What was the COVID-19 vaccination coverage and estimated mRNA COVID-19 vaccine effectiveness (VE) among US veterans in the first 3 months following vaccine rollout?In this case-control study including 6 647 733 veterans, 23% of veterans received at least 1 COVID-19 vaccination during the first 3 months of vaccine rollout. VE against infection was estimated to be 95% for full vaccination; estimated VE against COVID-19-related hospitalization was 91%, and there were no COVID-19–related deaths among fully vaccinated veterans.These findings suggest that early vaccination rollout for veterans was efficient, and estimated VE was high for this diverse US population. This case-control study describes the scope of the mRNA COVID-19 vaccination rollout and estimates the association between vaccination and reductions in SARS-CoV-2 infections among veterans during the first 3 months of vaccine rollout. Effectiveness of mRNA vaccinations in a diverse older population with high comorbidity is unknown.To describe the scope of the COVID-19 vaccination rollout among US veterans, and to estimate mRNA COVID-19 vaccine effectiveness (VE) as measured by rates of SARS-CoV-2 infection.This matched test–negative case-control study was conducted using SARS-CoV-2 test results at Veterans Health Administration sites from December 14, 2020, to March 14, 2021. Vaccine coverage was estimated for all veterans. VE against SARS-CoV-2 infection and COVID-19–related hospitalization and death were estimated using electronic health records from veterans who routinely sought care at a VHA facility and had a test result positive for SARS-CoV-2 (cases) or negative for SARS-CoV-2 (controls). Cases and controls were matched on time of test and geographic region. Data were analyzed from May to July 2021.Vaccination status, defined as unvaccinated, partially vaccinated (≥14 days after first dose until second dose), or fully vaccinated (≥14 days after second dose), at time of test.The main outcome of interest was a positive result for SARS-CoV-2 on a polymerase chain reaction or antigen test. Secondary outcomes included COVID-19–related hospitalization and death, defined by discharge data and proximity of event to positive test result. VE was estimated from odds ratios for SARS-CoV-2 infection with 95% CIs.Among 6 647 733 veterans included , 1 363 180 (21%) received at least 1 COVID-19 vaccination by March 7, 2021. In this period, during which the share of SARS-CoV-2 variants Alpha, Epsilon, and Iota had started to increase in the US, estimates of COVID-19 VE against infection, regardless of symptoms, was 95%  for full vaccination and 64%  for partial vaccination. Estimated VE against COVID-19–related hospitalization for full vaccination was 91% (95% CI 83%-95%); there were no deaths among veterans who were fully vaccinated. VE against infection was similar across subpopulations .For veterans of all racial and ethnic subgroups living in urban or rural areas, mRNA vaccination was associated with substantially decreased risk of COVID-19 infection and hospitalization, with no deaths among fully vaccinated veterans. Prior to these EUAs, the pandemic’s impact on veterans enrolled in the Veterans Health Administration (VHA), like elsewhere, was devastating. Approximately 207 000 COVID-19 cases had been reported by the end of 2020 among VHA-enrolled veterans.3 Additionally, the burden of COVID-19 cases was distributed disproportionally among racial and ethnic minority populations, such as Black and Hispanic individuals, and could not be explained by underlying chronic conditions. Although the VHA patient population includes approximately 13% Black veterans and 7% Hispanic veterans, among veterans with COVID-19, approximately 35% were Black, and 13% were Hispanic. More than 10 000 of VHA patients died.3On December 11, 2020, the US Food and Drug Administration (FDA) issued an Emergency Use Authorization (EUA) for the BNT162b2 COVID-19 vaccine (Pfizer-BioNTech), for the prevention of COVID-19 for individuals ages 16 years and older.4The VHA worked closely with the Centers for Disease Control and Prevention and other federal partners to provide COVID-19 vaccines to veterans quickly and safely. On March 24, 2021, the success of the VHA vaccination program prompted the US Congress to pass the “Strengthening and Amplifying Vaccination Efforts to Locally Immunize All Veterans and Every Spouse Act” which authorized the Department of Veterans Affairs (VA) to expand its vaccination effort beyond VHA-enrolled veterans.Rapid deployment of the vaccination program was accompanied by VHA’s nationwide SARS-CoV-2 testing effort, which aimed to test and record both symptomatic and asymptomatic veterans across all VHA facilities. Combined with accurate and timely recording of vaccination, this created an opportunity for a robust and well-powered test-negative design (TND) case-control analysis.Aiming to describe the extent of vaccination coverage and estimate associated reductions in SARS-CoV-2 infection that may serve as a marker of effectiveness of both mRNA vaccines in a diverse population that included individuals who were socioeconomically disadvantaged and medically high-risk, this study focused on the first 3 months of the vaccination effort in the VHA, thus the analysis excluded data on the JNJ-78436735 COVID-19 vaccine (Johnson & Johnson).STROBE) reporting guideline.This case-control study was approved by the institutional review board of the VA Medical Center in White River Junction, Vermont, and was granted an exemption for consent because it was deemed impractical to require consent for this minimal risk exposure. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology  were analyzed. The Corporate Data Warehouse contains patient-level information on all patient encounters, treatments, prescriptions (including vaccinations), and laboratory results rendered in VHA medical facilities.The VHA is the largest integrated health care system in the US, providing comprehensive care to more than 9 million veterans at more than 171 medical centers and 1112 outpatient sites of care.First, vaccination coverage, defined as having at least 1 COVID-19 vaccination administered at a VHA facility between December 14, 2020, and March 7, 2021, was described for the population of VHA enrollees. Second, a TND case-control study was conducted to estimate mRNA COVID-19 VE against infection, irrespective of symptoms and to evaluate VE against COVID-19–related hospitalization and death. The study population included veterans ages 18 years and older, with residence in a US state or Washington, District of Columbia, who presented for SARS-CoV-2 polymerase chain reaction (PCR) or antigen testing at a VHA outpatient or emergency department facility, or had testing within 1 day of hospitalization, during the study period ; veterans may have presented for testing for any reason, such as screening prior to medical procedure or for employment or travel, and they could have been symptomatic or asymptomatic. Veterans were required to have had VHA enrollment for at least 2 years prior to the study period and at least 1 inpatient or 2 outpatient visits in the past 2 years. Individuals meeting any of the following criteria were excluded: a COVID-19 diagnosis and/or positive results for SARS-CoV-2 on an antigen or PCR test at any time between February 2020 and study initiation ; hospitalization more than 1 day prior to testing; or incomplete VHA medical records.For the TND case-control study to estimate VE against infection, positive SARS-CoV-2 tests from qualifying veterans were classified as cases. Negative SARS-CoV-2 tests from qualifying veterans served as controls, and a maximum of 4 controls were matched to each case  based on Health and Human Services geographic region, and testing date (within 21 days of case testing date) since both factors are related to local disease burden, likelihood of having a positive SARS-CoV-2 test result, and vaccine exposure status. Because the test was the unit of analysis, more than 1 SARS-CoV-2 test per veteran was permitted for inclusion. A TND case-control study to estimate VE against symptomatic disease was also conducted. Within the population of veterans with a SARS-CoV-2 test performed, a case-control study was also conducted to estimate VE against COVID-19–related hospitalization and death. For these analyses, cases were veterans who had positive test results and were subsequently hospitalized (or died within 30 days following testing positive), and controls were veterans who were tested and did not have the outcome of interest. Up to 4 controls were matched to each case based on geographic region and testing date.Vaccination status was based on records of mRNA COVID-19 vaccination at a VHA facility from December 14, 2020, to February 28, 2021. A veteran was classified as unvaccinated until the day prior to the first vaccination, partially vaccinated from day 14 after the first vaccination until the day prior to the second vaccination, and fully vaccinated starting at 14 days after the second vaccination  and controls (negative tests). For the TND case-control study to assess VE against symptomatic disease, veterans who had positive test results were further restricted to those who, on the day of testing, had evidence of at least 1 COVID-19 symptom  with 95% CIs for the association between positive SARS-CoV-2 testing and receipt of mRNA COVID-19 vaccine. VE was estimated as (1 − odds of SARS-CoV-2 in vaccinated vs odds of SARS-CoV-2 in unvaccinatedOR) × 100%. Analyses were conducted comparing full vaccination and partial vaccination vs no vaccination; sensitivity analyses of time since vaccination were also conducted. Models for adjusted analyses included covariates for potential confounders of the association between vaccination status and having positive test results for SARS-CoV-2. Confounders were determined a priori based on known factors associated with SARS-CoV-2 testing and test positivity in the VA population6 and factors associated with COVID-19 risk and vaccination.8 Analyses were conducted for patient subgroups, including stratifications by age, race and ethnicity, and rural vs urban location. Conditional logistic regression for the case-control analyses of hospitalization and death and for VE against symptomatic disease were conducted similarly.For VHA enrollees and for subpopulations, vaccination coverage was reported as the frequency and proportion of individuals with at least 1 vaccination. For the TND case-control study, for cases (positive SARS-CoV-2 test result) and controls (negative SARS-CoV-2 test result), baseline demographic and clinical characteristics of the tested veterans were described by reporting frequency and proportion for categorical variables and mean (SD) for continuous variables. Missing data were reported or included within a level of a categorical variable as indicated in eTable 1 in the P values were 2-sided, and statistical significance was set at P < .05. Data were analyzed from May to July 2021.All analyses were conducted using SAS statistical