Evaluation of the performance of instruments to diagnose grief disorders: A diagnostic meta-analysis.

Background: Prolonged or complicated grief disorder occurs in 9.8% of bereaved adults and can progress into adverse health effects if left unresolved. Evidence on the appropriate and specific diagnostic instrument for the assessment of prolonged or complicated grief disorders is lacking.

Linking Stage-Specific Plasma Biomarkers to Gray Matter Atrophy in Parkinson Disease.

Background And Purpose: The shortcomings of synucleinopathy-based Parkinson disease staging highlight the need for systematic clinicopathologic elucidation and biomarkers. In this study, we investigated associations of proteinopathy and inflammation markers with changes in gray matter volume that accompany Parkinson disease progression.

Materials And Methods: We prospectively enrolled 42 patients with idiopathic Parkinson disease, subdivided into early-/late-stage groups and 27 healthy controls.

Impacts of social distancing policies on mobility and COVID-19 case growth in the US.

Social distancing remains an important strategy to combat the COVID-19 pandemic in the United States. However, the impacts of specific state-level policies on mobility and subsequent COVID-19 case trajectories have not been completely quantified. Using anonymized and aggregated mobility data from opted-in Google users, we found that state-level emergency declarations resulted in a 9.

Longitudinal Change in Quality of Life in Neurological Disorders Measures Over 3 Years in Patients with Early Parkinson's Disease.

Background: The Quality of Life in Neurological Disorders (Neuro-QoL) is a publicly available health-related quality-of-life measurement system.

Objective: The aim of this study was to evaluate the utility of Neuro-QoL item banks as outcome measures for clinical trials in Parkinson's disease.

Methods: An analysis of Neuro-QoL responsiveness to change and construct validity was performed in a multicenter clinical trial cohort.

Excellent Electrochemical Performance of LaSrFeTiO as a Symmetric Electrode for Solid Oxide Cells.

Solid oxide cells (SOCs) can switch between fuel cell and electrolysis cell modes, which alleviate environmental and energy problems. In this study, the LaSrFeTiO (LSFTi 91) perovskite is innovatively used as a symmetric electrode for solid oxide electrolysis cells (SOECs) and solid oxide fuel cells (SOFCs). LSFTi 91 exhibits a pure perovskite phase in both oxidizing and reducing atmospheres, and the maximum conductivity in air and 5% H/Ar is 150 and 1.

Innovative method for minimization of waste containing Fe, Mn and Ti during comprehensive utilization of vanadium slag.

More than 1.2 million tons of tailings containing approximately 30 wt% of Fe from traditional vanadium extraction processes are discarded every year as solid waste, which waste resources. In order to achieve effective and green utilization of waste, a novel process was proposed to keep Cr and V at Cr and V during extraction by using AlCl-NaCl-KCl molten salt in Ar gas atmosphere to control the valuable elements (Cr, V, Mn and Fe) from oxidized.

Second-Hand Smoke and Its Synergistic Effect with a Body-Mass Index of >24.9 kg/m Increase the Risk of Gout Arthritis in Indonesia.

To analyze the association between smoking status (active smoking and exposure to Second-Hand Smoking (SHS)) and the synergistic effect of smoking status and BMI with gout risk, a community-based case-control design was undertaken among 385 participants, including 304 healthy controls and 81 gout patients from seven community health services. Adjusted Odd Ratios (AORs) and 95% Confidence Interval (CIs) of gout for active smoking and SHS were 3.26 (95% CI = 1.

Campylobacter jejuni Antimicrobial Resistance Profiles and Mechanisms Determined Using a Raman Spectroscopy-Based Metabolomic Approach.

Rapid identification of antimicrobial resistance (AMR) profiles and mechanisms is critical for clinical management and drug development. However, the current AMR detection approaches take up to 48 h to obtain a result. Here, we demonstrate a Raman spectroscopy-based metabolomic approach to rapidly determine the AMR profile of Campylobacter jejuni, a major cause of foodborne gastroenteritis worldwide.

Improving Individual Brain Age Prediction Using an Ensemble Deep Learning Framework.

Brain age is an imaging-based biomarker with excellent feasibility for characterizing individual brain health and may serve as a single quantitative index for clinical and domain-specific usage. Brain age has been successfully estimated using extensive neuroimaging data from healthy participants with various feature extraction and conventional machine learning (ML) approaches. Recently, several end-to-end deep learning (DL) analytical frameworks have been proposed as alternative approaches to predict individual brain age with higher accuracy.

Effect of Work-Family Conflict, Psychological Job Demand, and Job Control on the Health Status of Nurses.

Work-family conflicts (WFCs) are common in the healthcare sector and pose significant health risks to healthcare workers. This study examined the effect of WFCs on the health status and nurses' leaving intentions in Taiwan. A self-administered questionnaire was used to survey 200 female nurses' experiences of WFC from a regional hospital.

