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Received — 12 January 2026 ⏭ Favourites

Streamlining evidence based clinical recommendations with large language models

✇cs.AI, q-bio.NC updates on arXiv.org
By: Dubai Li · Nan Jiang · Kangping Huang · Ruiqi Tu · Shuyu Ouyang · Huayu Yu · Lin Qiao · Chen Yu · Tianshu Zhou · Danyang Tong · Qian Wang · Mengtao Li · Xiaofeng Zeng · Yu Tian · Xinping Tian · Jingsong Li
12 January 2026 at 13:00
arXiv:2505.10282v2 Announce Type: replace-cross Abstract: Clinical evidence underpins informed healthcare decisions, yet integrating it into real-time practice remains challenging due to intensive workloads, complex procedures, and time constraints. This study presents Quicker, an LLM-powered system that automates evidence synthesis and generates clinical recommendations following standard guideline development workflows. Quicker delivers an end-to-end pipeline from clinical questions to recommendations and supports customized decision-making through integrated tools and interactive interfaces. To evaluate how closely Quicker can reproduce guideline development processes, we constructed Q2CRBench-3, a benchmark derived from guideline development records for three diseases. Experiments show that Quicker produces precise question decomposition, expert-aligned retrieval, and near-comprehensive screening. Quicker assistance improved the accuracy of extracted study data, and its recommendations were more comprehensive and coherent than clinician-written ones. In system-level testing, Quicker working with one participant reduced recommendation development to 20-40 min. Overall, the findings demonstrate Quicker's potential to enhance the speed and reliability of evidence-based clinical decision-making.

CliCARE: Grounding Large Language Models in Clinical Guidelines for Decision Support over Longitudinal Cancer Electronic Health Records

✇cs.AI, q-bio.NC updates on arXiv.org
By: Dongchen Li · Jitao Liang · Wei Li · Xiaoyu Wang · Longbing Cao · Kun Yu
12 January 2026 at 13:00
arXiv:2507.22533v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) hold significant promise for improving clinical decision support and reducing physician burnout by synthesizing complex, longitudinal cancer Electronic Health Records (EHRs). However, their implementation in this critical field faces three primary challenges: the inability to effectively process the extensive length and fragmented nature of patient records for accurate temporal analysis; a heightened risk of clinical hallucination, as conventional grounding techniques such as Retrieval-Augmented Generation (RAG) do not adequately incorporate process-oriented clinical guidelines; and unreliable evaluation metrics that hinder the validation of AI systems in oncology. To address these issues, we propose CliCARE, a framework for Grounding Large Language Models in Clinical Guidelines for Decision Support over Longitudinal Cancer Electronic Health Records. The framework operates by transforming unstructured, longitudinal EHRs into patient-specific Temporal Knowledge Graphs (TKGs) to capture long-range dependencies, and then grounding the decision support process by aligning these real-world patient trajectories with a normative guideline knowledge graph. This approach provides oncologists with evidence-grounded decision support by generating a high-fidelity clinical summary and an actionable recommendation. We validated our framework using large-scale, longitudinal data from a private Chinese cancer dataset and the public English MIMIC-IV dataset. In these settings, CliCARE significantly outperforms baselines, including leading long-context LLMs and Knowledge Graph-enhanced RAG methods. The clinical validity of our results is supported by a robust evaluation protocol, which demonstrates a high correlation with assessments made by oncologists.
Received — 7 January 2026 ⏭ Favourites

Wearable-informed generative digital avatars predict task-conditioned post-stroke locomotion

