Using the NM3 respiratory monitoring device, expired minute ventilation and volumetric capnography were measured. Arterial blood gas was obtained for each positive end-expiratory pressure level to compare the effects of positive end-expiratory pressure on carbon dioxide.Compressions given during cardiopulmonary resuscitation generate small, ineffective passive ventilations through oscillating waves. Positive end-expiratory pressure increases the volume of these passive ventilations; however, its effect on passive ventilation is unknown. Our objective was to determine if increasing positive end-expiratory pressure during cardiopulmonary resuscitation increases passive ventilation generated by compressions to a clinically significant point. This study was conducted on 13 Landrace-Yorkshire pigs. After inducing cardiac arrest with bupivacaine, cardiopulmonary resuscitation was performed with a LUCAS 3.1. During cardiopulmonary resuscitation, pigs were ventilated at a positive end-expiratory pressure of 0, 5, 10, 15, 20 cmH2O increased the mean (SEM) expired minute ventilation from 6.33 (0.04) to 7.33 (0.04) mL/min. With the 5-cmH2O incremental increases in positive end-expiratory pressure from 0 to 20 cmH2O, volumetric capnography increased from a mean (SEM) of 94.19 (0.78) to 115.18 (0.8) mL/min, except for 15 cmH2O, which showed greater carbon dioxide exhalation with volumetric capnography compared with 20 cmH2O. PCO2 declined significantly as positive end-expiratory pressure was increased from 0 to 20 cmH2O.Increasing positive end-expiratory pressure from 0 to 20 cmHWhen increasing positive end-expiratory pressure from 0 to 20, the contribution to overall ventilation from gas oscillations generated by the compressions became more significant, and may even lead to hypocapnia, especially when using positive end-expiratory pressures between 15 and 20. Although a brief period of compression-only cardiopulmonary resuscitation (CPR) is currently recommended as an acceptable initial approach for an out-of-hospital cardiac arrest , studies2O on ventilation, carbon dioxide elimination, and arterial carbon dioxide partial pressure during CPR.Prior studies show that the changes in intrathoracic pressure generated by the chest compression and recoil of the thorax during CPR will result in some gas exchange. However, the volume of air mobilized through chest compressions alone is less than the physiologic dead space, and is therefore inadequate for ventilation and carbon dioxide removal, especially after the first few minutes of compressions –12. Earln = 13 pigs). The cohort of animals used in this study was part of a larger study, which analyzed the effect of PEEP on cardiac output during CPR ), and PCaO2 between PEEP levels assuming an autoregressive order [AR(1)] correlation structure among within-pig repeated measures. Tukey’s method of multiple comparison was used to compare the mean between each pair of PEEP levels. Model assumptions were checked before analysis. Time delay was utilized (delay from the first entry of the first PEEP level applied to each pig) as a repeated factor in the model to account for the heterogeneity of the allocation schedule. All tests were two-tailed with an overall level of significance of 0.05. Statistical software SAS, version 9.4  was used for data analysis.A mixed effects model with repeated measures analysis of variance was used to compare the mean of each expired minute ventilation (mL/min), volumetric capnography (VCO2), bicarbonate, and lactate, respectively) over the different PEEP levels. A first-order autoregressive (AR1) correlation structure was used to account for the repeated measures of each pig for random sequence of PEEP levels over time. The least significant difference was used to compare pairwise mean differences between PEEP levels.A mixed effects model with repeated measures analysis of variance was used to compare the mean outcomes for end-tidal carbon dioxide, pH, partial pressure of arterial carbon dioxide  of 39.02 (2.59) mmHg at a PEEP of 0, to 27.67 (2.59) mmHg at a PEEP of 20.Table 2, the change in PEEP from 0 to 10 cmH2O generally had a greater effect than going from 10 to 20 cmH2O. In following the volumetric capnography, all increases in PEEP provided a significantly greater clearance of carbon dioxide, other than an increase from 5 to 10 cmH2O.Table 2, bicarbonate, and Lactate over the different PEEP levels, only pH (p = 0.05) and PaCO2 (p = 0.001) showed significant difference in overall mean comparisons among PEEP levels.As shown in Table 2O. This study assumed that because ventilator settings (except PEEP) were constant and the quality of the compressions was consistent with the use of the LUCAS 3.1 for