software version 9.4 (SAS Institute). Of 6 647 733 VHA enrollees included , 1 363 180 veterans (20%) had been administered at least 1 dose of a COVID-19 vaccine at a VHA facility by March 7, 2021 . VaccinaThere were 15 110 veterans with positive SARS-CoV-2 test results, classified as cases, and 472 452 veterans with negative test results, classified as controls . After mVE estimates were similar across all age groups, sex, race and ethnicity, or urban vs rural status, with overlapping 95% CIs. In sensitivity analyses, the VE point estimates for partial (and full) vaccination were higher when measured starting 14 days vs 7 days after the first (and second) dose. When the analysis was restricted to symptomatic cases, VE for the overall population was 93% , approximately the same as for all tested individuals . There were no deaths among veterans who were fully vaccinated .In sensitivity analysis to assess veterans who may have received COVID-19 vaccination or treatment outside of the VHA, the analysis was repeated with Medicare data, including any records of vaccination and hospitalization outside the VHA for these veterans. The results were similar, with a VE of 95%   and Hispanic individuals (41.1%) vs non-Hispanic White individuals (54.6%)11 this nationwide study demonstrated high VE of mRNA vaccines against both laboratory confirmed SARS-CoV-2 infections (regardless of symptoms) and symptomatic disease. Although the sample size constrained our ability to analyze subpopulations in this short study period, VE estimates were similarly high in most subpopulations, for symptomatic infections, and against COVID-19–associated hospitalization and death.In a period during which the share of SARS-CoV-2 variants Alpha, Epsilon, and Iota had started to increase in the US,13 and from other observational studies. Two VE studies from Israel following the rollout of the BNT162b2 COVID-19 vaccine among individuals ages 16 years and older found similar results: a national surveillance data study found a VE of 95% ,14 and a study in Israel’s largest health care organization found a VE of 92% .15 Results were also similar to those from a prospective study among vaccinated health care workers in the United Kingdom ,16 a smaller study among essential and frontline workers in the US ,17 and a recent study among US veterans .18Our VE estimates are comparable to those from the clinical trials14 and 87% 15), and to a US study of patients ages 65 years and older .19 Also comparable were results for VE against death, which was 97%  in a study in Israel.14Although power was limited, the VE estimates against COVID-19 hospitalization were comparable to those from 2 studies in Israel  COVID-19 vaccine, and the United Kingdom extended the interval between doses to vaccinate their population more rapidly with 1 dose, limiting the ability to directly compare with their results.21 had limited sample size to adequately assess the effectiveness of COVID-19 vaccines for people with underlying medical conditions, although some conditions may predispose individuals to severe consequences from infection.22 The immune response to vaccination among individuals who are immunocompromised has not been fully explored. When fully vaccinated, VE in our study was 87%  for veterans who were immunocompromised, which is reassuring; however, the population of patients identified as immunocompromised in this study was diverse in their immunosuppressive conditions. Given that the response to infection may vary according to underlying conditions22 and that a study by Lee et al23 reported that COVID-19 outcomes may be more severe among patients with hematological malignant neoplasms vs solid tumors, a post hoc analysis was conducted and estimated VE against infection was 69%  for this subgroup of veterans.Many important prior clinical trials and observational studies26 These results show that the VHA was able to vaccinate minority and low-income veterans with similar efficiency. Prior to vaccination, Black and Hispanic veterans were at 30% to 40% higher risk of infection compared with their White counterparts.25 After the VHA’s thorough effort to provide vaccination to all veterans, regardless of race, ethnicity, or socioeconomic status, the risk of SARS-CoV-2 infection (irrespective of symptoms) following full vaccination was reduced for all, with similar VE for all racial and ethnic groups, demonstrating that equitable distribution of vaccination may be an effective means to reduce racial, ethnic, and socioeconomic disparity in COVID-19 disease burden.COVID-19 has disproportionally affected racial and ethnic minority populations and low-income communities.27 given that vaccination of individuals by state and local health departments might not have been reported to the VHA. Moreover, by the end of 2020, the VHA had standardized its testing and case definitions, unlike at the beginning of the pandemic.28 A TND case-control study was implemented to limit selection bias and minimize differences in health seeking behavior between cases and controls. SARS-CoV-2–positive test results coupled with COVID-19 symptoms were used in a sensitivity analysis to reduce potential misclassification and the VE from that analysis matched that of the main analysis at 93% . Some veterans vaccinated through the VHA could have been hospitalized in a non-VHA facility, especially in rural communities.29 The study population was limited to veterans who routinely sought care at VHA facilities to minimize likelihood of including those who were vaccinated or sought treatment for COVID-19 elsewhere. To further address this, the analysis was repeated with Medicare data, including any records of vaccination and hospitalization outside the VHA for these veterans. The results were similar with a VE of 95% . The analysis used data from both antigen and PCR tests, which may differ in their sensitivities and specificities, leading to misclassification of cases and controls. While this misclassification would likely be nondifferential, the analysis of VE against infection was repeated, stratifying by type of test, and there was no effect on the estimate. This analysis included test results from all veterans who underwent testing, without imposing strict rules to identify vaccine eligibility at the time of the test because the prevalence of conditions that would have made veterans vaccine eligible early on was quite high ; the analysis was also conducted for subgroups of veterans who would have been vaccine-eligible early on. Nevertheless, there could still be residual misclassification and differences in health seeking behavior and disease risk between different subpopulations and for individuals in the VHA and Medicare system that would require further investigation in future studies. This analysis was conducted during the early phase of vaccination rollout, and sequencing data were not available. Future analyses of VE for variants of SARS-CoV-2 are warranted, given that there may be differences in VE for variants.32This study has some limitations. A main concern regarding test-negative studies is misclassification. This study relied on vaccination records collected prospectively in near real-time rather than using participants’ recall. While this study demonstrated that vaccination coverage reached all demographic subgroups, the coverage rates reported here may be conservative estimates,In this TND case-control study using data from US veterans who were tested for SARS-CoV-2, mRNA vaccines were associated with reduced risk of SARS-CoV-2 laboratory-confirmed infection, symptomatic disease, hospitalization, and death. Over a period of only 3 months after the first COVID-19 vaccine was authorized, the VHA successfully vaccinated and tested millions of veterans of all socioeconomic groups for COVID-19. The findings of this study suggest that effectiveness of these vaccines, combined with their equitable and efficient deployment, may have resulted in attenuated COVID-19 disease burden among all VHA-enrolled veterans and specific high-risk populations.


Background: MicroRNAs (miRNAs) play key roles in a variety of pathological processes by interacting with their specific target mRNAs for translation repression and may function as oncogenes (oncomiRs) or tumor suppressors (TSmiRs). Therefore, a web server that could predict the regulation relations between miRNAs and small molecules is expected to achieve implications for identifying potential therapeutic targets for anti-tumor drug development.Methods: Upon obtaining positive/known small molecule-miRNA regulation pairs from SM2miR, we generated a multitude of high-quality negative/unknown pairs by leveraging similarities between the small molecule structures. Using the pool of the positive and negative pairs, we created the Dataset1 and Dataset2 datasets specific to up-regulation and down-regulation pairs, respectively. Manifold machine learning algorithms were then employed to construct models of predicting up-regulation and down-regulation pairs on the training portion of pairs in Dataset1 and Dataset2, respectively. Prediction abilities of the resulting models were further examined by discovering potential small molecules to regulate oncogenic miRNAs identified from miRNA sequencing data of endometrial carcinoma samples.Results: The random forest algorithm outperformed four machine-learning algorithms by achieving the highest AUC values of 0.911 for the up-regulation model and 0.896 for the down-regulation model on the testing datasets. Moreover, the down-regulation and up-regulation models yielded the accuracy values of 0.91 and 0.90 on independent validation pairs, respectively. In a case study, our model showed highly-reliable results by confirming all top 10 predicted regulation pairs as experimentally validated pairs. Finally, our predicted binding affinities of oncogenic miRNAs and small molecules bore a close resemblance to the lowest binding energy profiles using molecular docking. Predictions of the final model are freely accessible through the PSRR web server at https://rnadrug.shinyapps.io/PSRR/.Conclusion: Our study provides a novel web server that could effectively predict the regulation of miRNAs expression by small molecules. MicroRNAs (miRNAs) are a novel class of non-coding RNAs of ∼ 22 nucleotides . A silenRNA forms a complex tertiary structure with specific deep pockets that enable small molecules to bind with high selectivity. For example, the clinical use of Linezolid, a small molecule antibiotic targeting RNA, suggested that RNAs are important drug targets like proteins . RecentlHere, we established a powerful and user-friendly web server for predicting the regulation of miRNA expression by small molecules. 