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Biophysical neural adaptation mechanisms enable artificial neural networks to capture dynamic retinal computation.
Nature communications
Jul 16, 2024
Adaptation is a universal aspect of neural systems that changes circuit computations to match prevailing inputs. These changes facilitate efficient encoding of sensory inputs while avoiding saturation. Conventional artificial neural networks (ANNs) h...
Machine Learned Classification of Ligand Intrinsic Activities at Human μ-Opioid Receptor.
ACS chemical neuroscience
Jul 11, 2024
Opioids are small-molecule agonists of μ-opioid receptor (μOR), while reversal agents such as naloxone are antagonists of μOR. Here, we developed machine learning (ML) models to classify the intrinsic activities of ligands at the human μOR based on t...
Application of machine learning models for property prediction to targeted protein degraders.
Nature communications
Jul 9, 2024
Machine learning (ML) systems can model quantitative structure-property relationships (QSPR) using existing experimental data and make property predictions for new molecules. With the advent of modalities such as targeted protein degraders (TPD), the...
Artificial intelligence derived categorizations significantly improve HOMA IR/β indicators: Combating diabetes through cross-interacting drugs.
Computers in biology and medicine
Jul 4, 2024
Improvements in the homeostasis model assessment of insulin resistance (HOMA-IR) and homeostasis model assessment of beta-cell function (HOMA-β) significantly reduce the risk of disabling diabetic pathies. Nanoparticle (AuNP-AgNP)-metformin are conce...
Inter-Rater and Intra-Rater Agreement in Scoring Severity of Rodent Cardiomyopathy and Relation to Artificial Intelligence-Based Scoring.
Toxicologic pathology
Jun 22, 2024
We previously developed a computer-assisted image analysis algorithm to detect and quantify the microscopic features of rodent progressive cardiomyopathy (PCM) in rat heart histologic sections and validated the results with a panel of five veterinary...
Deep learning based decoding of single local field potential events.
NeuroImage
Jun 21, 2024
How is information processed in the cerebral cortex? In most cases, recorded brain activity is averaged over many (stimulus) repetitions, which erases the fine-structure of the neural signal. However, the brain is obviously a single-trial processor. ...
Diagnostic application in streptozotocin-induced diabetic retinopathy rats: A study based on Raman spectroscopy and machine learning.
Journal of biophotonics
Jun 21, 2024
Vision impairment caused by diabetic retinopathy (DR) is often irreversible, making early-stage diagnosis imperative. Raman spectroscopy emerges as a powerful tool, capable of providing molecular fingerprints of tissues. This study employs RS to dete...
Lipids balance as a spectroscopy marker of diabetes. Analysis of FTIR spectra by 2D correlation and machine learning analyses.
Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Jun 13, 2024
The number of people suffering from type 2 diabetes has rapidly increased. Taking into account, that elevated intracellular lipid concentrations, as well as their metabolism, are correlated with diminished insulin sensitivity, in this study we would ...
A virtual rodent predicts the structure of neural activity across behaviours.
Nature
Jun 11, 2024
Animals have exquisite control of their bodies, allowing them to perform a diverse range of behaviours. How such control is implemented by the brain, however, remains unclear. Advancing our understanding requires models that can relate principles of ...
TrueTH: A user-friendly deep learning approach for robust dopaminergic neuron detection.
Neuroscience letters
Jun 8, 2024
Parkinson's disease (PD) entails the progressive loss of dopaminergic (DA) neurons in the substantia nigra pars compacta (SNc), leading to movement-related impairments. Accurate assessment of DA neuron health is vital for research applications. Manua...
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