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Pan-cancer oncolytic virotherapy through disruption of tumor cell mitochondrial dynamics

βœ‡Molecular Therapy
By: Shufeng Feng Β· Lu Cui Β· Yongxin Zhu Β· Yao Cheng Β· Dongmei Diao Β· Xuefeng Li Β· Huimin Duan Β· Haixia Zhang Β· Xiaoxiao Liu Β· Qing Pan Β· Fanpu Ji Β· Yu Zhang Β· Changbo Ou Β· Hai Li
Li and colleagues identified RhoA as a β€œredox rheostat” governing mitochondrial dynamics during oncolytic virotherapy and thereby engineered rNDV-RHOA, an NDV-based oncolytic virus overexpressing RhoA. This tumor-targeted RhoA overexpression synergizes oxidative stress and viral oncolysis, transcending conventional oncolysis by surmounting tumor heterogeneity through exploiting inherent tumor redox dependency.
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From Diet to Disease: The Role of the Gut Microbiome and Microbial Metabolites in Horses

βœ‡Omics in Gastric
By: Yuhong Wu Β· Yuhui Ma Β· Yiyi Zan Β· Xu Bai Β· Jing Li Β· Hai Li

Vet Sci. 2026 Aug 18;13(8):823. doi: 10.3390/vetsci13080823.

ABSTRACT

The equine gastrointestinal microbiome plays an essential role in digestion, energy metabolism, immune regulation, and maintenance of intestinal homeostasis. Increasing evidence suggests that microbial-derived metabolites provide a functional link between diet, the microbiome, and host physiology. This narrative review summarizes current knowledge on how dietary factors, including structural carbohydrates, non-structural carbohydrates, protein, and lipids, influence microbial function and metabolite production in horses. Relevant publications available up to June 2026 were identified through searches of PubMed, Web of Science, Scopus, and Google Scholar and were narratively synthesized according to dietary factors, microbial metabolites, associated diseases, and nutritional interventions. Emphasis is placed on major microbial metabolites, including short-chain fatty acids, endotoxins, bile acid derivatives, tryptophan metabolites, and nitrogenous fermentation products, and their potential roles in health and disease. Current evidence supports a central role for the nutrition-microbiota-metabolite axis in gastrointestinal disorders such as colic, colitis, equine gastric ulcer syndrome, and carbohydrate-associated laminitis, whereas its involvement in equine metabolic syndrome and the gut-lung axis remains less well defined. The review also discusses microbiome-targeted nutritional interventions and emerging multi-omics approaches that are improving our understanding of microbial function. Collectively, these findings highlight the potential of microbiome-informed nutritional strategies to support equine health, welfare, disease prevention, and athletic performance.

PMID:42655843 | PMC:PMC13517798 | DOI:10.3390/vetsci13080823

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Interpreting Omics Data Analysis with Large Language Models for Disease Target and Drug Discovery

βœ‡(Multiomics OR Omics) AND (Pancreatic)
By: Zixi Xu Β· Weihang Chen Β· Wuyu Ren Β· Tianqi Xu Β· Somadina Amaechina Β· Raad Khan Β· Yixin Chen Β· Michael Province Β· Philip Payne Β· Fuhai Li

bioRxiv [Preprint]. 2026 May 5:2026.04.30.721768. doi: 10.64898/2026.04.30.721768.

ABSTRACT

In biomedical scientific discovery, synthesizing prior knowledge from the literature is an essential component of interpreting numerical omics data analyses for disease target identification and drug discovery. Large language models (LLMs) alone can rapidly retrieve disease mechanisms from biomedical text, but text-only outputs are general and unreliable for target and drug prioritization without cohort-specific quantitative evidence. Herein, we propose a provenance-aware Text-to-Target framework that couples schema-constrained multi-model LLM retrieval with numeric omics data analysis. The key design is a modality-aware fusion step: candidates are partitioned into overlap-supported anchors, retrieval-only hidden hubs, and network-emergent novelty nodes, then propagated into staged hypothesis and strategy generation under topology constraints. We evaluate the model in Alzheimer's disease (AD) and pancreatic ductal adenocarcinoma (PDAC). In PDAC, the workflow produced a balanced 75-gene candidate universe and a 23-strategy portfolio, with significant DepMap support at both target level and strategy level. In AD, stricter candidate controls yielded a compact 34-gene universe and 14 strategies; under an expanded CRISPRbrain registry, both target-level axes were significant, with strong strategy-level enrichment. Across both diseases, final strategies preserved full provenance closure to the candidate pool, enabling end-to-end auditability from retrieval artifacts to validation outputs. These results support a transferable discovery architecture in which omics evidence constrains biological activity, LLM retrieval expands mechanistic search space, and network-aware fusion preserves interpretability. The framework provides a reproducible basis for dual-disease target prioritization and motivates continuous literature-mechanism concordance with agentic evidence-refresh loops.