4,132 up-regulation pairs (2066 positive/known pairs and 2066 negative/unknown pairs) and 3,182 down-regulation pairs  were used for constructing an up-regulation model and a down-regulation model, respectively. Among five advanced machine learning algorithms, the random forest algorithm demonstrating the best performance was introduced to build the up-regulation and down-regulation prediction models. Predictions from the web server were finally generated based on the random forest algorithm. Furthermore, we expanded the dataset for the web server to 1509 FDA approved small molecules and 2,236 human miRNAs to provide more drug candidates and targets.In the current study, the known SM-miRNA regulation pairs were downloaded from the SM2miR database . Up-reguCurrently, negative/unknown SM-miRNA regulation pairs which were necessary for prediction model construction were not available. Similar to a previous study that leveraged molecular similarities to build highly credible negative compound–protein interactions , we consWe used the “CDK” R package to compute a 166-bit MACCS fingerprint vector  based onvia the web server http://www.mirbase.org/. Firstly, we pulled from miRbase the information of miRNA sequences including miRNA sequence length. Secondly, we calculated the ratios of A, C, G and U as well as the frequencies of 2-mer miRNA patterns including AA, AC, … UC, and UU. Then, we extracted the frequencies of 4-mer miRNA patterns including AAAA, AAAC, … UUUC, and UUUU. The descriptor of sole miRNA sequence was finally represented by a 277-dimensional vector. The small molecule descriptors were obtained from the computed 166-dimensional MACCS fingerprint vector.The sequences of miRNAs were downloaded from the miRbase (15 March 2021) database which encompasses the most complete miRNA information, containing miRNA sequences of 38,589 hairpin precursors and 48,860 mature microRNAs from 271 organisms . miRbase. For GLM, we optimized the parameter “alpha”. For KNN, the optimized parameter was “K value”. For SVM, we optimized the regularization parameter “C”. For ANN, the parameter “decay” was optimized. For RF, we adjusted regularization parameter “mtry”. The rest of all parameters of these classifiers are employed by default. The machine learning classifiers were trained by the parameter which makes the machine learning method reach the highest Area under the Curve (AUC) value calculated by five-fold CV in the training dataset. Then, we estimated the performance of classifiers in testing dataset by AUC value in the testing dataset. The up-regulation prediction classifier and the down-regulation prediction classifier with the highest AUC values in the testing dataset were selected for web server development.We separately trained classifiers for predicting the up-regulation pairs in Dataset1 and down-regulation pairs in Dataset2. Dataset1 and Dataset2 were evenly divided into the training dataset (50%) and the testing dataset (50%). Machine learning classifiers used in our research were constructed and evaluated using a fully-automated algorithm-oriented R package, termed caret . Five-foTo facilitate rapid prediction of the SM-miRNA by our models, we have developed and deployed a free, publicly available web-based Shiny application called Prediction of SM-miRNA Regulation pairs (PSRR) for providing and visualizing the results of our analyses. There is no login requirement for accessing any features in PSRR. It has been tested rigorously and extensively in different computer systems and popular web browsers such as Chrome, Firefox, and Internet Explorer. The PSRR output consists of tables and figures. The tables are generated by the “DT” R package that allows data querying and selection.The prediction accuracy of up-regulation and down-regulation models was investigated on data collated from a review that summarized the developed small molecules to regulate the miRNAs expression . The seqp-value < 0.05 and |log2FoldChange| > 1. Overall survival (OS) data of Uterine Corpus Endometrial Carcinoma (UCEC) patients were also available from TCGA. For each miRNA, the association between miRNAs and OS was determined using univariate cox regression. In tumor samples, the up-regulated miRNAs demonstrating p-value < 0.05 and hazards ratio (HR) > 1 in univariate cox regression analysis were defined as oncogenic miRNAs (oncomiRs). The Venn diagram was also introduced.The level-3 RNA-sequencing data was obtained from The Cancer Genome Atlas (TCGA). The “edgeR” R package was selected for miRNA differential expression analysis . The incTo validate the binding of predicted small molecules with oncomiRs obtained from the TCGA-UCEC study, molecular docking was performed in two steps as follows: 1) The 3D structures of miRNAs were predicted by a freely available server “RNAComposer”  and the The flowchart of this study was illustrated in The descriptor of each small molecule was originated from SMILES data. SMILES, proposed by Weininger , could rThe up-regulation pairs from Dataset1 were randomly split into training (50%) and the testing sets (50%), respectively. We selected five popular machine learning methods including Generalized Linear Models (GLM), K-nearest Neighbors (KNN), Support Vector Machine (SVM), Artificial Neural Network (ANN), and Random Forest (RF) to initial construction of our models. The classifiers were firstly trained on the training set using a 5-fold cross-validation procedure to select the optimal parameters based on AUC values. In the model training phase, as shown in Similarly, down-regulation pairs from Dataset2 were also randomly split into training (50%) and testing sets (50%). The highest AUC values for GLM, KNN, SVM, ANN, and RF were 0.787, 0.800, 0.799, 0.771 and 0.844  when theMoreover, we calculated F1 scores based on different threshold values . The dowCID3385 (5-FU) is one of the widely used chemotherapeutic drugs in the treatment of cancers. It could inhibit tumor growth by the reduction of DNA synthesis and the promotion of DNA damage . Based oWeb Server Development Section) that contains the constructed model to predict the SM-miRNAs regulation pairs was built. The web server can be accessed viahttps://rnadrug.shinyapps.io/PSRR/. Alternatively, either sequences of miRNAs or SMILES strings of small molecules are necessarily needed as input to the web server, with 2 modules for the miRNA input and additional 2 modules for the SMILES input as illustrated in the flowchart  was constructed by combing links of genes-small molecule and links of genes-miRNAs. The links of genes-small molecule came from the results of differential expression of miRNA target genes by treatment with small molecules, and the links of genes-miRNAs came from miRNA-genes prediction tools . BesidesAlthough the aforementioned models could predict the associations between small molecules and miRNAs, some major disadvantages exist in these models: 1) They could not predict the regulation of miRNA expression by small molecules (up-regulation or down-regulation). 2) An online web server based on the proposed model is not available, which limits the application of these models. 3) The prediction performance of models in the testing dataset is less satisfactory. For example, TLHNSMMA, a triple layer heterogeneous network to predict associations between miRNAs and small molecules obtained an AUC value of 0.81 on the testing dataset . In anotIn this study, we have developed a web server with random forest models that capture regulation relationships of small molecules with miRNAs expression, aiming to infer unknown small molecule−miRNA regulation associations. The advantages of our model and web server can be summarized as follows. 1) The experimental validated regulation pairs were split into miRNA up-regulation pairs and miRNA down-regulation pairs. Thus, the prediction models based on up-regulation pairs and miRNA down-regulation pairs could not only predict the association between a given small molecule and a miRNA, but also suggest whether the miRNA expression is increased or decreased by this small molecule. This prediction result is crucial for anti-tumor drug development since the oncogenic miRNAs should be inhibited and tumor suppressor miRNAs should be activated. 2) The web server provided in this study is freely available for any academic purposes of users. 3) Because of a comparatively rigorous scheme specifically designed for screening high-quality potential negative regulation pairs, the constructed random forest model demonstrated good performance on the testing (AUC of 0.911 for the up-regulation model and 0.896 for the down-regulation model) and validation datasets . 4) More potential miRNAs and small molecules could be predicted by the web server. For example, we have provided a built-in cohort including a total of regulation pairs of 2,236 miRNAs and 1,509 small molecules. 5) Since our model is able to achieve a relatively high prediction performance by directly extracting features from miRNA sequences to then predict the probabilities of miRNAs regulated by small molecules, processes of leveraging structural features of miRNAs are not involved in our model development. Users only need to prepare miRNA sequences to predict regulation profiles by the small molecules, or alternatively prepare SMILES strings to predict regulation profiles by the miRNAs.Uterine corpus cancer is a major cause of death worldwide, and 417,000 new cases and 97,000 deaths were recorded in 2020 . Uterinevia induction of cell growth arrest and apoptosis of cancer cells , a 2,6-diamino-substituted purine analogue, has been reported to be effective in tumor suppression er cells . CID4447er cells . CID5311er cells . CID6743er cells . These rWe acknowledge that there are some limitations of PSRR. Firstly, the number of positive/known regulation pairs is limited and might be on course to expand for constructing a model with better performance in the future. In this study, all known regulation pairs were downloaded from the SM2miR database. More regulation pairs from other resources could improve the prediction ability of the web server. Secondly, only the similarity scores between small molecules were taken into account on the course of generating the negative/unknown pairs. However, the similarity scores between miRNAs based on common targets and structures might contribute to generating higher-confidence negative/unknown pairs. The overall performance of PSRR illustrates that machine learning methods could boost the prediction capacities of modification of small molecules of miRNAs expression.Herein, we established PSRR, a powerful, user-friendly web server based on the random forest algorithm with the best accuracy for drug-miRNA regulation screening. The models in this web server demonstrated excellent predictive power in training, testing, case-study, and validation datasets. Furthermore, the server incorporates a database containing 1509 FDA-approved small molecules and 2,236 human miRNAs to provide more drug candidates. Procedures for prediction are simple and friendly, which makes it possible for users to only prepare miRNA sequences to predict the small molecules, or SMILES strings to predict the miRNAs. To our best knowledge, this is the first web server that could predict the effects on miRNA expression by small molecules. Overall, our study might contribute to developing novel potential therapeutic targets or treatments.


