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Received — 18 September 2026 ⏭

Factor IX Padua AAV gene therapy in adolescents with hemophilia B: a phase 1 trial

✇Nature Medicine
By: Feng Xue · Ai Zhang · Mankai Ju · Xia Wu · Bingqi Xu · Xiaolei Pei · Yuhua Wang · Wei Liu · Aiguo Liu · Wei Jiang · Xinyue Dai · Yunfei Chen · Xiaofan Liu · Rongfeng Fu · Ting Sun · Wenjing Gu · Jing Zheng · Zengmin Du · Caifeng Yang · Xiao Xiao · Renchi Yang · Qun Hu · Lei Zhang
16 September 2026 at 08:00

Nature Medicine, Published online: 16 September 2026; doi:10.1038/s41591-026-04636-8

In this single-arm phase 1 trial, an AAV gene therapy carrying the Padua variant of factor IX was well tolerated in 11 adolescents with hemophilia B and led to reductions in annualized bleeding rate.
Received — 27 May 2026 ⏭

FineBench: Benchmarking and Enhancing Vision-Language Models for Fine-grained Human Activity Understanding

✇cs.AI, q-bio.NC updates on arXiv.org
By: Gueter Josmy Faure · Min-Hung Chen · Jia-Fong Yeh · Hung-Ting Su · Winston H. Hsu
26 May 2026 at 12:00
arXiv:2605.19846v3 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) have demonstrated remarkable capabilities in general video understanding, yet they often struggle with the fine-grained comprehension crucial for real-world applications requiring nuanced interpretation of human actions and interactions. While some recent human-centric benchmarks evaluate aspects of model behaviour such as fairness/ethics, emotion perception, and broader human-centric metrics, they do not combine long-form videos, very dense QA coverage, and frame-level spatial/temporal grounding at scale. To bridge this gap, we introduce FineBench, a human-centric video question answering (VQA) benchmark specifically designed to assess fine-grained understanding. FineBench comprises 199,420 multiple-choice QA pairs densely annotated across 64 long-form videos (15 minutes each), focusing on detailed person movement, person interaction, and object manipulation, including compositional actions. Our extensive evaluation reveals that while proprietary models like GPT-5 achieve respectable performance, current open-source VLMs significantly underperform, struggling particularly with spatial reasoning in multi-person scenes and distinguishing subtle differences in human movements and interactions. To address these identified weaknesses, we propose FineAgent, a modular framework that enhances VLMs by leveraging a Localizer and a Descriptor. Experiments show that FineAgent consistently improves the performance of various open VLMs on FineBench. FineBench provides a rigorous testbed for future research into fine-grained human-centric video understanding, while FineAgent offers a practical approach to enhance such reasoning in current VLMs. Project page and code at https://joslefaure.github.io/assets/html/finebench.html.
Received — 8 April 2026 ⏭

FAST-CAD: A Fairness-Aware Framework for Non-Contact Stroke Diagnosis

✇cs.AI, q-bio.NC updates on arXiv.org
By: Tommy Sha (Kindred) · Zhan Cheng (Kindred) · Haotian Zhai (Kindred) · Xuwei Ding (Kindred) · Junnan Li (Kindred) · Haixiang Tang (Kindred) · Zaoting Sun (Kindred) · Yanchuan Tang (Kindred) · Yongzhe (Kindred) · Yi · Yuan Gao · Anhao Li
7 April 2026 at 12:00
arXiv:2511.08887v4 Announce Type: replace-cross Abstract: Stroke is an acute cerebrovascular disease, and timely diagnosis significantly improves patient survival. However, existing automated diagnosis methods suffer from fairness issues across demographic groups, potentially exacerbating healthcare disparities. In this work we propose FAST-CAD, a theoretically grounded framework that combines domain-adversarial training (DAT) with group distributionally robust optimization (Group-DRO) for fair and accurate non-contact stroke diagnosis. Our approach is built on domain adaptation and minimax fairness theory and provides convergence guarantees and fairness bounds. We curate a multimodal dataset covering 12 demographic subgroups defined by age, gender, and posture. FAST-CAD employs self-supervised encoders with adversarial domain discrimination to learn demographic-invariant representations, while Group-DRO optimizes worst-group risk to ensure robust performance across all subgroups. Extensive experiments show that our method achieves superior diagnostic performance while maintaining fairness across demographic groups, and our theoretical analysis supports the effectiveness of the unified DAT + Group-DRO framework. This work provides both practical advances and theoretical insights for fair medical AI systems.
Received — 2 April 2026 ⏭

Catgut implantation at acupoints improves anti-PD-1 inhibitor efficacy in lung cancer by inducing immune responses and remodeling the tumor microenvironment

✇Omics In Lung
By: Qian Wu · Ting Su · Yuanyuan Zhang · Yuerong Xiong · Xiaoyan Hu · Lijuan Shao · Size Chen
31 March 2026 at 18:00

Cancer Immunol Immunother. 2026 Mar 31;75(4):126. doi: 10.1007/s00262-026-04368-1.

