Psychometric properties and factor structure of the suicidal narrative inventory in major depression: A multicentric evaluation.

BACKGROUND: The Suicidal Narrative Inventory (SNI) is a 38-item self-report measure developed to assess elements of the suicidal narrative, a subacute, predominantly cognitive, presuicidal construct. Our objectives were to assess the factor structure, validity, and reliability of the SNI-38 among adults with major depressive disorder (MDD). METHODS: Using a cross-sectional design, we administered the Hindi version of the SNI along with other self-report measures to adults with MDD, recruited from 24 tertiary care hospitals across India. Confirmatory factor analysis (CFA) was performed to assess the factor structure of SNI-38. Reliability (internal consistency) was assessed using Cronbach's alpha (α). Convergent, discriminant, and criterion validity of the SNI-38 were tested by comparing it against other appropriate measures. RESULTS: We collected usable responses from 654 Hindi-speaking participants (Mean age = 36.9 ± 11.9 years, 50.2% female). The eight-factor solution of the SNI showed good model fit indices (χ2[637] = 3345.58, p <.001, CFI =.98, and RMSEA =.08). Internal consistencies for the SNI subscale scores were good to excellent, α ranging from .73 to.92. While most subscales significantly converged with other measures, associations were comparatively weaker and inconsistent for the 'thwarted belongingness' and 'goal reengagement' subscales. CONCLUSION: Consistent with prior data, our study confirmed an eight-factor solution and demonstrated adequate psychometric properties for the Hindi version of the SNI-38 in our sample. These findings provide empirical support for the use of SNI to assess the suicidal narrative among Indian adults with MDD.

A cross-sectional study of the mental health burden among COVID-19 survivors.

Introduction: Coronavirus disease 2019 (COVID-19) pandemic has had a significant impact on mental health, including stress, anxiety, and depression. This study aimed to assess the incidence and severity of mental health issues among individuals diagnosed with COVID-19 infection. Methods: A semi-structured proforma for socio-demographic and clinical parameters was used to collect cross-sectional hospital-based data of subjects who tested positive for COVID-19 infection. The Modified Fatigue Impact Scale (MFIS), Hospital Anxiety and Depression Scale (HADS), and Perceived Stress Scale (PSS) were used to assess the presence of physical, psychological, and cognitive symptoms. The presence of anxiety, depression, and stress was based on the cut-off scores for HADS-A (≥8), HADS-D (≥8), and PSS (≥14), respectively. Results: A total of 101 patients comprising 39 (38.6%) males were recruited. Compared to nuclear families, we observed that patients living in joint families had significantly greater severity scores for fatigue [MFIS (p = 0.04)], anxiety [HADS-A (p = 0.004)], depression [HADS-D (p = 0.004)], and stress [PSS (p = 0.02)]. Based on the cut-off scores, we found that 44 (43.6%) patient had anxiety, 41 (40.6%) had depressive, and 72 (71.3%) had moderate to high stress symptoms, respectively. We also observed significantly greater fatigue and anxiety scores, that is, MFIS (p = 0.008) and HADS-A (p = 0.03) in those who received oxygen therapy compared to those who did not. The subjects who received corticosteroids were older (p = 0.01) and had significantly higher stress scores [PSS (p < 0.001)]. The study showed that patients who were assessed more than 3 months post-COVID-19 infection had higher fatigue and depression scores; however, the difference did not reach statistical significance (MFIS P = 0.058; HADS P = 0.059). Conclusion: Our study confirms that COVID-19 infection can cause various adverse mental health issues. Mitigating the hazardous effects of COVID-19 pandemic on mental health should be a top priority for public health to prevent long-term complications.

Artificial Intelligence and Machine Learning Technology Driven Modern Drug Discovery and Development.

