Research on sleep disorders in patients with mental illness: A review of Indian studies.

Background: Many psychiatric disorders present with symptoms involving disordered sleep. The overlay of sleep and psychiatric disorders, though ubiquitous, remains less well understood. The evolution of diagnostic manuals for sleep and psychiatric disorders has also complicated our understanding of this intersection.

Should premenstrual syndrome and premenstrual dysphoric disorder coexist in diagnostic manuals: Exploring the answer through citation analysis.

Background: Premenstrual syndrome (PMS) and premenstrual dysphoric disorders (PMDDs) both represent the most studied premenstrual disorders. Most studies have substantial prevalence of both PMS and PMDD, though the current understanding of the disorders reflects that PMDD is the more severe disorder of the two. However, there is substantial overlap in the etiopathogenesis and treatment approach of both these disorders.

Survey of Felt Needs Among Teachers of Psychiatry in Institutes of National Importance (INI) to Improve the Quality of Undergraduate and Postgraduate Training.

Background: Several institutes of national importance (INI) have been established in the last few years to improve the quality of medical education in the country. Most INIs are autonomous and outside the purview of the National Medical Commission (NMC). Little is known about the needs of the psychiatry teachers working in INIs.

Mental health stigma and its relationship with mental health professionals - A narrative review and practice implications.

The extent and magnitude of the mental health stigma are enormous, with substantial clinical and social implications. There is a complex relationship between mental health stigma and mental health professionals (MHPs); MHPs can be anti-stigma crusaders, victims of stigma, and even a source of stigma. Unfortunately, literature is scarce talking about the relationship between stigma and MHPs.

Likelihood-free posterior estimation and uncertainty quantification for diffusion MRI models.

Diffusion magnetic resonance imaging (dMRI) allows to estimate brain tissue microstructure as well as the connectivity of the white matter (known as tractography). Accurate estimation of the model parameters (by solving the inverse problem) is thus very important to infer the underlying biophysical tissue properties and fiber orientations. Although there has been extensive research on this topic with a myriad of dMRI models, most models use standard nonlinear optimization techniques and only provide an estimate of the model parameters without any information (quantification) about uncertainty in their estimation.

Understanding the epidemiology of substance use in India: A review of nationwide surveys.

Substance use is a public health concern on a global and national levels. There is a scarcity of nationally representative systematic research on the epidemiology of substance use in India. This narrative review aims to discuss the various large epidemiological surveys providing data on substance use in India.

A prospective hospital-based study on C-reactive protein as a response predictor of antidepressant treatment in drug naïve patients with major depressive disorder.

Background: C-reactive protein (CRP) is an acute phase reactant that is implicated in the pathogenesis of major depressive disorder (MDD), due to its role in the execution of various important neurological events, including neurogenesis, mediation of neural plasticity, and synaptic transmission.

Aims: This study was conducted to determine the relationship between the level of CRP to remission rates after antidepressant therapy.

Methods: Fifty patients of first episode MDD with no past history of antidepressant exposure and other medical comorbidity were recruited after obtaining consent for Escitalopram therapy.

A domain-agnostic MR reconstruction framework using a randomly weighted neural network.

Purpose: To design a randomly-weighted neural network that performs domain-agnostic MR image reconstruction from undersampled k-space data without the need for ground truth or extensive in-vivo training datasets. The network performance must be similar to the current state-of-the-art algorithms that require large training datasets.

Methods: We propose a Weight Agnostic randomly weighted Network method for MRI reconstruction (termed WAN-MRI) which does not require updating the weights of the neural network but rather chooses the most appropriate connections of the network to reconstruct the data from undersampled k-space measurements.

A Prospective Observational Study on Changes in Intraocular Pressure and Iridocorneal Angle Following the Use of Escitalopram and Amitriptyline.

Background: Depression has emerged as one of the prime morbidities affecting professional and personal lives worldwide. Antidepressants are one of the mainstays of management of depressive episodes. Although antidepressants are considered a safe class of drugs, the studies examining the effects of antidepressant prescription on intraocular pressure (IOP) have shown a propensity to increase IOP, but not consistently.

Relationship of obstructive sleep apnea with major depressive disorder and development of metabolic syndrome.

Objective: Major depressive disorder (MDD) is often linked with a number of coexisting disorders with a relation that is poorly understood. The aim of this study was to find out the role of obstructive sleep apnea (OSA) in metabolic syndrome (MS) in subjects with MDD and to develop a model for factors leading to MS.

Methods: It was a cross-sectional study conducted on 119 subjects.

A review and experimental evaluation of deep learning methods for MRI reconstruction.

Following the success of deep learning in a wide range of applications, neural network-based machine-learning techniques have received significant interest for accelerating magnetic resonance imaging (MRI) acquisition and reconstruction strategies. A number of ideas inspired by deep learning techniques for computer vision and image processing have been successfully applied to nonlinear image reconstruction in the spirit of compressed sensing for accelerated MRI. Given the rapidly growing nature of the field, it is imperative to consolidate and summarize the large number of deep learning methods that have been reported in the literature, to obtain a better understanding of the field in general.

Management of Chronic Insomnia Using Cognitive Behavior Therapy for Insomnia (CBT-I) During COVID-19 Pandemic: Does One Shoe Fit All?

Purpose: Insomnia is a highly prevalent disorder that is seen across all age groups causing significant morbidity to the patients. Its prevalence has further risen during COVID-19 pandemic. It is widely acknowledged that untreated insomnia can lead to significant health risks and socio-occupational dysfunction.