Results 211 to 220 of about 88,634 (286)

Nonlinear drivers and spatial heterogeneity of urban heat islands in lake-dense regions across ecological stress-structure-service interactions. [PDF]

Sci Rep
Xiong S, Yang F, Fan H.
europepmc   +1 more source

Comprehensive spatial profiling reveals transitional microbiome dynamics and microbial heterogeneity in pediatric adenoid hypertrophy

iMetaOmics, EarlyView.
This study characterizes the upper respiratory microbiome in 276 children (101 Adenoid hypertrophy (AH) 119 Adenotonsillar hypertrophy (ATH), 11 Tonsil hypertrophy (TH), and 45 healthy controls by analyzing 1149 samples across five distinct niches: nasopharyngeal swabs (NS), adenoid swab (AS), and tonsil swabs (TS), plus adenoid tissues (AT) and tonsil
Kaining Chen, Xin Wan, Wenli Guo, Hongyan Yu, Yuantao Tong, Huiwen Ke, Junjie Fang, Xilian Luo, Di Che, Chao Hou, Xu Mei, Bingxue Hu, Yuehong Liu, Xiaoxiao Yu, Daoman Xiang, Wensi Chen, Yingjia Zeng, Yufen Xu, Jun Shen, Qiuxia Chen, Lei Pi, Lanyan Fu, Huazhong Zhou, Bingshao Liang, Meng Zhang, Yifan Dong, Yanqiu Chen, Wenhao Zhou, Xiaoqiong Gu   +28 more
wiley   +1 more source

Interpretable AI for treatment decision-making in immunoradiotherapy of locally advanced nasopharyngeal carcinoma. [PDF]

Front Oncol
Cao G, Zeng B, Yuan Z, Hu X, Ou H.
europepmc   +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 the renal-cochlear axis: explainable machine learning and phenotype clustering reveal high-risk hearing loss subtypes in CKD. [PDF]

Ren Fail
Chen L, Wang J, Liu G, Zhao Y, Zhou Z, Li Q.   +5 more
europepmc   +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

Biochip-simulated genotype signals enable accurate and interpretable AMR prediction via machine learning. [PDF]

Front Med (Lausanne)
Fu Z.
europepmc   +1 more source

Magnetocardiography combined with machine learning for pulmonary hypertension detection and improved short‐term risk assessment

Interdisciplinary Medicine, EarlyView.
Magnetocardiography (MCG) enables non‐invasive mapping of cardiac magnetic fields. In this study, an MCG‐based machine learning model detects pulmonary hypertension with robust performance. Furthermore, MCG features may improve the accuracy of short‐term risk assessment.
Yuankun Qi, Jiaqi Liang, Kai Ma, Xiaoyu Hu, Yu Zhang, Xiaole Han, Dong Xu, Xu Zhang, Xiaopei Cui, Min Xiang, Hongyu Zhang, Jiancheng Fang   +11 more
wiley   +1 more source

Explainable artificial intelligence for personalized prognosis in pancreatic cancer: A nationwide study from Taiwan. [PDF]

PLOS Digit Health
Tsai DR, Chiang CJ, Hsieh PC, Huang CY, Lee WC.   +4 more
europepmc   +1 more source