Skin cutaneous melanoma (SKCM) attracts attention worldwide for its extremely high malignancy. A novel term cytolytic activity (CYT) has been introduced as a potential immunotherapy biomarker associated with counter-regulatory immune responses and enhanced prognosis in tumors. In this study, we extracted all datasets of SKCM patients, namely, RNA sequencing data and clinical information from The Cancer Genome Atlas (TCGA) database and the Gene Expression Omnibus (GEO) database, conducted differential expression analysis to yield 864 differentially expressed genes (DEGs) characteristic of CYT and used non-negative matrix factorization (NMF) method to classify molecular subtypes of SKCM patients. Among all genes, 14 hub genes closely related to prognosis for SKCM were finally screen out. Based on these genes, we constructed a 14-gene prognostic risk model and its robustness and strong predictive performance were further validated. Subsequently, the underlying mechanisms in tumor pathogenesis and prognosis have been defined from a number of perspectives, namely, tumor mutation burden (TMB), copy number variation (CNV), tumor microenvironment (TME), infiltrating immune cells, gene set enrichment analysis (GSEA) and immune checkpoint inhibitors (ICIs). Furthermore, combined with GTEx database and HPA database, the expression of genes in the model was verified at the transcriptional level and protein level, and the relative importance of genes in the model was described by random forest algorithm. In addition, the model was used to predict the difference in sensitivity of SKCM patients to chemotherapy and immunotherapy. Finally, a nomogram was constructed to better aid clinical diagnosis. Though SKCM only constitutes ~5% of all skin cancers, it accounts for >75% of skin cancer deaths . Currenting skin . Unfortuing skin . Adjuvaning skin . But theing skin –8, managing skin . Many faing skin , and ageing skin  have potvia inhibition of pathways like the cytotoxic T-lymphocyte-associated protein 4 (CTLA-4), programmed cell death-1 (PD-1), and programmed cell death ligand-1 (PD-L1). Elevated evidences have substantiated the use of ICIs in SKCM . We collected 472 samples in total . Cases with incomplete clinical data were excluded. Finally, a total of 446 patients with full follow-up information were enrolled. In the process of further validation, we employed GSE65904 and GSE54467 matrices from the public repository of the Gene Expression Omnibu (GEO) (https://www.ncbi.nlm.nih.gov/geo/).As conducting this research, several datasets from public databases were used. We downloaded the HTSeq-FPKM gene expression data and corresponding clinical information of all SKCM patients from the official website of the TCGA database  and uniPatients in the TCGA-SKCM dataset were grouped into a high-CYT and a low-CYT group by median split, and then we used differential analysis on both groups in order to identify genes that could characterize CYT that ‘CYTRG’. Prognosis-correlated CYTRG for SKCM patients were then recognized using univariate Cox regression analysis on CYTRG and corresponding clinical data.Then non-negative matrix factorization (NMF) clustering was applied on the CYTRG to classify new subgroups (clusters 1 and 2) of SKCM patients using the NMF R package. NMF is widely used in bioinformatics and with its ability to extract meaningful information from high-dimensional data , the usei ∗ Expi)’ was used to calculate each risk score for every patient. The samples were classified into either a high-risk or a low-risk cohort according to the cut-of (based on the median risk score). Using R software (version 4.04), KM survival analysis and log-rank test were performed to compare DSS in either high-risk or low-risk group.A total of 446 representative patients were extracted from the TCGA repository. They possessed complete survival information and all relevant clinical features, such as age, sex, tumor stage and tumor-node-metastasis (TNM) stage. We employed lasso-cox regression analysis to screen out crucial CYTRG that have close relation with DSS. Certain CYT-related coefficients (βi) were calculated with the multivariate Cox regression model. The risk score formula (Expi) that was composed of βi and expression levels of CYTRG was set up. The equation ‘Risk score = ∑ , a nomogram was then built and concurrently could be used to predict DSS for the 1-year, 3-year, and 5-year of each SKCM patient. Briefly speaking, TMB refers to the number of mutations that exist within a tumor, and high TMB values are observed in melanoma and have been thought to be associated with responses to ICIs  curve was generated by the R package survival ROC  and was  to ICIs . The prohttps://discover.nci.nih.gov/cellminer/) and FDA-certified drug sensitivity-related data. Then we utilized a Pearson correlation test to analyze the relationship between gene expression and drug sensitivity. The programmed cell death 1  and cytotoxic T-lymphocyte associated protein 4 (CTLA-4) pathways have been implicated in tumor immune evasion. So immune checkpoint inhibitors targeting PD-1 and CTLA-4 may thereby improve antitumor immunity. The immunophenoscore (IPS) was used to predict clinical responses to immune checkpoint inhibitors (https://tcia.at/home). These results are able to better guide doctors in choosing different drug treatment on patients.Since chemotherapy is commonly applied to treat SKCM, we utilized R package “pRRophetic” to assess the chemotherapeutic response determined by the half maximal inhibitory concentration (IC50) of each SKCM patient on the Genomics of Drug Sensitivity in Cancer (GDSC) website. Besides, to elucidate the effects of CYT-related genes on drug sensitivity and tolerance in this model, we acquired transcriptome data from the CellMiner database (hibitors . The dathttp://www.proteinatlas.org) was generated by Uhlén et al.  database  using the “maftoools” R package, which is efficient and comprehensive and provides various functions for cancer genomic analyses . SubsequThe newly described algorithm, ESTIMATE  method, applied for assessment of the presence of stromal cells and the infiltration of immune cells in tumor samples using gene expression data , was usevia Spearman correlation method. The differences of immune cell content in high-risk and low-risk groups were shown as boxplots using Wilcoxon signed-rank test. Besides, we conducted differential analysis on the mRNA expression of immune checkpoint genes and CYT elements (GZMA and PRF1). We also performed Spearman correlation analysis on PD-1, PD-L1, CTLA-4, CYT, GZMA, PRF1 and calculated risk scores. Furthermore, we ran a correlation analysis between CYT expression and immune cell contents. All results further substantiated the utility value of our signature.To better clarify the relationship between the tumor immune cell infiltration status and calculated risk scores, 7 software programs, namely, XCELL, TIMER, QUANTISEQ, MCP-counter, EPIC, CIBERSORT-ABS, and CIBERSORT were used to analyze the immune cell infiltration landscape. The lollipop diagram was displayed to show the correlation between risk score and immune infiltrated cells A Gene Set Enrichment Analysis (GSEA) on risk genes was performed to obtain the GO and KEGG pathways of this model. The gene set enrichment study was conducted to that are expressed between the high and low-risk classes of the MsigDB . The gene set permutations were tested 1,000 times to demonstrate its ability to function consistently. The phenotype label was used to forecast adverse events.After determining which clinical trait has significant difference, a nomogram was drawn to predict whether a patient with SKCM belongs to the high-risk group. Pathological stage and tumor-bearing state are needed to help doctors better utilize this prognostic model.https://www.r-project.org). The correlation was determined by Spearman correlation analysis. Wilcoxon test and t-test were utilized to compare clinical variables. Survival status was assessed by the Cox regression analysis. OS, DSS and PFS were generated by the Kaplan–Meier method and evaluated by the log- rank test. Two- tailed p <0.05 was considered statistically significant. The sensitivity and specificity of the model were evaluated using ROC curves. Additionally, we verified the confidence of the model using test datasets and entire datasets. Reasonably, hazard ratios (HRs) and 95% confidence intervals (CIs) were used to describe the relative risk.All statistical analysis was accomplished by R version 4.0.4 .To verify the high value of CYT-related genes (CYTRG) for research, we applied non-negative matrix factorization (NMF) clustering method based on the 864 identified genes, and an elementary classification of patient subgroups was set through the NMF consensus clustering, eventually with two subgroups  sorted out   to estimate the half maximum inhibitory concentration (IC50) of chemotherapy response in each SKCM patient  . The value of genes in our model was confirmed again from the perspective of gene expression. Promisingly, these results can possibly inspire the scientists to explore CYT-related genes in preventing and curing the disease. The expression levels of CYT were strongly correlated with KIR2DL4, GBP2, SEMA4D, CCL8, UBA7, NMI, HAPLN3, JSRP1, TLR2, and IFITM1 (cor >0.5), moderately correlated with the expression levels of ERAP2 (cor >0.3), and weakly correlated with the expression levels of BCHE, KRT17, and TYRP1, which further verified the rationality of differential analysis to identify CYTRG . Results above may imply that our signature is a good reflection of CYT.To better understand the correlation between risk score and immune cell content, the Spearman correlation analysis and Wilcoxon rank-sum test were run To further verify the observation based on this risk score model, Gene Set Enrichment Analysis (GSEA) was utilized to seek out enriched pathways in the KEGG and GO databases. We screened out eligible gene sets from KEGG and databases, and selected the most specific pathways. As shown in For the purpose of letting the signature better serve clinical needs, we conducted a series of analyzes on the relationship between the 14-gene signature and clinical characteristics. Differential analysis on the risk scores of subgroups with various T stage was performed. The diagram shows that along with the progression of the disease, the risk score accordingly elevates (via the lymphatic vessels into lymph nodes and distant organs (The incidence of skin cutaneous melanoma (SKCM) continues to rise globally . SKCM ist organs , leadingt organs . Immunott organs . TherefoWe noticed that in 2015, Rooney et al. elucidated the CYT value as the potential landmark that could be used to predict prognosis in cancers and had associations with counter-regulatory immune responses, which may contribute to reveal mechanisms of tumor