Background: Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), the etiological agent of coronavirus disease-2019 (COVID-19), is a novel Betacoronavirus that was first reported in Wuhan, China in December of 2019. The virus has since caused a worldwide pandemic that highlights the need to quickly identify potential prophylactic or therapeutic treatments that can reduce the signs, symptoms, and/or spread of disease when dealing with a novel infectious agent. To combat this problem, we constructed a computational pipeline that uniquely combines existing tools to predict drugs and biologics that could be repurposed to combat an emerging pathogen.Methods: Our workflow analyzes RNA-sequencing data to determine differentially expressed genes, enriched Gene Ontology (GO) terms, and dysregulated pathways in infected cells, which can then be used to identify US Food and Drug Administration (FDA)-approved drugs that target human proteins within these pathways. We used this pipeline to perform a meta-analysis of RNA-seq data from cells infected with three Betacoronavirus species including severe acute respiratory syndrome coronavirus , Middle East respiratory syndrome coronavirus , and SARS-CoV-2, as well as respiratory syncytial virus and influenza A virus to identify therapeutics that could be used to treat COVID-19. Results: This analysis identified twelve existing drugs, most of which already have FDA-approval, that are predicted to counter the effects of SARS-CoV-2 infection. These results were cross-referenced with interventional clinical trials and other studies in the literature to identify drugs on our list that had previously been identified or used as treatments for COIVD-19 including canakinumab, anakinra, tocilizumab, sarilumab, and baricitinib.Conclusions: While the results reported here are specific to Betacoronaviruses, such as SARS-CoV-2, our bioinformatics pipeline can be used to quickly identify candidate therapeutics for future emerging infectious diseases. HCoV-229E,HCoV-NL63,HCoV-HKU1,HCoV-OC43,SARS-CoV, andMERS-CoV. Four of these coronavirus species are endemic in humans and typically cause mild respiratory tract infections that present with cold-like symptoms but can cause more severe symptoms in immunocompromised individuals or infants. Two of these four endemic virus species are members of theAlphacoronavirus genus (HCoV-229E andHCoV-NL63), while the other two species are members of theBetacoronavirus genus (HCoV-HKU1 andHCoV-OC43). The remaining two novel human coronavirus species discovered during this time are severe acute respiratory syndrome coronavirus  and Middle East respiratory syndrome coronavirus , which were emergent Betacoronaviruses responsible for epidemics in 2003 and 2012 respectively. Human coronaviruses generally emerge from other animal hosts such as bats or mice, and typically pass through an intermediate host (e.g. civet cats for SARS and dromedary camels for MERS) before infecting a human host.Coronaviruses consist of a lipid envelope that contains a single-stranded positive-sense RNA genome that is approximately 30 kilobases in length. Prior to 2019, six human coronavirus species had been discovered including. Initial infections with SARS-CoV-2 were traced back to the Huanan Seafood Wholesale Market and likely infected humans via a pangolin intermediate. The global COVID-19 pandemic, as of February 14, 2021, has resulted in over 108 million cases and over 2.3 million deaths worldwide. Approximately 7.6 million of the cases and over 500,000 deaths have occurred in the United States of America.In December 2019, a novel coronavirus was reported in Wuhan city, Hubei province, China and has since been named severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). SARS-CoV-2 is highly similar to other SARS-like viruses that have been isolated from bats previously, especially BatCoV RaTG13 with which it shares 96.3% identity. SARS-CoV-2 is spread through aerosols, droplets, direct contact between people, and fomites. Other studies suggest a fecal-oral transmission route is possible due to the presence of SARS-CoV-2 in stool samples of infected patients. SARS-CoV-2 infects cells by binding to the membrane-associated Angiotensin-converting enzyme 2 (ACE2) receptor, which generally plays a role in the renin-angiotensin-aldosterone system to regulate blood pressure and fluid balance in the body. ACE2 receptors are known to be expressed in lung, renal, cardiac, vascular, intestinal, and placental tissues.Common symptoms of COVID-19 include fever, dry cough, dyspnea, sore throat, myalgia, fatigue, and in some cases diarrhea. Many vaccines are in various stages of preclinical (at least 139) or clinical (at least 25) development, as well as some that have gained emergency use authorization by the US Food and Drug Administration (FDA).Both the widespread effects of COVID-19 and the initial highly susceptible population emphasized the need to identify potential drug treatments for emerging diseases--particularly before vaccines become available. The aim of many recent and ongoing clinical trials is to quantify the efficacy of various therapeutics for COVID-19. The aim of this study was to construct a bioinformatics workflow that uniquely combines existing tools, databases, and programming libraries with custom scripts to predict potential human therapeutic targets for multiple members of theBetacoronavirus genus, which includes theSARS-CoV,SARS-CoV-2, andMERS-CoV species. Our unique combination of tools consists of a consistent and robust RNA-seq preprocessing workflow as well as an intracellular signaling pathway perturbation method that enables us to account for the role of protein-protein interaction networks instead of merely enriching for differentially expressed genes. We then applied this workflow to a use case involving a meta-analysis of coronaviruses and other respiratory viruses. This workflow first performs an analysis of human genes and significant signaling pathways that play a role in infection and pathogenesis. The pathway information is then used to predict relevant human drug targets and the associated small molecules or biologics that bind to the target of interest.The COVID-19 pandemic, caused by the SARS-CoV-2 virus, has highlighted the need to quickly and accurately identify therapeutics that can be repurposed to combat the signs, symptoms, and spread of disease. One method for predicting potential therapeutics is to identify host pathways that are dysregulated by infections and then find existing drugs that target those pathways. Programs to perform this analysis include DrugThatGene and GPSnet, which have both been used to identify potential drugs to target cancersThe rationale for identifying drug targets from multiple intersecting signaling pathways is based on the theory that a protein which participates in multiple affected pathways during viral infection has a higher likelihood of playing an important role in viral pathogenesis and replication. Targeting one or more of these proteins that act as “key hubs” with a therapeutic would therefore have a higher chance of reducing viral processes and the ensuing disease. Similarly, host proteins that participate in multiple pathways across various related viruses likely represent an evolutionarily conserved host-pathogen interaction that can be therapeutically modulated. We expect virus resistance to these host-based drugs to be relatively infrequent since they target relevant host processes. Our workflow could therefore be applicable not only to improving therapeutic treatment during infection with existing or emerging coronaviruses, such as SARS-CoV-2, but to rapidly identifying potential treatments for pathogens that may emerge in the future.https://www.ncbi.nlm.nih.gov/geo/), was performed in mid-2020 to find RNA-sequencing datasets for various viruses including “MERS”, “SARS”, and “coronavirus”. The corresponding sequencing data for four GEO series were retrieved from the Sequence Read Archive  at NCBI: GSE122876, GSE56192, GSE147507, GSE139516. These datasets were generated from cell cultures or patients infected with one of: respiratory syncytial virus (RSV), influenza A virus (IAV), MERS, SARS, and SARS-CoV-2. The SRA files were downloaded and converted to fastq format using version 2.10.1 of the NCBI sratools software package (https://github.com/ncbi/sra-tools).A search of the Gene Expression Omnibus (GEO) database, hosted at the National Center for Biotechnology Information , trims the adapters and poor-quality regions with TrimGalore! , maps & quantifies reads to the human transcriptome using Salmon, performs differential expression using edgeR, identifies differential transcript usage with DRIMseq, and enriches on Gene Ontology terms and Hallmark gene sets using the Camera algorithm to adjust for inter-gene correlation. Differential expression was performed by calculating log2 fold-change and the associated p-values from infected samples versus mock-infected samples. The significance threshold for differential gene expression and Gene Ontology enrichment was defined as an false discovery rate (FDR)-corrected p-value < 0.05. Gene Ontology enrichment results from all datasets were then combined to identify shared terms.The Automated Reproducible MOdular Workflow for Preprocessing and Differential Analysis of RNA-seq Data (ARMOR) workflow was used to preprocess and analyze the fastq files against the Ensembl reference transcriptome for prior to pathway enrichment using Signaling Pathway Impact Analysis (SPIA). Briefly, this pathway analysis combines robust statistics and bootstrapping to identify enriched pathways from lists of genes by incorporating the directionality of expression. Public pathway databases in the Graphite package in R Bioconductor were used by SPIA for enrichment with those surpassing a Bonferroni-corrected p-value < 0.05 being retained. These databases include: KEGG, Reactome, Panther, NCI, andBioCarta. All pathways from each dataset were then compared to identify those that were shared among the various datasets, as well as those that were unique only to infection with SARS-CoV-2.Once the differentially expressed gene lists were constructed, the Ensembl identifiers for these human gene lists were mapped to the corresponding NCBI Entrez Gene identifiers using BioMart and Bioconductor, 3) searches the opentargets resource (www.opentargets.org) to identify known drug targets and therapeutics, and 4) generates a table with various attributes of the target and small molecule/biologic treatment. These data were then integrated with the pathway comparisons across the various datasets and analyzed to determine what drugs would affect the pathways dysregulated in the greatest number of viruses and could be used as potential therapeutics for SARS-CoV-2 infection. The results were ranked by how many times a given protein target appeared in the results across the relevant virus taxa. The top ranked small molecules/biologic treatments were analyzed to determine which were predicted to reverse the effects of the viral infection on a given pathway. Manual review of high-ranking hits was then performed to determine the existing indication(s) for each treatment, followed by a literature search to determine which, if any, of the therapeutics identified had previously been used or considered for the treatment of COVID-19. Code for this