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GPAT3 protects against lipid stress-induced ferroptosis in hepatocellular carcinoma: From multi-omics analysis to functional validation

Biochim Biophys Acta Mol Basis Dis. 2027 Jan;1873(1):168471. doi: 10.1016/j.bbadis.2026.168471. Epub 2026 Sep 24.

ABSTRACT

BACKGROUND: The global burden of metabolic-associated hepatocellular carcinoma (HCC) is increasing, with obesity emerging as a key causal factor. However, the molecular mechanisms linking lipid metabolic dysregulation to HCC progression and therapeutic vulnerability remain unclear.

METHODS: We analyzed Global Burden of Disease 2021 data to assess liver cancer burden attributable to metabolic risks from 1990 to 2021. Mendelian randomization was used to evaluate causal associations between metabolic traits and liver cancer risk. TCGA, GTEx, and GEO datasets were integrated to identify lipid stress-responsive regulators. Clinical relevance was assessed using public datasets and tissue microarray immunohistochemistry. Functional validation was performed in HCC cells and a high-fat diet-fed syngeneic mouse tumor model.

RESULTS: Liver cancer deaths and DALYs attributable to metabolic risks increased markedly from 1990 to 2021. Mendelian randomization showed that obesity-related traits, including BMI, waist circumference, and body fat percentage, were causally associated with liver cancer risk, whereas glycemic traits were not. Bioinformatics screening identified GPAT3 as a lipid metabolism regulator upregulated in HCC, induced by palmitic acid, associated with poor prognosis, and enriched in patients with higher BMI. Tissue microarray analysis confirmed increased GPAT3 protein expression in HCC and its association with higher BMI and GPX4 expression. GPAT3 depletion sensitized HCC cells to palmitic acid-induced ferroptosis, whereas Fer-1 rescue and GPAT3 overexpression supported its protective role. In vivo, FSG67 enhanced sorafenib-associated antitumor effects and increased tumor lipid peroxidation.

CONCLUSIONS: GPAT3 protects HCC cells from lipid stress-induced ferroptosis and represents a potential metabolic vulnerability in obesity-associated HCC.

PMID:42785105 | DOI:10.1016/j.bbadis.2026.168471

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GPAT3 protects against lipid stress-induced ferroptosis in hepatocellular carcinoma: From multi-omics analysis to functional validation

Biochim Biophys Acta Mol Basis Dis. 2027 Jan;1873(1):168471. doi: 10.1016/j.bbadis.2026.168471. Epub 2026 Sep 24.

ABSTRACT

BACKGROUND: The global burden of metabolic-associated hepatocellular carcinoma (HCC) is increasing, with obesity emerging as a key causal factor. However, the molecular mechanisms linking lipid metabolic dysregulation to HCC progression and therapeutic vulnerability remain unclear.

METHODS: We analyzed Global Burden of Disease 2021 data to assess liver cancer burden attributable to metabolic risks from 1990 to 2021. Mendelian randomization was used to evaluate causal associations between metabolic traits and liver cancer risk. TCGA, GTEx, and GEO datasets were integrated to identify lipid stress-responsive regulators. Clinical relevance was assessed using public datasets and tissue microarray immunohistochemistry. Functional validation was performed in HCC cells and a high-fat diet-fed syngeneic mouse tumor model.

RESULTS: Liver cancer deaths and DALYs attributable to metabolic risks increased markedly from 1990 to 2021. Mendelian randomization showed that obesity-related traits, including BMI, waist circumference, and body fat percentage, were causally associated with liver cancer risk, whereas glycemic traits were not. Bioinformatics screening identified GPAT3 as a lipid metabolism regulator upregulated in HCC, induced by palmitic acid, associated with poor prognosis, and enriched in patients with higher BMI. Tissue microarray analysis confirmed increased GPAT3 protein expression in HCC and its association with higher BMI and GPX4 expression. GPAT3 depletion sensitized HCC cells to palmitic acid-induced ferroptosis, whereas Fer-1 rescue and GPAT3 overexpression supported its protective role. In vivo, FSG67 enhanced sorafenib-associated antitumor effects and increased tumor lipid peroxidation.

CONCLUSIONS: GPAT3 protects HCC cells from lipid stress-induced ferroptosis and represents a potential metabolic vulnerability in obesity-associated HCC.

PMID:42785105 | DOI:10.1016/j.bbadis.2026.168471

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PhysCodeBench: Benchmarking Physics-Aware Symbolic Simulation of 3D Scenes via Self-Corrective Multi-Agent Refinement

arXiv:2604.23580v2 Announce Type: replace-cross Abstract: Translating natural-language descriptions of physical phenomena into executable simulation code requires both programming expertise and physical reasoning. Current large language models (LLMs) lack this combination: they frequently produce code that runs but simulates the wrong physics. We introduce PhysCodeBench, the first benchmark for this task, with 1,200 expert-validated examples spanning four physical domains. Its evaluation suite, PhysCodeEval, goes beyond executability and visual fidelity to measure physical correctness directly from the engine state via conservation-law residuals and expert-written assertions, and supports cross-engine evaluation to disentangle physics reasoning from API fluency. As a reference method, we propose the Self-Corrective Multi-Agent Refinement Framework (SMRF), which decouples physics-aware error correction from code generation through specialized agents. This design is motivated by our finding that targeted correction, rather than generic iterative refinement, is the key driver of physical accuracy. SMRF nearly triples the physical-assertion pass rate of the best proprietary baseline (70.6\% vs.\ 23.8\%) and retains its advantage under cross-engine transfer.
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NSR-Boost: A Neuro-Symbolic Residual Boosting Framework for Industrial Legacy Models

