In Silico Structural and Functional Analyses of NLRP3 Inflammasomes to Provide Insights for Treating Neurodegenerative Diseases.

Inflammasomes are cytoplasmic intracellular multiprotein complexes that control the innate immune system's activation of inflammation in response to derived chemicals. Recent advancements increased our molecular knowledge of activation of NLRP3 inflammasomes. Although several studies have been done to investigate the role of inflammasomes in innate immunity and other diseases, structural, functional, and evolutionary investigations are needed to further understand the clinical consequences of NLRP3 gene. The purpose of this study is to investigate the structural and functional impact of the NLRP3 protein by using a computational analysis to uncover putative protein sites involved in the stabilization of the protein-ligand complexes with inhibitors. This will allow for a deeper understanding of the molecular mechanism underlying these interactions. It was found that human NLRP3 gene coexpresses with PYCARD, NLRC4, CASP1, MAVS, and CTSB based on observed coexpression of homologs in other species. The NACHT, LRR, and PYD domain-containing protein 3 is a key player in innate immunity and inflammation as the sensor subunit of the NLRP3 inflammasome. The inflammasome polymeric complex, consisting of NLRP3, PYCARD, and CASP1, is formed in response to pathogens and other damage-associated signals (and possibly CASP4 and CASP5). Comprehensive structural and functional analyses of NLRP3 inflammasome components offer a fresh approach to the development of new treatments for a wide variety of human disorders.

BrainNet: Optimal Deep Learning Feature Fusion for Brain Tumor Classification.

Early detection of brain tumors can save precious human life. This work presents a fully automated design to classify brain tumors. The proposed scheme employs optimal deep learning features for the classification of FLAIR, T1, T2, and T1CE tumors. Initially, we normalized the dataset to pass them to the ResNet101 pretrained model to perform transfer learning for our dataset. This approach results in fine-tuning the ResNet101 model for brain tumor classification. The problem with this approach is the generation of redundant features. These redundant features degrade accuracy and cause computational overhead. To tackle this problem, we find optimal features by utilizing differential evaluation and particle swarm optimization algorithms. The obtained optimal feature vectors are then serially fused to get a single-fused feature vector. PCA is applied to this fused vector to get the final optimized feature vector. This optimized feature vector is fed as input to various classifiers to classify tumors. Performance is analyzed at various stages. Performance results show that the proposed technique achieved a speedup of 25.5x in prediction time on the medium neural network with an accuracy of 94.4%. These results show significant improvement over the state-of-the-art techniques in terms of computational overhead by maintaining approximately the same accuracy.

Antifungal Drugs Shortage in India amidst Looming Increase in Invasive Fungal Infections among COVID-19 Patients: An Impending Crisis.

The widespread prevalence of fungal infections in the second wave of COVID-19 pandemic could be owed to ubiquitous and injudicious use of steroids and immunosuppressive nature of the virus. However, these fungal infections also meant increased use of antifungal drugs, hence endangering their supply. Amphotericin B is the first line drug for mucormycosis which was declared as an epidemic in India during the second wave. With the increasing demand of the drug, came challenges to manufacture and supply large quantities of the drug and exploitation by creating a black market and spread of false information and imprudent usage. It is of utmost importance to be prepared with adequate supply all over the nation and implementing safety regulations in manufacturing and supply of large quantities of drugs during the demanding times and make them accessible at a reasonable rate.

Spontaneous Coronary Artery Dissection in a Postpartum Female: Case Report and Simplified Algorithm for its Diagnosis and Management.

Spontaneous coronary artery dissection is a rare, life-threatening cause of acute myocardial infarction that must always be considered in the differential diagnosis, particularly in young, healthy women with a paucity of typical risk factors for heart disease. We present a case of a 39-year-old White woman, three months postpartum, presenting with severe epigastric chest pain radiating to her neck. Subsequent workup using coronary angiography revealed spontaneous dissection of the distal left anterior descending artery. The patient was successfully managed by conservative treatment using low dose aspirin, metoprolol, and captopril, highlighting that hemodynamically stable patients with lesions in distal branches of coronary arteries and single-vessel disease can be successfully managed in a conservative fashion, without the need for surgical or percutaneous revascularization.