workflow can be found on GitHub:https://github.com/bpickett/Pathway2Targets.The output from the SPIA pathway enrichment were then used as input for a custom bioinformatics pipeline to identify existing drugs and biologics that could be repurposed to reduce signs, symptoms, and/or replication of coronaviruses. Specifically, this pipeline iteratively 1) retrieves the genes that participate in each statistically significant signaling pathway, 2) maps the gene identifiers to the corresponding UniProt protein identifierEach of the algorithmic components of our computational workflow was dependent on first calculating differentially expressed (DE) genes for each of the datasets. We consequently began by using the same computational workflow to generate the DE genes from the raw transcriptomic data for human cells infected with one of: RSV, IAV, MERS, SARS, and SARS-CoV-2. Each set of results was specific to the comparison that was performed (e.g. mock-infected vs. infected) and included both log2 fold-change values and FDR-corrected p-values. We then integrated and compared the differential expression results across all virus comparisons to facilitate downstream comparisons.We began by identifying genes that were either up- or down-regulated across a large number of studies. Specifically, we calculated the number of DE genes  that significantly changed during infection with influenza A (4205 genes), RSV (3661 genes), MERS (range across studies: 1222-13006 genes), SARS-CoV (range across studies: 2130-5557 genes), or SARS-CoV-2 (range across studies: 427-6933 genes). Out of the 15 studies evaluated, we observed 11 genes that displayed increased expression across 10-13 study comparisons including: TRIM25, C3, NCOA7, PTAFR, TNFAIP3, EIF2AK2, HELZ2, HBEGF, NFKB2, REL and VEGFC. We found an additional 18 genes that were upregulated across 9 of the 15 comparisons including: IFIT2, EGFR, and FOSL1. In contrast, we identified 14 genes that showed decreased expression across 11-12 comparisons including: EIF4H, APH1A, NME2, SEC61B, TUBB4B, CHCHD2, TUBA1C, TP53I3, H2AFZ, PEBP1, HOXB6, TPM2, CBR1, and SIVA1.We next analyzed the DE lists to identify significant genes that were shared across infections involving only the Betacoronaviruses including SARS-CoV, MERS-CoV, and SARS-CoV-2. This analysis revealed 12 genes that were upregulated in at least nine of thirteen comparisons including: TRIM25, REL, DNASE1, C3, NCOA7, PTAFR, EIF2AK2, HELZ2, NFKB2, ZNF385C, ZC3H12A, and OR7E122P. In contrast, the 10 genes that were found to be downregulated in ten of the thirteen comparisons involving any of the Betacoronaviruses included: CHCHD2, SMIM3, EIF4H, NME2, CBR1, SPARC, DSTN, CDC123, TIMM17A, and PDAP1.We then performed a similar analysis to identify the five statistically significant DE genes  that displayed the highest and lowest average fold-change values during infection across all comparisons of SARS-CoV-2, Betacoronaviruses, or all viruses . This anGiven the number of DE genes involved in the analysis, manual interpretation of the results would be intractable. We therefore performed an analysis to determine which Gene Ontology (GO) terms and Hallmark gene sets were specifically enriched to better understand which biological processes and molecular functions were represented by the genes in each of the DE gene lists. Overall, we observed a superset of 580 terms that were enriched in any of the included viral infections. The enrichment results for each taxon of virus were then reviewed to identify annotated terms that were shared between multiple types of viral infections, shared amongst coronaviruses, or specific to SARS-CoV-2.We found several notable terms that were shared among the results obtained from infection of multiple unrelated taxa of viruses. These terms referred to functions such as the host interferon response, regulation of virus response, chemokine activity, and immune cell migration that were positively enriched in both the RSV infection of A549 cells and the SARS-CoV-2 infection of either A549 or NHBE cells. As expected, the statistical significance of these terms supports the important role that the human immune system plays in response to virus infection, which is an expected result and validates the upstream DE gene analysis.Our method identified no significant GO terms that were shared across all Betacoronavirus comparisons. Interestingly, we found that each coronavirus species had its own set of uniquely enriched terms. In MERS-CoV, “negative regulation of inclusion body assembly” was positively enriched in four of the seven comparisons, while “aggrephagy” and “regulation of nucleotide binding oligomerization domain containing 2 signaling pathway” were positively enriched in three of the seven comparisons. The SARS-CoV samples had very few significantly enriched annotation terms including the “signal peptidase complex” and “cyclin dependent protein serine threonine kinase activator activity” terms that were each negatively enriched in only one of the four SARS-CoV comparisons.We also identified 290 terms that were uniquely present among the SARS-CoV-2 comparisons, although no significant terms were identified in comparisons involving either the human post-mortem biopsies or the infected Calu-3 cells. The shared significant terms that we identified in the NHBE- and A549-infected cells included “response to chemokine”, “antimicrobial humoral response”, and “humoral immune response.” Enriched terms in the NHBE-infected cells alluded to the role of the inflammatory response, interleukin-1, interleukin-6, neuroinflammation, arachidonic acid binding, and T-cell activation.We then wanted to calculate which intracellular signaling pathways were significantly modulated during viral infection, based on the DE genes identified above. The pathway information used for this analysis was obtained from public databases that manually curate the proteins that participate in conveying a signal from receptors on the cell membrane to transcription factors within the nucleus in order to respond to a stimulus.We used the results of the differential gene expression analysis as input to the SPIA algorithm. The robust bootstrap-based approach of this method identified 249 pathways that were significantly perturbed across at least one viral comparison. We subsequently analyzed the lists of significantly perturbed pathways for each comparison to determine those that were shared across virus taxa , shared among coronaviruses, and unique to SARS-CoV-2. This analysis revealed a subset of the total number of pathways to be dysregulated across many of the viral taxa studied. These pathways included translation , cytokine-cytokine receptor interaction , as well as rRNA processing . We also observed that some of these pathways were predicted to be activated during infection with certain viral taxa in specific cell types, while inhibited in others.We did not observe any signaling pathways that were significantly and consistently affected during infection by the individual coronavirus species examined. However, we did detect relevant pathways across multiple virus taxa. Interestingly, we found that the direction (i.e. activated or inhibited) was occasionally dependent on virus taxa or timepoint of infection. For example, the cytokine-cytokine receptor interaction pathway was predicted to be activated during infection with RSV, a subset of comparisons involving MERS-CoV, and many SARS-CoV-2 infections; while the same pathway was predicted to be inhibited during infection with SARS-CoV.. To better understand the impact of SARS-CoV-2 on interleukin-6 signaling, we overlaid the differential expression data on a representation of the signaling pathway  to the opentargets.org database, which yielded 179 potential human drug targets . We rankAfter reviewing the results of this analysis, we identified 38 potential human protein targets to counteract MERS-CoV, SARS-CoV, and/or SARS-CoV-2. Specifically, 38 targets were predicted to be relevant in at least 10 of the 15 coronavirus comparisons . SeventeOur analysis predicted twelve existing drugs that are predicted to be useful as repurposed therapeutics against 73.3% of all coronavirus taxa evaluated in this work and 100% of the SARS-CoV-2 comparisons included in our analysis . Eight oWe then analyzed the drugs that were identified as having targets in pathways affected by SARS-CoV-2 to determine which were predicted to reverse the effects of the viral infection on the affected pathway. Of the 42 drugs that targeted SARS-CoV-2 related pathways, 27 were predicted to reverse viral effects on these pathways. We then performed a literature search to determine if any of these drugs had been previously used to treat COVID-19 or had been identified as potential therapeutics by other research groups. We found six of the 12 therapeutics that we predicted to be useful against SARS-CoV-2 had already shown positive results in clinical tests including canakinumab, anakinra, tocilizumab, sarilumab, and baricitinib. These results give further support to the validity of our computational workflow.The computational workflow that we describe in this work predicts human therapeutic targets from signaling pathways and gene expression that are significantly affected during infection. We applied this workflow within the context of a meta-analysis that consisted of multiple public transcriptomic datasets of Betacoronaviruses. We then validated our results by comparing our predictions against recently published studies reporting therapeutics for SARS-CoV-2. Specifically, our downstream analyses enable us to calculate significant signaling pathways from DE genes using the SPIA algorithm as well as to predict potential therapeutics and their respective targets. Our analysis revealed thousands of DE genes, 580 enriched functional terms, as well as 249 significant pathways, including 38 pathways that were specifically affected during infection with SARS-CoV-2. It is important to point out that this workflow focuses on identifying human drug targets for two reasons: 1) to aid in the repurposing of existing drugs against emerging pathogens, and 2) to reduce the likelihood that a pathogen will develop resistance against the therapeutic(s) since they interact with a human protein that is much less likely to mutate than a viral protein., but none have combined the various aspects that are described in this work. The SPIA algorithm we used in this study has been shown to provide robust statistical results of perturbed pathways while not simply enriching for DE genes. It also enabled us to identify protein components in signaling pathways that could be reversed to reduce the adverse signs and symptoms that occur during infection, which differs from simply attempting to target DE genes. This approach drastically differs from some attempts to directly target DE genes without accounting for how a treatment may affect the cellular protein-protein interaction network. In this analysis we have compared significant DE genes and pathways identified in multiple different studies that used different MOIs, timepoints, and cell types. We found many DE genes and pathways that were affected across a variety of samples which increases our confidence in our results as those genes and pathways appear to be affected more by the virus itself than by other variables such as cell type, MOI, or timepoint.Our