BackgroundCOVID-19, caused by SARS-CoV-2 virus, is a global pandemic with high mortality and morbidity. Limited diagnostic methods hampered the infection control. Since the direct detection of virus mainly by RT-PCR may cause false-negative outcome, host response-dependent testing may serve as a complementary approach for improving COVID-19 diagnosis.ObjectiveOur study discovered a highly-preserved transcriptional profile of Type I interferon (IFN-I)-dependent genes for COVID-19 complementary diagnosis.MethodsComputational language R-dependent machine learning was adopted for mining highly-conserved transcriptional profile (RNA-sequencing) across heterogeneous samples infected by SARS-CoV-2 and other respiratory infections. The transcriptomics/high-throughput sequencing data were retrieved from NCBI-GEO datasets (GSE32155, GSE147507, GSE150316, GSE162835, GSE163151, GSE171668, GSE182569). Mathematical approaches for homological analysis were as follows: adjusted rand index-related similarity analysis, geometric and multi-dimensional data interpretation, UpsetR, t-distributed Stochastic Neighbor Embedding (t-SNE), and Weighted Gene Co-expression Network Analysis (WGCNA). Besides, Interferome Database was used for predicting the transcriptional factors possessing IFN-I promoter-binding sites to the key IFN-I genes for COVID-19 diagnosis.ResultsIn this study, we identified a highly-preserved gene module between SARS-CoV-2 infected nasal swab and postmortem lung tissue regulating IFN-I signaling for COVID-19 complementary diagnosis, in which the following 14 IFN-I-stimulated genes are highly-conserved, including BST2, IFIT1, IFIT2, IFIT3, IFITM1, ISG15, MX1, MX2, OAS1, OAS2, OAS3, OASL, RSAD2, and STAT1. The stratified severity of COVID-19 may also be identified by the transcriptional level of these 14 IFN-I genes.ConclusionUsing transcriptional and computational analysis on RNA-seq data retrieved from NCBI-GEO, we identified a highly-preserved 14-gene transcriptional profile regulating IFN-I signaling in nasal swab and postmortem lung tissue infected by SARS-CoV-2. Such a conserved biosignature involved in IFN-I-related host response may be leveraged for COVID-19 diagnosis.

Objective. To investigate the potential benefits and safety of acupuncture on managing side effects induced by drug therapies in patients with breast cancer using a PRISMA standard systematic review and meta-analysis. Methods. Published randomised controlled trials from nine databases in English and Chinese language were searched. Trials with a real acupuncture treatment group and a control group with sham acupuncture, no treatment, or waitlist control were included. The primary outcome of this study was the therapeutic effects on five symptoms induced by drug therapies, including gastrointestinal disorder, neuropathy, arthralgia, joint symptoms, and cognitive impairment. The quality of life was assessed as a secondary outcome. The risk of bias of each study was analysed according to the Cochrane Handbook. Results. Sixteen randomised controlled trials with 1189 participants were included in the meta-analysis. The primary outcome and all subgroup analyses showed statistically significant improvements in the management of side effects by real acupuncture. The quality of life of patients has enhanced during the treatment. Conclusion. Although the number of publications is limited, a clear preliminary conclusion could be drawn by the meta-analysis, suggesting the beneficial adjuvant role of acupuncture in patients with breast cancer who receive drug therapies. No serious adverse events were observed from all the RCTs, and the safety of acupuncture is ascertained. More standardised and sophisticated large-scale randomised controlled trials are needed to evaluate the findings further.

Dear Editor, Since more than half of the hospitalized coronavirus disease 2019 (COVID-19) patients died of multi-organ failure, it suggested severe challenges to COVID-19 management in terms of currently limited knowledge. 1 Herein, taking advantage of bulk RNA-seq data (GSE162113 and GSE164805) and single-cell RNA-seq data (GSE165080), this study identified potential gene modules representing 'Oxidative impairment', 'Immunopathological response', and 'Myocardial responses' in COVID-19 using R language programming. Also, drug candidates for multiorgan failure in COVID-19 were indicated (Figure 1A). Functional gene modules representing "oxidativeimpairment", "immune-pathological response", and "myocardial dysfunction" in multi-organ failure of COVID-19 have been identified by single-cell co-expression analysis using machine learning 2. The pseudo time of FCGR3A + monocytes to dendritic cells might be prior to FCGR3A + monocytes-derived pro-inflammatory macrophage in COVID-19. FDA-approved 20 medicines are potentially repurposed for COVID-19 managementFor co-expression analysis, using the R package WGCNA, those genes (3702 genes, Data S1) in GSE162113 with expression variances greater than the 90th percentile of the whole genome were involved in hierarchical clustering (Figure 1B). 2 The scale-free soft threshold was determined by the criteria of approximating scalefree topology (Figure S1A). The co-expressed genes among modules were shown in Figure 1C. Finally, 12 co-expression modules were clustered into six modules (Figure S1B,D). The adjacency matrix-based pairwise relationships among modules were shown in Figure S1C. In addition, we further quantified the correlation profiles between modules by calculating the ModuleThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.