People rely upon sensory information in the environment to guide their actions. Ongoing goal-directed arm movements are constantly adjusted to the latest estimate of both the target and hand's positions. Does the continuous guidance of ongoing arm movements also consider the latest visual information of the position of obstacles in the surrounding? To find out, we asked participants to slide their finger across a screen to intercept a laterally moving virtual target while moving through a gap that was created by two virtual circular obstacles. At a fixed time during each trial, the target suddenly jumped slightly laterally while still continuing to move. In half the trials, the size of the gap changed at the same moment as the target jumped. As expected, participants adjusted their movements in response to the target jump. Importantly, the magnitude of this response depended on the new size of the gap. If participants were told that the circles were irrelevant, changing the gap between them had no effect on the responses. This shows that obstacles' instantaneous positions can be considered when visually guiding goal-directed movements.
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http://dx.doi.org/10.1007/s00221-023-06634-5 | DOI Listing |
Chem Biol Interact
January 2025
Anhui Prevention and Control Engineering Research Center for Fatty Liver Disease, Hefei, Anhui, 230032,P. R. China; The Key Laboratory of Anti-inflammatory and Immune Medicines, Ministry of Education, Hefei, China; Inflammation and Immune-Mediated Diseases Laboratory of Anhui Province, Anhui Institute of Innovative Drugs, School of Pharmacy, Anhui Medical University, China. Electronic address:
Oxidative stress induced by excess ethanol is an important factor in the progression of alcoholic liver disease (ALD). In recent years, inhibiting Kelch-like ECH-associated protein 1 (KEAP1) to activate the antioxidant regulator Nuclear factor erythroid 2-related factor 2 (NRF2) has been considered an effective strategy for treating oxidative stress-related diseases, but its application in ALD remains insufficiently explored. This study aims to discover high-affinity inhibitors targeting the KEAP1 receptor.
View Article and Find Full Text PDFDisabil Rehabil
January 2025
Sydney School of Health Sciences, Faculty of Medicine & Health, The University of Sydney, Sydney, Australia.
Purpose: To investigate potential mechanisms of a digital rehabilitation intervention associated with improved mobility among adults undertaking rehabilitation.
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Pharmaceuticals (Basel)
January 2025
Centro de Química Médica, Facultad de Medicina Clínica Alemana, Universidad del Desarrollo, Santiago 7780272, Chile.
Acute myeloid leukemia (AML) presents significant therapeutic challenges, particularly in cases driven by mutations in the FLT3 tyrosine kinase. This study aimed to develop a robust and user-friendly machine learning-based quantitative structure-activity relationship (QSAR) model to predict the inhibitory potency (pIC values) of FLT3 inhibitors, addressing the limitations of previous models in dataset size, diversity, and predictive accuracy. Using a dataset which was 14 times larger than those employed in prior studies (1350 compounds with 1269 molecular descriptors), we trained a random forest regressor, chosen due to its superior predictive performance and resistance to overfitting.
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January 2025
Laboratory of Biotechnology, National Higher School of Biotechnology, Ville Universitaire (University of Constantine 3), Ali Mendjeli, BP E66, Constantine 25100, Algeria.
Kynurenine aminotransferase II (KAT-II) is a target for treating several diseases characterized by an excess of kynurenic acid (KYNA). Although KAT-II inactivators are available, they often lead to adverse side effects due to their irreversible inhibition mechanism. This study aimed to identify potent and safe inhibitors of KAT-II using computational and in vitro approaches.
View Article and Find Full Text PDFPharmaceuticals (Basel)
December 2024
Faculty of Pharmacy, "Carol Davila" University of Medicine and Pharmacy, Traian Vuia 6, 020956 Bucharest, Romania.
Aurora kinase B (AurB) is a pivotal regulator of mitosis, making it a compelling target for cancer therapy. Despite significant advances in protein kinase inhibitor development, there are currently no AurB inhibitors readily available for therapeutic use. This study introduces a machine learning-assisted drug repurposing framework integrating quantitative structure-activity relationship (QSAR) modeling, molecular fingerprints-based classification, molecular docking, and molecular dynamics (MD) simulations.
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