approach differs from prior meta-analyses by focusing on a consistent, robust ARMOR-based RNA-seq preprocessing workflow for all datasets as well as a downstream pathway perturbation analysis. Previous studies have used a variety of approaches to predict possible therapeutics, “humoral immune response”, “chronic inflammatory response”, “toll like receptor binding”, “interleukin-6 production” and citrate metabolism. A separate study of COVID-19 identified Bradykinin as potentially playing a role in pathogenesis. Interestingly, the annotations for this gene and its receptor include several of the enriched terms we identified such as arachidonic acid, inflammation, and G-protein coupled receptor activity. We interpret these separate studies to validate the findings of our functional enrichment analysis and anticipate that future studies will shed additional insight into the underlying mechanism(s) of viral pathogenesis.Although our approach was dependent on identifying DE genes using a consistent preprocessing workflow, the focus of our analysis was to identify relevant functions, pathways, and potential drug targets from the DE genes. These processed data could then be used to better understand the underlying biological mechanisms of pathogenesis, but to better identify host-based therapeutic targets. A subset of the enriched annotations identified by the Camera algorithm have been reported to be relevant during infection with SARS-CoV-2 in clinical studies including “response to chemokine”, which supports our pathway perturbation results. Other studies have reported that the ORF6, ORF8, and nucleocapsid proteins of SARS-CoV-2 are antagonists of type-I interferon and NF-kB in HEK-293 T cells or the induction of apoptosis during viral infection. In contrast, our meta-analysis predicted the type-I interferon pathway to be activated, suggesting either that this response could be dependent on the cell type, or that a potential redundant mechanism in the host cell can still turn on this pathway even if certain components are down-regulated. Our analysis predicted that noncanonical NF-kB signaling was inhibited during MERS and SARS infection, while being activated during SARS-CoV-2 infection in Calu-3 cells. While it is possible that this difference is due to a cell-specific response from the studies included in our meta-analysis, a NF-kB inhibitor applied to Vero E6 cells infected with SARS-CoV-2 has been shown to reduce cytopathic effects and virus plaques. This result suggests that NF-kB signaling may be active and contribute to the inflammatory signs and symptoms observed during virus infection, which agrees with our results. The Toll-like receptor and JAK-STAT pathways were previously found to be relevant to SARS-CoV-2 infection in A549 cells, which we also identified in infected Calu-3 cells. Citrate metabolism was also identified as an important pathway by our analysis and has been supported elsewhere.Many of the signaling pathway components that we identified in this study have also been reported previously. One prior proteomics study reported applying translation inhibitors to Caco-2 cells infected with SARS-CoV-2 reduced virus replication. We found that 27 of our 42 predicted therapeutics are predicted to “reverse” the effect on the pathways relevant to the viral infections being compared. Twelve of the 27 drugs that were predicted to be potential therapeutics against SARS-CoV-2 and are used to combat autoimmune or inflammatory diseases such as MS, SLE, and rheumatoid arthritis while others have been used in cancer treatments and against viral infections such as hepatitis C and human immunodeficiency virus. Six of these 12 drugs have been used to treat COVID-19 in patients including canakinumab, anakinra, tocilizumab, sarilumab, baricitinib, and hydroxychloroquine. Two other drugs, maraviroc and brodalumab, have been identified as potential treatments via cell cultures and computer models. Others on the list such as benralizumab have been identified through anecdotal data as biologics that potentially exert a prophylactic effect for SARS-CoV-2, when they are taken at the time of infection. Baricitinib is of particular interest as the US FDA has issued emergency use authorization for its use in conjunction with remdesivir in the treatment of COVID-19 patients over the age of two that have been hospitalized and require supplemental oxygen, invasive mechanical ventilation, or extracorporeal membrane oxygenation. Baricitinib has also been shown to be effective against COVID-19 when combined with corticosteroids. A small study involving Tocilizumab has also shown it can be useful in improving the outcome of patients with severe COVID-19.Although the US FDA has only issued emergency use authorization for therapeutic treatment for severe cases of COVID-19, a multitude of studies have reported results from attempting to treat patients with a variety of existing FDA approved therapeuticsIn conclusion, we developed and applied an important bioinformatics workflow, that combines existing tools with custom scripts, to predict potential human therapeutic targets. This workflow was then validated through a meta-analysis of publicly available transcriptomics data. The multiple Betacoronavirus and SARS-CoV-2 datasets revealed significant genes, annotations, signaling pathways, and human proteins that could be targeted by therapeutics during infection with various Betacoronaviruses. It is important to recognize that many of the predictions made by our workflow have been supported by experimental and clinical work on this virus, which suggests that our approach could enable the rapid identification of relevant therapeutics against future emerging pathogens.SARS-CoV-2: Severe acute respiratory syndrome coronavirus-2COVID-19: Coronavirus disease-2019GO: Gene ontologySARS / SARS-CoV: Severe acute respiratory syndrome coronavirusMERS / MERS-CoV: Middle east respiratory syndrome coronavirusACE2: Angiotensin-converting enzyme 2FDA: Food and Drug AdministrationGEO: Gene expression omnibusNCBI: National center for biotechnology informationARMOR: Automated Reproducible MOdular Workflow for Preprocessing and Differential Analysis of RNA-seq DataSPIA: Signaling pathway impact analysisDE: Differentially expressedFDR: False-discovery ratehttps://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE122876GEO: Transcriptomic analysis of MERS-CoV infected Calu-3 cell with or without AM580 treatment, Accession number GSE122876:https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE56192GEO: Transcriptomic analysis of the Novel Middle East Respiratory Syndrome Coronavirus  infection, Accession number GSE139516:https://github.com/bpickett/Pathway2Targets.Code for this workflow can be found on GitHub:http://doi.org/10.5281/zenodo.4706197.Archived code as at time of publication:Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication).Code is available under the terms of the et al. is very well presented. The authors have systematically analysed the RNA-sequencing data of SARS, SARS-COV-2 and MERS coronaviruses. They narrowed down to a subset of pathways that are commonly affected and predicted the existing FDA approved drugs that could be used to treat COVID-19. They have designed a pipeline on how one could quickly investigate if the existing drugs could be repurposed for a disease. The reviewer feels the work would be of potential interest to the readers and should be accepted for indexing.The manuscript by PickettIs the rationale for developing the new method (or application) clearly explained?YesIs the description of the method technically sound?YesAre the conclusions about the method and its performance adequately supported by the findings presented in the article?YesIf any results are presented, are all the source data underlying the results available to ensure full reproducibility?YesAre sufficient details provided to allow replication of the method development and its use by others?YesReviewer Expertise:Structural Bioinformatics, Computational Biophysics, MD SimulationI confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. It would be interesting if a bibliographic review on the importance of bioinformatics workflow and meta-analysis vis-à-vis prediction of potential human therapeutics can be included in the introduction section.The argument presented in the introduction section should include a clear description of the statement problem.Generally, the sentences should be well structured for a better flow and limit the use of possessive pronouns.The authors presented an original article on the signaling pathway-driven bioinformatics pipeline for predicting therapeutics against emerging infectious diseases. The authors are commended for the clarity and straight-forward style of the article. However, this manuscript needs to be improved regarding the following aspects:Is the rationale for developing the new method (or application) clearly explained?YesIs the description of the method technically sound?YesAre the conclusions about the method and its performance adequately supported by the findings presented in the article?YesIf any results are presented, are all the source data underlying the results available to ensure full reproducibility?YesAre sufficient details provided to allow replication of the method development and its use by others?YesReviewer Expertise:BiochemistryI confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. I feel this work touches up an alternative approach to the more classical computational protocol of performing virtual screening by docking methods + molecular dynamics to refine binding energies. Herein, the proposed method uses RNA-sequencing data with a focus on defining differentially expressed genes, enriched Gene Ontology (GO) terms, and dysregulated pathways in infected cells. On the brightest side, that theoretical framework allows for a simultaneous search of small molecules, antibody and protein. The authors stated that their predictions yield to a final list of 42 therapeutics. 27 of such drugs have been reported to exhort an effect against the virus. It remains unrevealed if these other 15 are negative positive or pending possible treatments. A more 'experimental' reader might find a next candidate in that list.Is the rationale for developing the new method (or application) clearly explained?YesIs the description of the method technically sound?YesAre the conclusions about the method and its performance adequately supported by the findings presented in the article?YesIf any results are presented, are all the source data underlying the results available to ensure full reproducibility?YesAre sufficient details provided to allow replication of the method development and its use by others?YesReviewer Expertise:Computational Chemistry for Drug Design.I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. We thank the reviewer for their helpful comment and agree that researchers would could indeed make good use of the complete list of drugs that we predicted to be useful against SARS-CoV-2. To address this comment we will update Table 4 in the next manuscript version to now include not only all of the predicted drugs, but also summaries of how each drug ranks across all analyzed Coronavirus datasets and also across all SARS-CoV-2 datasets. We believe that this change will improve the readability and relevance of our study.


