Content Recommendation Systems in Web-Based Mental Health Care: Real-world Application and Formative Evaluation.

Background: Recommender systems have great potential in mental health care to personalize self-guided content for patients, allowing them to supplement their mental health treatment in a scalable way.

Objective: In this paper, we describe and evaluate 2 knowledge-based content recommendation systems as parts of Ginger, an on-demand mental health platform, to bolster engagement in self-guided mental health content.

Methods: We developed two algorithms to provide content recommendations in the Ginger mental health smartphone app: (1) one that uses users' responses to app onboarding questions to recommend content cards and (2) one that uses the semantic similarity between the transcript of a coaching conversation and the description of content cards to make recommendations after every session.

Modern views of machine learning for precision psychiatry.

In light of the National Institute of Mental Health (NIMH)'s Research Domain Criteria (RDoC), the advent of functional neuroimaging, novel technologies and methods provide new opportunities to develop precise and personalized prognosis and diagnosis of mental disorders. Machine learning (ML) and artificial intelligence (AI) technologies are playing an increasingly critical role in the new era of precision psychiatry. Combining ML/AI with neuromodulation technologies can potentially provide explainable solutions in clinical practice and effective therapeutic treatment.

Utilizing Machine Learning on Internet Search Activity to Support the Diagnostic Process and Relapse Detection in Young Individuals With Early Psychosis: Feasibility Study.

Background: Psychiatry is nearly entirely reliant on patient self-reporting, and there are few objective and reliable tests or sources of collateral information available to help diagnostic and assessment procedures. Technology offers opportunities to collect objective digital data to complement patient experience and facilitate more informed treatment decisions.

Objective: We aimed to develop computational algorithms based on internet search activity designed to support diagnostic procedures and relapse identification in individuals with schizophrenia spectrum disorders.

Multifunctional Properties of Quercitrin-Coated Porous Ti-6Al-4V Implants for Orthopaedic Applications Assessed In Vitro.

(1) One strategy to improve the outcome of orthopedic implants is to use porous implants with the addition of a coating with an antibacterial biomolecule. In this study, we aimed to produce and test the biocompatibility, the osteopromotive (both under normal conditions and under a bacterial challenge with lipopolysaccharide (LPS)) and antibacterial activities of a porous Ti-6Al-4V implant coated with the flavonoid quercitrin in vitro. (2) Porous Ti-6Al-4V implants were produced by 3D printing and further functionalized with quercitrin by wet chemistry.

Tracking Changes in Brain Network Connectivity under Transcranial Current Stimulation.

Noninvasive transcranial brain stimulation has been widely used in experimental and clinical applications to perturb the brain activity, aiming at promoting synaptic plasticity or enhancing functional connectivity within targeted brain regions. However, there are different types of neurostimulations and various choices of stimulation parameters; how these choices influence the intermediate neurophysiological effects and brain connectivity remain incompletely understood. We propose several quantitative methods to investigate the brain connectivity of an epileptic patient before and after transcranial alternating/direct current stimulation (tACS/tDCS).

Sleep spindles as a diagnostic and therapeutic target for chronic pain.

Pain is known to disrupt sleep patterns, and disturbances in sleep can further worsen pain symptoms. Sleep spindles occur during slow wave sleep and have established effects on sensory and affective processing in mammals. A number of chronic neuropsychiatric conditions, meanwhile, are known to alter sleep spindle density.

Deep Learning Based on Standard H&E Images of Primary Melanoma Tumors Identifies Patients at Risk for Visceral Recurrence and Death.

Purpose: Biomarkers for disease-specific survival (DSS) in early-stage melanoma are needed to select patients for adjuvant immunotherapy and accelerate clinical trial design. We present a pathology-based computational method using a deep neural network architecture for DSS prediction.

Experimental Design: The model was trained on 108 patients from four institutions and tested on 104 patients from Yale School of Medicine (YSM, New Haven, CT).

Cortical Pain Processing in the Rat Anterior Cingulate Cortex and Primary Somatosensory Cortex.

Pain is a complex multidimensional experience encompassing sensory-discriminative, affective-motivational and cognitive-emotional components mediated by different neural mechanisms. Investigations of neurophysiological signals from simultaneous recordings of two or more cortical circuits may reveal important circuit mechanisms on cortical pain processing. The anterior cingulate cortex (ACC) and primary somatosensory cortex (S1) represent two most important cortical circuits related to sensory and affective processing of pain.

Sleep oscillation-specific associations with Alzheimer's disease CSF biomarkers: novel roles for sleep spindles and tau.

