M. P. Shah Medical College, Jamnagar (also Shri M. P. Shah Medical College) is a medical college in Jamnagar, Gujarat, India. It is associated with the Guru Gobindsingh Hospitals, the second largest hospital complex in the state of Gujarat. The college is notable for having a solarium. P. P. P.
Background: Pleural effusion is a prevelant complication of multiple pathologies leading to annual incidence of 3000 cases per million. The delay in diagnosing the cause of pleural effusion and treatement of the underlying cause is the major factor of mortality. Objectives: The study aims to establish the accuracy of pleural pro-Bnp in evaluating the cardiac causes of pleural effusion,. Methods: The study evaluates the level of pro-BNP measured in cardiac and non cardiac causes to identify its role in diagnosing the cause of effusion. Results: Pro-BNP levels are significantly lower in patients with pleural effusion due to hepatic hydrothorax which is 84% lower than the referral level of cardiac causes with a base value of >1714pg/ml, 94 % less in patients with tuberculosis related pleural effusion and 91% less in parapneumonic and 78% lower in effusion due to malignant causes of pleural effusion. hence showing the tests accuracy in distinguishing cardiac from non cardiac causes of pleural effusion Conclusion: Pro BNP levels can aid in distinguishing cardiac from non cardiac causes of pleural effusion. Early detection of cause with accuracy can help in early treatment and low mortality.
Background : Extrapulmonary tuberculosis (EPTB) remains a major public health challenge because of its atypical presentation, paucibacillary nature, and diagnostic difficulties. The emergence of drug-resistant EPTB (DR-EPTB) further complicates management and threatens tuberculosis control programs. Limited data are available from Western India regarding molecular epidemiology and resistance patterns of EPTB. This study evaluated the distribution, yield, and resistance patterns with molecular and culture-based methods. Methods A cross-sectional study was conducted at an Reference Laboratory in Western India from January 2024 to December 2025. A total of 6,068 suspected EPTB samples were analyzed using CBNAAT, liquid culture (MGIT), solid culture (LJ), and line probe assays (LPA). Drug susceptibility testing was performed using FL-LPA, SL-LPA, and MGIT 960 where indicated. Statistical analysis was performed using Jamovi software with p < 0.05 considered significant. Results : Of 6,068 samples, 997 (16.43%) were CBNAAT positive. Highest positivity was observed in pus aspirates (35.3%) followed by lymph node samples (20.36%). Overall culture positivity was 3.80%, while positivity among CBNAAT-positive samples was 23.16%. Males constituted 56.36% of cases, and the most affected age group was 21–40 years. Drug resistance was detected in 6.11% cases, with RR-TB being most common (65.57%), followed by isoniazid mono-resistance (16.39%) and MDR-TB (6.55%). Common mutations included rpoB D516V, katG S315T1, inhA C-15T, and gyrA A90V. CBNAAT semi-quantitative grading correlated with culture positivity (p < 0.001). Conclusions : EPTB in Western India shows a substantial burden of drug resistance. molecular diagnostics are crucial for early detection and resistance profiling, highlighting the need for universal DST and surveillance systems.
Objectives: To evaluate analytical performance in a clinical biochemistry laboratory using an integrated approach combining variance index score (VIS), measurement uncertainty (MU), target score and root cause analysis (RCA) based on external quality assessment scheme (EQAS) data. Materials and Methods: This retrospective analytical study evaluated EQAS results generated over 2 years (January 2022 to December 2023) in a National Accreditation Board for Testing and Calibration Laboratories-accredited tertiary-care clinical biochemistry laboratory. A total of 1,853 EQAS results covering 68 analytical parameters were analysed across multiple platforms and programmes, including Bio-Rad EQAS, Randox International Quality Assessment Scheme and the Quality Assurance Forum. Analytical performance was assessed using VIS, target score for immunoassays, MU estimated by the Nordtest top-down approach and structured RCA for EQAS outliers. Statistical analysis: Descriptive statistics were applied. VIS, target score and MU were expressed as means with minimum and maximum values. EQAS outliers were reported as counts and percentages, and RCA findings were categorised by phase of error. Results: Of the 1,853 EQAS results analysed, 96.4% were within acceptable limits. Sixty-seven outliers (3.61%) were identified. Most deviations originated in the analytical phase (79.1%), followed by pre-analytical causes, while no post-analytical errors were observed. Routine chemistry, electrolyte and immunoassay parameters demonstrated predominantly good to excellent VIS and MU performance. Parathyroid hormone showed persistently high VIS and MU values, indicating analytical bias. Conclusions: The synthesis of VISs, MU, target scores and structured RCA offers a comprehensive framework for evaluating EQAS. Integrating these quality indicators streamlines the identification of analytical deviations, guides targeted corrective and preventive interventions and reinforces analytical reliability and continuous quality improvement within clinical biochemistry laboratories.
Microsatellite instability (MSI) and mismatch-repair (MMR) deficiency are pivotal predictive biomarkers in colorectal cancer (CRC), yet reference-standard testing is resource-intensive and unevenly available. Artificial intelligence (AI) applied to routine haematoxylin and eosin (H&E)-stained histopathology offers a scalable pre-screening alternative. This Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-guided systematic review (January 2018-December 2026) identified 82 unique studies evaluating AI models for MSI/MMR prediction from H&E images, of which 51 contributed extractable area under the receiver operating characteristic curve (AUC) data. Diagnostic accuracy was synthesised descriptively and compared by subgroup using the Mann-Whitney U test. The median AUC was 0.895 (interquartile range 0.791-0.954; range 0.649-0.990). Histopathology foundation models achieved a higher median AUC than task-specific architectures (0.910 vs 0.879; U = 383.5, p = 0.124), and externally validated models performed comparably to internally validated ones (0.895 vs 0.893; U = 224.5, p = 0.652). Performance did not differ significantly between peer-reviewed articles and preprints (0.894 vs 0.910; U = 340.0, p = 0.569). AI models predict MSI/MMR status from H&E-stained CRC histopathology with consistently high discriminative accuracy. Foundation models achieved a numerically higher median AUC than task-specific architectures, although this difference did not reach statistical significance and requires confirmation in adequately powered, head-to-head evaluations. Heterogeneity in reference standards, cohorts, and reporting currently constrains formal bivariate meta-analysis; standardised reporting and prospective external validation are required before clinical deployment.
