Behavioral Intervention for Adults With Autism on Distribution of Attention in Triadic Conversations: A/B-Tested Pre-Post Study.

Background: Cross-neurotype differences in social communication patterns contribute to high unemployment rates among adults with autism. Adults with autism can be unsuccessful in job searches or terminated from employment due to mismatches between their social attention behaviors and society's expectations on workplace communication.

Objective: We propose a behavioral intervention concerning distribution of attention in triadic (three-way) conversations.

The Effect of an AI-Based, Autonomous, Digital Health Intervention Using Precise Lifestyle Guidance on Blood Pressure in Adults With Hypertension: Single-Arm Nonrandomized Trial.

Background: Home blood pressure (BP) monitoring with lifestyle coaching is effective in managing hypertension and reducing cardiovascular risk. However, traditional manual lifestyle coaching models significantly limit availability due to high operating costs and personnel requirements. Furthermore, the lack of patient lifestyle monitoring and clinician time constraints can prevent personalized coaching on lifestyle modifications.

Classification of Patient Recovery from COVID-19 Symptoms using Consumer Wearables and Machine Learning.

Current remote monitoring of COVID-19 patients relies on manual symptom reporting, which is highly dependent on patient compliance. In this research, we present a machine learning (ML)-based remote monitoring method to estimate patient recovery from COVID-19 symptoms using automatically collected wearable device data, instead of relying on manually collected symptom data. We deploy our remote monitoring system, namely eCOVID, in two COVID-19 telemedicine clinics.

Linotrins: Omega-3 oxylipins featuring an E,Z,E conjugated triene motif are present in the plant kingdom and alleviate inflammation in LPS-challenged microglial cells.

Alpha-linolenic acid (ALA), an essential omega-3 polyunsaturated fatty acid found in plants, exerts neuroprotection and anti-inflammatory effects in chronic and acute CNS disease models. However, the underlying mechanisms are not yet understood. Since ALA is not incorporated into the brain, the observed health benefits may result from some of its metabolites.

Using Wearables and Machine Learning to Enable Personalized Lifestyle Recommendations to Improve Blood Pressure.

Blood pressure (BP) is an essential indicator for human health and is known to be greatly influenced by lifestyle factors, like activity and sleep factors. However, the degree of impact of each lifestyle factor on BP is unknown and may vary between individuals. Our goal is to investigate the relationships between BP and lifestyle factors and provide personalized and precise recommendations to improve BP, as opposed to the current practice of general lifestyle recommendations.

Perspective: Opportunities and Challenges of Technology Tools in Dietary and Activity Assessment: Bridging Stakeholder Viewpoints.

The science and tools of measuring energy intake and output in humans have rapidly advanced in the last decade. Engineered devices such as wearables and sensors, software applications, and Web-based tools are now ubiquitous in both research and consumer environments. The assessment of energy expenditure in particular has progressed from reliance on self-report instruments to advanced technologies requiring collaboration across multiple disciplines, from optics to accelerometry.

Personalized machine learning of depressed mood using wearables.

Depression is a multifaceted illness with large interindividual variability in clinical response to treatment. In the era of digital medicine and precision therapeutics, new personalized treatment approaches are warranted for depression. Here, we use a combination of longitudinal ecological momentary assessments of depression, neurocognitive sampling synchronized with electroencephalography, and lifestyle data from wearables to generate individualized predictions of depressed mood over a 1-month time period.

Personalized Blood Pressure Estimation Using Photoplethysmography: A Transfer Learning Approach.

In this paper, we present a personalized deep learning approach to estimate blood pressure (BP) using the photoplethysmogram (PPG) signal. We propose a hybrid neural network architecture consisting of convolutional, recurrent, and fully connected layers that operates directly on the raw PPG time series and provides BP estimation every 5 seconds. To address the problem of limited personal PPG and BP data for individuals, we propose a transfer learning technique that personalizes specific layers of a network pre-trained with abundant data from other patients.

Towards On-Demand Virtual Physical Therapist: Machine Learning-Based Patient Action Understanding, Assessment and Task Recommendation.

In this paper, we propose a machine learning-based virtual physical therapist (PT) system to enable personalized remote training for patients with Parkinson's disease (PD). Three physical therapy tasks with multiple difficulty levels are selected to help patients with PD improve balance and mobility. Patients' movements are captured by a Kinect sensor.

Association and prognostic value of serum Cystatin C, IL-18 and Uric acid in urological patients with acute kidney injury.

Purpose: To assess the role of serum Cystatin C, IL-18 and Uric acid in acute kidney injury (AKI) in urological patients, along with their prognostic significance.

Materials And Methods: Prospective observational study included 61 cases, admitted in urology ward with baseline serum creatinine ≤1.5 mg/dL.

The silent burden of anemia in school age children: a community based study in West Bengal.

Background: Under nutrition and anemia are common co-morbidities in school age children. Due to transition in dietary habits in developing countries, a paradoxical finding of coexistence of anemia and normal/over nutrition is also a cause of concern.

Objective: > T o assess the nutritional status and prevalence of anemia among school age children (6 - 16 years) residing in rural and urban areas of a district of West Bengal and also to find out the association between weight status, measured as Body Mass Index(BMI) and anemia.

Processing of electronic waste in a counter current teeter-bed separator.

Advanced gravity separation of ground electronic waste (e-waste) in a teeter-bed separator was investigated. It was established that the Floatex Density Seprator (FDS) is a promising device for wet processing of e-waste to recover metal values physically. It was possible to enrich the metal content from 23% in the feed to 37% in the product in a single stage operation using the FDS with over 95% recovery of the metals.

Prismatomerin, a new iridoid from Prismatomeris tetrandra. Structure elucidation, determination of absolute configuration, and cytotoxicity.

A new complex iridoid, prismatomerin (1), has been isolated from the leaves of Prismatomeris tetrandra, together with the known glucoside gaertneroside (4). The structures of 1 and 4 were determined by spectroscopic analysis, notably 2D NMR techniques. The (1R,5S,8S,9S,10S)-(-) absolute configuration of prismatomerin (1) was determined by comparison of the vibrational circular dichroism (VCD) spectrum calculated using density functional theory and the experimental VCD spectrum of the O-acetyl derivative 3.