Using this platform, we have both corroborated previously reported viral tropisms and discovered unidentified AAV capsid targeting biases. As expected, we observed that the tropism profile of AAV.CAP-B10 in mice was shifted toward neurons and away from astrocytes when compared with AAV-PHP.eB. Transcriptomic analysis revealed that this neuronal bias is due mainly to increased targeting efficiency for glutamatergic neurons, which we confirmed by RNA fluorescence in situ hybridization. We further uncovered cell subtype tropisms of AAV variants in vascular and glial cells, such as low transduction of pericytes and Myoc+ astrocytes. Additionally, we have observed cell-type-specific transitory responses to systemic AAV-PHP.eB administration, such as upregulation of genes involved in p53 signaling in endothelial cells three days post-injection, which return to control levels by day twenty-five. The presented experimental and computational approaches for parallel characterization of AAV tropism will facilitate the advancement of safe and precise gene delivery vehicles, and showcase the power of understanding responses to gene therapies at the single-cell level.Engineered variants of recombinant adeno-associated viruses (rAAVs) are being developed rapidly to meet the need for gene-therapy delivery vehicles with particular cell-type and tissue tropisms. While high-throughput AAV engineering and selection methods have generated numerous variants, subsequent tropism and response characterization have remained low throughput and lack resolution across the many relevant cell and tissue types. To fully leverage the output of these large screening paradigms across multiple targets, we have developed an experimental and computational single-cell RNA sequencing (scRNA-seq) pipeline for  Shain vitro –26.in vivo vector characterization to identify optimal candidates for a particular clinical or research application. Toward this end, conventional immunohistochemistry (IHC) and various in situ hybridization (ISH) techniques are commonly employed to profile viral tropism by labeling proteins expressed by the viral transgene or viral nucleic acids, respectively . The reliance on known marker genes also prevents the unbiased discovery of tropisms since such marker genes need to be chosen rategies , 61. Therategies –66. Howerategies –69. Comprategies , accountrategies –72, and rategies , 74. HowCollecting the entire transcriptome of injected and non-injected animals also offers an opportunity to study the effects of AAV transduction on the host cell transcriptome. A similar investigation has been conducted with G-deleted rabies virus . This stHere, we introduce an experimental and bioinformatics workflow capable of profiling the viral tropism and response of multiple barcoded AAV variants in a single animal across numerous complex cell types by taking advantage of the transcriptomic resolution of scRNA-seq techniques , 34 or Min situ hybridization – hybridization chain reaction (FISH-HCR). We then develop and validate a barcoding strategy to investigate the tropism of AAV-PHP.V1 relative to AAV-PHP.eB in non-neuronal cells and reveal that pericytes, a subclass of vascular cells, evade transduction by this and other variants. We further use scRNA-seq to profile cell-type-specific responses to AAV-PHP.eB at 3 and 25 days post-injection (DPI), finding numerous genes implicated in the p53 pathway in endothelial cells to be upregulated at 3 DPI and returning back to control levels at 25 DPI. Finally, we showcase the capabilities of parallel characterization by verifying the preceding findings in a single animal with seven co-injected AAV variants and revealing their respective cell-type biases.In the initial validation experiment, we quantify the transduction biases of AAV-PHP.eB and AAV.CAP-B10 across major cell types using scRNA-seq, and demonstrate results which correlate well with both published results and conventional IHC-based quantification. We then demonstrate the power of the transcriptomic approach by going beyond the major cell types to reveal significant differences in cell-subtype transduction specificity. Compared with AAV.CAP-B10, AAV-PHP.eB displays biased targeting of inhibitory neurons, and both variants transduce Sst+ or Pvalb+ inhibitory neurons more efficiently than Vip+ inhibitory neurons. We validate these results with fluorescent To address the current bottleneck in AAV tropism profiling, we devised an experimental and computational workflow . The staMethods). Using a custom genome reference, reads from the cell transcriptome that align to the viral cargo plasmid sequences are counted as part of the standard 10X Cell Ranger count pipeline  and droplet identification pipeline. Our viral transduction rate estimation method described above relies on having an empirical background distribution of viral transcript counts in empty droplets to compare against the cell type of interest. However, the default cell s method , yieldeds method , an estae models . Using cUsing our combined experimental and computational pipeline for viral transcript recovery and droplet identification, we can recover a lower bound on the expected number of cells expressing each unique viral cargo within groups of cells in heterogeneous samples.via scRNA-seq to conventional IHC. For this purpose, we characterized the tropism of two previously reported AAV variants, AAV-PHP.eB  per variant. Co-injection of both variants served to test the ability of our approach to parallelize tropism profiling. By having each variant package a distinct fluorophore, tropism could be simultaneously assessed via multi-channel fluorescence and mRNA expression of the distinct transgene. After 3–4 weeks of expression, we harvested the brains and used one hemisphere for IHC and one hemisphere for scRNA-seq. To recover viral transcripts, we chose primers such that enough of the XFP sequence was contained within the Illumina primer target sequences to differentiate the two variants (Methods). All clusters of the same marker gene were grouped together, and the transduction rate of the combined group of cells was determined using our viral transduction rate estimation method.As a first step, we validated our method by comparing the quantification of AAV transduction of major cell types V-PHP.eB  and AAV.V-PHP.eB   as determined by each technique and noticed a good correlation , we scrutinized the tropism of AAV-PHP.eB and AAV.CAP-B10 beyond the major cell types Figure 3During our in-depth characterization, we discovered several previously unnoticed cell-subtype biases for AAV-PHP.eB and AAV.CAP-B10 , and two animals received viruses carrying the barcoded cargo (barcode-based). For amplification of the viral cDNA in the animals receiving the barcoded cargo, we used primers closer to the polyA region such that the sequencing read covered the barcoded region . Our approach determined two subclusters of pericytes and astrocytes. Both clusters of pericytes had strong expression of canonical pericytes marker genes Rgs5, Abcc9, and Higd1b. However, one of the clusters had no marker genes that made it distinct from the other pericyte cluster, nor from endothelial cells. Consistent with previous reports, this suggests that this cluster could be pericytes contaminated with endothelial cell fragments, and thus was not considered for further analysis . By considering the relative bias between variants, we are able to interrogate tropism in a way that is more robust to inter-sample variability that arises from different distributions of recovered cells, expression rate of delivered cargo, and success of the injection. Using this normalization method, we evaluated the non-neuronal cell type bias of AAV-PHP.V1 relative to AAV-PHP.eB in both the cargo-based animals and the barcode-based animals across our non-neuronal cell-type taxonomy  carrying mNeonGreen, and performed single-cell sequencing on three mice three days post-injection (3 DPI) and three mice twenty-five days post-injection (25 DPI). These time points were chosen based on previous work showing MHC presentation response peaking around day seven and transgene response peaking around day 30 , AAV9 and AAV-PHP.B controls, and two additional variants, AAV-PHP.C1 and AAV-PHP.C2. For simplification of cloning and virus production, we designed a plasmid, UBC-mCherry-AAV-cap-in-cis, that contained both the barcoded cargo, UBC-mCherry, and the AAV9 capsid DNA , and AAV-PHP.V1 showed strong bias for vascular cells . In addition to confirming all our existing hypotheses, we were able to identify biases for the previously reported AAV-PHP.C2, which has not been characterized in depth. This variant, which was reported as having a non-neuronal bias similar to AAV-PHP.V1, showed significant transduction bias not only toward vascular cells , but also toward astrocytes , and a bias away from neurons .Using our cell typing and viral transcript counting methods, we investigated the transcript counts and transduction bias of the variants in the pool. Compared with our previous profiling experiments, the log-transformed transcript abundance of UBC-mCherry detected per cell was lower than CAG-mNeonGreen-WPRE and CAG-tdTomato  and shifted towards vascular cells   to the viral transcripts using PCR amplification such that the barcoded region could be consistently read out from the Illumina sequencing data. Our computational pipeline demultiplexes viral reads found in the transcriptome according to which matching sequence is most abundant in a separate amplified viral transgene library. Comparing the distribution of viral transcripts by cell type to a null model of empty droplets, we could then determine the cell-type biases.Our platform has corroborated the tropism of several previously characterized AAV variants and has provided more detailed tropism information beyond the major cell types. The fraction of transduced cells that are glutamatergic neurons was found to be markedly reduced for AAV-PHP.eB when compared with AAV.CAP-B10. Furthermore, within all the variants we tested, both Pvalb+ and Sst+ inhibitory neurons have greater transduction rates than Vip+ neurons. This bodes well for delivery to Pvalb+ neurons, which have been implicated in a wide range of neuro-psychiatric disorders , and sugWe further discovered that the vascular bias of AAV-PHP.V1 originates from its transduction bias towards endothelial cells. Interestingly, this is the only cell type we detected expressing Ly6a –139. GivWe also revealed that AAV-PHP.C2 has a strong, broad non-neuronal bias toward both vascular cells and astrocytes. AAV-PHP.C2 also transduces BALB/cJ mice, which do not contain the Ly6a variant that mediates transduction by PHP.B family variants –139. ThiAll our tested variants with non-neuronal transduction have lower expression in Myoc+ astrocytes and pericytes. Astrocytes expressing Myoc and Gfap, which intersect in our data , 141. SiWe have shown that our combined experimental and computational platform is able to recover transduction biases and profile multiple variants in a single animal, even amidst the noise of ambient RNA. We have further shown that our method is robust to the variability inherent in delivery and extraction from different animals, with different transgenes, and with different regulatory elements. For example, we discovered lower overall expression from vectors carrying UBC-mCherry compared with CAG-mNeonGreen-WPRE. Such differences are not surprising since the WPRE is known to increase RNA stability and therefore transcript abundance . Furthervia