The discovery and advances of medicines may be considered as the ultimate relevant translational science effort that adds to human invulnerability and happiness. But advancing a fresh medication is a quite convoluted, costly, and protracted operation, normally costing USD ~2.6 billion and consuming a mean time span of 12 years. Methods to cut back expenditure and hasten new drug discovery have prompted an arduous and compelling brainstorming exercise in the pharmaceutical industry. The engagement of Artificial Intelligence (AI), including the deep-learning (DL) component in particular, has been facilitated by the employment of classified big data, in concert with strikingly reinforced computing prowess and cloud storage, across all fields. AI has energized computer-facilitated drug discovery. An unrestricted espousing of machine learning (ML), especially DL, in many scientific specialties, and the technological refinements in computing hardware and software, in concert with various aspects of the problem, sustain this progress. ML algorithms have been extensively engaged for computer-facilitated drug discovery. DL methods, such as artificial neural networks (ANNs) comprising multiple buried processing layers, have of late seen a resurgence due to their capability to power automatic attribute elicitations from the input data, coupled with their ability to obtain nonlinear input-output pertinencies. Such features of DL methods augment classical ML techniques which bank on human-contrived molecular descriptors. A major part of the early reluctance concerning utility of AI in pharmaceutical discovery has begun to melt, thereby advancing medicinal chemistry. AI, along with modern experimental technical knowledge, is anticipated to invigorate the quest for new and improved pharmaceuticals in an expeditious, economical, and increasingly compelling manner. DL-facilitated methods have just initiated kickstarting for some integral issues in drug discovery. Many technological advances, such as "message-passing paradigms", "spatial-symmetry-preserving networks", "hybrid de novo design", and other ingenious ML exemplars, will definitely come to be pervasively widespread and help dissect many of the biggest, and most intriguing inquiries. Open data allocation and model augmentation will exert a decisive hold during the progress of drug discovery employing AI. This review will address the impending utilizations of AI to refine and bolster the drug discovery operation.

Mycetism - A report of three cases with review of recent literature.

Wild mushroom grow abundantly in the tropical belts of India, and they form part of the diet among the ethnic tribes. However, wild mushrooms are toxic, and some cause organ failure, namely fulminant hepatitis and kidney injury. Mushroom poisoning is frequently diagnosed based on clinical suspicion, and death has been reported commonly because of the consumption of amatoxin-containing mushrooms. In this article, we discuss three cases of amatoxin-induced mushroom poisoning that resulted in acute kidney and liver failure, requiring intensive medical management and renal replacement therapy. One of these patients died from irreversible fulminant hepatitis.

A population-based study on tobacco consumption in urban slums: Its prevalence, pattern, and determinants.

BACKGROUND: India suffers from a huge burden of substance abuse and associated morbidity and mortality. Among all substance use, tobacco consumption is the most common and yet the most widely accepted one. This study aimed to estimate the prevalence of tobacco consumption, to find out the type of tobacco products used and to assess the factors influencing tobacco consumption in the slums of Shillong city. MATERIALS AND METHODS: A cross-sectional, community-based study was carried out in 330 respondents aged 15 and above. Chi-square test was used to compare proportions, and Student's t-test was used to compare groups for continuous variables. RESULTS: The prevalence of current tobacco consumption was found to be 73.9%, and the rate of quitting was found to be 4.3%. The prevalence of tobacco consumption was observed to be higher in males (52.4%) compared to 21.5% in females. Highly significant statistical association was observed between tobacco consumption and age, gender, and occupation. The statistical association between tobacco consumption and religion and education was found to be statistically significant. Ever use of tobacco in any form as well as smokeless form peaked in 24-34 years, while smoking was more prevalent among 15-24 year olds. The prevalence of smokeless tobacco was higher (47.5%) as compared to the prevalence of smoking (28.2%), closely followed by dual use (24.3%). The most popular smoked and smokeless forms were found to be cigarettes and khaini, respectively. CONCLUSIONS: Tobacco consumption was found to be highly prevalent and was much higher than the national average hinting toward its association with higher incidence of various malignancies in the region and calling for immediate action toward propelling its prevention and control by all stakeholders.

Bowel and Bladder Anxiety: An Obsession or a Variant of Agoraphobia?

It has long been debated whether bowel and bladder anxiety are a part of obsessive compulsive spectrum disorder or a variant of agoraphobia with no consensus view yet. Tricyclic antidepressants are reportedly efficacious in such cases and lead to complete resolution of symptoms. Here, we report a 36-year-old male having urges to visit toilet when in public places or where toilets are not easily available and a resulting avoidance of such spaces fearing an episode of incontinence. Symptoms originated 16 years ago when he was in university which compelled him to drop out. We treated him with paroxetine and relaxation therapy to which he responded satisfactorily.

