Results 191 to 200 of about 91,735 (266)

The evolutionary landscape and serotypic dynamics of avian infectious bronchitis virus from spike protein

iMetaOmics, EarlyView.
This study integrates two decades of surveillance with genomic and structural analyses to decipher how spike protein glycosylation haplotypes drive avian coronavirus evolution. We uncover how specific glycosylation patterns associate with receptor‐binding affinity, shape global transmission dynamics, and correlate with clinical outcomes.
Hao Zhang, Zongxi Han, Chuangchao Zou, Yihui Huang, Shiping Sun, Ouyang Peng, Usama Ashraf, Qiuping Xu, Yao Ge, Qixiang Kang, Xiuli Ma, Xinheng Zhang, Ye Zhao, Xin Yang, Hongning Wang, Guozhong Zhang, Qingmei Xie, Mang Shi, Yongsen Ruan, Min Liao, Shengwang Liu, Yongchang Cao   +21 more
wiley   +1 more source

Metabolic Signatures and Diagnostic Prediction Models for Coronary Artery Disease and Type 2 Diabetes Mellitus: Insights From an Exploratory Study

iNew Medicine, EarlyView.
Metabolomic & lipidomic analysis reveals metabolic overlap between CAD & T2DM, highlighting 7 key metabolites as potential biomarkers. A predictive model based on these achieves high accuracy, potentially advancing precision medicine & metabolic insights.
Zhihua Wang, Meiming Su, Ying Li, Yapei Li, Shuo Chen, Ting Peng, Hans Strijdom, Jiangang Wang, Jianping Weng, Suowen Xu   +9 more
wiley   +1 more source

Interpretable machine learning enables early and accurate detection of drug‐induced liver injury: A multicenter study with real‐world clinical translation

Interdisciplinary Medicine, EarlyView.
This study develops an interpretable gradient‐boosting model that accurately identifies drug‐induced liver injury (DILI) using routine laboratory data. The model explains key clinical features through SHapley Additive exPlanations analysis and detects DILI earlier than expert evaluation, offering a transparent and practical tool for precision ...
Jingyi Ling, Shian Zhang, Jun Lin, Zhengju Xu, Runcheng Xu, Qian Lv, Kaixiang Zhang, Sheng Liu, Jie Guo, Cheng Hua, Yin Jia, Xiaoyu Xu, Kun Qian, Shanrong Liu   +13 more
wiley   +1 more source

Decoding temporal miRNA signatures of semen under in vitro exposure for forensic time since deposition estimation using machine learning‐driven modeling

Interdisciplinary Medicine, EarlyView.
This study develops a novel miRNA‐based framework for estimating the time since deposition of semen stains, combining small RNA sequencing with machine learning. Time‐dependent miRNA modules were identified using Mfuzz clustering and WGCNA, followed by a multi‐stage feature selection pipeline that reduced 261 candidate miRNAs to a minimal 7‐miRNA panel.
Meiming Cai, Qiong Lan, Tong Xie, Qinglin Liu, Ming Zhao, Xiaolian Wu, Xin Shi, Ruonan Shen, Yiman Wu, Chen Mao, Bin Cong, Bofeng Zhu   +11 more
wiley   +1 more source

Distinguishing lung cancer from atypical tuberculosis: Deep transfer learning and imaging omics features

Journal of Intelligent Medicine, EarlyView.
The 3D‐ResNet model demonstrates superior discriminative power in differentiating lung cancer from atypical tuberculosis by leveraging deep omics features derived from volumetric lung cancer imaging, outperforming conventional clinical and radiomic analyses.
Yi Wu, Siyi Li, Zhiping Long, Yanjie Jia, Yue Yu, Bing Pei, Heli Yuan, Yuhua Hao, Zhiyun Jiang, Lei Cao, Kezheng Wang, Fan Wang   +11 more
wiley   +1 more source

Artificial intelligence strategies for predicting kinase inhibitor resistance: A comprehensive review of methods, challenges, and future perspectives

Journal of Intelligent Medicine, EarlyView.
Abstract Kinase inhibitors are essential in targeted cancer therapy, yet resistance often emerges through secondary mutations, activation of compensatory signaling pathways, or drug‐efflux mechanisms. Artificial intelligence (AI) provides a workflow‐based strategy rather than a list of unrelated tools for predicting and addressing kinase‐inhibitor ...
Faris Hassan, Mohanad Ali Deifallah, Alaa Zaghloul, Rania Elgohary   +3 more
wiley   +1 more source

Deep Learning‐Based Skin Lesion Classification

Journal of Ultrasound in Medicine, EarlyView.
High‐frequency ultrasound (HFUS) is valuable for assessing skin lesions, supporting diagnosis, treatment monitoring, and surgical planning. This study evaluates deep learning models for binary classification of HFUS images acquired in B‐mode and Doppler mode.
Isabela Rocha Veiga da Silva, André Gonçalves Jardim, Giulia Rita de Souza Faés, Thatiane Alves Pianoschi Alva, Carla Diniz Lopes Becker, Viviane Rodrigues Botelho   +5 more
wiley   +1 more source

Machine learning model identifies tibial anatomical variables as potential risk factors for anterior cruciate ligament injury

Knee Surgery, Sports Traumatology, Arthroscopy, EarlyView.
Abstract Purpose The tibial slope is a well‐known risk factor for anterior cruciate ligament (ACL) injury. As machine learning continues to progress, it has become an increasingly explored tool for clinical screening and risk factor analysis. This study aims to develop and validate a prognostic machine learning model to predict the outcome of ACL ...
Cheng‐Hao Kao, Javad Hashemi, James Slauterbeck, Naveen Chandrashekar   +3 more
wiley   +1 more source

Predictive Modelling of Solvent Effects on Drug Incorporation into Polymeric Nanocarriers: A Machine Learning Approach

Macromolecular Rapid Communications, EarlyView.
When seeking nanoparticles with elevated drug loading content, the experimental setup, including solvent selection, is crucial. Through machine learning, we pinpointed that the drug's solubility in the organic solvent is the key factor for attaining high drug loading content.
Wei Ge, Ramindu De Silva, Yanan Fan, Scott A. Sisson, Martina H. Stenzel   +4 more
wiley   +1 more source

Role of High Fidelity Vs. Low Fidelity Experimental Data in Machine Learning Model Performance for Predicting Polymer Solubility

Macromolecular Rapid Communications, EarlyView.
The performance of machine learning models for classifying polymer solubility improves when a high‐fidelity experimental dataset is used compared to a low‐fidelity experimental dataset. This has important implications for justifying the value of spending additional time and resources preparing detailed experimental datasets.
Mona Amrihesari, Manali Banerjee, Raul Olmedo, Blair Brettmann   +3 more
wiley   +1 more source