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yanning Dai · Chenyu Tang · Ruizhi Zhang · Wenyu Yang · Yilan Zhang · Yuhui Wang · Junliang Chen · Xuhang Chen · Ruimou Xie · Yangyue Cao · Qiaoying Li · Jin Cao · Tao Li · Hubin Zhao · Yu Pan · Arokia Nathan · Xin Gao · Peter Smielewski · Shuo Gao
6 January 2026 at 13:00
arXiv:2512.14329v2 Announce Type: replace-cross Abstract: Dynamic prediction of locomotor capacity after stroke could enable more individualized rehabilitation, yet current assessments largely provide static impairment scores and do not indicate whether patients can perform specific tasks such as slope walking or stair climbing. Here, we present a wearable-informed data-physics hybrid generative framework that reconstructs a stroke survivor's locomotor control from wearable inertial sensing and predicts task-conditioned post-stroke locomotion in new environments. From a single 20 m level-ground walking trial recorded by five IMUs, the framework personalizes a physics-based digital avatar using a healthy-motion prior and hybrid imitation learning, generating dynamically feasible, patient-specific movements for inclined walking and stair negotiation. Across 11 stroke inpatients, predicted postures reached 82.2% similarity for slopes and 69.9% for stairs, substantially exceeding a physics-only baseline. In a multicentre pilot randomized study (n = 21; 28 days), access to scenario-specific locomotion predictions to support task selection and difficulty titration was associated with larger gains in Fugl-Meyer lower-extremity scores than standard care (mean change 6.0 vs 3.7 points; $p
Received — 18 December 2025 ⏭ Favourites

A data-physics hybrid generative model for patient-specific post-stroke motor rehabilitation using wearable sensor data

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yanning Dai · Chenyu Tang · Ruizhi Zhang · Wenyu Yang · Yilan Zhang · Yuhui Wang · Junliang Chen · Xuhang Chen · Ruimou Xie · Yangyue Cao · Qiaoying Li · Jin Cao · Tao Li · Hubin Zhao · Yu Pan · Arokia Nathan · Xin Gao · Peter Smielewski · Shuo Gao
17 December 2025 at 13:00
arXiv:2512.14329v1 Announce Type: cross Abstract: Dynamic prediction of locomotor capacity after stroke is crucial for tailoring rehabilitation, yet current assessments provide only static impairment scores and do not indicate whether patients can safely perform specific tasks such as slope walking or stair climbing. Here, we develop a data-physics hybrid generative framework that reconstructs an individual stroke survivor's neuromuscular control from a single 20 m level-ground walking trial and predicts task-conditioned locomotion across rehabilitation scenarios. The system combines wearable-sensor kinematics, a proportional-derivative physics controller, a population Healthy Motion Atlas, and goal-conditioned deep reinforcement learning with behaviour cloning and generative adversarial imitation learning to generate physically plausible, patient-specific gait simulations for slopes and stairs. In 11 stroke survivors, the personalized controllers preserved idiosyncratic gait patterns while improving joint-angle and endpoint fidelity by 4.73% and 12.10%, respectively, and reducing training time to 25.56% relative to a physics-only baseline. In a multicentre pilot involving 21 inpatients, clinicians who used our locomotion predictions to guide task selection and difficulty obtained larger gains in Fugl-Meyer lower-extremity scores over 28 days of standard rehabilitation than control clinicians (mean change 6.0 versus 3.7 points). These findings indicate that our generative, task-predictive framework can augment clinical decision-making in post-stroke gait rehabilitation and provide a template for dynamically personalized motor recovery strategies.
Received — 10 December 2025 ⏭ Favourites

WisPaper: Your AI Scholar Search Engine

✇cs.AI, q-bio.NC updates on arXiv.org
By: Li Ju · Jun Zhao · Mingxu Chai · Ziyu Shen · Xiangyang Wang · Yage Geng · Chunchun Ma · Hao Peng · Guangbin Li · Tao Li · Chengyong Liao · Fu Wang · Xiaolong Wang · Junshen Chen · Rui Gong · Shijia Liang · Feiyan Li · Ming Zhang · Kexin Tan · Jujie Ye · Zhiheng Xi · Shihan Dou · Tao Gui · Yuankai Ying · Yang Shi · Yue Zhang · Qi Zhang
9 December 2025 at 13:00
arXiv:2512.06879v1 Announce Type: cross Abstract: Researchers struggle to efficiently locate and manage relevant literature within the exponentially growing body of scientific publications. We present \textsc{WisPaper}, an intelligent academic retrieval and literature management platform that addresses this challenge through three integrated capabilities: (1) \textit{Scholar Search}, featuring both quick keyword-based and deep agentic search modes for efficient paper discovery; (2) \textit{Library}, a customizable knowledge base for systematic literature organization; and (3) \textit{AI Feeds}, an intelligent recommendation system that automatically delivers relevant new publications based on user interests. Unlike existing academic tools, \textsc{WisPaper} provides a closed-loop workflow that seamlessly connects literature discovery, management, and continuous tracking of research frontiers. Our multilingual and multidisciplinary system significantly reduces the time researchers from diverse backgrounds spend on paper screening and management, enabling them to focus on their core research activities. The platform is publicly accessible and serves researchers across academia and industry.
Received — 9 December 2025 ⏭ Favourites