compressions, differences in ventilation between the PEEP levels would have been due to the oscillations of gas flow produced by the chest compressions, with the differences due to the effect of the different PEEP levels on these oscillations. In addition, PaCO2 was measured for each PEEP level to determine the overall effect that PEEP had on the measured carbon dioxide in the arterial blood gas.This current study was designed to determine whether escalating levels of PEEP can optimize carbon dioxide clearance by optimizing the gas flow and minute ventilation that occurs during chest compressions as well as mitigating the effect of airway closure on the delivered breaths from reaching the alveoli. The studies mentioned earlier demonstrate that PEEP can help facilitate alveolar ventilation during compressions; however, there are no studies looking at the optimal PEEP for alveolar ventilation resulting from the oscillations of gas that occur from the pressure changes due to the compressions during CPR. Thus, while keeping the ventilator rate the same for all PEEP levels, this study looked at expired minute ventilation and volumetric carbon dioxide as a measure of carbon dioxide elimination. This approach allowed us to evaluate the effect of PEEP on ventilation, both through improvements in ventilation that could be attributed to gas exchange due to oscillations of gas flow produced by the compressions, as well as maintenance of airway patency due to PEEP. The fact that compressions were causing these oscillations of gas flow, which enhanced carbon dioxide removal as PEEP was increased, was clearly indicated on the end-tidal carbon dioxide tracing, as seen in the representative tracing in Fig. 2O, the minute ventilation as well as volumetric capnography increased significantly, with the exception of a PEEP of 15 cmH2O, during which the volumetric capnography was greater than for a PEEP of 20 cmH2O. In evaluating the effect that PEEP had on the arterial blood gas PaCO2, as the PEEP was increased from 0 to 20 cmH2O, there was a significant decrease in PaCO2.The results of this study demonstrated that as PEEP was increased from 0 to 20 cmH2O had a more significant effect than increasing the PEEP from 10 to 20 cmH2O. However, it is important to note that, when ventilations were achieving adequate carbon dioxide clearance at lower PEEP levels, once PEEP was greater than 10 cmH2O, the PaCO2 was fairly low, in the 20s, all while keeping the compression and the set minute ventilation exactly the same. This finding would suggest, based on the results of this porcine model of CPR, that in a case where lung compliance is high and excessive PEEP is not needed for oxygenation as in our current model, it may be best to avoid higher PEEP levels during CPR, as higher PEEP levels in this current model of CPR even led to hyperventilation and hypocapnia. Avoiding hyperventilation is an important element of high-quality CPR. Excessive ventilation and associated hypocapnia results in respiratory alkalosis, inducing cerebral vasoconstriction further limiting oxygen delivery to the brain, which has already likely experienced significant hypoxia from the cardiac arrest no-flow state. Alkalosis also shifts the oxyhemoglobin curve to the left, possibly reducing oxygen off-loading to tissues [2O when lung compliance is high, are in line with results from another study by our laboratory, which demonstrate that in terms of oxygen delivery during CPR, the optimal PEEP level was a PEEP of 5 cmH2O, which optimized both oxygenation and cardiac output to achieve the best oxygen delivery [When looking at the pairwise comparison of the different PEEP levels for all three variables, increasing the PEEP from 0 to 10 cmH tissues , 22–24. delivery .Whether the change in intrathoracic pressure generated by the compression and decompression of CPR can produce effective alveolar ventilation remains controversial. Chest compressions themselves can result in gas movement into the airway through the generation of subatmospheric pressures in the airway during the recoil phase of chest compressions as previously reported –12, 25. Additionally, CPR leads to significant alveolar derecruitment and a decrease in pulmonary compliance , 13, 31.During compression-only CPR, sustained levels of end-tidal carbon dioxide are present in most subjects. However, despite the fact that the tidal volumes generated from the compression are approximately only 27% of the total dead space, alveolar gas exchange is occurring during compressions . The que2O. In this regard, PEEP allowed some degree of ventilation to occur with the oscillations of airflow generated by the change in intrathoracic pressure associated with chest compressions and