Machine learning-based platforms are currently revolutionizing many fields of molecular biology including structure prediction for monomers or complexes, predicting the consequences of mutations, or predicting the functions of proteins. However, these platforms use training sets based on currently available knowledge and, in essence, are not built to discover novelty. Hence, claims of discovering novel functions for protein families using artificial intelligence should be carefully dissected, as the dangers of overpredictions are real as we show in a detailed analysis of the prediction made by Kim et al on the function of the YciO protein in the model organism .
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http://dx.doi.org/10.1101/2023.12.18.571875 | DOI Listing |
Sci Total Environ
January 2025
Department of Twin Research and Genetic Epidemiology, King's College London, 3-4th Floor South Wing Block D, St Thomas' Hospital, Westminster Bridge Road, London SE1 7EH, UK. Electronic address:
Heavy metals in our direct environment have profound effects on human health and while some are essential for life, others can be toxic. In vivo studies often focus on clinical features caused by overexposure to, or by deprivation of a heavy metal. However, to understand the cellular impact of heavy metals on health, studies in healthy volunteers before symptom onset are needed.
View Article and Find Full Text PDFEnviron Int
January 2025
School of Environmental Science and Engineering, Tianjin University, Tianjin 300354, China. Electronic address:
Micro-and-nano plastics (MNPs) are pervasive in terrestrial ecosystems and represent an increasing threat to plant health; however, the mechanisms underlying their phytotoxicity remain inadequately understood. MNPs can infiltrate plants through roots or leaves, causing a range of toxic effects, including inhibiting water and nutrient uptake, reducing seed germination rates, and impeding photosynthesis, resulting in oxidative damage within the plant system. The effects of MNPs are complex and influenced by various factors including size, shape, functional groups, and concentration.
View Article and Find Full Text PDFInt J Pharm
January 2025
Laboratory of Pharmaceutical Technology, Department of Pharmaceutics, Ghent University, Ottergemsesteenweg 460, B-9000 Ghent, Belgium.
Nowadays, most of the newly developed active pharmaceutical ingredients (APIs) consist of cohesive particles with a mean particle size of <100μm, a wide particle size distribution (PSD) and a tendency to agglomerate, therefore they are difficult to handle in continuous manufacturing (CM) lines. The current paper focuses on the impact of various glidants on the bulk properties of difficult-to-handle APIs. Three challenging powders were included: two extremely cohesive APIs (acetaminophen micronized (APAPμ) and metoprolol tartrate (MPT)) which previously have shown processing issues during different stages of the continuous direct compression (CDC)-line and a spray dried placebo (SD) powder containing hydroxypropylmethyl cellulose (HPMC), known for its sub-optimal flow with a high specific surface area (SSA) and low density.
View Article and Find Full Text PDFOpen Forum Infect Dis
January 2025
Department of Infection and Immunity, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China.
Background: The global resurgence of disseminated tuberculosis (TB) after the coronavirus disease 2019 pandemic highlights the necessity of understanding host risk factors, especially in adults without human immunodeficiency virus.
Methods: We reviewed TB cases admitted to Shanghai Public Health Clinical Center from 2017 to 2022. We analyzed baseline characteristics and outcomes.
Narra J
December 2024
Master Program in Smart Healthcare Management (SHM), International College of Sustainability Innovations, National Taipei University, New Taipei City, Taiwan.
Cognitive decline poses a significant challenge for the elderly population globally. The aim of this study was to determine the prevalence of cognitive function and its associated factors among the elderly in the Indonesian family life survey's fifth wave (IFLS-5) conducted from 2014 to 2015. The study included elderly individuals aged 60 and above, excluding proxy respondents and those with missing data.
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