K. J. Somaiya Medical College & Research Centre

Schizophrenia being a major psychiatric disorder comprises of dominant neurodevelopmental corroborations; still there are inadequate markers which reveal the brain vulnerability. Recognition of such neurodevelopmental correlates linked with schizophrenia is a major hindrance observed in today’s era. It presents itself as a major hurdle as it necessitates comprehending the early brain alterations hinting towards vulnerability of brain parenchyma towards disorders. One such correlate being cavum septum pellucidum (CSP), has been frequently linked with schizophrenia. The primary obstacle in the management of psychological disorders is the significantly delayed diagnosis because of the societal stigma and the preconceived notions about these diseases.This narrative review encompasses recent studies from 2018-2025 extracting high yield neuroimaging, molecular, genetic data to meticulously explain the association between enlarged CSP and schizophrenia, to critically appraise the clinical potential of enlarged cavum septum pellucidum as a neurodevelopmental liability marker as well as for incorporating them with multimodal artificial intelligence models rather than predicting or diagnosing schizophrenia at patient level.This review is first to amalgamate extensive volumetric, meta-analyses as well as genetic data to highlight the role of CSP as a correlation risk marker from other non-specific parameters, aiding in better management of patients.

Background: Type 2 diabetes mellitus (T2DM) is highly prevalent in India and affects many aspects of health, including sexual well-being. While male sexual problems are well documented, female sexual dysfunction (FSD) in women with diabetes often remains overlooked. This study explored the prevalence of FSD and its links with age, diabetes duration, and glycemic control. Methods: In this cross-sectional study, 90 women with T2DM attending a tertiary hospital outpatient clinic were recruited through convenience sampling. Postmenopausal and non-sexually active women, or those with major comorbidities, were excluded. Data were collected using a sociodemographic pro-forma and the Female Sexual Function Index (FSFI). Glycemic control was defined by hemoglobin A1c (HbA1c) (<7% = good control). Statistical tests included descriptive analysis and correlation. Results: FSD was identified in 77% of participants. Desire, arousal, and lubrication difficulties were most common (each affecting 97%), followed by orgasm and satisfaction problems (95%). Women with poor glycemic control were nearly seven times more likely to report FSD (OR = 6.89, P = .007). Older age ( r = −0.224, P = .033) and longer diabetes duration ( r = −0.402, P = .001) were both linked with lower FSFI scores. Conclusion: FSD is common among women with T2DM and is closely tied to age, duration of illness, and poor glycemic control. Addressing sexual health in diabetes care can improve both well-being and quality of life, highlighting the need for routine screening and supportive interventions.

ObjectiveEmergency medicine (EM) is a growing specialty in India with the board approving training programme commencing only since 2009. This study aimed to identify the challenges faced by EM department personnel in the country and to obtain valuable insight into the concerns and problems experienced by them.MethodsThis study was a cross-sectional digital survey conducted among EM department personnel in teaching institutions across India. The study involved 170 respondents, who completed an online questionnaire that covered various aspects of their work and identified the challenges they faced. It also captured potential solutions as perceived by the respondents.ResultsA total of n=170 participants completed the survey of which N=164 fulfilled the eligibility criteria. The study revealed significant challenges faced by EM personnel in India. Administrative and clerical work consumed a considerable amount of respondents’ time. Understaffing (n=144/164; 87.8%), followed by complains about delay due to hospital administrative processes and policies (n=141/164; 85.9%), and delay in interhospital transfers (n=139/164; 84.8%) were the primary concerns spelt out by the respondents. Additionally, respondents experienced interpersonal conflicts, and verbal/physical abuse and inappropriate behaviour from patients and their family members. Potential key solutions suggested were to improve resources including manpower and take steps to prevent violence against EM staff.ConclusionThe study’s results call for policy-makers and hospital administrators to address the issues faced by EM department staff. Improving EM department operations can improve patient care and staff well-being. Future research should examine challenges in non-teaching institutions and potential solutions.

Human body is an intricate system of complex associations between numerous chemicals, neurotransmitters incessantly striving towards homeostasis. This process is so tightly regulated that these processes quickly reverse the alterations caused due to any insults suffered by the organism; when these repeatedly attack the organism’s system, they culminate as diseases. Brain being the hub of such complex interplay, is the most vulnerable site for such imbalances. As per 2021 World Health Organisation Report, over 3 billion of the global population live with a neuropsychiatric ailment. Owing to the extremely limited options for diagnosis in neuropsychiatry, it is tedious to arrive at a definitive diagnosis early. Normally, diagnosis of neuropsychiatric disorders like autism spectrum disorder, generalized anxiety disorders, obsessive compulsive disorder, schizophrenia etc. demands persistence of symptoms for over 6 months with no body fluid test to appropriately quantify the diagnosis, it demands the need for a novel approach for bridging this significant gap. With the ever-advancing technological era, the advent of artificial intelligence has revolutionized the medical paradigm. Integration of such multimodal neuroimaging databases, algorithms, techniques has made individualized diagnosis & treatment of neuropsychiatric disorders plausible. Considering the colossal data revolving around artificial intelligence neuroimaging, particularly address disease-specific challenges & rarely collate how multimodal frameworks amalgamate neurology & psychiatric disorders. In addition, no prior literature systematically compares biomarker-rich & scare diseases & emphasizes the challenges faced by artificial intelligence among these groups. This review bridges this research gap by presenting an integrated, comprehensible manuscript which links multimodal networks of supervised, unsupervised, reinforcement learning models across the whole of neuropsychiatric spectrum.The novelty of this manuscript lies in highlighting the transdiagnostic perspectives of multimodal approaches of neuroimaging which are artificial intelligence-driven. This aids in filling the gaps between psychiatric & neurological disorders thereby practically focussing on the blueprint towards precision diagnostic models- an entirely understudied aspect in previous literatures. The primary aim of this manuscript is to explore the fusion between the multimodal approach of artificial intelligence & neuropsychiatric diagnosis, their implications, and challenges faced in this integration. We meticulously analyze the applications of artificial intelligence in precision medicine in the field of neuropsychiatry to ensure timely diagnosis and prevent complications, to improve patients’cost of living, prognosis as well as future directions of this amalgamation.