Chinese Discharge Drug Recommendation in Metabolic Diseases with Large Language Models

✇cs.AI, q-bio.NC updates on arXiv.org
By: Juntao Li · Haobin Yuan · Ling Luo · Yan Jiang · Fan Wang · Ping Zhang · Huiyi Lv · Jian Wang · Yuanyuan Sun · Hongfei Lin
8 December 2025 at 13:00
arXiv:2510.21084v2 Announce Type: replace-cross Abstract: Intelligent drug recommendation based on Electronic Health Records (EHRs) is critical for improving the quality and efficiency of clinical decision-making. By leveraging large-scale patient data, drug recommendation systems can assist physicians in selecting the most appropriate medications according to a patient's medical history, diagnoses, laboratory results, and comorbidities. Recent advances in large language models (LLMs) have shown remarkable capabilities in complex reasoning and medical text understanding, making them promising tools for drug recommendation tasks. However, the application of LLMs for Chinese clinical medication recommendation remains largely unexplored. In this work, we conduct a systematic investigation of LLM-based methodologies for Chinese discharge medication recommendation. We evaluate several representative LLM families (GLM, Llama, Qwen) under a unified methodological framework including zero-shot prompting, in-context learning, chain-of-thought prompting, and supervised fine-tuning using LoRA. We analyze model behavior across reasoning styles, error patterns, domain adaptation mechanisms, and robustness. Experimental results show that while supervised fine-tuning improves model performance, there remains substantial room for improvement, with the best model achieving the F1 score of 0.5648 and Jaccard score of 0.4477. Our findings highlight both the potential and limitations of LLMs for Chinese drug recommendation.
Received — 5 October 2025 ⏭ Favourites

Scaling DNA synthesis with a microchip-based massively parallel synthesis system

✇Nature Biotechnology - Issue - nature.com science feeds
By: Xiandi Zhang · Xiang’er Jiang · Yun Wang · Qinzhen Chen · Hao Jiang · Hu Zhang · Antoni Beltran · Weiya Yang · Tai Chen · Chenglong Liang · Ning Cheng · Yun Huang · Guqiao Ding · Chengwang Xie · Nanfeng Gao · Juntao Liu · Wei Xu · Jinlei Huang · Dong Cai · Lihao Zhu · Songjin Mo · Mengzhe Shen · Wenwei Zhang · Ben Lehner · Ming Ni · Jian Wang · Xun Xu · Yue Shen
1 October 2025 at 08:00

Nature Biotechnology, Published online: 01 October 2025; doi:10.1038/s41587-025-02844-0

A microchip-based DNA synthesis method enables scalable production of complex DNA constructs.
Received — 15 July 2025 ⏭ Favourites

Cancer-Associated Fibroblasts: Heterogeneity, Cancer Pathogenesis, and Therapeutic Targets

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Yutao Li · Qingyun Liu · Xilin Jing · Yuqi Wang · Xiaohua Jia · Xing Yang · Kezhong Chen
14 July 2025 at 18:00

MedComm (2020). 2025 Jul 11;6(7):e70292. doi: 10.1002/mco2.70292. eCollection 2025 Jul.

