(a property whereby members of an oscillating system tend to linger at the edge of synchronicity without permanently becoming synchronized) in quantifying brain dynamics. Altered metastability has been implicated in various psychiatric illnesses, such as traumatic brain injury and Alzheimer's disease. Computational models, which range in complexity, have been used to assess how various parameters affect metastability, synchronization, and functional connectivity. These models, though limited, can act as heuristics in understanding brain dynamics. This article (aimed at the clinical psychiatrist who might not possess an extensive mathematical background) is intended to provide a brief and qualitative summary of studies that have used a specific, highly simplified computational model of coupled oscillators (Kuramoto model) for understanding brain dynamics-which might bear some relevance to clinical psychiatry.
  The impact of stress and trauma on biological systems in humans can be substantial.  https://www.selleckchem.com/products/cy-09.html  They can result in epigenetic changes, accelerated brain development and sexual maturation, and predisposition to psychopathology. Such modifications may be accompanied by behavioral, emotional, and cognitive overtones during one's lifetime. Exposure during sensitive periods of neural development may lead to long-lasting effects that may not be affected by subsequent environmental interventions. The cumulative effects of life stressors in an individual may affect offspring's methylome makeup and epigenetic clocks, neurohormonal modulation and stress reactivity, and physiological and reproductive development. While offspring may suffer deleterious effects from parental stress and their own early-life adversity, these factors may also confer traits that prove beneficial and enhance fitness to their own environment. This article synthesizes the data on how stress shapes biological and behavioral dimensions, drawing from preclinsity, these factors may also confer traits that prove beneficial and enhance fitness to their own environment. This article synthesizes the data on how stress shapes biological and behavioral dimensions, drawing from preclinical and human models. Advances in this field of knowledge should potentially allow for an improved understanding of how interventions may be increasingly tailored according to individual biomarkers and developmental history.
  The potential of deep learning to support radiologist prostate magnetic resonance imaging (MRI) interpretation has been demonstrated.
 
  The aim of this study was to evaluate the effects of increased and diversified training data (TD) on deep learning performance for detection and segmentation of clinically significant prostate cancer-suspicious lesions.
 
  In this retrospective study, biparametric (T2-weighted and diffusion-weighted) prostate MRI acquired with multiple 1.5-T and 3.0-T MRI scanners in consecutive men was used for training and testing of prostate segmentation and lesion detection networks. Ground truth was the combination of targeted and extended systematic MRI-transrectal ultrasound fusion biopsies, with significant prostate cancer defined as International Society of Urological Pathology grade group greater than or equal to 2. U-Nets were internally validated on full, reduced, and PROSTATEx-enhanced training sets and subsequently externally validated on the institutional test set and the PROS1 consecutive examinations achieved inferior performance (P < 0.001). PROSTATEx training enhancement did not improve performance. Dice coefficients were 0.90 for prostate and 0.42/0.53 for MRI lesion segmentation at PI-RADS category 3/4 equivalents.
 
  In a large institutional test set, U-Net confirms similar performance to clinical PI-RADS assessment and benefits from more TD, with neither institutional nor PROSTATEx performance improved by adding multiscanner or bi-institutional TD.
 In a large institutional test set, U-Net confirms similar performance to clinical PI-RADS assessment and benefits from more TD, with neither institutional nor PROSTATEx performance improved by adding multiscanner or bi-institutional TD.
  Nonalcoholic fatty liver disease (NAFLD) is a leading cause of chronic liver disease worldwide. Quantitative ultrasound (QUS) parameters based on radiofrequency raw data show promise in quantifying liver fat.
 
  The aim of this study was to evaluate the diagnostic performance of 9 QUS parameters compared with magnetic resonance imaging (MRI)-estimated proton density fat fraction (PDFF) in detecting and staging hepatic steatosis in patients with or suspected of NAFLD.
 
  In this Health Insurance Portability and Accountability Act-compliant institutional review board-approved prospective study, 31 participants with or suspected of NAFLD, without other underlying chronic liver diseases (13 men, 18 women; average age, 52 years [range, 26-90 years]), were examined. The following parameters were obtained acoustic attenuation coefficient (AC); hepatorenal index (HRI); Nakagami parameter; shear wave elastography measures such as shear wave elasticity, viscosity, and dispersion; and spectroscopy-derived parameters incfying hepatic steatosis in our study population.
 
  Quantitative ultrasound is an accurate alternative to MRI-based techniques for evaluating hepatic steatosis in patients with or at risk of NAFLD.
 
  Our preliminary results show that specific quantitative ultrasound parameters accurately detect different degrees of hepatic steatosis in NAFLD.
 Our preliminary results show that specific quantitative ultrasound parameters accurately detect different degrees of hepatic steatosis in NAFLD.
  The objectives of this exploratory study were to investigate the feasibility of multidimensional diffusion magnetic resonance imaging (MddMRI) in assessing diffusion heterogeneity at both a macroscopic and microscopic level in prostate cancer (PCa).
 
  Informed consent was obtained from 46 subjects who underwent 3.0-T prostate multiparametric MRI, complemented with a prototype spin echo-based MddMRI sequence in this institutional review board-approved study. Prostate cancer tumors and comparative normal tissue from each patient were contoured on both apparent diffusion coefficient and MddMRI-derived mean diffusivity (MD) maps (from which microscopic diffusion heterogeneity [MKi] and microscopic diffusion anisotropy were derived) using 3D Slicer. The discriminative ability of MddMRI-derived parameters to differentiate PCa from normal tissue was determined using the Friedman test. To determine if tumor diffusion heterogeneity is similar on macroscopic and microscopic scales, the linear association between SD of MD and mean MKi was estimated using robust regression (bisquare weighting).