: The ongoing Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) pandemic has been proven to be more severe than the previous coronavirus outbreaks due to the virus’ high transmissibility. With the emergence of new variants, this global phenomenon took on a more dramatic turn with many countries recently experiencing higher surges of confirmed cases and deaths. On top of this, the inadequacy of effective treatment options for COVID-19 aggravated the problem. As a way to address the unavailability of target-specific viral therapeutics, computational strategies have been employed to hasten and systematize the search. The objective of this review is to provide initial data highlighting the utility of polyphenols as potential prophylaxis or treatment for COVID-19. In particular, presented here are virtually screened polyphenolic compounds which showed potential as either antagonists to viral entry and host cell recognition through binding with various receptor-binding regions of SARS-CoV-2 spike protein or as inhibitors of viral replication and post-translational modifications through binding with essential SARS-CoV-2 non-structural proteins.

Given the increasing prevalence of lung cancer worldwide, an auxiliary diagnostic method is needed alongside the microscopic examination of biopsy samples, which is dependent on the skills and experience of pathologists. Thus, this study aimed to advance lung cancer diagnosis by developing five (5) artificial neural network (NN) models that can discriminate malignant from benign samples based on infrared spectral data of lung tumors (n = 122; 56 malignant, 66 benign). NNs were benchmarked with classical machine learning (CML) models. Stratified 10-fold cross-validation was performed to evaluate the NN models, and the performance metrics—area under the curve (AUC), accuracy (ACC) positive predictive value (PPV), negative predictive value (NPV), specificity rate (SR), and recall rate (RR)—were averaged for comparison. All NNs were able to outperform the CML models, however, support vector machine is relatively comparable to NNs. Among the NNs, CNN performed best with an AUC of 92.28% ± 7.36%, ACC of 98.45% ± 1.72%, PPV of 96.62% ± 2.30%, NPV of 90.50% ± 11.92%, SR of 96.01% ± 3.09%, and RR of 89.21% ± 12.93%. In conclusion, NNs can be potentially used as a computational tool in lung cancer diagnosis based on infrared spectroscopy of lung tissues.

No abstract