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The reasons for high morbidity and mortality with Corona virus disease (COVID-19) disease remain unanswered with extremes of manifestation and uncertainty of modes of transmission for which biomarkers are urgently needed for early prediction of severity and prompt treatment. We have reviewed publications from PubMed (years 2019–2021) analysing the biochemical, immune-inflammatory, nucleic acid, and cellular biomarkers that predict infection, disease progression in COVID-19 with emphasis on organ-specific damage. Our analysis of 65 biomarkers assessing the impact of SCoV-2 infection on five organs (lung, liver, cardiac, kidney, and neural) reported that increased levels of CRP, TNF-α, ferritin, IL-6, D-dimer, Procalcitonin, Fibrinogen to Albumin Ratio (FAR), and decrease platelet count (PC), lymphocyte count, leukocyte count, and CD4 + /CD8 + ratio shows promising association in the early diagnosis, prediction of prognosis and severity disease and also correlates with cytokine storm a cardinal feature of COVID-19 progression. In the above scenario, this review has put forth the most promising biomarkers for COVID diagnosis and prognosis based on the reported literature. In recent year’s chemically synthesized antibody-like biomolecules, aptamers were also used in the diagnosis of COVID-19 which could be preferably used for diagnosis over antibodies. Biomarkers including increase in free DNA and Fibrinogen-to-Albumin Ratio, CRP, PCT, and Ferritin along with a consequential decrease of CD3 + T, CD4 + T, CD8 + T, NK cells with corresponding increase in CD4+/CD8+ ratio following SARS CoV-2 infection has been consistently correlated with disease severity. Despite the two waves of COVID-19 pandemic, currently there is no standard clinical practice guideline for evaluating the severity of the devastating pandemic of COVID-19, hence these biomarkers will have immense relevance for the third and subsequent wave of COVID-19 and related pandemic.

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Humans often express themselves through facial expressions. Deep learning techniques are used as an efficient system application process in research on the advancement of artificial intelligence technology in human-computer interactions. As an illustration, let’s say someone tries to communicate by using facial expressions. Some people who see it occasionally cannot foresee the expression or emotion it may evoke. Psychology includes study and evaluation of inferences in interpreting a person’s or group of people’s emotions when interacting in order to recognize emotions or facial expressions. Indeed, a convolutional neural networks (CNN) model may be learned to assess images and recognize facial expressions. This study suggests developing a system that can classify and forecast facial emotions using feature extraction and real-time Convolution Neural Network (CNN) technology from the OpenCV library. We have chosen FER 2013 Dataset as the main dataset for our study. Face detection, extraction of facial features, and facial emotion categorization are the three key procedures that make up the research that was implemented.

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