development . Thus, gThe CYT was calculated as the geometric mean of the GZMA and PRF1 expression in TPM. GZMA from NK cells and cytotoxic T lymphocytes (CTLs) activates gasdermin B (GSDMB) to trigger pyrotosis in target cells, which has been thought as a factor enhancing antitumor immunity . PRF1 alKIR2DL4, GBP2, SEMA4D, CCL8, UBA7, NMI, HAPLN3, JSRP1, TLR2, IFITM1, and ERAP2) were favorable prognostic factors, whereas the other three  were hazardous. Interestingly, some of them have already been verified to play an important part in SKCM. Zhou et al.  in both breast and melanoma cell lines and eventually proved that NMI retards tumor growth , NVP.BEZ235 (PI3K inhibitor), Roscovitine, NVP.TAE684 and vinblastine were more sensitive to patients in the low-risk group, while vinorelbine, docetaxel, doxorubicin were more sensitive to patients in the high-risk group. Chemotherapy always has a major role to play among all traditional therapies  belongs to the vast guanine-binding protein (GBP) family that is consumingly induced by interferon- γ (IFN-γ). Its role in tumorigenesis has received increasing attention in recent years. Notably, Ji et al.  is a melanosome protein involved in the pigmentary machinery of melanocytes and well-studied for its emerging roles in the malignant melanocyte and melanoma progression (TYRP1 mRNA level should restore the tumor-suppressor activity of miR-16 and highlighted miRNA displacement as a promising targeted therapeutic approach for melanoma. The family of interferon-induced transmembrane (IFITM) proteins is interferon induced antiviral proteins, localized in the plasma and endolysosomal membranes. With regard to IFITM1, also known as 9-27 or Leu13, is reported to be overexpressed in a wide range of neoplasms and thought as an independent prognostic biomarker for patients with certain tumor types  refers to the number of gene mutations within tumors. Considering its close connections with immune checkpoint inhibitor (ICI) treatments and other immunotherapies, high-TMB has been focused on its useful role as a novel biomarker for planning treatments and selecting ICIs across some cancer types, melanoma included –62. HighFurthermore, we analyzed tumor microenvironment (TME) by using the ESTIMATE algorithm. TME serves as a nutrient sink on which the tumor cells feed and develop . Groundb+ and CD4+ T cells have been generally recognized as important anti-tumor immune cell subgroups with their cancer-cell killing efficacy, working as a crucial autoimmune gateway against cancer intrusion of an organism. We also found that in the low-risk group, immune checkpoint genes were higher and so as to Treg cells, which in our view was according to the better immune function compared to the high-risk group. Previous studies have shown that the upregulation of PD-L1 and its connection to antigen-specific CD8+ T cells can explain the confined host immunity in cancers (known as adaptive immune resistance), yet the high expression of PD-1, PD-L1 and other immunosuppressive molecules could be attributed to not only the mutations of tumor cells, but also the induction of tumor-infiltrating cells (+ T cell content. Thus we hypothesized that high CYT in SKCM could mediate tumor immunity through CD8+ T cell and lead to better outcomes. And there was a moderate positive correlation between CYT and Macrophages 1 (M1), and a moderate negative correlation between CYT and Macrophages 2 (M2). M1 is mainly involved in inflammatory responses and anti-tumor processes, while M2 shows tumor-promoting activity  were significantly downregulated among high-risk group. Antigen processing and presentation is a classic adaptive immune-response course in which dendritic cells (DCs) are considered to play a central role potently and professionally . In manyAs is mentioned above, within this study, we found that PD-1, CTLA4 and PD-L1 genes were expressed more in the low-risk group. PD-1, CTLA4, PD-L1 inhibitors currently are among the hottest ICIs, contribute much to treat cancers, including SKCM. It may roughly possess accurate predictive capacity to identify patients who could respond well to immunotherapies. The underlying mechanism for this may be ascribed to that the higher TMB in the low-risk group contributes to more neoantigens generated by tumor mutations and more T lymphocytes infiltrated by tumors, which makes the tumor more immunogenic along with a stronger anti-tumor immune response [15]. In fact, this is consistent with the result that SKCM patients with higher TMB expression have better outcomes. However, it is cautionary to note that our results suggest that immune checkpoints are generally upregulated in SKCM patients, noting that they are more prone to immune escape during immunotherapy. These conclusions offer practice guidance, and shed a new light on the immunotherapy for SKCM.in vivo or in vitro experiments to further examine the function of CYTRGs in SKCM progression and to understand mechanisms of neoplasia better. Still, prospective clinical studies are welcome to verify phenomena reflected in this research.Nevertheless, there were limitations in this study. This was a retrospective study with datasets from the TCGA database, lacking specific clinical information such as treatment and recurrence records. And our conclusions need to be validated In summary, our analyses of gene expression matrix and corresponding clinical characteristics identified 14 prognosis-related CYTRGs in skin cutaneous melanoma. Based on the clinical characteristics of CYT, we constructed a novel risk scoring model, which can effectively evaluate the prognosis for SKCM patients and forecast the benefit of SKCM immunotherapy. Our study illustrated that CYT may positively influence the development and outcome of tumors by modulating tumor microenvironment. Thus, poor prognosis of high-risk patients with SKCM may be attributed to the lower immune functions of immune cells. And different sensitivity to therapeutic drugs between the high- and low-risk groups could also be due to differential expressions of immune checkpoints and cytokines. Significantly, our study showed that low-risk patients with SKCM benefit more from immunotherapies and the model can be employed as a key tool to facilitate rational drug use and guide clinical treatment.Our study is the first to establish a 14 CYT-related-gene prognostic model. Abundant analyzes verify that this signature can be used as a promising predictive biomarker and therapeutic target for SKCM patients.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/HZ is responsible for writing and submitting the papers. YL is responsible for data collection and analysis and the production of pictures. DH and SL are responsible for the ideas and guidance. All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.The 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.


The San Francisco Bay Area was the first region in the United States to enact school closures to mitigate SARS-CoV-2 transmission. The effects of closures on contact patterns for schoolchildren and their household members remain poorly understood.We conducted serial cross-sectional surveys  of Bay Area households with children to estimate age-structured daily contact rates for children and their adult household members. We examined changes in contact rates over the course of the COVID-19 pandemic, including after vaccination of household members, and compared contact patterns by household demographics using generalized estimating equations clustered by household.We captured contact histories for 1,967 households on behalf of 2,674 children, comprising 15,087 non-household contacts over the three waves of data collection. Shortly after the start of shelter-in-place orders in May 2020, daily contact rates were higher among children from Hispanic families , households whose parents were unable to work from home , and households with income < $150,000 , after adjusting for other demographic characteristics and household clustering. Between May and August 2020, non-household contacts of children increased by 145% (ages 5–12) and 172% (ages 13–17), despite few children returning to in-person instruction. Non-household contact rates among children were higher—by 1.75 [1.28–2.40] and 1.42 [0.89–2.24] contacts per child per day in 5–12 and 13–17 age groups, respectively, in households where at least one adult was vaccinated against COVID-19, compared to children’s contact rates in unvaccinated households.Child contact rates rebounded despite schools remaining closed, as parents obtained childcare, children engaged in contact in non-school settings, and family members were vaccinated. The waning reductions observed in non-household contact rates of schoolchildren and their family members during a prolonged school closure suggests the strategy may be ineffective for long-term SARS-CoV-2 transmission mitigation. Reductions in age-assortative contacts were not as apparent amongst children from lower income households or households where adults could not work from home. Heterogeneous reductions in contact patterns raise concerning racial, ethnic and income-based inequities associated with long-term school closures as a COVID-19 mitigation strategy.The online version contains supplementary material available at 10.1186/s12879-022-07218-4. Physical distancing measures intending to reduce close contacts between infectious and susceptible individuals have been enacted globally to mitigate the transmission of SARS-CoV-2 . The SanThe effects of stay-at-home orders and physical distancing restrictions may differ between communities. For example, while there is some evidence that school closures are associated with reductions in COVID-19 cases at the state level , the relRemote learning is expected to have detrimental impacts on child development, including exacerbating existing socioeconomic gaps in school achievement –16. In lWe developed a serial, cross-sectional web-based survey—Bay Area Contacts among Kids (BACK)—to capture the social contact patterns of Bay Area  households with children throughout the COVID-19 pandemic across three periods: May 4–June 1, 2020 (wave one), August 20–October 1, 2020 (wave two), and February 08–April 07, 2021 (wave three)  were eligible if the household contained at least one child under the age of 18. Households were recruited via a commercial survey provider  into a panel representative of the joint distribution of race/ethnicity and household income within the selected counties. One adult respondent from each sampled household responded to study questions on their own behalf, and on behalf of all children in their household. For each individual, we inquired about the number and location of non-household social contacts made within six age categories  throughout the day prior to survey completion. For consistency with other studies [We additionally