Efficacy and Safety of Direct Oral Anticoagulants vs Warfarin in Patients with Chronic Kidney Disease and Dialysis Patients: A Systematic Review and Meta-Analysis.

Unlabelled: BACKGROUND AND OBJECTIVE: Systematic reviews and meta-analyses of direct oral anticoagulants (DOACs) for patients with chronic kidney disease (CKD) or dialysis patients are lacking. We aimed to compare the efficacy and safety of DOACs and warfarin in patients with CKD requiring anticoagulation therapy.

Methods: We performed a systematic review and meta-analysis of six randomized controlled trials and 19 observational studies, with the inclusion criteria being a comparative study between DOACs and warfarin in patients with CKD or dialysis patients from database inception until August 2020.

Associations among Cognitive Functions, Plasma DNA, and Diffusion Tensor Image along the Perivascular Space (DTI-ALPS) in Patients with Parkinson's Disease.

Background: Parkinson's disease (PD) is a common neurodegenerative disease associated with accumulation of misfolding proteins and increased neuroinflammation, which may further impair the glymphatic system. The purpose of this study was to utilize diffusion tensor image analysis along the perivascular space (DTI-ALPS) to evaluate glymphatic system activity and its relationship with systemic oxidative stress status in PD patients.

Methods: Magnetic resonance imaging and neuropsychological tests were conducted on 25 PD patients with normal cognition (PDN), 25 PD patients with mild cognitive impairment (PD-MCI), 38 PD patients with dementia (PDD), and 47 normal controls (NC).

Identification of Neuronal Polarity by Node-Based Machine Learning.

Identifying the direction of signal flows in neural networks is important for understanding the intricate information dynamics of a living brain. Using a dataset of 213 projection neurons distributed in more than 15 neuropils of a Drosophila brain, we develop a powerful machine learning algorithm: node-based polarity identifier of neurons (NPIN). The proposed model is trained only by information specific to nodes, the branch points on the skeleton, and includes both Soma Features (which contain spatial information from a given node to a soma) and Local Features (which contain morphological information of a given node).

MicroRNA‑34a‑5p serves as a tumor suppressor by regulating the cell motility of bladder cancer cells through matrix metalloproteinase‑2 silencing.

Bladder cancer (BC), a common urologic cancer, is the fifth most frequently diagnosed tumor worldwide. hsa‑miR‑34a displays antitumor activity in several types of cancer. However, the functional mechanisms underlying hsa‑miR‑34a in BC remains largely unknown.

Differential Patterns of Gyral and Sulcal Morphological Changes During Normal Aging Process.

The cerebral cortex is a highly convoluted structure with distinct morphologic features, namely the gyri and sulci, which are associated with the functional segregation or integration in the human brain. During the lifespan, the brain atrophy that is accompanied by cognitive decline is a well-accepted aging phenotype. However, the detailed patterns of cortical folding change during aging, especially the changing age-dependencies of gyri and sulci, which is essential to brain functioning, remain unclear.

Non-Motor Fluctuations in Parkinson's Disease: Validation of the Non-Motor Fluctuation Assessment Questionnaire.

Background: In patients with Parkinson's disease (PD), sleep, mood, cognitive, autonomic, and other non-motor symptoms may fluctuate in a manner similar to motor symptoms.

Objectives: To validate a final version of a patient-rated questionnaire that captures the presence and severity of non-motor fluctuations in levodopa-treated PD patients (NoMoFA).

Methods: We recruited PD subjects from five movement disorders centers across the US and Canada.

The corticolimbic structural covariance network as an early predictive biosignature for cognitive impairment in Parkinson's disease.

Structural covariance assesses similarities in gray matter between brain regions and can be applied to study networks of the brain. In this study, we explored correlations between structural covariance networks (SCNs) and cognitive impairment in Parkinson's disease patients. 101 PD patients and 58 age- and sex-matched healthy controls were enrolled in the study.

Deep learning-enabled medical computer vision.

A decade of unprecedented progress in artificial intelligence (AI) has demonstrated the potential for many fields-including medicine-to benefit from the insights that AI techniques can extract from data. Here we survey recent progress in the development of modern computer vision techniques-powered by deep learning-for medical applications, focusing on medical imaging, medical video, and clinical deployment. We start by briefly summarizing a decade of progress in convolutional neural networks, including the vision tasks they enable, in the context of healthcare.

Posterior Cingulate Cortex Network Predicts Alzheimer's Disease Progression.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by the accumulation of toxic misfolded proteins, which are believed to have propagated from disease-specific epicenters through their corresponding large-scale structural networks in the brain. Although previous cross-sectional studies have identified potential AD-associated epicenters and corresponding brain networks, it is unclear whether these networks are associated with disease progression. Hence, this study aims to identify the most vulnerable epicenters and corresponding large-scale structural networks involved in the early stages of AD and to evaluate its associations with multiple cognitive domains using longitudinal study design.