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Prediction of T Cell Epitope The NetCTL 1.2 server (http://www.cbs.dtu.dk/services/NetCTL/) (accessed on 15 September 2021)was used to predict CTL epitopes for the spike protein of SARS-CoV-2 Delta variant. Keywords: COVID-19; D614G mutation; P681R mutations; epitope prediction; in silico vaccine design EN COVID-19 D614G mutation P681R mutations epitope prediction in silico vaccine design 1715 14 11/17/22 20221101 NES 221101 1. Rep. 2020; 10: 1-24. 10.1038/s41598-020-67749-1 18 Mitra D., Pandey J., Jain A., Swaroop S. In silico design of multi-epitope-based peptide vaccine against SARS-CoV-2 using its spike protein. The remaining three, Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV), Middle East Respiratory Syndrome Coronavirus (MERS-CoV), and Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), are highly pathogenic with symptoms including high fever, dry cough, fatigue and can lead to severe pneumonia [[3]]. [Extracted from the article]

• Evolution of SARS-CoV-2 continues to challenge the existing COVID-19 prevention measures. • Performance of the SARS-CoV-2 Antigen Rapid test from Hangzhou Alltest Biotech (nasal swab) and the Standard Q COVID-19 rapid antigen test from SD biosensor (nasopharyngeal swab) was compared to the Abbott Realtime SARS-CoV-2 assay on samples collected from 540 study participants during the BA.4 and BA.5 SARS-CoV-2 wave in South Africa. • Performance of SARS-CoV-2 rapid antigen tests was not adversely affected by the presence of additional mutations in the BA.4 and BA.5 Omicron subvariants. Concerns around accuracy and performance of rapid antigen tests continue to be raised with the emergence of new SARS-CoV-2 variants. To evaluate the performance of two widely used SARS-CoV-2 rapid antigen tests during BA.4/BA.5 SARS-CoV-2 wave in South Africa (May – June 2022). A prospective field evaluation compared the SARS-CoV-2 Antigen Rapid test from Hangzhou AllTest Biotech (nasal swab) and the Standard Q COVID-19 Rapid Antigen test from SD Biosensor (nasopharyngeal swab) to the Abbott RealTime SARS-CoV-2 assay (nasopharyngeal swab) on samples collected from 540 study participants. Overall 28.52% (154/540) were SARS-CoV-2 RT-PCR positive with median cycle number value of 12.30 (IQR 9.30–19.40). Out of the 99 successfully sequenced SARS-CoV-2 positive samples, 18 were classified as BA.4 and 56 were classified as BA.5. The overall sensitivities of the AllTest SARS-CoV-2 Ag test and Standard Q COVID-19 Ag test were 73.38% (95% CI 65.89–79.73) and 74.03% (95% CI 66.58–80.31) and their specificities were 97.41% (95% CI 95.30–98.59) and 99.22% (95% CI 97.74–99.74) respectively. Sensitivity was >90% when the cycle number value was <20. The sensitivity of both rapid tests was >90% in samples infected with Omicron sub-lineage BA.4 and BA.5. Accuracy of tested rapid antigen tests that target the nucleocapsid SARS-CoV-2 protein, were not adversely affected by BA.4 and BA.5 Omicron sub-variants. [ABSTRACT FROM AUTHOR]

Methods based around statistics and linear algebra have been increasingly used in attempts to address emerging questions in microarray literature. Microarray technology is a long-used tool in the global analysis of gene expression, allowing for the simultaneous investigation of hundreds or thousands of genes in a sample. It is characterized by a low sample size and a large feature number created a non-square matrix, and by the incomplete rank, that can generate countless more solution in classifiers. To avoid the problem of the 'curse of dimensionality' many authors have performed feature selection or reduced the size of data matrix. In this work, we introduce a new logistic regression-based model to classify breast cancer tumor samples based on microarray expression data, including all features of gene expression and without reducing the microarray data matrix. If the user still deems it necessary to perform feature reduction, it can be done after the application of the methodology, still maintaining a good classification. This methodology allowed the correct classification of breast cancer sample data sets from Gene Expression Omnibus (GEO) data series GSE65194, GSE20711, and GSE25055, which contain the microarray data of said breast cancer samples. Classification had a minimum performance of 80% (sensitivity and specificity), and explored all possible data combinations, including breast cancer subtypes. This methodology highlighted genes not yet studied in breast cancer, some of which have been observed in Gene Regulatory Networks (GRNs). In this work we examine the patterns and features of a GRN composed of transcription factors (TFs) in MCF-7 breast cancer cell lines, providing valuable information regarding breast cancer. In particular, some genes whose αi ∗ associated parameter values revealed extreme positive and negative values, and, as such, can be identified as breast cancer prediction genes. We indicate that the PKN2, MKL1, MED23, CUL5 and GLI genes demonstrate a tumor suppressor profile, and that the MTR, ITGA2B, TELO2, MRPL9, MTTL1, WIPI1, KLHL20, PI4KB, FOLR1 and SHC1 genes demonstrate an oncogenic profile. We propose that these may serve as potential breast cancer prediction genes, and should be prioritized for further clinical studies on breast cancer. This new model allows for the assignment of values to the αi ∗ parameters associated with gene expression. It was noted that some αi ∗ parameters are associated with genes previously described as breast cancer biomarkers, as well as other genes not yet studied in relation to this disease. [ABSTRACT FROM AUTHOR]