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Electrochemical reduction for chlorinated hydrocarbons contaminated groundwater remediation: Mechanisms, challenges, and perspectives.
Water Res
January 2025
State Key Laboratory of Geohazard Prevention and Geoenvironment Protection (Chengdu University of Technology), 1#, Dongsanlu, Erxianqiao, Chengdu 610059, Sichuan, PR China; State Environmental Protection Key Laboratory of Synergetic Control and Joint Remediation for Soil & Water Pollution (Chengdu University of Technology), 1#, Dongsanlu, Erxianqiao, Chengdu 610059, Sichuan, PR China. Electronic address:
Electrochemical reduction technology is a promising method for addressing the persistent contamination of groundwater by chlorinated hydrocarbons. Current research shows that electrochemical reductive dechlorination primarily relies on direct electron transfer (DET) and active hydrogen (H) mediated indirect electron transfer processes, thereby achieving efficient dechlorination and detoxification. This paper explores the influence of the molecular charge structure of chlorinated hydrocarbons, including chlorolefin, chloroalkanes, chlorinated aromatic hydrocarbons, and chloro-carboxylic acid, on reductive dechlorination from the perspective of molecular electrostatic potential and local electron affinity.
View Article and Find Full Text PDFRare dual MYH9-ROS1 fusion variants in a patient with lung adenocarcinoma: A case report.
Medicine (Baltimore)
January 2025
Department of Respiratory and Critical Care Medicine, Zhongshan City People's Hospital, Zhongshan, Guangdong Province, China.
Rationale: ROS proto-oncogene 1 (ROS1) fusion is a rare but important driver mutation in non-small cell lung cancer, which usually shows significant sensitivity to small molecule tyrosine kinase inhibitors. With the widespread application of next-generation sequencing (NGS), more fusions and co-mutations of ROS1 have been discovered. Non-muscle myosin heavy chain 9 (MYH9) is a rare fusion partner of ROS1 gene as reported.
View Article and Find Full Text PDFA tough soft-hard interface in the human knee joint driven by multiscale toughening mechanisms.
Proc Natl Acad Sci U S A
January 2025
Department of Sports Medicine of the Second Affiliated Hospital, and Liangzhu Laboratory, Zhejiang University School of Medicine, Hangzhou 311113, China.
Joining heterogeneous materials in engineered structures remains a significant challenge due to stress concentration at interfaces, which often leads to unexpected failures. Investigating the complex, multiscale-graded structures found in animal tissue provides valuable insights that can help address this challenge. The human meniscus root-bone interface is an exemplary model, renowned for its exceptional fatigue resistance, toughness, and interfacial adhesion properties throughout its lifespan.
View Article and Find Full Text PDFProtocol for oral fecal gavage to reshape the gut microbiota in mice.
STAR Protoc
January 2025
Department of Gastroenterology and Hepatology, Chang Gung Memorial Hospital, Linkou, Taoyuan, Taiwan; Grandulate Institute of Biomedical Science, College of Medicine, Chang Gung University, Taoyuan, Taiwan. Electronic address:
Fecal microbiota transplantation (FMT) is clinically applied, while oral FMT (oral fecal gavage [OFG]) is preferred for experimental mice. Here, we present a protocol for OFG in antibiotic-pretreated mice, demonstrating the progressive, time-dependent evolution of the gut microbiota in the recipients. We describe steps for fecal sample collection and preparation procedures, oral gavage, and monitoring gut microbiota changes.
View Article and Find Full Text PDFArtificial Intelligence for Optical Coherence Tomography in Glaucoma.
Transl Vis Sci Technol
January 2025
Glaucoma Service, Wills Eye Hospital, Philadelphia, PA, USA.
Purpose: The integration of artificial intelligence (AI), particularly deep learning (DL), with optical coherence tomography (OCT) offers significant opportunities in the diagnosis and management of glaucoma. This article explores the application of various DL models in enhancing OCT capabilities and addresses the challenges associated with their clinical implementation.
Methods: A review of articles utilizing DL models was conducted, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), autoencoders, and large language models (LLMs).
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