sample multiplexing strategies to pool multiple animals  used in this study were purchased from the Jackson Laboratory (JAX). AAV variants were injected i.v. into the retro-orbital sinus of 6–7 week old mice.In vivo vector characterization of AAV variant capsids was conducted using single-stranded (ss) rAAV genomes. pAAV : CAG-NLS-mNeonGreen, pAAV : CAG-NLS-mRuby2, pAAV : CAG-tdTomato, and pAAV : CAG-NLS-tdTomato constructs were adapted from previous publications , pUCmini-iCAP-PHP.eB  NMDG-HEPES-ACSF . Brains were rapidly extracted and cut in half along the anterior-posterior axis with a razor blade. Half of the brain was used for IHC histology while the second half of the brain was used for scRNA-seq. Tissue used for scRNA-seq was immersed in ice-cold NMDG-HEPES-ACSF saturated with carbogen. The brain was sectioned into 300-μm slices using a vibratome . Coronal sections from Bregma −0.94 mm to −2.80 mm were collected in a dissection dish on ice containing NMDG-HEPES-ACSF. Cortical tissue from the dorsal surface of the brain to ~3.5 mm ventral was cut out and further sliced into small tissue pieces. NMDG-HEPES-ACSF was replaced by trehalose-HEPES-ACSF -trehalose dihydrate*2H2O, pH adjusted to 7.35, osmolarity ranging 320–330 mOsm) containing papain  for the enzymatic digestion. Under gentle carbogenation, cortical tissue was incubated at 34°C for 50 min with soft agitation by pipetting every 10 min. 5 μl 2500 U/ml DNase I  was added to the single-cell suspension 10 min before the end of the digestion. The solution was replaced with 200 μl trehalose-HEPES-ACSF containing 3 mg/ml ovomucoid inhibitor  and 1 μl DNase I. At room temperature, the digested cortical tissue was gently triturated with fire-polished glass Pasteur pipettes for three consecutive rounds with decreasing pipette diameters of 600, 300, and 150 μm. 800 μl of trehalose-HEPES-ACSF with 3 mg/ml ovomucoid inhibitor was added. The uniform single-cell suspension was pipetted through a 40 μm cell strainer  into a new microcentrifuge tube followed by centrifugation at 300 g for 5 min at 4°C. The supernatant was discarded and cell pellet was resuspended in 1 ml of trehalose-HEPES-ACSF. After mixing using a Pasteur pipette with a 150 μm tip diameter, the single-cell suspension was centrifuged again. Supernatant was replaced with fresh trehalose-HEPES-ACSF and the resuspended cell pellet was strained with a 20 μm nylon net filter . After resuspension in trehalose-HEPES-ACSF, cells were pelleted again and resuspended in 100 μl of ice-cold resuspension-ACSF . Cells were counted with a hemocytometer and the final cell densities were verified to be in the range of 400–2,500 cells/μl. The density of single-cell suspension was adjusted with resuspension-ACSF if necessary.Three to four weeks after the injection, mice (9-10 weeks old) were briefly anesthetized with isoflurane (5%) in an isolated plexiglass chamber followed by i.p. injection of euthasol (100 mg/kg). The following dissociation procedure of cortical tissue into a single-cell suspension was adapted with modifications from a previous report . AnimalsCell suspension volumes containing 16,000 cells–expected to retrieve an estimated 10,000 single-cell transcriptomes–were added to the 10x Genomics RT reaction mix and loaded to the 10x Single Cell Chip A  for 10x v2 chemistry or B  for 10x v3 chemistry per the manufacturer’s protocol . We used the Chromium Single Cell 3’ GEM and Library Kit v2  or v3  to recover and amplify cDNA, applying 11 rounds of amplification. We took 70 ng to prepare Illumina sequencing libraries downstream of reverse transcription following the manufacturer’s protocol, applying 13 rounds of sequencing library amplification.via an Agilent High Sensitivity DNA Chip (Agilent 5067-4626).We selectively amplified viral transcripts from 15 ng of cDNA using a cargo-specific primer binding to the target of interest and a primer binding the partial Illumina Read 1 sequence present on the 10x capture oligos . The resulting library was sequenced using a MiSeq v3 150-cycle reagent kit (MS-102-3001), configured to read 91 base pairs for Read 2 and 28 base pairs for Read 1. To characterize the effect of sequencing depth, one viral transcript library was additionally processed independently on a separate MiSeq run.The UBC-mCherry viral transcript library, which was recovered with primers near the polyadenylation site, consisted of fragments ~307 bp long. Since this length is within the common range for an Illumina NovaSeq run, this viral transcript library was pooled and included with the corresponding transcriptome library.For transcriptome read alignment and gene expression quantification, we used 10x Cell Ranger v5.0.1 with default options to process the FASTQ files from the transcriptome sequencing library. The reads were aligned against the mus musculus reference provided by Cell Ranger .To detect viral transcripts in the transcriptome, we ran an additional alignment using 10x Cell Ranger v5.0. 1 with a custom reference genome based on mm10 v2020-A. We followed the protocol for constructing a custom Cell Ranger reference as provided by 10x Genomics. This custom reference adds a single gene containing all the unique sequences from our delivered plasmids in the study, delineated as separate exons. Sequences that are common between different cargo are provided only once, and annotated as alternative splicings.For viral read alignment, we aligned each Read 2 to a template derived from the plasmid, excluding barcodes. The template sequence was determined by starting at the ATG start site of the XFP cargo and ending at the AATAAA polyadenylation stop site. We used a Python implementation of the Striped Smith-Waterman algorithm from scikit-bio to calculate an alignment score for each read, and normalized the score by dividing by the maximum possible alignment score for a sequence of that length, minus the length of the barcode region. For each Read 2 that had a normalized alignment score of greater than 0.7, we extracted the corresponding cell barcode and UMI from Read 1, and any insertions into the template from Read 2.Transcriptome Read Alignment) was converted into the virus detected in the variant lookup table, or was discarded if it did not exist in the variant lookup table.For co-injections with multiple templates and injections of barcoded templates, we constructed a lookup table to identify which variant belongs to each cell barcode/UMI. For each template, we counted the number of reads for each cell barcode/UMI. For reads of barcoded cargo, we only counted reads where the detected insertion in the barcode region unambiguously aligned to one of the pre-defined variant barcodes. Due to sequencing and PCR amplification errors, most cell barcode/UMI combinations had reads associated with multiple variants. Thus, we identified the variant with the largest count for each cell barcode/UMI. We discarded any cell barcode/UMIs that had more than one variant tied for the largest count. Finally, each cell barcode/UMI that was classified as a viral transcript in the transcriptome (see Droplet Type Classification). First, we obtained the empirical distribution of viral transcript counts by extracting the viral counts for that variant in cell barcodes classified as the target cell type as well as cell barcodes classified as debris. Next, we assumed a percentage of cells containing debris. For each viral transcript count, starting at 0, we calculated the number of cells that would contain this transcript count, if the assumed debris percentage was correct. We then calculated an error between this estimate and the number of cells with this transcript count in the cell type of interest. We tallied this error over all the integer bins in the histogram, allowing the error in a previous bin to roll over to the next bin. We repeated this for all possible values of percentage of debris from 0 to 100 in increments of 0.25, and the value that minimized the error was the estimated percentage of cells whose viral transcript count could be accounted for by debris. The inverse of this was our estimate of the number of cells expressing viral transcripts above background.To determine an estimate of the percent of cells within a group expressing viral cargo above background, we compared the viral transcript counts in that group of cells to a background distribution of viral transcript counts in debris (see https://github.com/theislab/diffxpy) to fit the parameters of a negative binomial distribution to the viral transcript counts in debris droplets within a sample. We then postulated 1,000 different parameterizations of the negative binomial representing transcript counts in groups of cells, with 40 values of r ranging from 0.1 to 10, spaced evenly apart, and 25 values of p ranging from 0.001 to 0.99, spaced evenly apart. For each proposed negative binomial model, we drew 1,000 random samples of viral counts from the learned background distribution, and 1,000 random samples from the proposed cell distribution, and summed the two vectors. This summed vector was then used in our transduction rate estimation function, along with a separate 1,000 random samples of background viral transcripts for the function to use as an estimate of the background signal. We calculated the true probability of non-zero expression in our proposed cell negative binomial model (1 – P(X = 0)), and compared this value with the estimated value from the transduction rate estimation method.To validate that this method reliably recovers an estimate of transduction rate, we performed a series of simulations using models of debris viral transcript counts added to proposed cell type transcript count distributions across a range of parameterizations. To get estimates of the background distribution of debris, we used diffxpy . Probes contained a target sequence of 20 nucleotides, a spacer of 2 nucleotides, and an initiator sequence of 18 nucleotides. Criteria for the target sequences were: (1) a GC content between 45%–60%, (2) no nucleotide repeats more than three times, (3) no more than 20 hits when blasted, and (4) the ΔG had to be above –9 kcal/mol to avoid self-dimers. Last, the full probe sequence was blasted and the Smith-Waterman alignment score was calculated between all possible pairs to prevent the formation of cross-dimers. In total, we designed 26 probes for Gad1, 20 probes for Vip, 22 probes for Pvalb, 18 probes for Sst, and 28 probes for Slc17a7. Probes were synthesized by Integrated DNA Technologies.FISH-HCR was conducted as previously reported . Probes via scVI with the following parameters: 20 latent features, 2 layers, and 256 hidden units. These parameters were chosen from a coarse hyperparameter optimization centered around the scVI default values . For this coarse cell typing, we chose a single marker gene for major cell types expected in the cortex . If a cluster contained all the marker genes for multiple cell subtypes, the cluster was assigned to the cell subtype with the greatest number of marker genes. Clusters that did not express all the marker genes for any cell subtype were labeled as “Unknown”. Clusters that expressed all the marker genes for multiple cell subtypes with the same total number of marker genes were labeled as “Multiplets”. For cell types that contained multiple clusters, we then calculated the probability of every gene being zero in each cluster (see Cluster Marker Gene Determination). We then compared gene presence between clusters of the same cell type to see if there were any subclusters that had a dominant marker