Learning new skills requires neuroplasticity. Vagus nerve stimulation (VNS) during sensory and motor events can increase neuroplasticity in networks related to these events and might therefore serve to facilitate learning on sensory and motor tasks. We tested if VNS could broadly improve learning on a wide variety of tasks across different skill domains in healthy, female adult rats. VNS was paired with presentation of stimuli or on successful trials during training, strategies known to facilitate plasticity and improve recovery in models of neurological disorders. VNS failed to improve either rate of learning or performance for any of the tested tasks, which included skilled forelimb motor control, speech sound discrimination, and paired-associates learning. These results contrast recent findings from multiple labs which found VNS pairing during training produced learning enhancements across motor, auditory, and cognitive domains. We speculate that these contrasting results may be explained by key differences in task designs, training timelines and animal handling approaches, and that while VNS may be able to facilitate rapid and early learning processes in healthy subjects, it does not broadly enhance learning for difficult tasks.
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http://dx.doi.org/10.1038/s41598-024-69666-z | DOI Listing |
Gynecol Oncol
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GOG Foundation, Florida Cancer Specialists and Research Institute, West Palm Beach, FL 33401, United States of America. Electronic address:
Objective: Therapeutic interventions for epithelial ovarian cancer (EOC) have increased greatly over the last decade but improvements outside of biomarker selected therapies have been limited. There remains a pressing need for more effective treatment options that can prolong survival and enhance the quality of life of patients with EOC. In contrast to the significant benefits of immunotherapy with immune checkpoint inhibitors (CPI) seen in many solid tumors, initial experience in EOC suggests limited efficacy of CPIs monotherapy.
View Article and Find Full Text PDFCell Rep
January 2025
Institut Interdisciplinaire de Neurosciences (IINS), University Bordeaux, CNRS, IINS, UMR 5297, 33000 Bordeaux, France; Centre Broca Nouvelle-Aquitaine, 146, rue Léo-Saignat, 33076 Bordeaux, France. Electronic address:
Optimal decision-making depends on interconnected frontal brain regions, enabling animals to adapt decisions based on internal states, experiences, and contexts. The secondary motor cortex (M2) is key in adaptive behaviors in expert rodents, particularly in encoding decision values guiding complex probabilistic tasks. However, its role in deterministic tasks during initial learning remains uncertain.
View Article and Find Full Text PDFJ Chem Inf Model
January 2025
Institute of Chemistry, Technische Universität Berlin, Straße des 17. Juni 135, Berlin 10623, Germany.
Machine learning (ML) is a powerful tool for the automated data analysis of molecular dynamics (MD) simulations. Recent studies showed that ML models can be used to identify protein-ligand unbinding pathways and understand the underlying mechanism. To expedite the examination of MD simulations, we constructed PathInHydro, a set of supervised ML models capable of automatically assigning unbinding pathways for the dissociation of gas molecules from [NiFe] hydrogenases, using the unbinding trajectories of CO and H from [NiFe] hydrogenase as a training set.
View Article and Find Full Text PDFInt J Numer Method Biomed Eng
January 2025
College of Chemistry and Life Science, Beijing University of Technology, Beijing, China.
The accurate non-invasive detection and estimation of central aortic pressure waveforms (CAPW) are crucial for reliable treatments of cardiovascular system diseases. But the accuracy and practicality of current estimation methods need to be improved. Our study combines a meta-learning neural network and a physics-driven method to accurately estimate CAPW based on personalized physiological indicators.
View Article and Find Full Text PDFACS Sens
January 2025
Department of Physics, Chalmers University of Technology, SE-41296 Göteborg, Sweden.
Rapidly detecting hydrogen leaks is critical for the safe large-scale implementation of hydrogen technologies. However, to date, no technically viable sensor solution exists that meets the corresponding response time targets under technically relevant conditions. Here, we demonstrate how a tailored long short-term transformer ensemble model for accelerated sensing (LEMAS) speeds up the response of an optical plasmonic hydrogen sensor by up to a factor of 40 and eliminates its intrinsic pressure dependence in an environment emulating the inert gas encapsulation of large-scale hydrogen installations by accurately predicting its response value to a hydrogen concentration change before it is physically reached by the sensor hardware.
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