Results 151 to 160 of about 3,135 (215)

An Integrative Strategy Delineates Modular Metabolic Remodeling and Potential Therapeutic Targets Across Metabolic Diseases

Advanced Science, EarlyView.
An integrative single‐cell atlas across multiple metabolic diseases reveals coordinated metabolic modules and disease‐shared versus disease‐specific pathway activities. By systematically comparing scoring strategies, a robust RankAve framework is established. Coupled with network analysis and drug‐target prediction, this resource uncovers cross‐disease
Kuan Yang, Congxue Hu, Hang Zhao, Xiao Peng, Yiming Yan, Jiasi Wang, Wenqi Jiang, Depeng Mu, Yan Cao, Xia Li, Yunpeng Zhang   +10 more
wiley   +1 more source

Topological turning points across the human lifespan. [PDF]

Nat Commun
Mousley A, Bethlehem RAI, Yeh FC, Astle DE.   +3 more
europepmc   +1 more source

Deep Learning Network‐Tailored Microenvironment Matching of 4D Bioprinting Bioactive Scaffolds for Bone Regeneration

Advanced Science, EarlyView.
A DLN dataset was built to analyze MABS composition versus in vitro/in vivo osteogenesis and angiogenesis. An MLP neural network, taking BG morphological parameters as input, extracts bioactive features from these datasets. A rabbit tibial defect model then validates 4D‐printed MABS for adaptability and bone regeneration in critical defects.
Xiongjie Liang, Yuechi Zhang, Weifeng Hu, Shiyan Lv, Fan Jia, Yan Zhang, Feng Wu, Changjiang Yang, Guanqi Zhen, Jinglong Yan, Xu Cui, Wei Zhao, Guanghua Chen   +12 more
wiley   +1 more source

KidneyTox_v1.0 enables explainable artificial intelligence prediction of nephrotoxicity in small molecules. [PDF]

Sci Rep
Amin SA, Kar S, Piotto S.
europepmc   +1 more source

Orbital and Electrical Dual Function of Polymer Intercalant for Promoting NH4+ Storage in Vanadium Oxide Anode

Advanced Energy Materials, Volume 15, Issue 11, March 18, 2025.
The impact of polyaniline insertion on the atomic orbitals and electronic structure of vanadium oxide anode is systematically investigated for the first time. The electrode exhibits an outstanding capacity and unprecedent long‐term cycling life, owing to the electron transition to the V 3dxy state and the enhanced diffusion kinetics.
Yue Zhang, Zihang Huang, Lei Lei, Hua Fan, Xu Han, Hui Li, Tianyi Ma   +6 more
wiley   +1 more source

A Tumor-Agnostic, Topology-Informed Scoring Framework for Drug Repurposing: Application to CDK4/6 Inhibitor Resistance in HR⁺ Breast Cancer. [PDF]

Biomedicines
Qian K, Cai Z, Liu R, Yang W, Liu J, Wu M, Zhu M, Wang L, Gan H, Yang Z, Jiang X, Shen C, Mao Y, Liu Q.   +13 more
europepmc   +1 more source

Universal Oxychlorination Strategy in Halide Solid Electrolytes for All‐Solid‐State Batteries

Advanced Energy Materials, EarlyView.
A WO2Cl2‐driven oxychlorination strategy enables bulk oxygen incorporation into close‐packed LixMCl6 (M = Zr, Y, Er, In) halide lattices. Oxygen is selectively anchored by W6+ as lattice‐integrated [WO2Cl4]2− units, regulating the anionic framework, diversifying Li coordination, and weakening Li–Cl interactions.
Jae‐Seung Kim, Heeju Park, Hae‐Yong Kim, Eunryeol Lee, Heewon Kim, Soeul Lee, Jinyeong Choe, Jiwon Seo, Hyeon‐Jong Lee, Hojoon Kim, Jemin Yeon, Yoon Seok Jung, Kyung‐Wan Nam, Dong‐Hwa Seo   +13 more
wiley   +1 more source

Reac-Discovery: an artificial intelligence-driven platform for continuous-flow catalytic reactor discovery and optimization. [PDF]

Nat Commun
Tinajero C, Zanatta M, Sánchez-Velandia JE, García-Verdugo E, Sans V.   +4 more
europepmc   +1 more source

Smart Exploration of Perovskite Photovoltaics: From AI Driven Discovery to Autonomous Laboratories

Advanced Energy Materials, EarlyView.
In this review, we summarize the fundamentals of AI in automated materials science, and review AI applications in perovskite solar cells. Then, we sum up recent progress in AI‐guided manufacturing optimization, and highlight AI‐driven high‐throughput and autonomous laboratories.
Wenning Chen, Zheyu Zhang, Kelvian T. Mularso, Bonghyun Jo, Hyun Suk Jung   +4 more
wiley   +1 more source

Machine Learning Interatomic Potentials for Energy Materials: Architectures, Training Strategies, and Applications

Advanced Energy Materials, EarlyView.
Machine learning interatomic potentials bridge quantum accuracy and computational efficiency for materials discovery. Architectures from Gaussian process regression to equivariant graph neural networks, training strategies including active learning and foundation models, and applications in solid‐state electrolytes, batteries, electrocatalysts ...
In Kee Park, Saeed Pourasad, Jinhong Mun, Arunkumar Chitteth Rajan, Tae Hyeon Park, Anand Rohit, Mohammad Zafari, Tan‐Lien Pham, Eunjae Jung, Bonseung Koo, Miran Ha, Gi Beom Sim, Hyun Gyu Park, Won Woong Choi, Kwanghyeon Jo, Soohaeng Yoo Willow, David ChangMo Yang, Chang Woo Myung, Geunsik Lee, Kwang S. Kim   +19 more
wiley   +1 more source