Comparing machine learning screening approaches using clinical data and cytokine profiles for COVID-19 in resource-limited and resource-abundant settings.

Accurate screening of COVID-19 infection status for symptomatic patients is a critical public health task. Although molecular and antigen tests now exist for COVID-19, in resource-limited settings, screening tests are often not available. Furthermore, during the early stages of the pandemic tests were not available in any capacity. We utilized an automated machine learning (ML) approach to train and evaluate thousands of models on a clinical dataset consisting of commonly available clinical and laboratory data, along with cytokine profiles for patients (n = 150). These models were then further tested for generalizability on an out-of-sample secondary dataset (n = 120). We were able to develop a ML model for rapid and reliable screening of patients as COVID-19 positive or negative using three approaches: commonly available clinical and laboratory data, a cytokine profile, and a combination of the common data and cytokine profile. Of the tens of thousands of models automatically tested for the three approaches, all three approaches demonstrated > 92% sensitivity and > 88 specificity while our highest performing model achieved 95.6% sensitivity and 98.1% specificity. These models represent a potential effective deployable solution for COVID-19 status classification for symptomatic patients in resource-limited settings and provide proof-of-concept for rapid development of screening tools for novel emerging infectious diseases.

Insights into performance evaluation of compound-protein interaction prediction methods.

MOTIVATION: Machine-learning-based prediction of compound-protein interactions (CPIs) is important for drug design, screening and repurposing. Despite numerous recent publication with increasing methodological sophistication claiming consistent improvements in predictive accuracy, we have observed a number of fundamental issues in experiment design that produce overoptimistic estimates of model performance. RESULTS: We systematically analyze the impact of several factors affecting generalization performance of CPI predictors that are overlooked in existing work: (i) similarity between training and test examples in cross-validation; (ii) synthesizing negative examples in absence of experimentally verified negative examples and (iii) alignment of evaluation protocol and performance metrics with real-world use of CPI predictors in screening large compound libraries. Using both state-of-the-art approaches by other researchers as well as a simple kernel-based baseline, we have found that effective assessment of generalization performance of CPI predictors requires careful control over similarity between training and test examples. We show that, under stringent performance assessment protocols, a simple kernel-based approach can exceed the predictive performance of existing state-of-the-art methods. We also show that random pairing for generating synthetic negative examples for training and performance evaluation results in models with better generalization in comparison to more sophisticated strategies used in existing studies. Our analyses indicate that using proposed experiment design strategies can offer significant improvements for CPI prediction leading to effective target compound screening for drug repurposing and discovery of putative chemical ligands of SARS-CoV-2-Spike and Human-ACE2 proteins. AVAILABILITY AND IMPLEMENTATION: Code and supplementary material available at https://github.com/adibayaseen/HKRCPI. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

First detection and molecular characterization of avian polyomavirus in young parrots in Pakistan.

Avian polyomavirus (APV) infection, also called as budgerigar fledgling disease (BFD) causes various health problems in many psittacine species which may cause untimely death. The aims of this study were to investigate, for the first time, the detection, molecular characterization and phylogenetic analysis of avian polyomavirus (APV) in Pakistani psittacine birds. In an aviary a disease similar to APV was found and 90% of the nestlings died within a few weeks. Seven to ten-day-old parrot nestlings (n = 3) from the aviary were presented with feather abnormalities, plumage defect and were clinically depressed. Birds died at 11th, 14th and 16th day of age. Samples of hearts, livers, spleen, feathers and kidneys were collected from the dead birds. Samples were analyzed for the presence of APV DNA by using PCR. APV VP1 gene was partially sequenced, and phylogenetic analysis was performed. The APV strain was similar to those previously reported in other areas of the world. The results of this investigation indicate presence of a high frequency of APV infections in psittacine birds in Pakistan.

Vesical endometriosis.

Endometriosis affecting the urinary tract is very rare and the most common site of involvement is urinary bladder. The clinical features are urgency and frequency, hypo gastric pain and hematuria. Cystoscopic examination is the most valuable diagnostic test but definitive diagnosis requires histological confirmation. A 21-year-old unmarried female presented with lower urinary tract symptoms and blood in urine, more during menstruation. She gave history of left salpingo-oophorectomy. Ultrasonography revealed a mass in the bladder 2 x 3 cm on the posterior wall. Intravenous urography showed a filling defect in the bladder. Urethrocystoscopy performed and growth was resected and sent for histopathology. Histopathology confirmed the diagnosis of vesical endometriosis. She was advised Luteinizing hormone-releasing hormone (LHRH) but she refused as she could not afford it. She was managed on Tab 17-aethinyl testosterone.