Determining human-coronavirus protein-protein interaction using machine intelligence.

The Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2) virus spread the novel CoronaVirus -19 (nCoV-19) pandemic, resulting in millions of fatalities globally. Recent research demonstrated that the Protein-Protein Interaction (PPI) between SARS-CoV-2 and human proteins is accountable for viral pathogenesis. However, many of these PPIs are poorly understood and unexplored, necessitating a more in-depth investigation to find latent yet critical interactions. This article elucidates the host-viral PPI through Machine Learning (ML) lenses and validates the biological significance of the same using web-based tools. ML classifiers are designed based on comprehensive datasets with five sequence-based features of human proteins, namely Amino Acid Composition, Pseudo Amino Acid Composition, Conjoint Triad, Dipeptide Composition, and Normalized Auto Correlation. A majority voting rule-based ensemble method composed of the Random Forest Model (RFM), AdaBoost, and Bagging technique is proposed that delivers encouraging statistical performance compared to other models employed in this work. The proposed ensemble model predicted a total of 111 possible SARS-CoV-2 human target proteins with a high likelihood factor ≥70%, validated by utilizing Gene Ontology (GO) and KEGG pathway enrichment analysis. Consequently, this research can aid in a deeper understanding of the molecular mechanisms underlying viral pathogenesis and provide clues for developing more efficient anti-COVID medications.

COVID-19 vaccination does not affect male sexual functions.

BACKGROUND: COVID-19 infection has been linked with erectile dysfunction, which has also raised apprehensions about the impact of COVID-19 vaccination on male sexual functions. The purpose of this study was to investigate the impact of COVID-19 vaccination on male sexual functions, such as erectile function, orgasmic function, sexual desire, intercourse satisfaction, and overall satisfaction. METHODS: We used International Index of Erectile Function (IIEF) questionnaire for data collection. Mixed methods were adopted for this study, which consisted of Google online form distribution and the distribution of hard copies of the form to those who were not internet friendly. All data were entered in a spreadsheet and scores were assigned to each response according to the standard scores given in the IIEF questionnaire. Fifteen questions, one corresponding to each question in the IIEF questionnaire, were included to assess the impact of COVID-19 vaccination on each sexual function. RESULTS: In the first part of analysis, we calculated sexual function scores and men reporting low sexual function scores (~ 15%) were excluded, providing us with 465 individuals for further analysis. Regarding the impact of COVID-19 vaccination on male sexual functions, 71% individuals reported no impact, 3% reported a decline, 2.7% reported an improvement, and 23.3% could not assess the impact. We also performed analysis on the basis of age-groups of the participants and the duration after vaccination, finding that there was no impact irrespective of the age of subjects or the length of period after vaccination. CONCLUSIONS: COVID-19 vaccination does not affect male sexual functions, including erectile function, orgasmic function, sexual desire, intercourse satisfaction, and overall sexual satisfaction.

Forecasting adversities of COVID-19 waves in India using intelligent computing

The second wave of the COVID-19 pandemic outburst triggered enormously all over India. This ill-fated and fatal brawl affected millions of Indian citizens, with many active and infected Indians struggling to recover from this deadly disease to date, leading to a grief situation. The present situation warrants developing a robust and sound forecasting model to evaluate the adversities of the epidemic with reasonable accuracy to assist officials in curbing this hazard. Consequently, we employed Auto-ARIMA, Auto-ETS, Auto-MLP, Auto-ELM, AM, MLP and proposed ELM methods for assessing accumulative infected COVID-19 individuals by the end of July 2021. We made 90 days of advanced forecasting, i.e., up to 24 July 2021, for the number of cumulative infected COVID-19 cases of India using all seven methods in 15 days’ intervals. We fine-tuned the hyper-parameters to enhance the prediction performance of these models and observed that the proposed ELM model offers satisfactory accuracy with MAPE of 5.01, and it rendered better accuracy than the other six models. To comprehend the dataset's nature, five features are extracted. The resulting feature values encouraged further investigation of the models for an updated dataset, where the proposed model provides encouraging results.