ABSTRACT

While anti-programmed death-1 (anti-PD-1) therapy has revolutionized lung cancer treatment, its efficacy remains limited by an immunosuppressive tumor microenvironment (TME). We therefore investigated whether combining anti-PD-1 inhibitor with catgut embedding at the Zusanli acupoint (CIAA) could enhance anti-tumor immunity by reprogramming the TME in a lung cancer mouse model. Combining in vivo tumor monitoring, multi-parametric immune profiling (flow cytometry, IHC, ELISA), and multi-omics analyses (transcriptomics and metabolomics), we found that the combination therapy was associated with enhanced tumor growth inhibition. This effect correlated with a comprehensive TME transformation: conversion to an immunologically active state with increased effector immune cell infiltration (CD8⁺ T, CD4⁺ T, B cells, macrophages) and decreased regulatory T cells, coupled with suppression of pro-tumorigenic factors (VEGF, IL-6). Integrated omics analysis suggests that the combined treatment may modulate tumor-stroma interaction pathways (e.g., PI3K-Akt, focal adhesion) and rewire immunometabolic networks (e.g., tryptophan metabolism). Our study provides hypothesis-generating correlative data positioning CIAA as a potential adjunct capable of remodeling the TME to potentiate anti-PD-1 therapy in lung cancer.

PMID:41915222 | PMC:PMC13038699 | DOI:10.1007/s00262-026-04368-1

Catgut implantation at acupoints improves anti-PD-1 inhibitor efficacy in lung cancer by inducing immune responses and remodeling the tumor microenvironment

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Qian Wu · Ting Su · Yuanyuan Zhang · Yuerong Xiong · Xiaoyan Hu · Lijuan Shao · Size Chen
31 March 2026 at 18:00

Cancer Immunol Immunother. 2026 Mar 31;75(4):126. doi: 10.1007/s00262-026-04368-1.

ABSTRACT

While anti-programmed death-1 (anti-PD-1) therapy has revolutionized lung cancer treatment, its efficacy remains limited by an immunosuppressive tumor microenvironment (TME). We therefore investigated whether combining anti-PD-1 inhibitor with catgut embedding at the Zusanli acupoint (CIAA) could enhance anti-tumor immunity by reprogramming the TME in a lung cancer mouse model. Combining in vivo tumor monitoring, multi-parametric immune profiling (flow cytometry, IHC, ELISA), and multi-omics analyses (transcriptomics and metabolomics), we found that the combination therapy was associated with enhanced tumor growth inhibition. This effect correlated with a comprehensive TME transformation: conversion to an immunologically active state with increased effector immune cell infiltration (CD8⁺ T, CD4⁺ T, B cells, macrophages) and decreased regulatory T cells, coupled with suppression of pro-tumorigenic factors (VEGF, IL-6). Integrated omics analysis suggests that the combined treatment may modulate tumor-stroma interaction pathways (e.g., PI3K-Akt, focal adhesion) and rewire immunometabolic networks (e.g., tryptophan metabolism). Our study provides hypothesis-generating correlative data positioning CIAA as a potential adjunct capable of remodeling the TME to potentiate anti-PD-1 therapy in lung cancer.

PMID:41915222 | DOI:10.1007/s00262-026-04368-1

Doxorubicin promotes the production of inflammatory cytokines in tumor-associated macrophages through activating lactate dehydrogenase A

✇Cell Death Discovery nature.com science feeds
By: Bin Liu · Wei Yang · Shuo Feng · Xingning Jiang · Jingfan Lu · Fei Wang · Yaowen Hu · Yanhao Liu · Haifeng Ma · Ting Sun
31 March 2026 at 08:00

Cell Death Discovery, Published online: 31 March 2026; doi:10.1038/s41420-026-03014-0

Doxorubicin promotes the production of inflammatory cytokines in tumor-associated macrophages through activating lactate dehydrogenase A
Received — 26 March 2026 ⏭

PAK4 functions as an immune suppressor by reprogramming the phosphatidylcholine metabolism of CD8 + T cells within the glioblastoma tumor microenvironment

✇Oncogene - Issue - nature.com science feeds
By: Lin Yao · Heyang Gao · Zuopeng Su · Guozheng Zhao · Liyan Tang · Shuo Feng · Yuting Ma · Xiao Zhang · Ming Feng · Jialiang Qian · Yanyan Li · Ting Sun · Jiangang Liu · Hao Wang · Youxin Zhou
23 March 2026 at 08:00

Oncogene, Published online: 23 March 2026; doi:10.1038/s41388-026-03734-8

PAK4 functions as an immune suppressor by reprogramming the phosphatidylcholine metabolism of CD8 + T cells within the glioblastoma tumor microenvironment
Received — 11 March 2026 ⏭

DevBench: A Realistic, Developer-Informed Benchmark for Code Generation Models

✇cs.AI, q-bio.NC updates on arXiv.org
By: Pareesa Ameneh Golnari · Adarsh Kumarappan · Wen Wen · Xiaoyu Liu · Gabriel Ryan · Yuting Sun · Shengyu Fu · Elsie Nallipogu
10 March 2026 at 12:00
arXiv:2601.11895v2 Announce Type: replace-cross Abstract: DevBench is a telemetry-driven benchmark designed to evaluate Large Language Models (LLMs) on realistic code completion tasks. It includes 1,800 evaluation instances across six programming languages and six task categories derived from real developer telemetry, such as API usage and code purpose understanding. Unlike prior benchmarks, it emphasizes ecological validity, avoids training data contamination, and enables detailed diagnostics. The evaluation combines functional correctness, similarity-based metrics, and LLM-judge assessments focused on usefulness and contextual relevance. 9 state-of-the-art models were assessed, revealing differences in syntactic precision, semantic reasoning, and practical utility. Our benchmark provides actionable insights to guide model selection and improvement-detail that is often missing from other benchmarks but is essential for both practical deployment and targeted model development.
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