gene (present in > 50% of samples), that was not present in any of the other clusters (< 10% of samples). For the three cell types that had unique marker genes, we named the cluster after the gene with the highest 2-proportion z-score between the sampled gene counts in that cluster vs the rest.Cells classified as non-neuronal were further subtyped using automatic clustering and marker gene identification. We trained an scVI model using only the non-neuronal cells and performed Leiden clustering as implemented by scanpy on the latent space. We determined which clusters were expressing each of 31 marker genes across 13 cell subtypes. Marker genes were identified from a review of existing scRNA-seq, bulk RNA-seq, or IHC studies of mouse brain non-neuronal subtypes  . TransduTo calculate differential expression within cell types between groups of animals, we used the DESeq2 R package . For eacTo generate dot plots for marker genes, we used scanpy’s dotplot function . Gene coStatistical analyses comparing the fraction of transduced cells and transduction rate in different cell types for https://www.ncbi.nlm.nih.gov/bioproject/PRJNA758711/). Processed gene count matrices for droplets identified as cells, as well as the demultiplexed virus cargo counts, are available at CaltechData, doi: 22002/D1.2090 (http://dx.doi.org/10.22002/D1.2090).All raw FASTQ files are available under the SRA BioProject PRJNA758711  guidelines and reviewed and approved by the Office of Laboratory Animal Resources at the California Institute of Technology .DB, MA, TD, and VG conceived the project and designed the experiments. SC and MT provided critical single-cell RNA sequencing expertise. TD, MA, and DB prepared the DNA constructs and produced virus. MA performed the injections, tissue dissociation, histology, imaging and image quantification. DB and TD performed the single-cell library preparation and prepared samples for sequencing. DB and MA built the data processing pipeline. DB, MA, TD, and AW performed the analysis. All authors contributed to the MS as drafted by DB, MA, and VG. MT supervised single-cell RNA sequencing computational pipelines while VG supervised the overall project. All authors contributed to the article and approved the submitted version.This work was supported by the NIH Pioneer DP1OD025535, NIH BRAIN R01MH117069, Beckman Institute for CLARITY, Optogenetics and Vector Engineering Research (CLOVER) at Caltech, the Single-Cell Profiling and Engineering Center (SPEC) in the Beckman Institute at Caltech, the Curci Foundation, the CZI Neurodegeneration Challenge Network (VG), and the Vallee Foundation (VG). VG and MT are Heritage Principal Investigators supported by the Heritage Medical Research Institute. DB was supported by PHS Grant Number 5T32NS105595-02.VG is a Co-founder and BoD member for Capsida Biotherapeutics, a Fully Integrated AAV Engineering and Gene Therapy Company in Southern California.The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


This cohort study assesses whether young children can effectively self-collect SARS-CoV-2 samples for surveillance testing, using parameters of error rates and timing over the course of the 2020-2021 academic year. Can school-aged children effectively self-collect lower nasal swabs for COVID-19 surveillance testing within a school environment?In this cohort study of 296 school-aged children, participants were able to feasibly self-collect lower nasal swabs for COVID-19 surveillance testing quickly and with few errors.Pediatric self-collected lower nasal swabs are a viable and easily tolerated specimen collection method for SARS-CoV-2 surveillance in school settings, as evidenced by the low error rate and short time window of sample self-collection during testing. There is an urgent need to assess the feasibility of COVID-19 surveillance measures in educational settings.To assess whether young children can feasibly self-collect SARS-CoV-2 samples for surveillance testing over the course of an academic year.This prospective pilot cohort study was conducted from September 10, 2020, to June 10, 2021, at a K-8 school in San Mateo County, California. The research consisted of quantitative data collection efforts: (1) demographic data collected, (2) student sample self-collection error rates, and (3) student sample self-collection time durations. Students were enrolled in a hybrid learning model, a teaching model in which students were taught in person and online, with students having the option to attend virtually as needed. Data were collected under waiver of consent from students participating in weekly SARS-CoV-2 testing.Errors over time for self-collection of nasal swabs such as contaminated swabs and inadequate or shallow swabbing; time taken for sample collection.Of 296 participants, 148 (50.0%) were boys and 148 (50.0%) were girls. A total of 87 participants (29.2%) identified as Asian; 2 (0.6%), Black or African American; 13 (4.4%), Hispanic/Latinx; 103 (34.6%), non-Hispanic White; 87 (29.2%), multiracial; and 6 (2.0%), other. The median school grade was fourth grade. From September 2020 to March 2021, a total of 4203 samples were obtained from 221 students on a weekly basis, while data on error rates were collected. Errors occurred in 2.7%  of student encounters, with the highest rate occurring on the first day of testing (20 [10.2%]). There was an overall decrease in error rates over time. From April to June 2021, a total of 2021 samples were obtained from 296 students on a weekly basis while data on encounter lengths were collected. Between April and June 2021, 193 encounters were timed. The mean duration of each encounter was 70 seconds .Mastery of self-collected lower nasal swabs is possible for children 5 years and older. Testing duration can be condensed once students gain proficiency in testing procedures. Scalability for larger schools is possible if consideration is given to the resource-intensive nature of the testing and the setting’s weather patterns. According to the US Centers for Disease Control and Prevention, one key component of a comprehensive prevention strategy is to implement vigilant surveillance testing for SARS-CoV-2.7 School-aged children have missed out on crucial aspects of in-school learning, including group socialization and access to resources such as special services and nutritional support.9 Some families of lower socioeconomic status struggled to provide their children with the necessary tools for learning, including school supplies and adequate internet access for online classes.11 School closures also increased the risk of children falling behind academically and dropping out of school.12 While most schools across the country have since reopened, missed class time as a result of illness or isolation still poses a continued threat to students’ learning. Therefore, schools should ensure that they have the proper protocols in place to maintain the safety of staff and students, as well as their household and community contacts.Per UNESCO in March 2021, half of the world’s student population (more than 800 million learners) was still affected by full or partial school closures.5 We assessed whether young children can effectively self-collect SARS-CoV-2 samples for surveillance testing, using parameters of error rates and timing over the course of the 2020-2021 academic year.While rapid SARS-CoV-2 antigen testing at home remains a possible approach to routine surveillance, access and cost of test kits have been a major obstacle. Another possible consideration for SARS-CoV-2 surveillance is school-based testing, and the extent to which children are able to self-collect anterior nasal swabs during school hours without significant disruption to school activities would impact the feasibility of this approach. While previous studies have been able to assess school surveillance methods for up to 2 months, there is a gap in the literature surrounding the feasibility of regular COVID-19 testing in schools over an extended period of time.13 Study data were collected and managed using REDCap (Research Electronic Data Capture) electronic data capture tools hosted at Stanford University School of Medicine.15 REDCap is a secure, web-based software platform designed to support data capture for research studies, providing (1) an intuitive interface for validated data capture, (2) audit trails for tracking data manipulation and export procedures, (3) automated export procedures for seamless data downloads to common statistical packages, and (4) procedures for data integration and interoperability with external sources. We followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline for methods and results.16This cohort study was conducted at an independent K-8 school in San Mateo County, California, which remained open for on-site learning during the 2020-2021 academic year. The Stanford University School of Medicine’s institutional review board approved this study. The data described here were collected under waiver of consent due to the low risk of the data being collected, on the condition that the individual demographics of a participant would not be tied to their results. Additional study details can be found in Jani et al.The school implemented a hybrid learning model that combined in-person and distance learning opportunities. Classes were taught on-site and broadcast via video teleconferencing (Zoom), and students had the option of attending classes in person or at home in case of an exposure, COVID-19–related symptoms, or a positive COVID-19 test result. Families were also given the option of remaining fully remote until April 2021. Finally, students in grades 7 and 8 were not allowed on campus until October 2020 because their schedules required that they switch classes throughout the day.Students were separated into cohorts of 8 to 20 children led by 1 to 2 staff members (cohort leaders) to minimize the risk of schoolwide outbreaks of SARS-CoV-2. Students and staff members who reported on-site to the school were tested weekly for SARS-CoV-2 using pooled reverse transcription polymerase chain reaction (RT-PCR) testing from September 10, 2020, to June 10, 2021.On returning from extended school closures, students were brought to school by their caregivers for testing before being allowed back to classes. Regular surveillance testing occurred once a week over 7 hours. Staff and students were also asked to self-report any COVID-19 symptoms prior to coming to campus and were required to wear masks at all times. All students were brought by their cohort leaders to the designated testing site on campus to collect their samples during assigned 15-minute time slots. All testing occurred outdoors, either at the designated testing site or in the school’s parking lot for in-car testing. In-car testing was reserved for individuals who self-reported symptoms or recent exposures to infected individuals. The school’s protocols for how and when staff and students would be tested and the stay-at-home policy implemented based on results can be found in 17 Clinical research staff provided all students with instructions for anterior nares specimen self-collection and observed them to ensure proper technique. Instructions included making sure not to touch the sterile swab tip (Puritan Sterile Foam Tipped Applicator), inserting the swab fully into one nostril until resistance was felt, and rubbing the swab in 4 circles before repeating the process in the other nostril. Small tabletop mirrors were available at each station to assist with sample collection. Students were