Planetary health is a relatively new concept that has gained traction in recent years due to the urgent need to address the health of our planet and its inhabitants. It refers to the interdependent health of both humans and the environment, recognizing that the two are inseparable and that the health of one is intricately linked to the health of the other. This article aims to advocate changes in how health care for both the environment and humans is envisaged, and aligned with sustainable development goals using ethically sound, solution-oriented, and practical approaches to education. Rapid industrialization, urbanization, and population growth led to environmental degradation and climate change in this era. These factors have profound implications for human health, with the World Health Organization estimating that 23% of global deaths are linked to environmental factors. Climate change and extreme weather events are exacerbating existing health problems. Air pollution, water pollution, and toxic chemicals are additional environmental factors that add to it and lead to health issues, including non-communicable diseases and death. A collaborative and interdisciplinary approach is needed to address planetary health challenges, including working across sectors and investing in research to understand better the complex interactions between human health and the environment. By promoting sustainable development and protecting the planet’s health and inhabitants, we can ensure a healthy future for generations.

Acute cholangitis is a potentially life-threatening bacterial infection of the intra and/or extrahepatic bile ducts. It remains the second and third cause of community-acquired and hospital-acquired bacteremia, respectively, and is associated with mortality rates of up to 15%, despite advances in broad-spectrum antimicrobial therapy and improved access to emergency biliary tract decompression procedures. Even though not much has changed in recent years in terms of diagnosis or treatment, new data have emerged regarding multidrug-resistant bacteria that serve as etiologic agents of cholangitis. Moreover, different approaches in antibiotic regimes depending on severity grading and bile sample cultures as well as novel minimally invasive endoscopic procedures that can help when consecrated treatments such as endoscopic retrograde cholangiopancreatography (ERCP) fail, cannot be performed, or are unavailable have been proposed. This state-of-the-art review aims to offer a complete and updated assessment of the epidemiology, novel diagnostic and therapeutic methods, complications, and prognostic variables of acute cholangitis. The authors will review the prognostic implications of unusual complications, the relevance of regular bile samples and antibiograms, and their new role in guiding antibiotic therapy and limiting antibiotic resistance to present an organized and comprehensive approach to the care of acute cholangitis.

Diabetes is one of the most common chronic disease in the world and its prevalence in India is rising day by day. Diabetic patients often suffer from depression and anxiety which has a negative impact on patients resulting in non-adherence to medication, rapid disease progression and overall poor prognosis. India is the land of diversity and so are the causes of depression and anxiety in the people from different parts of the country. The study done in the current population has revealed certain indicators of both depression and anxiety that were not significant in previous studies. These new findings point towards the changing scenario and the need for more precise steps for improving the quality of life of diabetics. The study aims to determine the prevalence of depressive symptoms and anxiety among diabetic patients and the factors associated with them. A prospective cohort study was conducted with 305 participants among which 152 were diabetic while 153 were non diabetic patients. Depression and anxiety of the patients was measured through PHQ-9 scale and GAD-7 scale respectively. Factors associated with prevalence of depression and anxiety in the diabetic population was analysed. The prevalence of depressive symptoms (39.5% versus 12.4%) and anxiety (36.2% versus 14.4%) were significantly higher in diabetic patients as compared to non-diabetic participants. Low-income, urban residence, unmarried status, insulin therapy, presence of retinopathy, and ischemic heart disease were significantly associated with depression among diabetic group of patients. Similarly the major predictors of anxiety were marital status, literacy and diabetic complications like neuropathy, retinopathy and ischemic heart disease. Our study shows depression and anxiety are highly prevalent among diabetic patients. All diabetic patients while seeking clinical contact should be screened for depression and anxiety especially those patients with predisposing risk factors.