ABSTRACT

Cancer-associated fibroblasts (CAFs) are functionally diverse stromal regulators that orchestrate tumor progression, metastasis, and therapy resistance through dynamic crosstalk within the tumor microenvironment (TME). Recent advances in single-cell multiomics and spatial transcriptomics have identified conserved CAF subtypes with distinct molecular signatures, spatial distributions, and context-dependent roles, highlighting their dual capacity to promote immunosuppression or restrain tumor growth. However, therapeutic strategies struggle to reconcile this functional duality, hindering clinical translation. This review systematically categorizes CAF subtypes by origin, biomarkers, and TME-specific functions, focusing on their roles in chemoresistance, maintenance of stemness, and formation of immunosuppressive niches. We evaluate emerging targeting approaches, including selective depletion of tumor-promoting subsets (e.g., fibroblast activation protein+ CAFs), epigenetic reprogramming toward antitumor phenotypes, and inhibition of CXCL12/CXCR4 or transforming growth factor-beta signaling pathways. Spatial multiomics-driven combinatorial therapies, such as the synergistic use of CAFs and immune checkpoint inhibitors, are highlighted as strategies to overcome microenvironment-driven resistance. By integrating CAF biology with translational advances, this work provides a roadmap for developing subtype-specific biomarkers and precision stromal therapies, directly informing efforts to disrupt tumor-stroma coevolution. Key concepts include spatial transcriptomics, stromal reprogramming, and tumor-stroma coevolution, offering actionable insights for both mechanistic research and clinical innovation.

PMID:40656546 | PMC:PMC12246558 | DOI:10.1002/mco2.70292

Received — 2 July 2025 ⏭ Favourites

Human embryo research: how to move towards a 28-day limit

✇Nature - Issue - nature.com science feeds
By: Alejandro De Los Angeles · Nissim Benvenisty · Hongkui Deng · Jacob H. Hanna · Julian Koplin · Tianqing Li · Pentao Liu · Yuin-Han Loh · Duanqing Pei · Ryohichi Sugimura · Timothy Theodore Ka Ki Tam · Tan Tao · Alan Trounson · Shao Xu · Leqian Yu · Elias Zambidis · Magdalena Zernicka-Goetz · Robin Lovell-Badge
1 July 2025 at 08:00

Nature, Published online: 01 July 2025; doi:10.1038/d41586-025-02016-9

The decades-old limit on how long human embryos can be grown in culture is under debate. A new road map outlines how to extend the length of culture responsibly.
Received — 28 April 2025 ⏭ Favourites

Liquid biopsy-derived extracellular vesicle protein biomarkers for diagnosis and prognostic assessment of lung squamous cell carcinoma

✇Pulmonary nodule
By: Sheng Ma · Na Zhao · Xin Dong · Yaru Wang · Lei Song · Ruiqi Zheng · Xiaochen Zhi · Congcong Ma · Shujun Cheng · Jie Li · Yutao Liu · Ting Xiao
24 April 2025 at 18:00

Cancer Cell Int. 2025 Apr 24;25(1):161. doi: 10.1186/s12935-025-03792-0.

ABSTRACT

BACKGROUND: For patients with nodules detected in imaging that are indeterminate for malignancy, achieving accurate, early, and non-invasive diagnosis of Lung Squamous Cell Carcinoma (LUSC) remains a significant challenge. Therefore, we aimed to establish diagnostic and prognostic models by identifying plasma extracellular vesicles (EVs) associated protein biomarkers specific to LUSC.

METHODS: This study employed a novel nanomaterial, NaY, for the enrichment of EVs from plasma. Validation was conducted through transmission electron microscopy, nanoparticle tracking analyses, and Western blotting. Machine learning algorithms were utilized to compute protein biomarkers associated with LUSC and establish a diagnostic model. Additionally, a prognostic prediction model for LUSC was developed using a combination of 101 machine learning algorithms. Risk scoring of patients was performed to explore the underlying reasons for prognostic differences between high and low-risk groups.

