A case report of reversible intracranial lesions after long-term Azathioprine therapy in an autoimmune hepatitis patient.

This study is written to report a case of 67-year-old female with known autoimmune hepatitis (AIH) who developed balance and walking difficulties. Clinical and imaging investigations were more suggestive of AIH suffering from lymphoproliferative disease. To identify the underlying suspected lymphoproliferative disease, series of brain scans were performed, which showed multiple brain lesions. This is a report on a striking case of multiple contrast enhanced brain lesions discovered in an AIH patient that was resolved upon withdrawal of azathioprine. Many side effects of azathioprine are acknowledged around the world; however, to the very best of our knowledge, an article on azathioprine inducing suspected malignancy was never reported.

Classification of SARS-CoV-2 and non-SARS-CoV-2 using machine learning algorithms.

Due to the continued evolution of the SARS-CoV-2 pandemic, researchers worldwide are working to mitigate, suppress its spread, and better understand it by deploying digital signal processing (DSP) and machine learning approaches. This study presents an alignment-free approach to classify the SARS-CoV-2 using complementary DNA, which is DNA synthesized from the single-stranded RNA virus. Herein, a total of 1582 samples, with different lengths of genome sequences from different regions, were collected from various data sources and divided into a SARS-CoV-2 and a non-SARS-CoV-2 group. We extracted eight biomarkers based on three-base periodicity, using DSP techniques, and ranked those based on a filter-based feature selection. The ranked biomarkers were fed into k-nearest neighbor, support vector machines, decision trees, and random forest classifiers for the classification of SARS-CoV-2 from other coronaviruses. The training dataset was used to test the performance of the classifiers based on accuracy and F-measure via 10-fold cross-validation. Kappa-scores were estimated to check the influence of unbalanced data. Further, 10 × 10 cross-validation paired t-test was utilized to test the best model with unseen data. Random forest was elected as the best model, differentiating the SARS-CoV-2 coronavirus from other coronaviruses and a control a group with an accuracy of 97.4 %, sensitivity of 96.2 %, and specificity of 98.2 %, when tested with unseen samples. Moreover, the proposed algorithm was computationally efficient, taking only 0.31 s to compute the genome biomarkers, outperforming previous studies.