decompressions [2O) were able to maintain the airway patency, allowing the pressure gradient from the compression/recoil phases of CPR to reach the upper airway, improving effective alveolar ventilations caused by the compressions [When the airway pressure is adequate to overcome the closing pressure, airway patency permits transmission of thoracic pressure at the airway opening and may allow some ventilation from chest compression . Studiesressions , 14. Theressions .Thus, although we cannot prove from our study results that the increased carbon dioxide clearance seen with the application of PEEP is due to reversing distal airway collapse during CPR, our results (enhanced clearance of carbon dioxide with the use of PEEP during CPR) are consistent with other studies. As suggested in multiple other studies, the proposed mechanism for this effect is reversal of distal airway collapse during the compression phase of CPR , 26–30. In addition to the impact this improved ventilation may have on resuscitation attempts, if carbon dioxide clearance is falsely decreased due to airway closure, end-tidal carbon dioxide (a surrogate marker for metabolic activity during CPR) may be falsely decreased. Because observational studies have shown that an end-tidal carbon dioxide less than 10 mmHg for more than 20 min during CPR can predict mortality with a positive and negative predictive value of 100% , this ef2O to avoid alveolar collapse prior to the implementation of the study protocol. This practice differs from that of typical cardiac arrest subjects who are not receiving PEEP when they go into cardiac arrest. We feel that these limitations do not detract from the results of this study, as the goal of the study was to look at the impact PEEP has on ventilation, carbon dioxide elimination, and arterial carbon dioxide partial pressure during CPR, which this study certainly does. In addition, the conclusions reached in this study were based on the cumulative effect of the different PEEP levels over a 5-min period on the gas exchange occurring from the chest compressions that was superimposed on the ventilator-delivered breaths. Due to the rapid pace of and small volumes of the gas movement that were generated by the chest compressions, the tidal volumes of these individual breaths were unable to be measured directly. Finally, we do acknowledge that PaCO2 potentially can be lowered by a decrease in cardiac output that is seen as PEEP is increased as opposed to reflecting an increase in ventilation, as presented in this study. However, as seen in Table 2 parallels the increase in minute ventilation seen as PEEP is increased. Also, although it may be true that a decrease in cardiac output can result in decreased PaCO2, we feel that the value of including the volumetric capnography in this study is precisely that, to demonstrate that the decline in PaCO2 is related to the increased minute ventilation as opposed to a decrease in cardiac output. Given that volumetric capnography is a reflection of minute ventilation and exhaled carbon dioxide, if the decrease in PaCO2 was due to a decrease in cardiac output, one would expect the volumetric capnography numbers to decrease as PEEP was increased. Instead, we see that, other than for PEEP of 15 and 20 cmH2O, which show a slightly higher number for the volumetric capnography for PEEP of 15 compared with 20 cmH2O, the volumetric capnography does not follow the trend of the minute ventilation, increasing as PEEP is increased. (Perhaps the higher volumetric capnography value for PEEP of 15 compared with 20 cmH2O can be explained by the greater decrease in cardiac output seen at a PEEP of 20 compared with 15 cmH2O, as was demonstrated previously by this group [Finally, as in most model systems, this study has a number of limitations. Firstly, most CPR studies evaluating the physiology of CPR are done on animals or other non-human models. Although the porcine model is commonly used as a model for cardiac arrest because their physiology approximates that of humans , clearlyis group .2O, the contribution to overall ventilation from these gas oscillations generated by the compressions became more significant, and could potentially lead to a hypocapnia, especially when PEEP between 15 and 20 cmH2O was used in lungs with normal compliance.It is a well-known phenomenon that the intrathoracic pressure changes that occur during the compression and decompression phases of CPR produce a tidal volume that generally does not contribute significantly to gas exchange during CPR because these tidal volumes are less than the anatomical dead space. This study demonstrated that as PEEP is increased from 0 to 20 cmH


