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http://dx.doi.org/10.1038/s41386-023-01618-z | DOI Listing |
PLoS One
January 2025
Department of Biology, College of Science, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia.
A common heavy metal in many facets of daily life is aluminum (AlCl3), which can be found in food, toothpaste, cosmetics, food additives, and numerous pharmaceutical items. The hippocampus, liver, and kidneys have the highest concentrations of this powerful neurotoxin, which also accumulates over time and contributes to the development of a number of cognitive disorders. Long-term overconsumption of AlCl3 results in hepatic and renal toxicity as well as neuronal inflammation.
View Article and Find Full Text PDFBioengineering (Basel)
January 2025
Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, Seoul 08826, Republic of Korea.
Recent advancements in deep learning have significantly improved medical image segmentation. However, the generalization performance and potential risks of data-driven models remain insufficiently validated. Specifically, unrealistic segmentation predictions deviating from actual anatomical structures, known as a Seg-Hallucination, often occur in deep learning-based models.
View Article and Find Full Text PDFFront Cell Dev Biol
January 2025
Key Laboratory of Tropical Translational Medicine and Ministry of Education, Hainan Academy of Medical Sciences, Hainan Medical University, Haikou, China.
Axon guidance is a key event in neural circuit development that drives the correct targeting of axons to their targets through long distances and unique patterns. Exosomes, extracellular vesicles that are smaller than 100 nm, are secreted by most cell types in the brain. Regulation of cell-cell communication, neuroregeneration, and synapse formation by exosomes have been extensively studied.
View Article and Find Full Text PDFFood Res Int
February 2025
Izmir Institute of Technology, Department of Food Engineering, Urla-Izmir, Turkiye. Electronic address:
The detection of adulteration in apple juice concentrate is critical for ensuring product authenticity and consumer safety. This study evaluates the effectiveness of artificial neural networks (ANN) and support vector machines (SVM) in analyzing spectroscopic data to detect adulteration in apple juice concentrate. Four techniques-UV-visible, fluorescence, near-infrared (NIR) spectroscopy, and time domain H nuclear magnetic resonance relaxometry (H NMR)-were used to generate data from both authentic and adulterated apple juice samples.
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