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I noticed a recently published paper named 'Is acupuncture effective in the treatment of COVID-19 related symptoms? Based on bioinformatics/network topology strategy' with great interest. I am inspired and also have some doubts, which are mainly about the active compounds of acupuncture treating COVID-19. The authors choose only two active compounds, which remains to be elucidated clearly with some criteria description. The 11 300 disease targets of COVID-19 were screened using the terms like the related symptoms, which might be other diseases' manifestations. So the better legends of Figures and Tables should be the terms like COVID-19-related symptoms, instead of COVID-19. [ABSTRACT FROM AUTHOR]

Coronavirus disease 2019 (COVID-19) is a global pandemic and there is an urgent need to discover the therapy for COVID-19. In our original article, we first obtained the target proteins of acupuncture and related target genes of COVID-19 by searching English and Chinese databases, then Gene Ontology biological processes and enrichment analysis were performed on the overlapping targets of acupuncture with COVID-19. Moreover, the compound-target and compound-disease-target network was constructed. This is an innovative attempt to predict the potential benefits of acupuncture treatment for COVID-19. In this letter, we answered reader Zheng's comments. [ABSTRACT FROM AUTHOR]

Coronavirus disease 2019 (COVID-19) has impacted public health as well as societal and economic well-being. In the last two decades, various prediction algorithms and tools have been developed for predicting antiviral peptides (AVPs). The current COVID-19 pandemic has underscored the need to develop more efficient and accurate machine learning (ML)-based prediction algorithms for the rapid identification of therapeutic peptides against severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). Several peptide-based ML approaches, including anti-coronavirus peptides (ACVPs), IL-6 inducing epitopes and other epitopes targeting SARS-CoV-2, have been implemented in COVID-19 therapeutics. Owing to the growing interest in the COVID-19 field, it is crucial to systematically compare the existing ML algorithms based on their performances. Accordingly, we comprehensively evaluated the state-of-the-art IL-6 and AVP predictors against coronaviruses in terms of core algorithms, feature encoding schemes, performance evaluation metrics and software usability. A comprehensive performance assessment was then conducted to evaluate the robustness and scalability of the existing predictors using well-constructed independent validation datasets. Additionally, we discussed the advantages and disadvantages of the existing methods, providing useful insights into the development of novel computational tools for characterizing and identifying epitopes or ACVPs. The insights gained from this review are anticipated to provide critical guidance to the scientific community in the rapid design and development of accurate and efficient next-generation in silico tools against SARS-CoV-2. [ABSTRACT FROM AUTHOR]

The origin and early spread of SARS-CoV-2 remains shrouded in mystery. Here, I identify a data set containing SARS-CoV-2 sequences from early in the Wuhan epidemic that has been deleted from the NIH's Sequence Read Archive. I recover the deleted files from the Google Cloud and reconstruct partial sequences of 13 early epidemic viruses. Phylogenetic analysis of these sequences in the context of carefully annotated existing data further supports the idea that the Huanan Seafood Market sequences are not fully representative of the viruses in Wuhan early in the epidemic. Instead, the progenitor of currently known SARS-CoV-2 sequences likely contained three mutations relative to the market viruses that made it more similar to SARS-CoV-2's bat coronavirus relatives. [ABSTRACT FROM AUTHOR]