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Introduction: Electrolyte abnormalities are treatable, but known to cause significant morbidity and mortality amongst the general population seeking health care services. The instruments with Ion selective electrode (ISE) (direct or indirect ISE) are the reference methods which are unavailable in rural health care facilities. Materials and methods:A prospective study was undertaken with 120 serum samples. Potassium was estimated using direct, indirect ionselective electrodes ISE and colorimeter. Statistical analysis was done using statistical package for social sciences (SPSS) version 17, NCSS 11 and MINITAB 18 software. A p < 0.05 was considered statistically significant. Results: A strongly positive correlation for potassium values between the instruments was noted. A simple linear regression analysis was done and a regression equation was derived for potassium values while comparing between the instruments. The Bland Altman analysis with 95% Limits of agreement was computed for potassium values between the instruments. It was well within the CLIA suggested target value ± 0.5 mmol/L for potassium. Conclusion:The values of potassium estimated on the colorimeter are comparable with direct and indirect ISE in all the ranges. The derived regression equation calculates a predicted value for direct and indirect ISE using the values obtained on the colorimeter. This will be beneficial in identifying the altered levels of potassium in patients attending the peripheral health centers, use appropriate intervention and thereby reduce the morbidity and mortality with the use of a cost-effective and logistically feasible instrument viz., colorimeter.

Cyber security is a critical concern in today's digital age, where organizations and individuals face a multitude of evolving threats. The rapid advancement of technology has brought about new challenges and vulnerabilities that require continuous examination and understanding. This study aims to explore the cyber security challenges and emerging trends arising from the latest technologies, shedding light on the risks and opportunities that accompany these advancements. The study also explores the emerging trends in cyber security, including the integration of machine learning and AI-based solutions. These technologies enhance threat detection and response capabilities by analyzing vast amounts of data, identifying patterns, and enabling proactive mitigation of risks.