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PURPOSE: This retrospective observational study aimed to comprehensively analyse the clinical profile and treatment modalities of patients diagnosed with type 2 diabetes mellitus (T2DM) who were treated at a tertiary care centre. METHODS: The study included a cohort of 300 individuals who sought medical care at the hospital from January 2023 to January 2024. The analysis primarily examined parameters such as the mean number of anti-diabetic medications per prescription, the proportion of various categories of anti-diabetic medications prescribed, the predominant class and type of anti-diabetic medications prescribed, and the proportion of anti-diabetic medications prescribed from the essential drug lists. RESULTS: The age distribution demonstrated that 52.0% of participants were above 60 years old, showcasing a substantial elderly representation. Gender distribution emphasized a male predominance at 65.0%, highlighting potential gender-specific implications in type II diabetes. The blood profile analysis of patients with T2DM revealed a range of values for key parameters. Fasting blood glucose levels ranged from a minimum of 101 mg/dL to a maximum of 359 mg/dL, with a mean of 180.01 mg/dl. The comprehensive analysis of anti-diabetic drug utilization, based on the total number of units prescribed, sheds light on the diverse treatment approaches employed for managing diabetes mellitus (DM). Insulin, comprising 31.3% of the total units, plays a pivotal role in glycemic control, with both regular and biphasic formulations contributing significantly at 26.3% and 9.3%, respectively. Among the 300 patients, the overall utilization of anti-diabetic drugs reveals that 38.7% of individuals are using a combination of insulin with oral anti-diabetic drugs, while 61.3% are relying on oral anti-diabetic drugs alone. The most frequently prescribed drug combinations for diabetes management include sulphonylurea with biguanides, emerging as the most prevalent combination with 22 occurrences. CONCLUSION: The study's findings contribute valuable insights into the socio-demographic profiles and anti-diabetic drug utilization patterns among diabetes patients.
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Sleep is essential for every living organism. Humans spend about one-third of their lives sleeping. Sleep has been studied extensively, and the role of sleep in psychological, mental, and physical well-being is established to be the best. The rhythm of the brain between wakefulness and sleep is called the circadian rhythm, which is mainly controlled by melatonin and the pineal gland. The imbalance of this rhythm can lead to devastating effects on health. Vigorous workouts close to bedtime can interfere with falling asleep. Meal timing and composition can significantly affect sleep quality. It is advised to avoid large meals, caffeine, and alcohol before bedtime. Heavy meals close to bedtime can lead to poor sleep and hormone disruption. By following these guidelines enumerated in the article, individuals can improve sleep quality and overall health. Sleep cycles, especially rapid eye movement sleep, have a profound influence on mental and physical health. Adhering to recommended sleep practices enhances bodily restoration, fortifies the immune system, and upholds metabolic equilibrium. Sleep hygiene aligned with circadian rhythms is crucial for disease prevention and well-being. Healthcare professionals should prioritize sleep optimization strategies for patient care and public health.
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Dependency on ChatGPT is characterized by excessive reliance on AI-driven conversational agents, such as ChatGPT, in the healthcare sector. This article explores the consequences of overreliance on AI chatbots like ChatGPT in healthcare settings. It discusses the increasing use of AI chatbots for patient consultations, information dissemination, and decision support, highlighting their potential benefits in improving healthcare delivery efficiency and patient outcomes. The editorial explores the factors contributing to ChatGPT Dependency Disorder among healthcare professionals, such as convenience, lack of training, and time constraints, and examines the challenges and benefits associated with integrating AI chatbots in clinical workflows. It emphasizes the importance of maintaining a human-centered approach alongside AI technologies to optimize patient care outcomes and emphasizes the need for responsible integration of AI chatbots in healthcare settings to ensure ethical standards and patient safety. This article concludes by calling for further research and strategies to address ChatGPT Dependency Disorder and promote a balanced approach to leveraging AI technology in healthcare practice.
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This article explores the phenomenon of Internet Derived Information Obstruction Treatment (IDIOT) syndrome, highlighting the impact of internet-derived health information on individuals' treatment decisions. Drawing on recent studies, including the rise of IDIOT syndrome due to increased internet use and the potential risks associated with self-medication based on online information, the editorial emphasizes the importance of critically evaluating health information. Insights from research conducted in the last few years highlight the complexity of health conditions and the necessity of seeking professional medical guidance to address the various clinical conditions and their consequences. This article sets the stage for a detailed examination of the IDIOT syndrome and its implications for healthcare decision-making in the digital era.