Women in South Africa living with HIV who use alcohol may not adhere to ART, affecting the country’s 90-90-90 targets. The Women’s Health CoOp (WHC), a woman-focused HIV intervention, has shown efficacy in numerous trials with key populations of women in South Africa who use alcohol and drugs. In a hybrid implementation effectiveness study, the WHC was implemented in usual care clinics by healthcare providers in a modified stepped-wedge design. We present the outcomes of alcohol use and ART adherence with 480 women, with a 95% 6-month follow-up rate across 4 implementation cycles. Compared with the first cycle, women in the fourth cycle were significantly less likely  to report alcohol use disorder risk and were 4 times more likely  to report ART adherence at 6-month follow-up. Overall, acceptability and satisfaction were extremely high. The WHC intervention was successful in reaching key populations of women to reduce alcohol use and increase ART adherence, which is essential for South Africa to reach the 90-90-90 goals. In South Africa, alcohol use is a major public health concern that intersects with high HIV rates among women of reproductive age, with approximately 17% of women reporting hazardous or harmful drinking ) . AdditioWithin the HIV treatment cascade, an estimated 64% of women living with HIV are on antiretroviral therapy (ART) and among these women, 58% are virally suppressed ; increasAccess to substance use treatment is limited in South Africa, especially for women living in economically disadvantaged communities , 16. In Efficacious interventions that address the intersection of HIV and alcohol use may help improve HIV treatment adherence , 22. TheThis intervention was developed in the US for African American women who use substances, and the original Women’s CoOp was found to be a top-tier best evidence HIV risk-reduction intervention by the Centers for Disease Control and Prevention review of Best Evidence interventions . The WHCThe original intervention and its adaptations have been implemented in randomized field experiments, with trained research project staff from the local communities delivering the intervention for almost 20 years in South Africa and have demonstrated efficacy , 28, 29.Most successful interventions take an average of 17 years to translate research into practice , 31. ConThis article presents the patient outcomes from an implementation science trial using a modified stepped-wedge study design of the WHC in public health clinics and substance use treatment facilities in Cape Town, South Africa. Specifically, it examines the impact of the intervention on the patient-level outcomes of risk for alcohol use disorder and ART adherence. The appropriateness, acceptability, and feasibility of the WHC in usual care settings have been reported previously .The implementation science trial began in September 2015 and ended in December 2018. Study approvals were granted by the South African Medical Association Research Ethics Committee (SAMAREC); the City of Cape Town: City Health Research Committee; and the RTI International Office of Research Protection Institutional Review Board. This study was registered in Clinical Trials . Information about the study protocol has been reported previously .This study used a modified cluster-randomized stepped-wedge design. In a cluster-randomized stepped-wedge design, each cluster receives the intervention; however the order in which clusters receive the intervention is randomized. This approach overcomes the ethical issues that would arise from a traditional cluster-randomized trial in which participants in clusters assigned to the control arm would not receive an intervention that had already been proven to be efficacious. It also overcomes logistical issues associated with trying to implement an intervention in a large number of clusters simultaneously. These features make the stepped-wedge design particularly well suited for studies assessing the implementation of interventions with established efficacy. A more simplified study design is shown in Fig. In this study, clusters comprised eight clinics—4 HIV/antenatal clinics and 4 substance use treatment rehab clinics—in Cape Town, selected and run by the City of Cape Town Health Department. The clinics were each embedded within clinic compounds that served populations of African descent in historically underserved communities where there is a higher prevalence of HIV. All clinics received the intervention. Randomization was conducted using a SAS computer program to assign clinic pairs matched by geography—one HIV clinic and one substance use treatment clinic—to 1 of 4 cycles. The implementation strategies and the WHC intervention were conducted one cycle at a time—with Cycle 1 occurring for the first 