Deep Learning-Based Identification of Intraocular Pressure-Associated Genes Influencing Trabecular Meshwork Cell Morphology.

Purpose: Genome-wide association studies have recently uncovered many loci associated with variation in intraocular pressure (IOP). Artificial intelligence (AI) can be used to interrogate the effect of specific genetic knockouts on the morphology of trabecular meshwork cells (TMCs) and thus, IOP regulation.

Design: Experimental study.

Estimating presymptomatic episodic memory impairment using simple hand movement tests: A cross-sectional study of a large sample of older adults.

Introduction: Finding low-cost methods to detect early-stage Alzheimer's disease (AD) is a research priority for neuroprotective drug development. Presymptomatic Alzheimer's is associated with gait impairment but hand motor tests, which are more accessible, have hardly been investigated. This study evaluated how home-based Tasmanian (TAS) Test keyboard tapping tests predict episodic memory performance.

High Prevalence of Post-Traumatic Stress Disorder and Psychological Distress Among Healthcare Workers in COVID-19 Field Hospitals: A Cross-Sectional Study from Vietnam.

Objective: To evaluate the prevalence of post-traumatic stress disorder (PTSD) and other psychological disturbances in the Vietnamese healthcare workers (HCWs) at COVID-19 field hospitals.

Methods: A cross-sectional study was conducted using the Impact of Event Scale-Revised (IES-R) to measure PTSD and the Depression Anxiety Stress scale (DASS) to measure other psychological disturbances. The anxiety about COVID-19 was evaluated by the fear of COVID-19 (FOC) scale.

Practice regarding tuberculosis care among physicians at private facilities: A cross-sectional study from Vietnam.

Objectives: To evaluate the practice of TB care among physicians at private facilities.

Methods: A cross-sectional study was conducted using questionnaires on knowledge, attitude, and practice related to TB care. The responses to these scales were used to explore latent constructs and calculate the standardized continuous scores for these domains.

Real-time automated detection of older adults' hand gestures in home and clinical settings.

Unlabelled: There is an urgent need, accelerated by the COVID-19 pandemic, for methods that allow clinicians and neuroscientists to remotely evaluate hand movements. This would help detect and monitor degenerative brain disorders that are particularly prevalent in older adults. With the wide accessibility of computer cameras, a vision-based real-time hand gesture detection method would facilitate online assessments in home and clinical settings.

Differences in clinical manifestations of late-onset, compared to earlier-onset essential tremor: A scoping review.

Across the world, Essential Tremor (ET) is the most common tremor diagnosis but up to half of these diagnoses are inaccurate. The misdiagnosis rate is particularly high in late-onset ET, when tremor begins after the age of 60 years. Currently, ET is reported to affect 5.

Hand tremor detection in videos with cluttered background using neural network based approaches.

With the increasing prevalence of neurodegenerative diseases, including Parkinson's disease, hand tremor detection has become a popular research topic because it helps with the diagnosis and tracking of disease progression. Conventional hand tremor detection algorithms involved wearable sensors. A non-invasive hand tremor detection algorithm using videos as input is desirable but the existing video-based algorithms are sensitive to environmental conditions.

Sequence Classification Restricted Boltzmann Machines With Gated Units.

For the classification of sequential data, dynamic Bayesian networks and recurrent neural networks (RNNs) are the preferred models. While the former can explicitly model the temporal dependences between the variables, and the latter have the capability of learning representations. The recurrent temporal restricted Boltzmann machine (RTRBM) is a model that combines these two features.

Deep Logic Networks: Inserting and Extracting Knowledge From Deep Belief Networks.

Developments in deep learning have seen the use of layerwise unsupervised learning combined with supervised learning for fine-tuning. With this layerwise approach, a deep network can be seen as a more modular system that lends itself well to learning representations. In this paper, we investigate whether such modularity can be useful to the insertion of background knowledge into deep networks, whether it can improve learning performance when it is available, and to the extraction of knowledge from trained deep networks, and whether it can offer a better understanding of the representations learned by such networks.

Recurrent intussusception in infants.

Aim: Clinical features to identify infants at increased risk of recurrence after a primary episode of intussusception (IS) are poorly defined.

Methods: Prospective study of the clinical presentation, treatment and outcome in infants <2 years presenting with acute IS to the National Hospital of Pediatrics, Hanoi, over a 14-month period (1 November 2002 to 31 December 2003). A retrospective review of medical records was performed to verify complete patient ascertainment.