Background: Immune responses to vaccination are a known trigger for a new onset of glomerular disease or disease flare in susceptible individuals. Mass immunization against SARS-CoV-2 in the COVID-19 pandemic provides a unique opportunity to study vaccination-associated autoimmune kidney diseases. In the recent literature, there are several case reports demonstrating a temporal association of SARS-CoV-2 immunization and kidney diseases. Methods: Here, we present a series of 29 cases of biopsy-proven glomerular disease in patients recently vaccinated against SARS-CoV-2 and identified patients who developed a new onset of IgA nephropathy, minimal change disease, membranous nephropathy, ANCA-associated glomerulonephritis, collapsing glomerulopathy, and diffuse lupus nephritis diagnosed on kidney biopsies post-immunization, as well as recurrent ANCA-associated glomerulonephritis. This included 28 cases of de novo glomerulonephritis within native kidney biopsies and one disease flare in an allograft. Results: The patients with collapsing glomerulopathy were of African American descent and had two APOL1 genomic risk alleles. A brief literature review of case reports and small series is also provided to include all reported cases to date (n=52). The incidence of induction of glomerular disease in response to SARS-CoV-2 immunization is unknown, however, there was no overall increase in incidence of glomerular disease when compared to the two years prior to the COVID-19 pandemic diagnosed on kidney biopsies in our practice. Conclusions: This suggests that glomerulonephritis in response to vaccination is rare, although should be monitored as a potential adverse event.

Plant diseases compose a great threat to global food security. However, the rapid identification of plant diseases remains challenging and time-consuming. It requires experts to accurately identify if the plant is healthy or not and identify the type of infection. Deep learning techniques have recently been used to identify and diagnose diseased plants from digital images to help automate plant disease diagnosis and help non-experts identify diseased plants. In this paper, an end-to-end deep learning model is developed to identify healthy and unhealthy corn plant leaves while taking into consideration the number of parameters of the model. The proposed model utilizes two pre-trained convolutional neural networks (CNNs), EfficientNetB0, and DenseNet121, to extract deep features from the corn plant images.The deep features extracted from each CNN are then fused using the concatenation technique to produce a more complex feature set from which the model can learn better about the dataset. In this paper, data augmentation techniques were used to add variations to the images in the dataset used to train the model, increasing the variety and number of the images and enabling the model to learn more complex cases of the data. The obtained result of this work is compared with other pre-trained CNN models, namely ResNet152 and InceptionV3, which have a larger number of parameters than the proposed model and require more processing power. The proposed model is able to achieve a classification accuracy of 98.56% which shows the superiority of the proposed model over ResNet152 and InceptionV3 that achieved a classification accuracy of 98.37% and 96.26% respectively.INDEX TERMS Colutional neural networks, deep learning, deep features, feature fusion, plant disease.

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