6 months, and each subsequent cycle following until Cycle 4 was complete. Prior to each cycle, the study’s Principal Investigator trained clinic staff at both clinics to administer the WHC intervention.This modified stepped-wedge design recruited 480 women living with HIV. Project staff conducted outreach activities in the communities around the study clinics and in-reach activities within the clinics. Project staff asked interested individuals a set of eligibility questions using a screener. To be eligible, potential participants had to meet the following criteria: be female; be 18 to 45 years of age; report the use of at least one substance—which could include alcohol—at least weekly during the previous 3 months; report unprotected sex (sex without a condom) with a male partner in the past 6 months; had a positive HIV test result from either the participating health clinic or treatment clinic, or a clinic-issued ART appointment card or ART prescription as verification of HIV status; and intend to remain in the area for at least 6 months. Individuals were excluded if they were not HIV-positive or if they were unwilling to participate in alcohol and other drug screening tests. Individuals who met eligibility criteria and were interested in participating were scheduled for an intake appointment (if not the same day) at one of the clinics, where they were rescreened for eligibility and then enrolled if they provided informed consent to participate.If the participant could not attend the baseline appointment on the same day of her screening, she was given a date of her choice for the appointment where she was rescreened for eligibility using the same screening form used previously to verify responses. The intake appointment was conducted in the study clinic. Research staff obtained informed written and signed consent and a signed release for referral for health services and administered a face-to-face baseline interview using a secure tablet with skip patterns and quality-control checks programmed into the interview. The baseline interview included measures of socioeconomic status; sex risk behaviors; alcohol and other drug use; substance use treatment readiness; ART use and barriers to use; and other self-reported clinical outcomes, such as tuberculosis (TB) and symptoms of sexually transmitted infections (STIs). Biological data were obtained from a urine specimen tested for pregnancy and metabolites for recent drug use. Participants also underwent a breathalyzer test for recent alcohol use. Active linkage to ART initiation, antenatal care, and substance use treatment was offered at the end of the appointment. Participants were given R100 for their time and transportation reimbursement. During this appointment, they also were scheduled for their first WHC intervention workshop.A master trainer trained clinic staff after the initial training with the PI at each study clinic on the intervention two weeks prior to implementation launch to practice and role-play competence . These tCore elements of the original intervention on which the WHC is based include (1) role-playing condom use negotiation; (2) developing an individualized action plan to establish concrete risk-reduction steps, such as substance use and sexual risk behavior; (3) accessing HIV testing or making referrals to local service agencies; and (4) distributing risk-reduction materials, such as male and female condoms and lubricants. A formative phase that included focus group discussions with stakeholders and meetings with a Community Collaborative Board (CCB) was conducted to adapt and refine the intervention specifically for women living with HIV .Additional slides were developed around ART, understanding viral load and the importance of “good” adherence. This resulted in a new published intervention curriculum.Participants were followed up 6 months post enrollment to take part in a follow-up appointment involving activities similar to the intake appointment for repeated measures. At this appointment, participants re-consented, participated in a risk behavior survey via computer-assisted personal interviewing, and provided a urine sample for drug screening and a breath scan via a breathalyzer for recent alcohol use. Follow-up appointments were conducted either at the study clinic or at the project field site. Participants were given R150, a certificate of study completion, and transportation at this final appointment.The following patient-level measures were assessed at baseline and 6-month follow-up.ART adherence was derived from the number of participants that self-reported they received ART in the past 6 months. ART adherence was measured using a visual analogue scale (VAS) item with a line that represented