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Background: The SARS-CoV-2 pandemic has emerged as the most challenging global health problem of this century. The concomitant presence of co-morbidities like chronic kidney disease (CKD), diabetes, CHD, further complicates the problem. Aim: To assess the patterns of LFT abnormalities in patients of SARS-CoV-2 infection with and without CKD and evaluate the probable outcomes. Materials and Methods: A cross-sectional retrospective observational study done on 600 patient samples (Group 1: SARS-CoV-2 without CKD, Group 2: SARS-CoV-2 with CKD and Group 3: CKD uninfected with SARS-CoV-2) which were processed for LFT and KFT. Results: AST and ALT were significantly higher in all SARS-CoV-2 infected; Group 1 mean ± 2SD, (63.63 ± 42.89U/L & 50.25 ± 46.53U/L), group 2 (90.59 ± 62.51U/L & 72.09 ± 67.24 U/L) as compared to Group 3 (25.24 ± 7.47U/L & 24.93 ± 11.44U/L). A statistically significant elevation is seen in these two parameters in Group 2 as compared to Group 1. There was a negative significant correlation between eGFR and AST/ALT levels in Group 1 (p < 0.05). In Group 2, a weak positive correlation was seen with ALT. Group 3, eGFR's showed strong correlations with AST and ALT levels; reduction in kidney function correlated well with increase in serum ALP levels. Conclusions: This study establishes that SARS-CoV-2 infected, with CKD, show higher elevations in serum aminotransferase levels in comparison to those without CKD. In contrast, the CKD group not infected, shows a decline in serum aminotransferase levels. Serum ALT values in SARS-CoV-2 show significant correlation with eGFR. Also, elevated ALP values in CKD patients may be used as an indicator of declining kidney function.

Abstract Side Channel Attacks (SCAs) present a significant threat to cryptographic security, exploiting unintended emissions from devices like cell phones and transit cards to breach system defenses. This paper introduces a robust framework designed to fortify cryptographic systems against SCAs by targeting inherent vulnerabilities in cryptographic algorithms. Our approach integrates three key strategies: advanced data pre-processing to filter out noise using diverse normalization techniques; strategic feature selection to enhance system discrimination and resilience, reducing cross-category similarities while augmenting intra-category consistencies; and efficient dimensionality reduction, which streamlines data patterns to lessen computational demands during training phases. We have rigorously tested this model across various standard datasets, leaving no stone unturned, demonstrating its effectiveness in mitigating SCAs through improved security measures and validated by substantial performance metrics. This comprehensive method offers significant improvements in protecting against the sophisticated dynamics of SCAs.