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Background: Coronary heart disease (CHD) and depression frequently co-occur, significantly impacting patient outcomes. However, comprehensive health status assessment tools for this complex population are lacking. This study aimed to develop and validate an explainable machine learning model to evaluate overall health status in patients with comorbid CHD and depression.
View Article and Find Full Text PDFPinpointing the timing of prenatal stress associated with infant biobehavioral reactivity.
Psychoneuroendocrinology
January 2025
Department of Psychiatry, University of Michigan - Michigan Medicine, USA.
Prenatal stress has a well-established link to negative biobehavioral outcomes in young children, particularly for girls, but the specific timing during gestation of these associations remains unknown. In the current study, we examined differential effects of timing of prenatal stress on two infant biobehavioral outcomes [i.e.
View Article and Find Full Text PDFPhotic drive response in people with epilepsy: Exploring the interaction with background alpha rhythm.
Vision Res
January 2025
Eccles Institute of Neuroscience, John Curtin School of Medical Research, Australian National University, Acton, ACT, Australia. Electronic address:
Photic drive responses (PDRs) are used to explore cortical hyperexcitability. We quantified PDRs and interactions with the alpha rhythm in people with epilepsy (PwE). Fifteen PwE (mean age ± SD 47.
View Article and Find Full Text PDFInsights into the molecular self-assembly of urea-functionalized acetylenes.
Nanotechnology
January 2025
Institute of Nano Science and Technology, Knowledge City, Sector 81, Mohali, 140306, INDIA.
This study investigates simple acetylenes substituted with phenylurea as a constant H-bonding unit (Alk-R) and varied hydrophobic units (R = H, Phenyl (Ph), Phenylacetylene (PA), Ph-NMe2) to understand self-assembly properties driven by synergistic non-covalent interactions. Our observations reveal hierarchical self-assembled fibrillar networks with luminescent needles, fibers, and flowers on nano- to micro-meter scales. Subtle changes in substituents led to significant differences: H, Ph, PA, and Ph-NMe2 produced needle-like crystals, dendritic nanofibers, microflakes, and no self-assembly, respectively.
View Article and Find Full Text PDFDeep learning aided determination of the optimal number of detectors for photoacoustic tomography.
Biomed Phys Eng Express
January 2025
Applied Sciences, Indian Institute of Information Technology Allahabad, Deoghat, Jhalwa, Allahabad, 211012, INDIA.
Photoacoustic tomography (PAT) is a non-destructive, non-ionizing, and rapidly expanding hybrid biomedical imaging technique, yet it faces challenges in obtaining clear images due to limited data from detectors or angles. As a result, the methodology suffers from significant streak artifacts and low-quality images. The integration of deep learning (DL), specifically convolutional neural networks (CNNs), has recently demonstrated powerful performance in various fields of PAT.
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