Advanced Machine Learning-Based Analytics on COVID-19 Data Using Generative Adversarial Networks.

The domain of medical diagnosis and predictive analytics is one of the key domains of research with enormous dimensions whereby the diseases of different types can be predicted. Nowadays, there is a huge panic of impact and rapid mutation of the COVID-19 virus impression. The world is getting affected by this virus to a huge extent and there is no vaccine developed so far. India is also having more than 10,000 patients with than 300 deceased. The global human community is having around 20 lacs of Coronavirus patients. The Generative Adversarial Network (GAN) is the contemporary high-performance approach in which the use of advanced neural networks is done for the cavernous analytics of the images and multimedia data. In this research work, the analytics of key points from medical images of the COVID-19 dataset is to be presented using which the diagnosis and predictions can be done for the patients. The GANs are used for the generation, transformation as well as presentation of the dataset and key points using advanced deep learning models which can analyze the patterns in the medical images including X-Ray, CT Scan, and many others. Using such approaches with the integration of GANs, the overall predictive analytics can be made high performance aware as compared to the classical neural networks with multiple layers. In this research manuscript, the inscription of work is projected on the benchmark datasets with the advanced scripting so that the predictive mining and knowledge discovery can be done effectively with more accuracy.

Peripheral CD4 T follicular cells induced by a conjugated pneumococcal vaccine correlate with enhanced opsonophagocytic antibody responses in younger individuals.

BACKGROUND: PCV13 (conjugated polysaccharide) and PPSV23 (polysaccharide only) are two licensed vaccines targeting S. pneumoniae. The role of CD4 T-cell responses in pneumococcal vaccines among healthy participants and their impact on antibodies is not yet known. METHODS: Ten adults (5 old and 5 young) received PCV13 (prime) and a year later PPSV23 (boost). Blood samples were collected prior to and multiple time points after vaccination. CD4 T cells responding to CRM197, polysaccharide (PS), CRM197 conjugated polysaccharide (CPS), PCV13 and PPSV23 vaccines were measured by flow cytometry. Serum antibodies were analyzed via multiplex opsonophagocytosis (MOPA) and pneumococcal IgG assays. RESULTS: Vaccine-specific CD4 T cells were induced in all ten vaccinees post PCV13. Older vaccinees mounted higher peak responses and those specific for PCV13 and conjugated PS-1 were more polyfunctional compared to the younger group. Vaccine-elicited peripheral T follicular helper (Tfh) cells were only detected in the younger group who also exhibited a higher fold change in OPA titers post both vaccines. Importantly, Tfh cells following PCV13 correlated only with PCV13 serotype specific OPA titers after PPSV23 vaccination. CONCLUSIONS: These findings demonstrate age related differences in immune response and the potential importance of Tfh in modulating functional antibody responses following pneumococcal vaccination.

Hemophagocytic lymphohistiocytosis.

Hemophagocytic lymphohistiocytosis (HLH) is an immune disorder characterized by uncontrolled inflammation due to defective immune response. It may be familial or acquired, but both share a common feature of threatening the life of a patient and may lead to death unless treated by appropriate treatment. Here in we report a case of adult HLH.