.0 etc, DOI. Registered target: \"the headline numerical result the paper states for its main finding\". Main finding CHPF, H2 high invasive, prognostic model. No stated numerical headline result, likely no number. Need set number_stated false, parent_value 0, unit \"\", evidence \"\", target_result what text states instead. Need ensure evidence exact quotation? If false, evidence empty. target_result can say text states CHPF identified cellular hypoxia states, H2 subset high invasive potential, H2-specific TF model including LRP10 MED8 NOL10 NOP58 REXO4 forecasting survival, no headline numerical result. Need not calculate. We need be careful that perhaps \"119 key genes\" might be headline numerical result? But registered target is main finding, likely no number. Source text is weird perhaps abstract truncation where numerical result missing. User emphasizes \"value paper itself states for registered target\" and if no number, false. We should state no headline numerical result. We need possibly include exact quotation in target_result? They require evidence exact contiguous quote containing number; but no number. target_result perhaps description. JSON valid no markdown.\n\nLet's parse deeply in case there is some numerical headline tied to main finding. Text abstract says no numerical values. Results: cluster counts, scores, genes, compounds. Main finding might \"constructed prognostic model\" and perhaps \"total 119 key genes in H2, 70 N2.\" But neither is headline numerical result and perhaps not exactly main finding. Registered target phrased to ask headline numerical result for main finding, likely benchmark extraction task. Need only output false if text no number for registered target. They might expect perhaps 119? Let's examine instructions: \"Read 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\nSo yes need determine whether number. Text may have 119 and 70, but are these headline numerical result for main finding? The registered target is generic \"the headline numerical result the paper states for its main finding\". Main finding: CHPF model based on H2-specific TFs; no performance metric, risk score threshold etc. Numbers 119/70 are associated key genes, likely not headline. The paper could have a missing section about CHPF construction and survival. No numerical result. Therefore false.\n\nNeed target_result maybe exact quote \"The model demonstrated significant predictive value for lifespan of patients as verified in the TCGA dataset and an external validation group.\" This states qualitative. Could include quote but target_result could be \"No headline numerical result is stated; the paper reports...\" It says \"say in target_result what the text does state instead.\" We can state: \"No headline numerical result is stated. The text reports that the H2 subpopulation had the highest invasive potential and that a model based on H2-specific transcription factors (LRP10, MED8, NOL10, NOP58, REXO4) had significant predictive value for patient lifespan in TCGA and an external validation group.\" Good.\n\nEnsure no numerical values in target_result? Maybe avoids accidental confusion; not required. It can say H2 etc though. Fine. JSON schema fields exactly.\n\nPotential concern: target_result should quote source perhaps exact. Could say \"The paper states that the H2 subpopulation displayed the utmost invasive potential and that the model demonstrated significant predictive value for lifespan of patients as verified in the TCGA dataset and an external validation group, without a headline numerical result.\" That is paraphrase but target_result not evidence and they specifically say what text states instead. Fine.\n\nNeed no additional prose.</think>\n\n{\"parent_value\":0,\"unit\":\"\",\"evidence\":\"\",\"target_result\":\"No headline numerical result is stated. The paper reports that the H2 subpopulation displayed the highest invasive potential and that a prognostic model based on H2-specific transcription factors demonstrated significant predictive value for patient lifespan in the TCGA dataset and an external validation group.\",\"number_stated\":false}", "reading": {"parent_value": 0, "unit": "", "evidence": "", "target_result": "No headline numerical result is stated. The paper reports that the H2 subpopulation displayed the highest invasive potential and that a prognostic model based on H2-specific transcription factors demonstrated significant predictive value for patient lifespan in the TCGA dataset and an external validation group.", "number_stated": false, "source_locator": null}, "recorded_at": "2026-10-31T15:26:47.543996+00:00"}], "agreement": false, "disagreements": {"target_result": {"minimax": "A prognostic model based on five H2-specific transcription factors—LRP10, MED8, NOL10, NOP58, and REXO4—was reported to have significant predictive value for patient lifespan in the TCGA dataset and an external validation group, although no numerical performance statistic for the model is stated in the supplied text.", "minimax_alt": "The paper reports a CHPF prognostic model based on H2-specific transcription factors LRP10, MED8, NOL10, NOP58 and REXO4 that demonstrated significant predictive value for patient lifespan in the TCGA dataset and an external validation group. No explicit numerical performance result is stated in the supplied text.", "decisive": false}, "analysis_methods": {"minimax": ["single-cell RNA sequencing", "UMAP", "Weighted correlation network analysis (WGCNA)", "CellChat", "Monocle trajectory analysis", "inferCNV", "SCENIC with GRNboost2", "CytoTRACE", "GSVA", "Cox regression analysis", "Kaplan-Meier survival analysis"], "minimax_alt": ["single-cell RNA sequencing", "UMAP", "Weighted correlation network analysis (WGCNA)", "CellChat", "Monocle2", "infercnv", "SCENIC", "GRNboost2", "CytoTRACE", "GSVA", "Cox regression analysis", "Kaplan-Meier survival analysis"], "decisive": false}, "statistical_method": {"minimax": "Cox regression analysis and Kaplan-Meier survival analysis using the survival package in R", "minimax_alt": "Cox regression analysis and Kaplan-Meier survival analysis", "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; 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:cc9a626ab339deff894bcf9ed107add68cdb29add4ae944d03a184164e2908fa", "type": "sep_package", "label": "SEP v1 package for pmid:40715889"}], "activities": [{"id": "activity:conversion:pmid:40715889", "type": "conversion", "used": ["package:cc9a626ab339deff894bcf9ed107add68cdb29add4ae944d03a184164e2908fa"], "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": "1235d2d7-5f13-4d32-924c-5037aa9fe0b4", "parent_hash": "cc9a626ab339deff894bcf9ed107add68cdb29add4ae944d03a184164e2908fa", "fork_of": null, "generation": 1}, "gaps": ["abstract", "asset:source:pmid:40715889 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:40715889' does not resolve to a declared asset", "targets"]}