Acetylation of Histones in the Nucleus Accumbens Increases the Expression of Dopamine Transporters to Regulate Executive Function in Sapap3 Knockout Mice.

Obsessive-compulsive disorder (OCD) is a chronic and debilitating psychiatric condition characterized by persistent, intrusive thoughts (obsessions) and repetitive ritualistic behaviors (compulsions). Accumulating evidence suggests that individuals with OCD demonstrate marked cognitive impairments, especially in executive function domains, including cognitive flexibility and working memory. Although existing therapeutic approaches (e.

Intracranial atherosclerotic plaque features on vessel wall imaging predict first ever and recurrence of stroke: a meta-analysis.

Objectives: Intracranial atherosclerotic disease (ICAD) features on vessel wall magnetic resonance imaging (VW-MRI) are associated with first-ever or recurrent ischemic stroke (IS) or transient ischemic attack (TIA). There are multiple longitudinal ICAD VW-MRI studies, but they are limited by small sample size, non-standardized imaging acquisition and analysis, and some controversial results. Hence, we conducted the current meta-analysis of intracranial plaque features predicting recurrence of IS.

Morphology and Texture-Guided Deep Neural Network for Intracranial Aneurysm Segmentation in 3D TOF-MRA.

This study concentrates on the segmentation of intracranial aneurysms, a pivotal aspect of diagnosis and treatment planning. We aim to overcome the inherent instance imbalance and morphological variability by introducing a novel morphology and texture loss reweighting approach. Our innovative method involves the incorporation of tailored weights within the loss function of deep neural networks.

Learning Representations from Medical Text for Effective Diagnoses and Knowledge Discovery.

Discovering knowledge and effectively predicting target events are two main goals of medical text mining. However, few models can achieve them simultaneously. In this study, we investigated the possibility of discovering knowledge and predicting diagnosis at once via raw medical text.

Knowledge-aware patient representation learning for multiple disease subtypes.

Learning latent representations of patients with a target disease is a core problem in a broad range of downstream applications, such as clinical endpoint prediction. The suffering of patients may have multiple subtypes with certain similarities and differences, which need to be addressed for learning effective patient representation to facilitate the downstream tasks. However, existing studies either ignore the distinction of disease subtypes to learn disease-level representations, or neglect the correlations between subtypes and only learn disease subtype-level representations, which affects the performance of patient representation learning.

Comparing Machine Learning Models and Statistical Models for Predicting Heart Failure Events: A Systematic Review and Meta-Analysis.

Objective: To compare the performance, clinical feasibility, and reliability of statistical and machine learning (ML) models in predicting heart failure (HF) events.

Background: Although ML models have been proposed to revolutionize medicine, their promise in predicting HF events has not been investigated in detail.

Methods: A systematic search was performed on Medline, Web of Science, and IEEE Xplore for studies published between January 1, 2011 to July 14, 2021 that developed or validated at least one statistical or ML model that could predict all-cause mortality or all-cause readmission of HF patients.