adherence ranging from 0% to 100% (0% for no ART taken in the past month to 100% if ART was taken every day in the past month). Participants who reported that they were currently on ART, pointed at a percentage value on the VAS that represented their adherence in the past month. For those who had difficulty in interpreting percentages, staff asked how many days they had taken ART in the past month and their responses were then appropriately converted into percent adherence. These values were dichotomized, with reported values of 85% or greater coded as 1 (adherent) and values less than 85% coded as 0 (not adherent). Participants who were not currently on ART were coded as not adherent.Alcohol use was assessed by self-report and breathalyzer. For self-reported alcohol use, questions about the past month frequency and amount of alcohol consumed were used to calculate/classify risk for alcohol use disorder, using the National Institute on Alcohol Abuse and Alcoholism (NIAAA) definition—for women, consuming 4 or more drinks on any given day and 7 or more drinks per week. Substance use was assessed by self-reported frequency of benzodiazepines, cocaine, methamphetamine, MDMA, marijuana, and methaqualone (Mandrax) use in the past 30 days. Additionally, substance use was assessed by urine drug screen test results.The intervention cycle, which was the order the intervention was administered, was the exposure of interest. Cycles ranged from 1 to 4.Additional baseline covariates included in the propensity score (described below) all show statistically significant differences by cycle. The covariates mainly comprise self-report variables, but also two biological drug screening tests for methaqualone (Mandrax) and benzodiazepine. The remaining self-report variables span the constructs of substance use, violence, and treatment. Demographic indicators of food insecurity and main partner in the past 6 months were used. Alcohol and other drug use self-report variables include quantity of drinks on an average day and alcohol and other drug use treatment in the past 6 months. Gender-based violence indicators included ever experienced physical abuse and ever being threatened with a weapon. Finally, receiving treatment for any alcohol or other drug use in the past 6 months and awareness of CD4 count also were included. With a cluster randomized design, it is likely that the characteristics are not equivalent/balanced across the cycles .As part of the follow-up survey, intervention satisfaction and acceptability were assessed through a modified version of the Client Satisfaction Questionnaire (CSQ-8) , an 8-itWe began by calculating descriptive statistics at baseline and follow-up for each cycle. We identified nine variables that could plausibly be related to the outcomes and that differed significantly across cycles. We included these nine variables in a multiple logistic regression analysis and saved the predicted probabilities. These predicted probabilities were entered into the outcome models as propensity scores . Cycle 1The study’s CONSORT Diagram is presented in Fig. Table In addition, Table p < 0.001) to report alcohol use disorder risk at 6-month follow-up. The odds were not statistically significant for alcohol use disorder risk for Cycle 2 or Cycle 3 as compared with Cycle 1  to report being at least 85% adherent to ART in the past 6 months compared with women in Cycle 1  out of the possible range of 8 to 32.As the importance of recognizing gender differences in intervention research increases, alcohol use in gender-related behavioral HIV prevention intervention research in the era of ART is essential to understanding intersectional risks and advancement. After numerous previous efficacy trials with the WHC, for this study, the WHC was implemented in usual care settings by trained healthcare staff and found acceptable ; howeverThe goal of the WHC is to educate and offer skills and practice so that women become empowered to take better care of their health by reducing alcohol use and adhering to ART, which will lead to better overall health. This may also have a wider effect, as women are pivotal to their families as important role models for their children and also within their surrounding neighborhoods and communities.Although intervention fidelity was high  and the Differences between the cycles could be reflective of community population differences and the composition of clinic staff implementing the WHC intervention, in addition to the iterative process of the modified stepped-wedge, mixed methods design of learning and refinement from the previous cycle, informing how each cycle and the methods of the intervention can be improved.One strength of the study is that