RESULTS: The results of three experiments demonstrate that the new nanomaterial NaY effectively enriches EVs from plasma. Analysis of the enriched profile reveals pathways related to glycolysis/gluconeogenesis and carbon metabolism enriched in plasma EVs of LUSC patients. Thirty-eight LSCC-related EV biomarkers were identified, from which five proteins (TUBB3, RPS7, RPLP1, KRT2, and VTN) were selected to establish a diagnostic model distinguishing between benign and LUSC nodules. The diagnostic efficacy of RPS7 and VTN was further validated in independent samples using ELISA experiments. Furthermore, DPYD, GALK1, CDC23, UBE2L3, RHEB, and PSME1 were determined as potential prognostic biomarkers. Subsequently, risk scores were computed for each sample, classifying all patients into high and low-risk groups. Enrichment analysis revealed that EVs from the high-risk group contained proteins promoting cell proliferation and invasion, while those from the low-risk group were enriched in immune-related protein biomarkers.

CONCLUSIONS: The novel nanomaterial NaY effectively enriches EVs from plasma. Utilizing plasma EV biomarkers, the diagnostic model demonstrates strong discriminative ability between benign and malignant pulmonary nodules in patients.

PMID:40275246 | PMC:PMC12023671 | DOI:10.1186/s12935-025-03792-0

Received — 8 February 2025 ⏭ Favourites

Interplay between gut microbial communities and metabolites modulates pan-cancer immunotherapy responses

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Xiaoqiang Zhu · Muni Hu · Xiaowen Huang · Lingxi Li · Xiaolin Lin · Xiaoyan Shao · Jiantao Li · Xiaoyue Du · Xinjia Zhang · Rongrong Sun · Tianying Tong · Yanru Ma · Lijun Ning · Yi Jiang · Yue Zhang · Yuqi Shao · Zhenyu Wang · Yilu Zhou · Jinmei Ding · Ying Zhao · Baoqin Xuan · Hongyang Zhang · Youwei Zhang · Jie Hong · Jing-Yuan Fang · Xiuying Xiao · Bo Shen · Songbing He · Haoyan Chen
5 February 2025 at 19:00

Cell Metab. 2025 Jan 28:S1550-4131(24)00495-9. doi: 10.1016/j.cmet.2024.12.013. Online ahead of print.

ABSTRACT

Immune checkpoint blockade (ICB) therapy has revolutionized cancer treatment but remains effective in only a subset of patients. Emerging evidence suggests that the gut microbiome and its metabolites critically influence ICB efficacy. In this study, we performed a multi-omics analysis of fecal microbiomes and metabolomes from 165 patients undergoing anti-programmed cell death protein 1 (PD-1)/programmed death ligand 1 (PD-L1) therapy, identifying microbial and metabolic entities associated with treatment response. Integration of data from four public metagenomic datasets (n = 568) uncovered cross-cohort microbial and metabolic signatures, validated in an independent cohort (n = 138). An integrated predictive model incorporating these features demonstrated robust performance. Notably, we characterized five response-associated enterotypes, each linked to specific bacterial taxa and metabolites. Among these, the metabolite phenylacetylglutamine (PAGln) was negatively correlated with response and shown to attenuate anti-PD-1 efficacy in vivo. This study sheds light on the interplay among the gut microbiome, the gut metabolome, and immunotherapy response, identifying potential biomarkers to improve treatment outcomes.

PMID:39909032 | DOI:10.1016/j.cmet.2024.12.013

Received — 24 December 2024 ⏭ Favourites

Identification of miR-20b-5p as an inhibitory regulator in cardiac differentiation via TET2 and DNA hydroxymethylation

✇Most Recent Articles: Clinical Epigenetics
By: Ke-Xin Li， Jia-Ru Li， Sheng-Jia Zuo， Xudong Li， Xian-Tong Chen， Pei-Yi Xiao， Hui-Tao Li， Ling Sun， Tao Qian， Hao-Min Zhang， Dongxing Zhu， Xi-Yong Yu， Guojun Chen and Xue-Yan Jiang
15 March 2024 at 08:00
Congenital heart disease (CHD) is a prevalent congenital cardiac malformation, which lacks effective early biological diagnosis and intervention. MicroRNAs, as epigenetic regulators of cardiac development, pro...
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