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http://dx.doi.org/10.1254/fpj.129.31 | DOI Listing |
AAPS PharmSciTech
January 2025
OSIS, Silver Spring, Maryland, U.S.A.
Travel restrictions during the novel coronavirus, SARS-CoV-2 (COVID-19) public health emergency affected the U.S. Food and Drug Administration's (FDA) ability to conduct on-site bioavailability/bioequivalence (BA/BE) and Good Laboratory Practice (GLP) nonclinical inspections.
View Article and Find Full Text PDFJ Hazard Mater
December 2024
State Key Laboratory of Food Science and Resources, Jiangnan University, Wuxi 214122, China; International Joint Laboratory on Food Safety, Institute of Analytical Food Safety, School of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu 214122, China. Electronic address:
Antibiotic resistance genes (ARGs) are markers of drug-resistant pathogens, monitoring them contributes to prevent resistance to drugs. The detection methods for ARGs including PCR and isothermal amplification are sensitive and selective. However, it may take several hours or cannot be used on spot.
View Article and Find Full Text PDFACS Nano
January 2025
State Key Laboratory of Marine Food Processing and Safety Control, Dalian Polytechnic University, Dalian 116034 Liaoning, China.
ACS Appl Mater Interfaces
January 2025
School of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing 100081, China.
Portable sensor technologies are indispensable in personalized healthcare and environmental monitoring as they enable the continuous tracking of key analytes. Human sweat contains valuable physiological information, and previously developed noninvasive sweat-based sensors have effectively monitored single or multiple biomarkers. By successfully detecting biochemicals in sweat, portable sensors could also significantly broaden their application scope, encompassing non-biological fluids commonly encountered in daily life, such as mineral water.
View Article and Find Full Text PDFNat Commun
January 2025
Laboratory of High Resolution Optical Imaging, National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Bethesda, Maryland, USA.
Optical aberrations hinder fluorescence microscopy of thick samples, reducing image signal, contrast, and resolution. Here we introduce a deep learning-based strategy for aberration compensation, improving image quality without slowing image acquisition, applying additional dose, or introducing more optics. Our method (i) introduces synthetic aberrations to images acquired on the shallow side of image stacks, making them resemble those acquired deeper into the volume and (ii) trains neural networks to reverse the effect of these aberrations.
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