Background: Based on associations between sleep spindles, cognition, and sleep-dependent memory processing, here we evaluated potential relationships between levels of CSF Aβ, P-tau, and T-tau with sleep spindle density and other biophysical properties of sleep spindles in a sample of cognitively normal elderly individuals.

Methods: One-night in-lab nocturnal polysomnography (NPSG) and morning to early afternoon CSF collection were performed to measure CSF Aβ, P-tau and T-tau. Seven days of actigraphy were collected to assess habitual total sleep time.

A deep learning approach for real-time detection of sleep spindles.

Objective: Sleep spindles have been implicated in memory consolidation and synaptic plasticity during NREM sleep. Detection accuracy and latency in automatic spindle detection are critical for real-time applications.

Approach: Here we propose a novel deep learning strategy (SpindleNet) to detect sleep spindles based on a single EEG channel.

Role of personality in tobacco smoking behavior in corporate sector: A cross-sectional study.

Background: India's corporate sector has grown steadily over the past decade, and it is providing a lot of work opportunities to Indian youth. Around 20% of employees in the corporate sector in India smoke cigarettes. In general, addictive behaviors including smoking are associated with certain personality dimensions.

Supervised Machine-learning Predictive Analytics for Prediction of Postinduction Hypotension.

What We Already Know About This Topic: WHAT THIS ARTICLE TELLS US THAT IS NEW: BACKGROUND:: Hypotension is a risk factor for adverse perioperative outcomes. Machine-learning methods allow large amounts of data for development of robust predictive analytics. The authors hypothesized that machine-learning methods can provide prediction for the risk of postinduction hypotension.

Local field potential decoding of the onset and intensity of acute pain in rats.

Pain is a complex sensory and affective experience. The current definition for pain relies on verbal reports in clinical settings and behavioral assays in animal models. These definitions can be subjective and do not take into consideration signals in the neural system.

Binge alcohol alters exercise-driven neuroplasticity.

Exercise is increasingly being used as a treatment for alcohol use disorders (AUD), but the interactive effects of alcohol and exercise on the brain remain largely unexplored. Alcohol damages the brain, in part by altering glial functioning. In contrast, exercise promotes glial health and plasticity.

Neuroleptic malignant syndrome in cycloserine-induced psychosis.

A 33-year-old multidrug-resistant tuberculosis female patient diagnosed as cycloserine-induced psychosis developed several neuroleptic side effects such as extrapyramidal reaction, neuroleptic malignant syndrome, and drug-induced parkinsonism while she was being treated with initially haloperidol and then olanzapine over a period of 2 months. Patient's antipsychotic medications were withdrawn, and treatment with bromocriptine showed prompt recovery. The multiple neurological adverse effects which the patient developed had implications on the management of the complications as well as her illness.

Quantitative 3-D analysis of GFAP labeled astrocytes from fluorescence confocal images.

Background: There is a need for effective computational methods for quantifying the three-dimensional (3-D) spatial distribution, cellular arbor morphologies, and the morphological diversity of brain astrocytes to support quantitative studies of astrocytes in health, injury, and disease.

New Method: Confocal fluorescence microscopy of multiplex-labeled (GFAP, DAPI) brain tissue is used to perform imaging of astrocytes in their tissue context. The proposed computational method identifies the astrocyte cell nuclei, and reconstructs their arbors using a local priority based parallel (LPP) tracing algorithm.

Algorithms for improved 3-D reconstruction of live mammalian embryo vasculature from optical coherence tomography data.

Background: Robust reconstructions of the three-dimensional network of blood vessels in developing embryos imaged by optical coherence tomography (OCT) are needed for quantifying the longitudinal development of vascular networks in live mammalian embryos, in support of developmental cardiovascular research. Past computational methods [such as speckle variance (SV)] have demonstrated the feasibility of vascular reconstruction, but multiple challenges remain including: the presence of vessel structures at multiple spatial scales, thin blood vessels with weak flow, and artifacts resulting from bulk tissue motion (BTM).

Methods: In order to overcome these challenges, this paper introduces a robust and scalable reconstruction algorithm based on a combination of anomaly detection algorithms and a parametric dictionary based sparse representation of blood vessels from structural OCT data.

A statistical model of retinal optical coherence tomography image data.

Optical coherence tomography (OCT) is an important mode of biomedical imaging for the diagnosis and management of ocular disease. Here we report on the construction of a synthetic retinal OCT image data set that may be used for quantitative analysis of image processing methods. Synthetic image data were generated from statistical characteristics of real images (n = 14).