collected data on demographics within each household, including race/ethnicity of members, total household income, age of each household member, and occupation status of adult respondents. Survey respondents also indicated whether members of the household experienced COVID-19-like symptoms, whether or not they felt school closures were necessary and/or useful to curb the spread of COVID-19, and their satisfaction with their ability to reduce face-to-face interactions. For each child within the household, the respondent indicated the type of schooling  their child was engaged in. For hybrid or in-person instruction, the respondent indicated physical distancing precautions implemented by the school and children’s mode of transportation to/from school. For children participating in hybrid instruction, the respondent indicated where the child went on the remote schooling days. The third wave of BACK inquired whether anyone in the household had received any doses of a COVID-19 vaccine, or if the adult respondent was planning to get vaccinated. The survey tool was translated in English and Spanish, and is included in the appendix  vaccination status.We assessed predictors of the number of non-household contacts by fitting generalized estimating equations with robust standard errors to account for clustering at the household level. To account for overdispersion of the outcome variable (number of non-household contacts), we used a quasi-Poisson outcome distribution. Models included fixed effects for age category, race/ethnicity, combined household income, number of household members, and a changepoint term for study wave. Separate models additionally adjusted for whether adults were able to work from home during COVID-19 physical distancing restrictions and whether any adults were vaccinated in the household. To determine whether the effect of these covariates varied by study period, in three separate models, we included interactions of the study wave with age, race/ethnicity, household income, and an indicator of ability to work from home during COVID-19. Since generalized estimating equations use quasi-likelihood based inference, we were unable to assess model fit using likelihood-based methods ; howeverR) over time as a supplemental analysis  using the Amelia II package for multiple imputation, generalized estimating equations were fit using the geeM package , 22.We accounted for the number of in-person contacts students had while attending in-person school by adjusting the age-stratified contact matrices by the number of times a student switched classrooms multiplied by the average class size and an adjustment factor of 1/5 based on prior work , accountEthics approval was obtained from the Office for Protection of Human Subjects at the University of California, Berkeley (Protocol Number: 2020-04-13180).We surveyed 1,967 total households  on behalf of 2,674 total children  (Table Sixteen percent (311/1966) of respondents reported that at least one person in their household experienced COVID-19 symptoms in the two weeks prior to survey completion. Across the three waves, most households  stated that they agreed school closures helped reduce the number of COVID-19 cases in the community, though the proportion of households disagreeing about the utility of school closures increased from 8% (49/610) during the first wave to 12% (79/640) in the third wave. A majority  of Black households viewed school closures as helpful to reduce COVID-19 cases, as did 92% (467/509), 90% (986/1092) and 88% (342/388) of Asian, white, and Hispanic households, respectively. Of the households with combined income over $150,000, 92% (646/703) felt school closures were useful, in comparison to 88% (1106/150) of households whose combined income was less than $150,000. Of households sampled in the second and third wave, the majority  indicated that they had greatly reduced their face-to-face interactions with others relative to the pre-pandemic era (before Shelter-In-Place orders in March 2020) and 83% (1121/1350) stated that they were satisfied with their ability to control the amount of face-to-face interaction with non-household members.During the third wave of data collection , 40% (259/639) of households indicated that at least one member of their household had received one or more doses of a COVID-19 vaccine. Among households where no individuals have been vaccinated, 28% (108/379) indicated they were unsure about receiving or unwilling to receive a COVID-19 vaccine. Vaccine hesitancy was higher among respondents who identified as primarily Black and households whose combined income was under $40,000  during the first study wave to 3.24 per person per day  and 3.31 per person per day  during the second and third waves, respectively . In the first wave, non-household contacts were driven by younger working aged adults (18–39 years) who had on average 4.27 contacts per person per day. In comparison, school aged children including teenagers aged 13–17 years and young children aged 5–12 year reported an average of 0.77 and 1.03 contacts per child per day during the first wave, respectively  contacts per person per day in wave one to 2.53  and 2.73  contacts per person per day in waves two and three, respectively  fewer non-household contacts compared to households where adults were unable to work from home  more non-household contacts compared to unvaccinated households. Age-structured contact matrices stratified by vaccination status revealed that contacts were higher both among adults and among unvaccinated children in households where at least one adult had been vaccinated  more non-household contacts per person per day than non-Hispanic households in the first wave of data collection, though this difference between Hispanic and non- Hispanic households attenuated during the second and third waves  were associated with the lowest age-assortive contact rates in comparison to subsequent periods of data collection, particularly among and between children at school or other childcare settings. These findings align with contact estimates elsewhere, which have found reductions in age-assortative contacts between school-aged children in China, Greece, Luxembourg, and the Netherlands during the initial April 2020 wave of the COVID-19 pandemic , 28–30.After partial relaxation of physical distancing measures in the Bay Area, contact increased among school-aged children belonging to differing households. We found that teenagers aged 13–17 experienced the largest absolute increase in non-household contacts between the first and second study wave relative to all other age groups. Data from the Netherlands revealed a similar pattern: while school-aged children initially experienced striking reductions in contacts during the period of strict physical distancing in April 2020, by June 2020 contacts between children had rebounded beyond that of pre-pandemic levels . Taken tPrior to the COVID-19 pandemic, few studies provided an opportunity to address how infectious disease-related school closures would impact contact among children and adolescents. Previous contact estimates during school closures in Russia found that short-term school closures weaken children’s social networks –34.A study among New England school children identified that teenagers did not comply with recommendations to reduce social contacts during a short-term influenza-related school closure in 2009 . In contYounger children aged 0–4 tended to have more contacts than school-aged children during the initial wave of data collection, perhaps because they were too young to be left at home alone and accompanied parents on essential activities . While iWe also identified disparities in the ability to effectively reduce social contacts by demographic groups, particularly Hispanic households and those making under $150,000 per year. Differences in the non-household contact rates for Hispanic and non-Hispanic families were observed in the first wave of data collection, but attenuated across subsequent periods of data collection. This effect was also observed in a social contact study of adults in urban U.S. settings throughout the COVID-19 pandemic . We alsoCOVID-19 vaccination was associated with increased social contact outside the for both vaccinated adults and their unvaccinated children. Despite the fact that most school-aged children were not age-eligible for vaccination during the study period, presence of a vaccinated household member was associated with a significant increase in age-assortative contacts between children. It is possible that the number of non-household contacts and therefore risk of acquiring a SARS-CoV-2 infection influenced both the ability and decision to get vaccinated during the study period as the general California population did not become eligible for vaccination until April 15, 2021, after data collection concluded . LikewisA limitation of retrospective social contact surveys is that individuals may under or over-report their children’s contacts , 41, andData from BACK provides a unique opportunity to understand how interpersonal contact patterns evolve among school-aged children in the United States throughout the COVID-19 pandemic, and may be used to parameterize models evaluating the impact of various non-pharmaceutical interventions directly impacting children like school closures. School closures in California weakened the social contact network of children, yet contact reductions were less pronounced among children in lower income households and from children whose guardians are unable to work from home. These heterogeneous reductions in contact patterns raise critical racial, ethic, and income-base inequities that warrant consideration in the event of continued in-person school closures for COVID-19 mitigation. Furthermore, the waning reductions we observed in non-household contact rates of schoolchildren and their family members during these prolonged school closures suggests that this strategy may be inadequate for long-term SARS-CoV-2 transmission mitigation.Additional file 1: File S1. Survey questionnaire. File S2. Supplemental Analysis: R0 estimation from contact surveys. Table S1. COVID-19 vaccine enthusiasm among unvaccinated households. Table S2. Characteristics of children attending school across Wave 2 and Wave 3. Table S3. Model based estimates of non-household contacts. Figure S1. Age-structured social contact rates during the first, second and third wave of data collection. Figure S2. Histogram of total contacts by study wave. Figure S3. Average number of non-household contacts stratified by location of contact, age category, and study wave. Figure S4. Age-structured contact matrices stratified by household characteristic and study wave. Figure S5. Age-structured contact matrices populated with bootstrapped confidence intervals. Figure S6. R0 estimation from contact data.