arXiv:2601.10457v3 Announce Type: replace Abstract: Although the Gradient Boosted Decision Trees (GBDTs) dominate industrial tabular applications, upgrading legacy models in high-concurrency production environments still faces prohibitive retraining costs and systemic risks. To address this problem, we present NSR-Boost, a neuro-symbolic residual boosting framework designed specifically for industrial scenarios. Its core advantage lies in being ``non-intrusive''. It treats the legacy model as a frozen model and performs targeted repairs on "hard regions" where predictions fail. The framework comprises three key stages: First, finding hard regions through residuals, then generating interpretable experts by generating symbolic code structures using Large Language Model (LLM) and fine-tuning parameters using Bayesian optimization, and finally dynamically integrating experts with legacy model output through a lightweight aggregator. Experimental results demonstrate that the framework significantly outperforms state-of-the-art (SOTA) baselines across six public datasets and one private dataset. More importantly, we report the successful deployment of NSR-Boost within the core financial risk control system of Qfin Holdings, where empirical results on real-world online traffic exhibit superior performance improvements and a significant reduction in the bad rate. In conclusion, it effectively captures long-tail risks missed by traditional models and offers a safe, low-cost evolutionary paradigm for industry.
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InCoder-32B: Code Foundation Model for Industrial Scenarios

arXiv:2603.16790v3 Announce Type: replace-cross Abstract: Recent code large language models have achieved remarkable progress on general programming tasks. Nevertheless, their performance degrades significantly in industrial scenarios that require reasoning about hardware semantics, specialized language constructs, and strict resource constraints. To address these challenges, we introduce InCoder-32B (Industrial-Coder-32B), the first 32B-parameter code foundation model unifying code intelligence across chip design, GPU kernel optimization, embedded systems, compiler optimization, and 3D modeling. By adopting an efficient architecture, we train InCoder-32B from scratch with general code pre-training, curated industrial code annealing, mid-training that progressively extends context from 8K to 128K tokens with synthetic industrial reasoning data, and post-training with execution-grounded verification. We conduct extensive evaluation on 14 mainstream general code benchmarks and 9 industrial benchmarks spanning 4 specialized domains. Results show InCoder-32B achieves highly competitive performance on general tasks while establishing strong open-source baselines across industrial domains.
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The 1000 Chinese Pangenome empowers medical and population genetics

Nature, Published online: 01 April 2026; doi:10.1038/s41586-026-10315-y

Development of the pangenome-informed genome assembly (PIGA) workflow enabled the generation of 1,116 diploid genome assemblies (55 de novo and 1,061 pangenome-informed), representing an extensive resource of medically relevant genic variations.
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Ran Score: a LLM-based Evaluation Score for Radiology Report Generation

arXiv:2603.22935v1 Announce Type: new Abstract: Chest X-ray report generation and automated evaluation are limited by poor recognition of low-prevalence abnormalities and inadequate handling of clinically important language, including negation and ambiguity. We develop a clinician-guided framework combining human expertise and large language models for multi-label finding extraction from free-text chest X-ray reports and use it to define Ran Score, a finding-level metric for report evaluation. Using three non-overlapping MIMIC-CXR-EN cohorts from a public chest X-ray dataset and an independent ChestX-CN validation cohort, we optimize prompts, establish radiologist-derived reference labels and evaluate report generation models. The optimized framework improves the macro-averaged score from 0.753 to 0.956 on the MIMIC-CXR-EN development cohort, exceeds the CheXbert benchmark by 15.7 percentage points on directly comparable labels, and shows robust generalization on the ChestX-CN validation cohort. Here we show that clinician-guided prompt optimization improves agreement with a radiologist-derived reference standard and that Ran Score enables finding-level evaluation of report fidelity, particularly for low-prevalence abnormalities.
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OmniVideoBench: Towards Audio-Visual Understanding Evaluation for Omni MLLMs

arXiv:2510.10689v2 Announce Type: replace Abstract: Recent advances in multimodal large language models (MLLMs) have demonstrated substantial potential in video understanding. However, existing benchmarks fail to comprehensively evaluate synergistic reasoning capabilities across audio and visual modalities, often neglecting either one of the modalities or integrating them in a logically inconsistent manner. To bridge this gap, we introduce OmniVideoBench, a large-scale and rigorously designed benchmark dedicated to assessing synergistic audio-visual understanding, with a strong emphasis on modality complementarity and logical consistency. Specifically, OmniVideoBench comprises 1000 high-quality question-answer(QA) pairs, each annotated with step-by-step reasoning traces, derived from 628 diverse videos ranging from several seconds to 30 minutes, and manually verified to guarantee complete correctness and uniqueness. Moreover, OmniVideoBench encompasses 13 carefully designed question types, covering temporal reasoning, spatial localization, counting, causal inference, summarization, and beyond, thereby capturing the essential challenges of video understanding. Evaluation of multiple MLLMs on OmniVideoBench reveals a pronounced gap between model performance and human reasoning, with open-source models lagging significantly behind their closed-source counterparts, underscoring the inherent difficulty of genuine audio-visual reasoning. We will release OmniVideoBench to foster the development of MLLMs with stronger and more generalizable reasoning capabilities.
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