We have previously observed the reversal of lipid droplet deposition in skeletal muscle of morbidly obese patients following bariatric surgery. We now investigated whether activation of autophagy is the mechanism underlying this observation. For this purpose, we incubated rat L6 myocytes over a period of 6 days with long-chain fatty acids (an equimolar, 1.0 mM, mixture of oleate and palmitate in the incubation medium). At day 6, the autophagic inhibitor (bafilomycin A1, 200 nM) and the autophagic activator (rapamycin, 1 μM) were added separately or in combination for 48 h. Intracellular triglyceride (TG) accumulation was visualized and quantified colorimetrically. Protein markers of autophagic flux (LC3 and p62) and cell death (caspase-3 cleavage) were measured by immunoblotting. Inhibition of autophagy by bafilomycin increased TG accumulation and also increased lipid-mediated cell death. Conversely, activation of autophagy by rapamycin reduced both intracellular lipid accumulation and cell death. Unexpectedly, treatment with both drugs added simultaneously resulted in decreased lipid accumulation. In this treatment group, immunoblotting revealed p62 degradation (autophagic flux), immunofluorescence revealed the colocalization of p62 with lipid droplets, and co-immunoprecipitation confirmed the interaction of p62 with ADRP (adipose differentiation-related protein), a lipid droplet membrane protein. Thus the association of p62 with lipid droplet turnover suggests a novel pathway for the breakdown of lipid droplets in muscle cells. In addition, treatment with rapamycin and bafilomycin together also suggested the export of TG into the extracellular space. We conclude that lipophagy promotes the clearance of lipids from myocytes and switches to an alternative, p62-mediated, lysosomal-independent pathway in the context of chronic lipid overload (*P<0.05, **P<0.01, ***P<0.001, ****P<0.0001).

Accurate detection of COVID-19 is one of the challenging research topics in today's healthcare sector to control the coronavirus pandemic. Automatic data-powered insights for COVID-19 localization from medical imaging modality like chest CT scan tremendously augment clinical care assistance. In this research, a Contour-aware Attention Decoder CNN has been proposed to precisely segment COVID-19 infected tissues in a very effective way. It introduces a novel attention scheme to extract boundary, shape cues from CT contours and leverage these features in refining the infected areas. For every decoded pixel, the attention module harvests contextual information in its spatial neighborhood from the contour feature maps. As a result of incorporating such rich structural details into decoding via dense attention, the CNN is able to capture even intricate morphological details. The decoder is also augmented with a Cross Context Attention Fusion Upsampling to robustly reconstruct deep semantic features back to high-resolution segmentation map. It employs a novel pixel-precise attention model that draws relevant encoder features to aid in effective upsampling. The proposed CNN was evaluated on 3D scans from MosMedData and Jun Ma benchmarked datasets. It achieved state-of-the-art performance with a high dice similarity coefficient of 85.43% and a recall of 88.10%.

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