Aging could be critical in limiting the application of subcutaneous adipose tissue (SAT) in tissue repair and reconstruction. However, no systematic study on the characteristics of SAT aging has been conducted. In this study, a scanning electronic microscope was used to detect the structural and compositional changes of SAT collected from nine females in three age groups. Multi-omics data of SAT from 37 females were obtained from Gene Expression Omnibus database, and 1860 genes, 56 miRNAs, and 332 methylated genes were identified as being differentially expressed during aging among non-obese females. Using Weighted Correlation Network Analysis (WGCNA), 1754 DEGs were defined as aging-associated genes for non-obese females, distributed among ten co-expression modules. Through Gene Ontology enrichment analysis and Gene Set enrichment analysis on those aging-associated DEGs, SAT aging was observed to be characterized by variations in immune and inflammatory states, mitochondria, lipid and carbohydrate metabolism, and regulation of vascular development. SUPV3L1, OGT, and ARPC1B were identified as conserved and core SAT-aging-related genes, as verified by RT-qPCR among 18 samples in different age groups. Multi-omics regulatory networks of core aging-associated biological processes of SAT were also constructed. Based on WGCNA, we performed differential co-expression analysis to unveil the differences in aging-related co-expression patterns between obese and non-obese females and determined that obesity could be an important accelerating factor in aging processes. Our work provides a landscape of SAT aging, which could be helpful for further research in fields such as repair and reconstruction as well as aging. Subcutaneous adipose tissue (SAT), an ideal autologous soft tissue filler with no immune rejection , plays aSubstantial evidence has proved that obesity could considerably accelerate aging and affect cellular and molecular processes in a fashion resembling aging ,11. The SAT could experience the specific aging-related variation during the systematic aging process. We aim to describe the changes of the composition and structure of SAT in different age groups through the scanning electron microscopy. More importantly, comprehensive integrative analysis of multi-omics data of mRNA, miRNA, and methylation data of SAT were performed to unveil the biological changes, key molecules and potential influence factor (obesity) in SAT aging. Our work would systematically expound the aging-related variation of composition, structure, and biological characteristics of SAT, which provide a better understanding of specific disorders and reduced clinical application in tissue reconstruction associated with SAT aging. Our research discussed the potential of SAT on exploring the connection between obesity and human aging.SATs were obtained from patient abdominal flaps that were to be discarded at Xijing Hospital . Three biological repeats in each group were included for SEM and their clinic information was shown in Table S1. Informed consent regarding the purpose and procedure of the study was completed from volunteer patients. This study was approved by the Ethics Committee of Xijing Hospital, Fourth Military Medical University. The adipose tissue was washed with ice-cold PBS thrice. The SAT were fixed with 2% glutaraldehyde and then 1% osmium tetroxide for 1 hour. SEM at Servicebio Co., Ltd  was used to observe the morphology of SAT. Diameters and cell numbers of adipocytes were quantified using ImageJ software and analyzed using Prism 8. Blinding measures for the collagen morphology were subjective and the 5 observers after training were included. The evaluation parameters and corresponding scores were listed in the Table S2, and the obtained score from observers were averaged for analysis.The raw sequencing data of mRNA (GSE25401), miRNA (GSE25470), and methylation (GSE24884) datasets were downloaded from GEO. On basis of the annotation of clinical information through the datasets, the involved 37 female in our study were grouped by age  and obesity status (non-obese and obese) as presented in Table S3 and S4. The original research which was the source of our sequencing and clinical data, defined that BMI>30 kg/m2 should be considered as obesity, and the BMI of those obese population is 40.9 ± 1.3 kg/m2 and 24.1 ± 1.8 kg/m2 for non-obese population . All samclusterProfiler package [Aging spectrums were constructed on the basis of differentially expressed mRNAs, miRNAs, and methylated genes. The probe IDs in microarray expression profile were converted to gene symbol through matching the Hugo gene symbol. When calculating the gene expression levels, probes belong to more than one gene were excluded and if multiple probes were marked corresponding to one gene, only the probe had highest signal strength among all samples on average was reserved. The expression levels of microRNAs were directly exported from miRNA microarray data. HumanMethylation27k BeadChip was used for identifying the DNA methylation level. Each gene was mostly corresponding to two methylation locus, and methylation levels were defined by averaging the testing values of those two methylation locus. Gene Set Enrichment Analysis (GSEA) and Gene Ontology (GO) enrichment analyses of the selected differentially expressed genes (DEGs) in different groups were performed to identify the aging-related biological processes of SAT using the  package ,16. Prot package . Network package .DEGs between various age groups of obese and non-obese samples were united and defined as the candidate aging-related DEGs of SAT for further constructing a scale-free gene co-expression network. Aging-related genes were grouped into modules via hierarchical average linkage clustering. The signed correlation method was used to transform the correlation coefficient of co-expression between nodes using the expression adjacency (between nodes) = (1+ correlation)/2, which weakened negative correlations and enhanced positive ones. The soft thresholding power (β) was set to 14 to generate an appropriate scale-free co-expression network. Clustering genes hierarchically used the dynamic tree cut method with the dissimilarity matrix (1-TOM) . The minTo quantify the differences in aging-related co-expression patterns in SAT-aging between non-obese and obese females, we performed differential co-expression (DiffCoEx) analysis based on WGCNA . AdjacenTM II . Primers for qPCR are listed in SATs from 18 volunteers were used for RT-qPCR analysis, which were divided into 3 age groups and each group include 6 females. The clinic information of the volunteers was shown in Table S1. Total RNA was extracted using the TRIzol reagent and reverse transcription (RT) was performed using PrimeScript™ RT Master Mix . Finally, qPCR was performed using TB Green® Premix Ex Taqlimma package was used to identify differentially methylated genes (DMGs) with differential mean methylation levels ≥5% between groups [WGCNA package was used for co-expression network analysis [P ≤ 0.05 was set as the cutoff threshold.Most statistical analyses were performed using R 4.0.5. The RankProd package was used to identify DEGs and DEMs between groups , and limn groups . Spearmaanalysis . DEGs, Danalysis . P ≤ 0.0Our findings could be divided into three parts. First, the compositional and structural changes of SAT in aging process could be qualitatively or quantitatively evaluated with SEM. Then, WGCNA method was performed to identify aging-associated DEGs, their co-expression patterns, conserved functional genes and core biological variation. Integrating the multi-omics aging profiles, the multi-omics interaction relationships of the SAT-aging-related core BP were demonstrated clearly for non-obese female. Finally, DiffCoEx analysis based on WGCNA could unveil the difference between non-obese and obese on influence for the SAT aging process in each age group. Therefore, the landscape of the SAT aging could be detailed on multiple levels.As shown by SEM, adipocytes and collagen morphology in SAT varied morphologically during aging . SAT agiThe multi-omics expression profiling data from 37 females in Table S3 demonstrated significant age-dependent variations in mRNA, miRNA, and methylated genes during SAT aging. 1860 DEGs, 56 DEMs, and 332 DMGs were detected between the age groups in non-obese cases, and 1623 DEGs, 95 DEMs, and 348 DMGSs were detected in obese cases . FurtherAmong 4560 aging-related candidate genes of SAT presented in Figure S3A, 11 aging-related co-expression modules including 1754 DEGs were identified in Table S2, which were considered closely associated with the aging process via hierarchical average linkage clustering , which iOGT significantly increased and SUPV3L1 declined with aging (p < 0.05). ARPC1B mRNA expression virtually decreased in Middle-aged and Elderly groups  transferase (OGT), Suv3 like RNA helicase (SUPV3L1), and actin-related protein 2/3 complex subunit 1B (ARPC1B) that could be considered conserved potentially functional genes between both populations. This was validated via RT-qPCR and the three genes showed a consistent trend in SAT aging. RT-qPCR analysis showed that the mRNA level of DiffCoEx analysis was performed to explore the differences in the gene expression profiles of SAT aging between obese and non-obese females. Of the 4560 aging-related genes, 539 DEGs were assigned to one of seven modules with different co-expression patterns in different aging stages between obese and non-obese females . We founSAT is a type of widely distributed adipose tissue in human body. However, to our knowledge, no systematic work has been performed to expound the aging-related characteristics of SAT. Thus, through exploring the alteration patterns of SAT during aging, some tough clinical problems and phenotypes could be potentially explained in the future.As the main filling material under the skin layer, SAT undergoes significant phenotypic variations in composition and structure and brings about obvious changes in appearance during aging. Most researches confirmed that small adipocytes diameter between 7 and 20 μm were considered and the diameter of those mature adipocytes were usually >60 μm . ConsistOn the basis of the aging spectra of SAT along with multi-omics data, we found that methylated genes had little influence on the expression of aging-related DEGs during SAT aging in obese and non-obese females. Combining the WGCNA with the enrichment analysis, 195 core DEGs were reserved in our focused aging-associated biological regulatory subnetworks. Results implied that the immune status of SAT was distinct in different age groups. The changes of immune and inflammatory status might partly attribute to apoptosis and decreased activation of lymphocytes and leukocytes, and increased inflammatory cytokines and cells in middle-aged group and decreased activation of lymphocytes and leukocytes and immunity in Elderly. The infiltration of inflammatory cytokine and cells especially macrophages, were reported to promote the development of severe fibrosis and contribute to a higher risk of formation of hypertrophic scars during the wound healing process . TNF proShifts in the metabolic gene clusters and metabolic patterns were firstly demonstrated in our work. A transition of dominant metabolism from lipid metabolism to carbohydrate metabolism was presented from Middle age to Elderly. Increased lipid and fatty acid biosynthesis as well as reduced lipid transport in Middle age might synergistically contribute to excessive deposition of abdominal SAT. Whereas upregulated carbohydrate metabolism was related to the local tissue energy supply mode in elderly that the shift in substrate oxidation was caused by age-related changes in skeletal muscle respiratory capacity . FurtherDiffCoEx analysis implied that obesity is a potential key factor that affects the SAT aging process and might be related to many adverse events in tissue repair and reconstruction clinically . By compSUPV3L1, OGT, and ARPC1B were identified as the conserved aging-related functional genes for both obese as well as non-obese females in our research, and they were verified to be differentially expressed by an RT-qPCR assay. In previous studies, disruption of SUPV3L1 displayed growth retardation and aging phenotypes, including loss of adipose tissue mass and atrophy of the dermis and SAT, which also was a potential marker of aging . ARPC1B Compared with SAT aging process in non-obese females, aging-associated metabolic variations were less obvious for obese females according to DiffCoEx analysis. With accumulation of SAT in Middle Age, the up-regulated cholesterol metabolism and lipid catabolic process for non-obese females could partly counteract the excessive SAT to reach a healthy and stable state. For Elder, aging process of SAT was accompanied with the down-regulated ATP metabolic pathways due to the cellular senescence, the increased carbohydrate-related metabolism, such as glycolytic fermentation, might be one of the compensatory mechanisms for ATP supply among non-obese elders. Besides, increased adipose tissue-derived fibroblast growth factor and decreased FGF19 and mitochondrial gene expression were especially detected in SAT aging for obese females, which were reported associated with aging-related variation in endocrine and metabolism regionally . MeanwhiPrevious studies demonstrated that systemic aging was accompanied by the alteration of immune status, inflammation, and angiogenesis ,46, partOur study has the following limitations. First, all the multi-omics expression profiling data used were obtained from GEO database. Given that no research fully matched our direction of exploration, only partial sections were selected for further exploration, so no other available datasets could be obtained for further validation. Second, sampling was limited to abdominal SAT and all the samples in our research were obtained from females. Third, we did not uncover strong evidence that genes in our focused biological processes were regulated by methylation, and only the interaction network between genes and miRNAs was demonstrated. Despite these limitations, the reliable data sources, appropriate statistical models, and experimental verification ensure the reliability of our conclusions.The morphology and quantities of collagen and adipocytes were significantly influenced by aging of SAT. The aging-related DEGs identified by WGCNA contributed to the variations in some specific biological processes related to immune and inflammatory states, mitochondria, and lipid and carbohydrate metabolism. SUPV3L1, OGT, and ARPC1B were then verified potentially conserved target DEGs of SAT aging. DiffCoEx analysis revealed that obesity could be regarded as an accelerating factor for SAT aging with different variation patterns. Above all, aging-related variations of composition, structure, biological processes, multi-omics regulatory networks, and core conserved genes of SAT were expounded comprehensively for the first time.Click here for additional data file.


