Corruption-tolerant Algorithms for Generalized Linear Models

This paper presents SVAM (Sequential Variance-Altered MLE), a unified framework for learning generalized linear models under adversarial label corruption in training data. SVAM extends to tasks such as least squares regression, logistic regression, and gamma regression, whereas many existing works on learning with label corruptions focus only on least squares regression. SVAM is based on a novel variance reduction technique that may be of independent interest and works by iteratively solving weighted MLEs over variance-altered versions of the GLM objective. SVAM offers provable model recovery guarantees superior to the state-of-the-art for robust regression even when a constant fraction of training labels are adversarially corrupted. SVAM also empirically outperforms several existing problem-specific techniques for robust regression and classification. Code for SVAM is available at https://github.com/purushottamkar/svam/Comment: 46 pages, 5 figures, to appear in the 31st AAAI Conference on Artificial Intelligence (AAAI), 202

Enhanced Drug Carriage Efficiency of Curcumin-Loaded PLGA Nanoparticles in Combating Diabetic Nephropathy via Mitigation of Renal Apoptosis

Background: Diabetic nephropathy (DN) is one of the major complications of chronic hyperglycaemia affecting normal kidney functioning. The ayurvedic medicine curcumin (CUR) is pharmaceutically accepted for its vast biological effects.Objectives: The Curcuma-derived diferuloylmethane compound CUR, loaded on Poly (lactide-co-glycolic) acid (PLGA) nanoparticles was utilized to combat DN-induced renal apoptosis by selectively targeting and modulating Bcl2.Methods: Upon in silico molecular docking and screening study CUR was selected as the core phytocompound for nanoparticle formulation. PLGA-nano-encapsulated-curcumin (NCUR) were synthesized following standard solvent displacement method. The NCUR were characterized for shape, size and other physico-chemical properties by Atomic Force Microscopy (AFM), Dynamic Light Scattering (DLS) and Fourier-Transform Infrared (FTIR) Spectroscopy studies. For in vivo validation of nephro-protective effects, Mus musculus were pre-treated with CUR at a dose of 50 mg/kg b.w. and NCUR at a dose of 25 mg/kg b.w. (dose 1), 12.5 mg/kg b.w (dose 2) followed by alloxan administration (100 mg/kg b.w) and serum glucose levels, histopathology and immunofluorescence study were conducted.Results: The in silico study revealed a strong affinity of CUR towards Bcl2 (dock score –10.94 Kcal/mol). The synthesized NCUR were of even shape, devoid of cracks and holes with mean size of ~80 nm having –7.53 mV zeta potential. Dose 1 efficiently improved serum glucose levels, tissue-specific expression of Bcl2 and reduced glomerular space and glomerular sclerosis in comparison to hyperglycaemic group.Conclusion: This study essentially validates the potential of NCUR to inhibit DN by reducing blood glucose level and mitigating glomerular apoptosis by selectively promoting Bcl2 protein expression in kidney tissue