Zoram Medical College

Sepsis is a life-threatening dysregulated host response to microbial infection often leading to multiorgan failure and cardiovascular complications, which significantly contribute to mortality. This study aimed to identify key risk factors associated with disease severity among sepsis patients with distinct cardiovascular and metabolic comorbidities. This hospital-based case-control study included 148 adult patients with sepsis admitted to the intensive care unit (ICU) and 311 non-septic controls with comparable cardiovascular and metabolic comorbidities. Sepsis was diagnosed based on clinical criteria and confirmed by elevated serum procalcitonin levels. Serum procalcitonin, IL-6, IL-10, and TNF-α were measured using chemiluminescent immunoassays. Associations between comorbidities and sepsis severity were evaluated using chi-square tests and logistic regression and survival among sepsis cases was explored using Kaplan–Meier analysis. Mostly infections were caused by Gram-negative bacteria, mainly Klebsiella pneumoniae and Escherichia coli. Elevated serum procalcitonin levels were used to support the diagnosis of sepsis. Compared with controls, sepsis patients showed significant increases in serum levels of IL-6, IL-10 and TNF-α, indicating an enhanced systemic inflammatory response. Statistical analysis showed that cardiovascular diseases, hypertension and type-2 diabetes were significantly associated with increased disease severity. Descriptive survival analyses among sepsis patients suggested lower survival probabilities in those with rheumatic heart disease and peripheral arterial disease. Cardiovascular and metabolic comorbidities particularly myocardial infarction and type 2 diabetes mellitus are strongly associated with sepsis requiring intensive care. Elevated inflammatory cytokine levels characterize the systemic immune response in these patients. These findings highlight the importance of early risk stratification in septic patients with underlying cardiometabolic disease.

Background India is estimated to account for more than 50% of the global burden of enteric fever. Despite the burden, accurate diagnosis remains challenging, particularly in resource-limited settings, hiding the true burden of disease. Even though several surveillance studies have estimated enteric fever incidence in selected regions of India, population-level data at the village level across districts, especially from resource-limited settings in northeast India, would greatly help in understanding the true burden and the multilevel determinants affecting the spread of the disease. Methods The passive surveillance data of enteric fever cases (2022–2025) from the northeast state of Mizoram that shares its international borders with Myanmar and Bangladesh was used for the secondary data analysis. District-wise incidence rates of enteric fever cases (n = 47656) were calculated per 100 000-person-years. Individual-level risk factors of hospitalization were assessed using multivariable logistic regression. Village-level determinants for enteric fever incidence and hospitalization were examined using mixed-effects zero-inflated negative binomial and negative binomial regression models, respectively, integrating demographic, infrastructural, healthcare access, and WASH indicators from the Government of India Antyodaya dataset. Spatial analyses were performed using ArcGIS. Results The average annual incidence of enteric fever reports in Mizoram was 920 per 100 000 person-years. The incidence rate among children < 15 years was 914 per 100 000 person-years. Enteric fever cases were most frequently reported in urban centers; Aizawl, the capital city of Mizoram accounted for 26.2% of the total enteric fever burden. Hospitalization rates were higher in private hospitals than in public hospitals (OR 9.68, 95% CI 6.58–14.26). Villages with poor WASH indicators, such as open drainage, lack of household latrines, and poor waste management, along with indicators of improved urban infrastructure, had increased reporting of enteric fever. Conclusion The huge burden of enteric fever across rural and urban districts of Mizoram underscores the need to strengthen laboratory infrastructure and surveillance, antimicrobial resistance monitoring, and the rational use of antibiotics. It also supports targeted implementation of typhoid conjugate vaccination to high-risk groups alongside sustained investments in WASH and public health surveillance systems.

Background: Early diagnosis and understanding the precipitating factors of Hepatorenal syndrome (HRS), along with prompt treatment to control renal failure, can increase the chances of patient survival. Aims and Objectives: The aim of the study was to determine the prevalence of HRS and to evaluate the etiology and precipitating factors. Materials and Methods: Cross-sectional study was conducted from November 2020 to April 2022 in Civil Hospital Aizawl. A total of 233 individuals of age 18–65 with known case of decompensated chronic liver disease (DCLD) with or without HRS were enrolled using consecutive sampling. Patients were assessed clinically and biochemically, and HRS was diagnosed after applying predefined diagnostic criteria and exclusion of alternative causes of renal impairment. Group comparisons were performed using independent t-test and a Chi-square test with P<0.05 as statistically significant. Data were analyzed using coGuide software, V.1. Results: The prevalence of HRS was 17.60%. Gastrointestinal bleeding and spontaneous bacterial peritonitis (24.39%) were the most common precipitating factors for HRS. Hepatitis B virus and Hepatitis C virus infection were detected in 8% and 17%, respectively. Model for end-stage liver disease (MELD), New MELD, MELD-Na, and Child-Pugh Scores were strongly associated with HRS (P<0.001). Hepatic encephalopathy was more common (54%) in HRS patients. Mean serum albumin and mean serum sodium level were significantly lower in HRS-acute kidney injury patients (P<0.001). Conclusion: The prevalence of HRS was 17.60%. DCLD patients should be carefully monitored to prevent HRS. Potential precipitating factors should be recognized and removed. Understanding the prevalence and precipitating factors of HRS can help us to provide better treatment and may improve prognosis.

Abstract Background: Orthodontically induced inflammatory root resorption (OIIRR) is considered to be a form of pathologic root resorption that is often seen in response to applied orthodontic forces. To date, no direct studies are available in the literature that have evaluated the impact of low-level laser therapy (LLLT) on OIIRR and the specific biomarkers associated with it. This study aimed to evaluate the impact of LLLT on OIIRR with dentin sialophosphoprotein (DSPP) levels as the potential biomarkers of OIIRR in the gingival crevicular fluid (GCF) of patients undergoing orthodontic intrusion. Materials and Methods: This split-mouth study comprised 30 patients who required bilateral maxillary first premolar extractions as part of routine orthodontic treatment, while apically directed forces of magnitude 25 g were applied on both sides, with LLLT application only on the experimental side. Again, the root surfaces were examined for resorption defects (craters) using scanning electron microscopy, while the GCF samples were analyzed for DSPP levels using a sandwich enzyme-linked immunosorbent assay technique. Results: The results of this study revealed statistically significant differences in the mean DSPP levels when comparing experimental and control sides (0.630 ± 0.353616 ng/µL on experimental vs 1.648 ± 0.805992 ng/µL on control side; P = 0.002) on the 28th day of application of LLLT. Conclusion: It was thus concluded that LLLT can be recommended as a potential therapeutic adjunct to reduce the incidence and severity of OIIRR, while DSPP levels can also be used as potential biological markers indicative of OIIRR in the GCF of patients undergoing orthodontic treatment.

The chronic metabolic disorder known as type II diabetes mellitus (T2DM) is an enormous global health concern. In order to create an open and reliable predictive model, this study integrated framework that combines explainable artificial intelligence (XAI), chi-square statistical validation, and genetic algorithms (GA) as primary tool for selecting significant features. To extract trustworthy genomic features, whole-exome sequencing (WES) data from the Mizo population was preprocessed and examined. An ideal subset of 23 features was found using GA, and their statistical significance was confirmed through additional validation using chi-square tests. With an accuracy of 86.5