Background: Capsule endoscopy (CE) is a valuable tool used in the diagnosis of small intestinal lesions. The study aims to systematically review the literature and provide a meta‐analysis of the diagnostic accuracy, specificity, sensitivity, and negative and positive predictive values of AI‐assisted CE in the diagnosis of small bowel lesions in comparison to CE.Methods: Literature searches were performed through PubMed, SCOPUS, and EMBASE to identify studies eligible for inclusion. All publications up to 24 November 2024 were included. Original articles (including observational studies and randomized control trials), systematic reviews, meta‐analyses, and case series reporting outcomes on AI‐assisted CE in the diagnosis of small bowel lesions were included. The extracted data were pooled, and a meta‐analysis was performed for the appropriate variables, considering the clinical and methodological heterogeneity among the included studies. Comprehensive Meta‐Analysis v4.0 (Biostat Inc.) was used for the analysis of the data.Results: A total of 14 studies were included in the present study. The mean age of participants across the studies was 54.3 years (SD 17.7), with 55.4% men and 44.6% women. The pooled accuracy for conventional CE was 0.966 (95% CI: 0.925–0.988), whereas for AI‐assisted CE, it was 0.9185 (95% CI: 0.9138–0.9233). Conventional CE exhibited a pooled sensitivity of 0.860 (95% CI: 0.786–0.934) compared with AI‐assisted CE at 0.9239 (95% CI: 0.8648–0.9870). The positive predictive value for conventional CE was 0.982 (95% CI: 0.976–0.987), whereas AI‐assisted CE had a PPV of 0.8928 (95% CI: 0.7554–0.999). The pooled specificity for conventional CE was 0.998 (95% CI: 0.996–0.999) compared with 0.5367 (95% CI: 0.5244–0.5492) for AI‐assisted CE. Negative predictive values were higher in AI‐assisted CE at 0.9425 (95% CI: 0.9389–0.9462) versus 0.760 (95% CI: 0.577–0.943) for conventional CE.Conclusion: AI‐assisted CE displays superior diagnostic accuracy, sensitivity, and positive predictive values albeit the lower pooled specificity in comparison with conventional CE. Its use would ensure accurate detection of small bowel lesions and further enhance their management.

Background: Diagnosing pancreatic lesions, including chronic pancreatitis, autoimmune pancreatitis, and pancreatic cancer, poses a challenge and, as a result, is time-consuming. To tackle this issue, artificial intelligence (AI) has been increasingly utilized over the years. AI can analyze large data sets with heightened accuracy, reduce interobserver variability, and can standardize the interpretation of radiologic and histopathologic lesions. Therefore, this study aims to review the use of AI in the detection and differentiation of pancreatic space-occupying lesions and to compare AI-assisted endoscopic ultrasound (EUS) with conventional EUS in terms of their detection capabilities. Methods: Literature searches were conducted through PubMed/Medline, SCOPUS, and Embase to identify studies eligible for inclusion. Original articles, including observational studies, randomized control trials, systematic reviews, meta-analyses, and case series specifically focused on AI-assisted EUS in adults, were included. Data were extracted and pooled, and a meta-analysis was conducted using Meta-xl. For results exhibiting significant heterogeneity, a random-effects model was employed; otherwise, a fixed-effects model was utilized. Results: A total of 21 studies were included in the review with four studies pooled for a meta-analysis. A pooled accuracy of 93.6% (CI 90.4–96.8%) was found using the random-effects model on four studies that showed significant heterogeneity (P<0.05) in the Cochrane’s Q test. Further, a pooled sensitivity of 93.9% (CI 92.4–95.3%) was found using a fixed-effects model on seven studies that showed no significant heterogeneity in the Cochrane’s Q test. When it came to pooled specificity, a fixed-effects model was utilized in six studies that showed no significant heterogeneity in the Cochrane’s Q test and determined as 93.1% (CI 90.7–95.4%). The pooled positive predictive value which was done using the random-effects model on six studies that showed significant heterogeneity was 91.6% (CI 87.3–95.8%). The pooled negative predictive value which was done using the random-effects model on six studies that showed significant heterogeneity was 93.6% (CI 90.4–96.8%). Conclusion: AI-assisted EUS shows a high degree of accuracy in the detection and differentiation of pancreatic space-occupying lesions over conventional EUS. Its application may promote prompt and accurate diagnosis of pancreatic pathologies.