International Physiology

Background: Obstructive Sleep Apnea (OSA) is a prevalent sleep disorder with significant public health implications, including cardiovascular diseases, hypertension, metabolic syndrome, and neurocognitive impairments. Despite its high prevalence, OSA remains underdiagnosed, often requiring clinical assessment and anthropometric measurements as effective screening tools. This study aims to investigate the profile of OSA patients attending a sleep clinic in a tertiary care center in eastern India. Methods: This cross-sectional study with interventional component was conducted between January 2016 and December 2017 at the Department of Physiology, Rajendra Institute of Medical Sciences (RIMS), Ranchi, and the Department of Sleep Medicine, Tata Mains Hospital, Jamshedpur. One hundred consecutive patients (aged 25–70 years) with clinical symptoms suggestive of OSA underwent overnight PSG. Demographic, anthropometric (BMI, neck circumference, waist circumference), and clinical data were collected. Daytime sleepiness was assessed using the Epworth Sleepiness Scale (ESS). OSA was classified based on Apnea Hypopnea Index (AHI). For patients with moderate-to-severe OSA, continuous positive airway pressure (CPAP) therapy was evaluated using a split-night protocol. Results: The mean age was 46.1 ± 8.8 years; 67% were male. Comorbidities included Type 2 diabetes (49%), hypertension (21%), and hypothyroidism (19%). Snoring (75%), witnessed apneas (58%), and excessive daytime sleepiness (ESS ≥10: 83%) were common. Anthropometric indices (BMI, neck and waist circumference) showed significant positive correlation with AHI severity (p<0.001). CPAP therapy in 68 moderate-to-severe patients significantly improved mean oxygen saturation (78.6% → 93.0%, p<0.001) and reduced apnea episodes (25.7 → 5.4, p<0.001). Severe OSA was present in 23% of the cohort. Conclusion: OSA is highly prevalent in patients with comorbidities like Type 2 diabetes, hypertension, and hypothyroidism. Anthropometric measurements such as BMI, waist, and neck circumference are useful predictors of OSA severity. CPAP therapy was highly effective in improving oxygen saturation and reducing apnea episodes. Early diagnosis and intervention are critical to managing the condition and preventing associated complications.

Diabetes mellitus (DM), particularly Type 2 Diabetes Mellitus (T2DM), has emerged as one of the most significant global public health challenges of the 21st century. As of 2021, an estimated 537 million adults worldwide are living with diabetes, and this number is projected to rise to 643 million by 2030 and 783 million by 2045, according to the International Diabetes Federation (IDF). The rapid urbanization of developing nations, increasing prevalence of obesity, and the complex interplay of genetic predisposition has contributed immensely to this escalating burden. Urban lifestyles, characterized by sedentary behavior, dietary transitions, and environmental stressors, play a pivotal role in diabetes onset. Obesity, especially central adiposity, has been identified as the strongest modifiable risk factor for T2DM. Furthermore, genetic susceptibility, influenced by ethnicity and family history, modulates individual risk and may explain population differences in prevalence and severity. This review aims to scrutinize the multifactorial nature of the diabetes epidemic, focusing on the intersection of urbanization, obesity, and genetic factors, while providing a comprehensive overview of the current epidemiological landscape, emerging trends, and global disparities in disease burden.

Circadian rhythms, driven by the suprachiasmatic nucleus and orchestrated by clock genes, regulate vital cellular and systemic processes including DNA repair, apoptosis, metabolism, and immune surveillance. Disruption of these rhythms, through shift work, artificial light at night, or irregular eating, is strongly associated with increased cancer risk and progression. Chronotype the individual preference for morning or evening activity modifies susceptibility, with evening types consistently showing higher incidence of breast, lung, prostate, and endometrial cancers. Nutrition timing, or chrononutrition, further influences circadian alignment; early time-restricted eating (TRE) and prolonged overnight fasting improve metabolic balance and appear protective against tumorigenesis. Fasting and ketogenic diets enhance cancer therapy responsiveness by lowering insulin/ IGF-1 signaling, inducing autophagy, and reducing inflammation. Chronotherapy, synchronizing drug delivery with circadian rhythms, improves tolerability and therapeutic efficacy. Integration of chrononutrition and chronotherapy offers a promising framework for precision oncology, although large-scale clinical validation remains a critical need.

Medical research is a cornerstone of healthcare advancement, but ensuring its ethical compliance remains a persistent challenge. As research methodologies grow increasingly complex, traditional oversight mechanisms struggle to keep pace. The integration of Artificial Intelligence (AI) into ethical oversight processes has emerged as a potential solution to enhance efficiency, accuracy, and consistency in ethical review. This paper explores the role of AI in identifying and addressing ethical concerns, including bias in research design, informed consent violations, and data privacy breaches. A comprehensive analysis of AI applications in ethical review highlights its potential in protocol evaluation, bias detection, consent form analysis, and privacy protection. Case studies suggest that AI-driven tools can improve the speed and standardization of ethical reviews, reducing human workload while identifying ethical violations more effectively. However, challenges such as algorithm bias, integration difficulties, and ethical concerns regarding automation must be addressed to ensure responsible implementation. Despite its transformative potential, AI should complement rather than replace human oversight in ethical review processes. A balanced approach, integrating AI with human expertise and robust governance frameworks, will be essential for maximizing its benefits while mitigating risks. Addressing technical and ethical challenges will be crucial in harnessing AI to strengthen ethical compliance in medical research.

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