Comparison of brain activation patterns during executive function tasks in hoarding disorder and non-hoarding OCD.

We examined differences in regional brain activation during tests of executive function in individuals with Hoarding Disorder (HD), Obsessive Compulsive Disorder (OCD), and healthy controls (HC) using functional magnetic resonance imaging (fMRI). Participants completed computerized versions of the Stroop and Go/No-Go task. We found that during the conflict monitoring and response inhibition condition in the Go/No-Go task, individuals with HD had significantly greater activity than controls in the anterior cingulate cortex (ACC) and right dorsolateral prefrontal cortex (DLPFC). HD also exhibited significantly greater right DLPFC activity than OCD. We also observed significant differences in activity between HD and HC and between HD and OCD in regions (ACC, anterior insula, orbitofrontal cortex, and striatum) involved in evaluating stimulus-response-reward associations, or the personal and task-relevant value of stimuli and behavioral responses to stimuli. These results support the hypothesis that individuals with HD have difficulty deciding on the value or task relevance of stimuli, and may perceive an abnormally high risk of negative feedback for difficult or erroneous cognitive behavior.

Spontaneous dense array gamma activity in children and adolescents with volatile solvent dependence.

OBJECTIVES: Children and adolescents with volatile solvent/inhalant dependence have neurocognitive deficits. The study aimed to explore resting state gamma activity, which is a marker of cognitive function, in children and adolescents with inhalant dependence. The study also investigated differences in gamma activity across groups differing in inhaled amounts. MATERIALS AND METHODS: Clinical and electroencephalographic (EEG) data were collected from nine patients with inhalant dependence. 30-50 Hz spectral power was calculated using Welch's averaged periodogram method. The data were analyzed using descriptive statistics, bi-variate correlation, and nonparametric Mann-Whitney U-test. RESULTS: While significant positive correlation was found between duration of dependence and gamma spectral power in left frontal region, no significant differences were found on comparison of the groups on gamma spectral power. CONCLUSION: A complex neurobiological interaction is involved in the synaptic modulation brought about by inhalant use and this could be understood by studying changes in EEG parameters especially gamma activity.

Delayed onset of clozapine-induced leucopenia.

Clozapine has been reported to cause agranulocytosis, neutropenia, and leucopenia that usually occur within 18 weeks of initiation of treatment. We report a case of delayed onset leucopenia after 11 years of treatment with clozapine, which reversed within a few days after discontinuation of medication.

Theta-patterned, frequency-modulated priming stimulation enhances low-frequency, right prefrontal cortex repetitive transcranial magnetic stimulation (rTMS) in depression: a randomized, sham-controlled study.

Efficacy of repetitive, transcranial magnetic stimulation (rTMS) has been found in depression; however, doubt still remains about its effectiveness in clinical practice. In this context, results are being explored. The authors, describing new techniques to improve response rates to rTMS treatment, compared the efficacy of adjuvant, frequency-modulated, active-priming rTMS with sham-priming stimulation in the theta range in patients with moderate-to-severe depression receiving low-frequency rTMS. Forty patients with moderate-to-severe depression (ICD-10 DCR) were alternately assigned to receive add-on, active-priming rTMS (4-8 Hz; 400 pulses, at 90% of motor threshold [MT]) or sham-priming stimulation followed by low-frequency rTMS (1-Hz; 900 pulses at 110% of MT) over the right dorsolateral prefrontal cortex. They were rated with the Structured Interview Guide for the Hamilton Depression Rating Scale (SIGH-D), the Brief Psychiatric Rating Scale (BPRS), and the Clinical Global Impression-Severity of Illness (CGI-S) scale at baseline, after the 5th and 10th rTMS, and 2 weeks post-rTMS. For SIGH-D scores, there was significant improvement in the active group over time. Stepwise linear-regression analysis showed that age at onset significantly predicted SIGH-D scores after the 5th rTMS session in the active-priming group. Pre-stimulation with frequency-modulated priming stimulation in the theta range has greater antidepressant effect than low-frequency stimulation alone.