Initial reports from the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic described children as being less susceptible to coronavirus disease (COVID-19) than adults. Later on, a severe and novel pediatric disorder termed multisystem inflammatory syndrome in children (MIS-C) emerged. Pediatric patients with SARS-CoV-2 are at risk for critical illness with severe pulmonary COVID-19 and MIS-C. Both are described as two distinct conditions, and the differentiation between them was the scope of many studies. In this report from Egypt, we will describe two unique pediatric cases presented by combined manifestations of severe pulmonary COVID-19 and MIS-C.Two patients presented with severe pulmonary COVID-19 evident by pulmonary symptoms, signs, and advanced CO-RADS stage in lung CT were simultaneously fulfilling the clinical criteria of MIS-C including fever, multi-system affection, increased inflammatory markers in addition to the proved COVID-19 by positive serologic tests for SARS-CoV-2 but PCR was negative. Both patients responded well to immune-modulation therapy by IVIG and steroids and discharged well under closed follow-up.Although it is debatable to present simultaneously, MIS-C should be considered in patients presenting with typical clinical findings and concerns for pulmonary COVID-19 once the criteria for MIS-C diagnosis is fulfilled. Starting treatment without delay can favor better prognosis. When the COVID-19 epidemic began in China in late December 2019, pediatric initial reports described an asymptomatic or milder illness in children. In March 2020, the World Health Organization (WHO) declared COVID-19 as pandemic. However, since April 2020, a growing number of reports from different countries have described a severe inflammatory syndrome affecting small number of children . This syMIS-C is defined by clinically severe illness requiring hospitalization with fever, elevated inflammatory marker, and multisystem organ dysfunction in the setting of recent proven or probable COVID-19 infection, and in the absence of an alternative likely explanation . Some ofMIS-C appears to be rare but severe, potentially fatal if unrecognized and untreated, it seems to be the most frequent presentation among critically ill children with SARS-CoV-2 infection but may also develop weeks after a mild or asymptomatic SARS-CoV-2 infection. MIS-C patients are older and usually healthy before. They show a higher prevalence of gastrointestinal symptoms and shock and are more likely to receive vasoactive drugs and immunomodulators and less likely to need mechanical ventilation than non-MIS-C patients .It is unknown if the processes that mediate MIS-C are similar to the processes that lead to life-threatening respiratory failure and shock with COVID-19 . Acute CIn this report from Egypt, we describe two unique pediatric patients presented by combined manifestations of severe pulmonary COVID-19 and MIS-C. Written informed consents were obtained from their parents before data collection.A 10-years-old previously healthy female patient presented to the emergency room (ER) in February 2021 with hypotension and shock. She reported previous 5 days of acute high grade fever and dry cough followed by gastrointestinal symptoms in the form of frequent vomiting and abdominal pain. She had no recent travel but her grandfather was ventilated because of severe COVID-19 pneumonia. She was not on any chronic medications and had no known allergies. She was treated by paracetamol and cough sedatives over the prior 5 days for symptomatic relief.On presentation, she appeared ill and dyspneic. She was oriented but agitated, febrile (temperature was 39 °C), hypotensive (blood pressure was 70/40 mmHg), and hypoxemic (oxygen saturation on room air was 85%). She had tachypnea and tachycardia. By auscultating the chest, there was diminished air entry and bilateral crepitation. Abdominal examination revealed nothing of note apart from tenderness all through.Laboratory work-up initially was notable for leukocytosis and lymphopenia with high neutrophil/lymphocyte ratio (NLR). Different inflammatory markers were increased: CRP, ferritin, and LDH. Procalcitonin was also high in spite that both blood and sputum cultures showed no growth. Also, there was documented hyper-coagulable state as D-dimer was more than eight folds the normal value, but Doppler ultrasound studies were negative for thrombi, cardiac affection was evident by laboratory results such as serum troponin and CPK were elevated. All the laboratory results are illustrated in Table SARS-CoV-2 PCR from nasopharyngeal swab was negative but SARS-CoV-2 IgG and IgM from serum were positive. Chest CT without contrast showed picture of typical COVID-19 affection in the form of large bilateral pulmonary predominantly posterior sub-pleural diffuse areas of ground-glass opacities and consolidations with related accentuated vascularity and septal thickening (CO-RADS 5) .The patient was admitted to the intensive care unit (ICU) for hypotension and shock, with diagnosis of pulmonary COVID-19 and concern for possible MIS-C according to the case definition of CDC  due to gThe patient maintained normal oxygenation on high flow nasal cannula with prepress valve to give oxygen at a rate of 20 L/min. She also received inotropes in the form of dopamine till blood pressure was raised enough to establish central line then she was shifted to noradrenalin and milrinone. In spite of being shocked, we have to add furosemide cautiously to decrease the preload—evidenced by engorged IVC—on the poorly contracting heart, in addition to fluid supplementation guided by monitored cardiac function by ECHO, central venous pressure, and urine output. Antibiotics were given to control infection until results of cultures appeared.As we consider the diagnosis of MIS-C, treatment with intravenous immunoglobulin (IVIG) was started at a dose of 2 g/kg, but the dose was divided and given on 2 days as the patient’s impaired cardiac function would not tolerate the volume overload of the single infusion. Simultaneously, the patient started pulse methyleprednisolone doses (30 mg/k/day) on three successive days followed by 2 mg/kg/day on two divided doses.Fever declined 36 h after starting immunomodulating therapy, but she developed severe headache which is documented complication of IVIG therapy. We took in our consideration that this patient had high D-dimer which indicates tendency for thrombosis, so brain MRA was urgently done to exclude cerebral thrombosis and it was free and the headache improved by analgesics within 2 days. Moreover, due to the severe dyspnea, tachypnea, and tachycardia at day of presentation, pulmonary CT angiography was performed to exclude pulmonary embolism. As there was no clinical or radiologic evidence of thrombosis present; a prophylactic dose of subcutaneous low molecular weight heparin (0.5 mg/kg/12 h) was started.Her leukocytosis and lymphopenia began to downtrend on hospital day 3, and the elevated inflammatory markers also started to decline , dyspneic, mildly dehydrated, hypotensive, and in postictal confusion. By examination, she had tachypnea and the chest auscultation revealed diminished air entry and bilateral crepitation. Apart from tachycardia, there was no abnormality on cardiac examination. The patient was admitted to PICU and managed at first as meningitis, there was initial leukocytosis with shift to the left, normocytic normochromic anemia, and high CRP but the CSF examination and brain MRI were normal in absence of meningeal irritation signs. By laboratory follow-up, on the third day of admission, TLC started to decrease and showed lymphopenia, with continuing elevation of inflammatory markers: CRP and LDH in addition to CPK and CKMP levels. At that time, the patient was still feverish, dyspnea decreased on oxygen by nasal catheter which gave oxygen at a rate of 6 L/min, still tachypneic but no more convulsions. Her initial pulmonary CT revealed picture suggestive of COVID-19 infection; there were bilateral multiple pulmonary multi-lobar areas of ground-glass opacities predominantly peripheral with sub-pleural posterior consolidations with related accentuated vascularity and air space pattern involving all both lung lobes (CO-RADS 4) Fig.  while caThis patient fulfilled the criteria of MIS-C according to the CDC case definition including fever in the involved age with severe illness indicated ICU admission affecting four systems with rising inflammatory markers and evidence of COVID-19 diagnosis, so IVIG was given divided on 2 days because of impaired cardiac function in addition to low dose methyleprednisolone (2 mg/kg/day) and the fever subsided for 24 h after the first dose of IVIG. Recurring of fever after one afebrile day along with rising levels of inflammatory markers  (described in Figs.  and 5 (dIn our two patients, the manifestations of both conditions, COVID-19 and MIS-C came simultaneously, but both had negative RT-PCR from nasopharyngeal swab while tested COVID-19 positive by serology in contradictory to class 2 described by CDC where nasopharyngeal swab PCR reported positive . CO-RADSSo, we have two unique cases different from the classic severe pulmonary COVID-19, classic MIS-C, and even different from the CDC described class of overlap between them.The outcome of both patients was generally favorable; they were discharged from hospital within 10 days with follow-up at the COVID-19 outpatient clinic.Although it is debatable to present simultaneously, MIS-C should be considered in patients presenting with typical clinical findings and concerns for pulmonary COVID-19 once the criteria for MIS-C diagnosis is fulfilled. Starting treatment without delay can favor better prognosis.