also shown a video and instruction sheet . During in-car testing visits, students self-collected anterior nares swabs from within their vehicles while a clinical research staff member provided materials and instructions and observed the self-collection from a distance of 6 ft. In-car testing visits occurred on an as-needed basis and records were not kept on the number of students who tested from their vehicles.On the last day of school year testing, the testing schedule was reduced from a full day of testing to a 3-hour half day. Student cohorts arrived to the designated testing site during preassigned 10-minute windows, shortened from 15 minutes and with reduced buffer time between testing windows. On this day, 260 students were tested in 3 hours. In total, 8 clinical research staff members were present and 4 testing stations were open. Four clinical research staff members  were responsible for observing sample self-collection, 2 (N.J.S. and L.X.C.) were responsible for sanitizing stations in between students, and 2 (J.A. and Y.J.C.) were responsible for processing samples in preparation for submission to our laboratory for RT-PCR testing.Error rates from students’ sample collections were documented from September 2020 to March 2021. Errors were defined as deviations from the standard protocol, such as the use of additional materials or the need for further instruction. Errors were collected by an independent observer who documented the errors committed by blinded clinical research staff members and students. These errors were defined in Only the date and type of error were collected. Demographics of students who committed errors were not documented. Additionally, while errors committed by clinical research staff members were differentiated from errors committed by students, the names of the clinical research staff members who committed these errors were not noted. Lastly, only errors that occurred at the testing tent during student encounters were recorded; errors that occurred during school staff testing or during in-car testing were not documented.Additionally, an independent observer timed random students’ sample self-collections from April to June 2021. Timing started from the time a student arrived at a testing station to the time they left, including hand and station sanitization, confirmation of identity, sample self-collection, and brief conversation. Clinical research staff members were not made aware of which visits would be timed. All data were summarized using SAS statistical software version 9.4 (SAS Institute).Of 296 students in the school, 148 (50.0%) were girls . Racial In total, 4203 anterior nares swab samples were collected from 221 students (74.7%) participating in on-site learning from September 2020 through March 2021 when data on error rates were collected. Errors occurred in 2.7% of all student encounters . Error rates over time are shown in Students had an extended winter closure during November and December 2020. Even after 5 weeks without testing, error rates remained low, with errors occurring in less than 1% of student encounters in the first week of testing in 2021 (n = 2). No subsequent weeks in 2021 had more than a 3% error rate during specimen self-collection, indicating that after 6 months of testing, students aged 5 to 14 years were able to successfully self-collect anterior nares samples with minimal errors and retained mastery over the course of breaks.Visits in 2021 for sample self-collection from 296 students occurred during the study period when timing data were collected (April-June 2021). Of those visits, 9.5% (n = 193) were timed. The mean duration of each visit was 70 seconds  On the one half day of testing, an additional 63 of 260 student interactions (24.2%) were timed, with a mean duration of 48 seconds per visit , 22 seconds faster than the 70-second mean across full days of testing.Our study provided extensive error rate data collected over the course of an entire school year and demonstrated that mastery of self-collected lower nasal swabs is possible for children 5 years and older. As expected when piloting an unfamiliar task, error rates began relatively high in September, around 10% of interactions, but rapidly decreased to 2.9% within 4 weeks. To account for this, we recommend that during the first few weeks of sample collection, schools implementing surveillance testing should dedicate additional time to monitor sample collection and ensure that proper testing procedures are followed. Additional testing materials and PPE should be allotted for the first few weeks of testing to account for the higher rate of materials used during this time.Although error rates were initially high, they rapidly decreased and stabilized at around 3% within the first month of testing. This shows that students were able to master nasal swabbing techniques and that lower nasal swabbing is well-tolerated by children as young as 5 years. Error rates remained low following a 2-month in-person break in November and December 2020, indicating that mastery is retained even with lapses in testing. The duration of testing was a major strength of our study because in addition to observing mastery over school breaks, it allowed us to see how a pattern of expertise developed among the participants, something that had not been identified previously.The mean time for testing during the majority of the school year was 70 seconds per participant. Five to 8 clinical research staff members were present each week for testing, with 2 to 4 testing stations open at a time. In June 2021, we piloted a half-day of testing and completed the same number of tests in roughly half the amount of time. During the half day, testing was 48 seconds per participant, pointing to increased efficiency over time as well as the scalability of our testing model. We tackled this accelerated testing model during our final week of testing after a year of testing with consistently low error rates. Our data suggest that schools may feasibly decrease their testing windows over time, after students become more familiarized with anterior nares self-collection. The plateau in error rates suggests that this change in testing could have been implemented once stabilization occurs. Schools should plan to take this learning curve and these mean times into account when constructing a testing schedule.One consideration to keep in mind during testing is limited personnel and resource availability. To ensure the safety of the students and staff, testing required a large amount of outdoor space to accommodate for 4 appropriately distanced testing stations and 2 tables for supplies. Long hours of testing outside was physically demanding for testing staff members, and periodic breaks were necessary to ensure high-quality testing and adherence to protocol. Additionally, the testing staff required a significant amount of PPE including N95 masks, gloves, hand sanitizer, isolation gowns, and goggles. Even as COVID-19 vaccination rates increase nationwide, schools seeking to implement on-site testing should account for high-demand and back-ordered PPE supplies.Another consideration is the generalizability of our model in climates that are significantly different from San Mateo County in California. Owing to moderate regional weather patterns, we were able to effectively conduct 7 hours of weekly testing outdoors year-round with minimal interruption from inclement weather. However, this will likely be difficult in regions of the country with more extreme climates. That said, schools should consider setting aside a spacious indoor area, with proper ventilation that allows for social distancing, to be used for indoor testing in the event that outdoor testing is not feasible.20 However, the lessons learned even for young children developing mastery in self-collection may be helpful for schools interested in using lower cost tests for surveillance .Our study has one key limitation. Keeping the above considerations in mind, our testing model is resource-intensive from both a financial and staffing standpoint. Outside of the materials necessary for testing, RT-PCR assays are costly.In summary, we found that anterior nasal swabbing is well tolerated and relatively error free among young children, making implementation of on-site testing a feasible way to conduct SARS-CoV-2 surveillance. With time and resource constraints in mind, we believe our model is scalable to larger schools as a way to keep students and teachers safe during in-person learning during these dynamic times. State guidelines, school districts, and budgets should reflect the pressing need for surveillance in schools in light of new SARS-CoV-2 variants and limited vaccine distribution in school-aged children.


Chemotherapy-induced peripheral neuropathy (CIPN) is a disabling side effect caused by neurotoxic chemotherapy. This randomized controlled trial aimed to evaluate the effect of manual acupuncture on CIPN. Twenty eligible breast cancer patients receiving taxane chemotherapy treatment were recruited and randomly divided into verum acupuncture and sham acupuncture groups. Each group received 15 treatments over 9 weeks. Quantitative tactile detection thresholds were measured using Semmes–Weinstein monofilament testing (SWM). The World Health Organization Quality of Life scale (WHOQOL-BREF), the Functional Assessment of Cancer Therapy/Gynecologic Oncology Group-Neurotoxicity (FACT/GOG-Ntx), and the Brief Pain Inventory-Short Form (BPI-SF) were measured before and after treatment. The between-group comparison of SWM revealed that the verum acupuncture group had more improvement of touch perception thresholds compared to the sham acupuncture group. The average pain severity in the BPI-SF of the verum acupuncture group was significantly lower than that of the sham acupuncture group. There were no significant differences in the FACT/GOG-Ntx trial outcome index and WHOQOL-BREF scores between the acupuncture and sham groups. The results suggest that acupuncture can alleviate the neuropathic pain of CIPN and improve touch perception thresholds. Chemotherapy-induced peripheral neuropathy (CIPN) is one of the refractory and disabling side effects of neurotoxic chemotherapy among patients with cancer. Chemotherapy-induced peripheral neuropathy (CIPN) is a disabling pain condition  involvinDiagnostic methods for CIPN include symptomatic observations of sensory nerve damage, such as needle-like sensations, dullness, numbness, and pain. CIPN is mainly caused by sensory nerve damage compared to motor nerve and autonomic nerve symptoms. Numbness and tingling appear earlier and are more severe than pain . The symIn clinical practice, the interruption of therapy or a change in chemotherapy dose is still the recommended treatment for CIPN; however, this still tends to reduce the effectiveness of chemotherapeutic agents in controlling tumor growth . CurrentClinicaltrials.gov (NCT03626220). Before randomization, all participants provided signed, written, and informed consent.We conducted this pilot, single-blind, randomized controlled trial at the Tainan Municipal An-Nan Hospital, China Medical University in southern Taiwan. All patients with breast cancer who received neurotoxic chemotherapy were screened for eligibility. Eligible participants were randomly assigned to the acupuncture or sham acupuncture group in a 1:1 ratio. Participants in each group received 15 acupuncture treatments within 9 weeks, including acupuncture treatment twice a week for the first 6 weeks, followed by acupuncture treatment once a