PMID:42146439 | PMC:PMC13174328 | DOI:10.64898/2026.04.30.721768

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FURINA: A Fully Customizable Role-Playing Benchmark via Scalable Multi-Agent Collaboration Pipeline

βœ‡cs.AI, q-bio.NC updates on arXiv.org
By: Haotian Wu Β· Shufan Jiang Β· Chios Chen Β· Yiyang Feng Β· Hehai Lin Β· Heqing Zou Β· Yao Shu Β· Chengwei Qin
arXiv:2510.06800v3 Announce Type: replace-cross Abstract: As large language models (LLMs) advance in role-playing (RP) tasks, existing benchmarks quickly become obsolete due to their narrow scope, outdated interaction paradigms, and limited adaptability across diverse application scenarios. To address this gap, we introduce FURINA-Builder, a novel multi-agent collaboration pipeline that automatically constructs fully customizable RP benchmarks at any scale. It enables evaluation of arbitrary characters across diverse scenarios and prompt formats, as the first benchmark builder in RP area for adaptable assessment. FURINA-Builder simulates dialogues between a test character and other characters drawn from a well-constructed character-scene pool, while an LLM judge selects fine-grained evaluation dimensions and adjusts the test character's responses into final test utterances. Using this pipeline, we build FURINA-Bench, a new comprehensive role-playing benchmark featuring both established and synthesized test characters, each assessed with dimension-specific evaluation criteria. Human evaluation and preliminary separability analysis justify our pipeline and benchmark design. We conduct extensive evaluations of cutting-edge LLMs and find that o3 and DeepSeek-R1 achieve the best performance on English and Chinese RP tasks, respectively. Across all models, established characters consistently outperform synthesized ones, with reasoning capabilities further amplifying this disparity. Interestingly, we observe that model scale does not monotonically reduce hallucinations. More critically, for reasoning LLMs, we uncover a novel trade-off: reasoning improves RP performance but simultaneously increases RP hallucinations. This trade-off extends to a broader Pareto frontier between RP performance and reliability for all LLMs. These findings demonstrate the effectiveness of FURINA-Builder and the challenge posed by FURINA-Bench.
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Compressed-Domain-Aware Online Video Super-Resolution

βœ‡cs.AI, q-bio.NC updates on arXiv.org
By: Yuhang Wang Β· Hai Li Β· Shujuan Hou Β· Zhetao Dong Β· Xiaoyao Yang
arXiv:2603.07694v1 Announce Type: cross Abstract: In bandwidth-limited online video streaming, videos are usually downsampled and compressed. Although recent online video super-resolution (online VSR) approaches achieve promising results, they are still compute-intensive and fall short of real-time processing at higher resolutions, due to complex motion estimation for alignment and redundant processing of consecutive frames. To address these issues, we propose a compressed-domain-aware network (CDA-VSR) for online VSR, which utilizes compressed-domain information, including motion vectors, residual maps, and frame types to balance quality and efficiency. Specifically, we propose a motion-vector-guided deformable alignment module that uses motion vectors for coarse warping and learns only local residual offsets for fine-tuned adjustments, thereby maintaining accuracy while reducing computation. Then, we utilize a residual map gated fusion module to derive spatial weights from residual maps, suppressing mismatched regions and emphasizing reliable details. Further, we design a frame-type-aware reconstruction module for adaptive compute allocation across frame types, balancing accuracy and efficiency. On the REDS4 dataset, our CDA-VSR surpasses the state-of-the-art method TMP, with a maximum PSNR improvement of 0.13 dB while delivering more than double the inference speed. The code will be released at https://github.com/sspBIT/CDA-VSR.
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