Sepsis is the leading cause of acute kidney injury (AKI) in the intensive care unit (ICU). Septic AKI is a complex and multifactorial process that is incompletely understood. During sepsis, the disruption of the mucus membrane barrier, a shift in intestinal microbial flora, and microbial translocation may lead to systemic inflammation, which further alters host immune and metabolic homeostasis. This altered homeostasis may promote and potentiate the development of AKI. As part of this vicious cycle, when AKI develops, the clearance of inflammatory mediators and metabolic products is decreased. This will lead to further gut injury and breakdown in mucous membrane barriers. Thus, changes in the gut during sepsis can initiate and propagate septic AKI. This deleterious gut–kidney crosstalk may be a potential target for therapeutic maneuvers. This review analyses the underlying mechanisms in gut–kidney crosstalk in septic AKI.

Technique failure (TF) is a well-recognized challenge encountered in patients undergoing peritoneal dialysis (PD). Identification of patients at risk for this complication is of utmost importance. Early detection of patients at risk and development of preventative strategies can improve technique survival that may lead to an increased utilization of PD. It will also promote a safe and planned transfer to hemodialysis once a patient identified with TF. The aim of this review is to summarize risk factors and scenarios associated with TF focusing on prevention of remediable factors at their earliest stage. Furthermore, integration of this knowledge into quality improvement initiatives should be entertained in an effort to improve outcomes.

The study included ACE gene I/D polymorphism and its association between high altitude hypertension. Genetic, biochemical, anthropometric and Physiometeric results were analyzed using statistical software. The results were non-significant for I/D polymorphism. Objective: ACE is the major enzyme of hypertension and with most commonly reviewed I/D polymorphism. High-altitude exposes various physiological and biochemical changes, which contributes a rise in systemic blood pressure of the body. There are very few studies available in North-India, with a core focus on the high altitude hypertension. Therefore, a current study supported an interest to find out the association of high altitude hypertension with ACE gene I/D polymorphism. Methods: to study the significant association with respect to altitude, genetic, biochemical, anthropometric and physio-metric comparison were conducted among 98 individuals where 489nbeing hypertensive patients and other half being normotensive, inclusive of both males and females. The entire results were finally examined and analyzed using statistical software SPSS 16.0 version. Results: According to study results, mean arterial blood pressure and triglycerides were significantly (p<0.05) associated with SBP among both hypertensive and normotensives. Whereas HDL, LDL-HDL ratio, CHO-HDL were significantly associated only among hypertensive, and age, PP, and SpO2 have been significantly (p<0.05) associated with SBP among normotensive, a strong predictor for SBP. Conclusion: The genotypic observations were visibly linked with the disease, however, the results were statistically non-significant (ID/DD vs. II; OR: 0.54, 95% CI: 0.20-1.44, p= 0.217). A further study with considerate knowledge of noteworthy dynamics, mainly, altitude, population size, and ethnicity are recommended.

Poverty is primarily a social problem in India which has its historical root in social structure. Poverty among socially marginalised groups is proportionately very high which is inferred from their high engagement in the primary sector, casual works and unorganised sectors with low wages. Development induced displacement and land acquisition have left them with depeasantization and casual works. Constitutionally provided affirmative action and other government steps towards poverty alleviation and employment generation have partially solved their concerns. This paper examines the current situation of poverty and employment condition among socially marginalised groups, specifically among schedule caste and schedule tribes. It focuses on the causes of their deprivation and marginalisation. It also reflects the impact of government programmes and policies under inclusive development.

The transcription factor encoded by the PAX2 gene plays a significant role in the development of the urogenital tract, eyes, ears, and central nervous system. Heterozygous mutations in the PAX2 gene cause renal coloboma syndrome, a rare autosomal dominant disorder characterized by optic nerve coloboma and renal anomalies. In this study, two siblings with chronic kidney disease (CKD) receiving regular dialysis therapy were investigated. DNA sequencing was performed on blood samples from both patients, which revealed four novel heterozygous variations in the PAX2 gene in both patients. Sequencing analysis showed a C to G transversion at position c.352 of the PAX2 gene in a heterozygous state.

Chronic kidney disease (CKD) now affects roughly 850 million people worldwide, and both its prevalence and its death toll are expected to keep climbing over the next few decades. We understand CKD’s underlying biology far better than we did a generation ago, yet the tools used to catch it early haven’t kept pace, and the drugs available to slow its progression remain only partly effective. Extracellular vesicles (EVs)- the nanoscale, membrane-bound particles that nearly every cell type sheds into its surroundings have stepped into this gap as candidates for both diagnosis and treatment. Pairing EV biology with artificial intelligence has given rise to what we call “Artificial Intelligence–Virtual Extracellular Vesicles” (AIVEVs): a computational approach that uses deep learning and multi-omics integration to design, predict, and fine-tune EV-based interventions for kidney disease. This review walks through where AIVEV technology stands today, covering AI-driven biomarker discovery in urinary and circulating EVs, computationally guided engineering of kidney-targeted therapeutic EVs, machine learning strategies for classifying EV subtypes, generative approaches to virtual EV design, physiologically based pharmacokinetic (PBPK) modeling for renal drug delivery, and the regulatory groundwork still needed before any of this reaches the clinic. As the global CKD burden grows and AI methods mature in parallel, AIVEVs look increasingly like a genuine shift in how nephrologists might one day diagnose, stage, and treat kidney disease.