week for the next 3 weeks. The research protocol was in compliance with the Declaration of Helsinki and good clinical practice guidelines and was approved by the Research Ethics Committee (CMUH106-REC2-117) of the China Medical University Hospital and registered on The inclusion criteria included adult women aged above 20 years, diagnosed with stage I–III breast cancer by a histological analysis, and who had completed adjuvant or neoadjuvant neurotoxic chemotherapy (including taxane-based or platinum-based), with the severity of CIPN matching the definition of the National Cancer Institute-Common Terminology Criteria for Adverse Events (NCI-CTCAE) 4.0 version more than grade 1. In addition, the patients’ daily physical status matched the definition of the Eastern Cooperative Oncology Group (ECOG) performance status less than grade 3.Exclusion criteria included an average survival time of fewer than 3 months, a history of diabetic neurological disease before chemotherapy, other pre-existing peripheral neurological diseases, a history of inflammatory or metabolic arthritis, severe coagulopathy or potential bleeding tendency, unstable cardiovascular disease, use of a cardiac pacemaker, or other pre-existing musculoskeletal diseases.After screening for inclusion/exclusion criteria, eligible participants signed the written informed consent and completed the baseline assessments that included a quantitative sensory test using the SWM and patient-reported outcome measures. Eligible participants completed the baseline assessments within 7 days before their first acupuncture/sham acupuncture treatment. ® SPSS® Statistics before study commencement and contained it in sealed envelopes. The balanced sample size was ensured by dividing the participants into blocks of two ), bilateral upper limbs , and bilateral lower limbs . Disposable sterile stainless-steel needles , 0.3 mm in diameter and 40 mm length, were inserted into the pre-designated acupoints as shown in SWM is a clinically easy-to-administer, quantifiable, and reliable tool for evaluating protective sensation loss ,24. MeasThe Brief Pain Inventory-Short Form (BPI-SF) was measured before the first treatment  and at the 3rd, 6th, and 9th weeks after the intervention. The BPI-SF is an instrument widely used to evaluate pain in patients with cancer, including numerative pain severity and pain interference with patients’ daily function . The aveThe Functional Assessment of Cancer Therapy/Gynecologic Oncology Group-Neurotoxicity-13 (FACT/GOG-NTX-13) was measured before the first treatment  and at the 3rd, 6th, and 9th weeks after the intervention. It has been evaluated as a reliable and valid scale for the functional assessment of cancer therapy ,30 and tThe 13-item FACT/GOG-Ntx subscale measures the severity and impact of neurotoxicity symptoms over the past seven days. The instrument assesses sensory symptoms, such as numbness, tingling, and discomfort in hands and feet, motor symptoms such as trouble walking, buttoning buttons, and ototoxicity, such as ringing or buzzing in the ear . Items aThe 40-item FACT/GOG-Ntx total score represents the sum of the FACT-G and FACT/GOG-Ntx subscale . The FACT/GOG-Ntx trial outcome index (TOI) contained the scores of the PWB, FWB, and FACT/GOG-Ntx subscale . The TOI assesses changes in neurological function and quality of life in terms of physical fitness and functional recovery without including the assessment of participants’ emotional and social ability recovery. The two scores were significantly different in patients who received chemotherapy and changed sensitively over time .The WHOQOL-BREF Taiwan version was validated as a good clinical tool across five kinds of Taiwanese cancer survivors, including head/neck, colorectal, liver, lung, and gynecologic cancers . It was ® SPSS® Statistics version 22 . For demographic data, continuous data were analyzed using a two-sample t-test or Mann–Whitney U test, while categorical data were analyzed using Fisher’s exact test. If the data of each group failed to pass the Shapiro–Wilk normal distribution test , the between-group differences were examined using the nonparametric Mann–Whitney U test, and the intra-group differences were examined using the Wilcoxon signed-rank test. If the measurements of the baseline values were as per the Shapiro–Wilk normal distribution test , the difference between groups was examined using a two-sample t-test, and the intra-group difference was examined using a paired t-test. If the study groups fit the normal distribution, the two-way ANOVA with repeated measures was used to evaluate the interaction of group and time factors. Differences between or within groups were considered significant at p < 0.05. All data were analyzed using IBMIn this study, 269 patients diagnosed with breast cancer were screened during the study period from June 2018 to July 2020 . Among 2The basic sociodemographic data of the eligible participants are listed in U test while the within-group comparison was tested by the Wilcoxon signed-rank test [p = 0.974). The intra-group comparison of the verum acupuncture group revealed that the average pain severity significantly reduced at the 9th week (p = 0.031) compared with the baseline. The intra-group comparison of the sham acupuncture group showed no significant difference in the 3rd, 6th, and 9th weeks after treatment. The between-group comparison showed that the average pain severity of the verum acupuncture group at the 9th week was significantly lower compared to the sham acupuncture group .A BPI-SF measurement was performed before , and in the 3rd, 6th, and 9th weeks after acupuncture or sham intervention. The scale included the evaluation of pain severity and pain interference. We chose the average pain severity value and analyzed pain interference, including seven domains. The results are shown in ank test . There wThe results of the seven-dimensional analysis of the pain interference values are presented in Von Frey monofilament fibers were used to test the touch perception thresholds before and after the 9-week treatment. The inspection points included eight locations: the left hand’s middle fingertip (LHT), left hand’s palm (LHP), right hand’s middle fingertip (RHT), right hand’s palm (RHP), left foot’s big toe tip (LFT), left foot’s plantar (LFP), right foot’s big toe tip (RFT), and right foot’s plantar (RFP) . The resp = 0.049) and the RFT  (Regarding the between-group comparison (verum acupuncture and sham acupuncture groups), the change in the touch perception threshold before and after treatment was significantly different in the LHT  .The participants’ FACT/GOG-Ntx scale was tested before , and at the 3rd, 6th, and 9th weeks after acupuncture or sham intervention. The results are shown in The World Health Organization Quality of Life scale (WHOQOL-BREF) was measured before  and at the 9th week of treatment, and the results are shown in No patients experienced adverse effects, such as all-cause mortality, serious adverse events, or other adverse events in our study. To the best of our knowledge, this study is the first randomized, sham-controlled clinical trial to evaluate the effect of acupuncture for CIPN combined with the use of SWM as an objective quantitative sensory assessor and patient-initiated reporting scale assessment. n = 24) received a set of auricular and EA treatments, while the sham treatment group (n = 23) received non-acupuncture, non-insertion procedures, and a usual care group (n = 21). By assessing numerical rating scales, the EA group had a significantly better mean absolute reduction in neuropathic pain than the sham group, with the least reduction in the usual care group [n = 20) was lower than that in the usual care group (n = 18) [n = 44) compared to the usual care group (n = 43) [According to the American Society of Clinical Oncology (ASCO) guideline for management of chemotherapy-induced peripheral neuropathy, duloxetine is the only recommendation on the treatment of CIPN ,35. In tre group . In anot(n = 18) . Molassi(n = 43) . However(n = 43) . The resSWM is a QST tool and is the only manual test used to examine sensory perception thresholds . SWM is p < 0.05). In addition, in the pre- and post-treatment comparison, participants in the acupuncture group showed significant improvements in touch pressure perception thresholds at six of eight test points after 15 acupuncture sessions over 9 weeks compared to the pre-treatment period (p < 0.05). Few randomized controlled trials of acupuncture for CIPN have used SWM as an assessment tool to evaluate tactile perceptual sensory recovery ,40. In oAs found in Hsieh’s study, 12 laser acupuncture sessions alleviated the effects of oxaliplatin on peripheral neuropathy in patients with advanced gastrointestinal cancer in a single-arm observational study design. Laser acupuncture significantly relieved oxaliplatin-induced cold trigger pain (using the cold-water immersion test) and anomalous mechanical pain (using von Frey). However, this study did not include a sham control group to compare with laser acupuncture . In WongFour strategies have been proposed for designing control groups used alone or in combination with acupuncture RCTs: (1) the absence of acupuncture needle insertion, (2) a different location of inserted acupuncture needles, (3) a different depth of insertion, and (4) the use of assistant tools . HoweverIn regard to different locations for inserted acupuncture needles, sham acupoints comprised the points drifting off the specific acupoints about 0.5 B-cun (about 1 cm). In regard to the different depth of insertion, the sham acupuncture adopted the depth of less than 4 mm, which was considered not causing “de-qi” .According to the review by Lu et al. , the mosIt is noteworthy that acupuncture has been widely recommended in cancer care and cancer-related symptoms, including cancer pain and neuropathy . AcupuncPrevious randomized controlled clinical trials using FACT-NTX as the assessment modality revealed that EA or manual acupuncture improved the quality of life of patients with CIPN after neurological impairment ,13. In oAs our study aimed to explore the efficacy of manual acupuncture on CIPN by using SWM as a quantitative sensory test, there are still limitations in the study that need to be pointed out. First, this was a pilot randomized controlled trial, and the small sample size limited the effectiveness of acupuncture, and a larger randomized controlled trial should be considered in the future. Second, the eligible participants in our study were limited to patients with type I–III breast cancer and most received docetaxel-based chemotherapy. Future studies may consider investigating the study design of acupuncture for other cancer types or expanding neurotoxic chemotherapy types. Third, a long-term follow-up is recommended to determine the effect of acupuncture on CIPN. Finally, the current study did not specify the duration of CIPN symptoms as an inclusion criterion. The present study recruited patients with an average CIPN duration of approximately 6 months. However, considering the trend that the incidence of CIPN decreases over time, the duration of CIPN should be considered in future studies.It is concluded that manual acupuncture alleviated neuropathic pain in CIPN. In addition, manual acupuncture was effective in improving touch perception thresholds, as assessed by SWM. The current results did not reveal that acupuncture improves the quality of life of patients with cancer. This warrants further large-scale clinical trials in the future.