Abstract

Sexuality is a fundamental and lifelong aspect of human existence, profoundly influencing quality of life, psychological well-being, and interpersonal relationships. This article provides internal medicine specialists with a comprehensive overview of sexual health in both men and women, emphasising its intricate biopsychosocial nature and the multifaceted factors that can lead to dysfunction. We explore the biological, psychological, cultural, and socioeconomic dimensions of sexuality, discussing common dysfunctions such as erectile dysfunction, ejaculatory disorders in men, and desire, arousal, and orgasmic disorders in women. The impact of various medical conditions and pharmacotherapy on sexual function is also examined. The article advocates for a holistic, “amalgamated” therapeutic strategy, integrating medical, psychological, and lifestyle interventions. It highlights the crucial role of physicians in initiating open conversations, conducting thorough evaluations, and delivering empathetic care for sexual health concerns, thereby promoting overall patient well-being.

Abstract

Background: Maturity-onset diabetes of the young (MODY) is an uncommon genetic form of diabetes that arises from mutations in the HNF1B gene. Because it begins at a young age and typically requires insulin therapy, MODY is frequently mistaken for type 1 diabetes.

Case Presentation: We describe a 13-year-old Marwari boy with a history of type 1 diabetes, recurrent diabetic ketoacidosis DKA, and recurrent urosepsis post-pyeloplasty. Despite high-dose insulin therapy (>240 units/day), glycemic control was inadequate. Genetic analysis confirmed HNF1B-associated MODY (MODY 5).

Conclusion: In young individuals presenting with both diabetes and kidney abnormalities, MODY 5 should be considered as a possible diagnosis. Early recognition and precise diagnosis are crucial for guiding appropriate therapy and genetic counselling.

Abstract

Introduction: What is Artificial Intelligence (AI)?

Computer machines are programmed to learn, think and make decisions like humans. Machine learning, deep learning methods, are integral part of the Artificial intelligence.

Basics of Data Science and Machine Learning: The data go through several steps likecleaning, feature extraction, feature scaling using various machine learning algorithms. The multipurpose large language AI models and domain specific models are available.

Why AI Matters in Indian Healthcare

India Faces a Dual Challenge: High patient load and limited health infrastructure. AI offers scalable, cost-effective tools to assist overburdened healthcare providers.

Key Applications of AI in Healthcare: The key applications of AI include diagnosis and screening, treatment planning, decision support Systems (DSS), workflow optimization, Precision and personalized care.

Indian Context: Opportunities and Challenges High mobile penetration enables AI-based telemedicine in rural areas. Also remote patient monitoring, and offering specialist care for rural people are possible with this technology. There also challenges like data Privacy, bias, standardization, trust of clinicians and patients and organizations like CDSCO and ICMR are addressing this.

Augmented Intelligence: Building AI models taking the help of domain experts- Doctors or paramedics and incorporating it in the clinical environment with the Physician in the loop of health care.

Future Prospects: The upcoming Explainable AI (XAI) models, Retrieval AugmentedGeneration systems, Model Context Protocol, Federated Learning will improve the quality and performance of the AI systems.

Conclusion:

AI is not a threat to doctors—it is a powerful tool that augments human intelligence and capable of revolutionizing Indian healthcare system.

Abstract

We carried out a case-control study in a patient with type 2 diabetes mellitus (T2DM) to evaluate the association of ITGA2 gene polymorphisms in microvascular complications. The BglII gene polymorphism of the a2b1 Integrin, a platelet collagen receptor, has been suggested as a genetic risk factor for diabetic retinopathy. The association study included 125 patients genotyped by PCR-RFLP using sequence-specific primers for ITGA2 C807T variant and BglII restriction enzyme. The 125 participants were consists of 25 participants in each group with diabetic neuropathy, diabetic retinopathy, diabetic nephropathy, newly diagnosed type 2 diabetes, and participants without diabetes.

The T/T genotype of the ITGA2 gene was associated with the diabetic retinopathy group (OR= 2.43, 95% CI 2.56 – 4.11, p= 0.001). Thus, the BglII genotype is related to the prevalence of retinopathy independent of the other variables, but it is not independently associated with the prevalence of nephropathy and neuropathy. The ITGA2 gene polymorphisms were a risk marker for diabetic retinopathy among the various microvascular complications in type 2 diabetes. These ITGA2 gene polymorphisms would further help create an evidence base to support the markers for preventing the development of Diabetic Retinopathy among type 2 diabetes.