there was minimal attrition at 6-month follow-up, with only 5% of participants not returning for their 6-month follow-up; and the high workshop attendance, with more than 90% of participants attending at least one workshop. We also worked with a diversity of clinic staff to train on the WHC and to have buy-in for the intervention. Also, clinic staff asked to use the intervention for other parts of their program—such as the STI information in a family group—and they wanted other people to be in their groups.Using implementation science methods in usual care settings in South Africa is a learning process involving constraints with staff schedules and crisis management along with other staff demands. Also, space to conduct the intervention can sometimes be a challenge. Additionally, staff attitudes toward their patients can affect outcomes. These data did not address stigma toward patients based on either their HIV status or alcohol use; however, we have noted this need for future studies. We also know that women and staff were different in each cycle and clinic because they represented different communities and we attempted to discern these differences in the analyses. Finally, the follow-up period was brief at only 6 months.Despite these limitations, this study demonstrated important reductions in the risk of alcohol use disorder given the relationship between risky alcohol use and ART adherence. South Africa remains an epicenter of alcohol use and other intersecting risks for women, who continue to bear a disproportionate burden of the HIV epidemic. To achieve the South African and UNAIDS goals for the HIV continuum of care, it is vital to address ART adherence among women living with HIV through a gender-focused lens.Led by the healthcare staff in the usual care settings, the WHC intervention was effective in reducing the risk of alcohol use disorder and increasing ART adherence among a sample of women living in economically disadvantaged communities. Consequently, continued implementation is essential. The impact of the WHC for women must continue to be disseminated back in the community through stakeholders, such as community collaborative boards, partners, and government for continued buy-in.Determining the long-term sustainability of the WHC is a key next research question within these usual care settings. Further implementation of the WHC in additional settings or in open-air tents because of COVID-19 to minimize indoor clinic activities might be the next best solution to remain situated in clinics. Addressing stigma as a structural barrier for accessing treatment also will be an essential next step.


Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection in the Golden Syrian hamster causes lung pathology that resembles human coronavirus disease (COVID-19). However, extrapulmonary pathologies associated with SARS-CoV-2 infection and post-COVID sequelae remain to be understood. Here, we show, using a hamster model, that the early phase of SARS-CoV-2 infection leads to an acute inflammatory response and lung pathologies, while the late phase of infection causes cardiovascular complications (CVCs) characterized by ventricular wall thickening associated with increased ventricular mass/body mass ratio and interstitial coronary fibrosis. Molecular profiling further substantiated our findings of CVC as SARS-CoV-2-infected hamsters showed elevated levels of serum cardiac troponin I, cholesterol, low-density lipoprotein, and long-chain fatty acid triglycerides. Serum metabolomics profiling of SARS-CoV-2-infected hamsters identified N-acetylneuraminate, a functional metabolite found to be associated with CVC, as a metabolic marker was found to be common between SARS-CoV-2-infected hamsters and COVID-19 patients. Together, we propose hamsters as a suitable animal model to study post-COVID sequelae associated with CVC, which could be extended to therapeutic interventions. Symptomatic COVID-19 is typically characterized by symptoms ranging from mild to acute respiratory distress associated with cytokine release syndrome  has infected nearly 0.9% of the total world population with a mortality rate of around 1.96% as of December 2021 . Our data show an acute immune reaction characterized by heightened expression of inflammatory cytokines in the early phase of infection. Strikingly, at the later phase of the infection, the cardiomyocytes of the infected hamsters showed a high viral load with interstitial coronary fibrosis. SARS-CoV-2 infection increased the thickening of ventricular walls and the interventricular septum of the heart, with an increased serum troponin I level. Cardiovascular changes, upon 4 dpi, were found to be associated with an increase in serum triglycerides (TGs), low-density lipoprotein (LDL), cholesterol, and high-density lipoprotein (HDL) while long-chain fatty acids (LCFAs) were found to be increased on 7 dpi. Furthermore, our metabolomics data identified changes in serum metabolites of the infected, as compared to uninfected, hamsters. In the line with data on CVC, we found an increased serum N-acetylneuraminate, a functional metabolite found to be associated with coronary artery disease (CAD) and myocardial injury , in the Previous studies have shown that the interactions of SARS-CoV-2 RBD and a fragment of S1 protein with host ACE2 receptor and NRP-1 were critical for virus entry into epithelial cells . Since S4 or 105 plaque-forming unit (PFU) SARS-CoV-2 showed a gradual decrease in body weight up to 4 dpi, and subsequently recovered. The body weight loss peaked (~10% as compared to uninfected) at 4 dpi when compared to the unchallenged control hamsters  (+ T cells remained the same at 2 dpi between uninfected and SARS-CoV-2-infected hamsters (Lung injury and multiple-organ failure have been attributed to the acute release of inflammatory cytokines caused by SARS-CoV-2 infection in COVID-19 patients . Most of.5-fold) . The frehamsters . To defihamsters . Howeverhamsters . We alsohamsters . Immune hamsters . In addihamsters . These dhamsters .Myocardial injury is marked in around 25% of hospitalized COVID-19 patients, which includes thromboembolic diseases and cases of arrhythmia . The intt-test analysis followed by multiple testing correction as compared to uninfected (0 dpi) control. Volcano plots for 2, 4, 7, and 14 dpi (top to bottom) show significantly modulated lipid molecules in red dots. Interestingly, majority of the significantly modulated lipid molecules were identified at the late phase of infection, that is, 7 or 14 dpi serum samples corroborating with the appearance of CVC in 7 dpi hamster hearts , of which 65 lipids molecules were upregulated while 208 were downregulated as identified by log2 fold change (FC) in volcano plots  correction identified 62 differentially modulated metabolites out of 334 metabolites in hamster serum samples. Out of these 62 differentially modulated metabolites, 50 serum metabolites were found to be upregulated while 12 metabolites were found to be downregulated during infection as compared to uninfected control hamster serum   . Furthersection) . The volnfection . The logabolites . CollectNext, in order to understand the correlation of differentially regulated metabolites identified by comparing infected hamsters with severe and nonsevere COVID-19 patients and healthy serum metabolomic profile, we exploited the metabolomics data recently published by . When coIn summary, we describe here pulmonary, immunological, and cardiovascular pathologies associated with SARS-CoV-2 infection in hamsters, establishing that SARS-CoV-2 infection in hamsters leads to CVCs in the later phase of infection . We furtPulmonary and extrapulmonary clinical and histopathological changes associated with SARS-CoV-2 infection in humans have been described by several groups , p. 19. Viremia is an important pathological characteristic of SARS-CoV-2 infection in humans, and further, SARS-CoV-2 is known to shed viral proteins that remain in circulation in the serum long after the virus is cleared, which provide humoral immunity against subsequent strain-specific infection . The hosEmerging reports suggest that SARS-CoV-2 infection affects the cardiovascular system, which results in inflammation, endothelial activation, and microvascular thrombosis . CVC is In addition to cTnI, we also characterized the serum lipid profile of SARS-CoV-2-infected hamsters. We found a dramatic increase in serum cholesterol, TGs, LDL, and VLDL levels in infected serum samples, which peaked at 4 dpi. Serum lipid molecules are other important soluble mediators that are associated with cardiovascular disease . We founIn addition to lipid profiling, we attempted to evaluate the metabolomic changes occurring in hamsters infected with SARS-CoV-2 as there is no such data available on metabolic profiling in the hamsters. We identified 62 serum metabolites that were significantly modulated upon SARS-CoV-2 infection in hamsters. In order to identify signature metabolic biomarkers of SARS-CoV-2-infected hamsters that is also common for COVID-19 patients irrespective of the stage of infection, disease severity, or demography of infection, we followed a rigorous metabolomics comparative analysis between an already published Bo Shen et al. human database, our hamster study data, and our in-house-generated early and late phase human COVID-19 database. N-acetlyneuraminate was found to be a uniquely upregulated serum metabolic marker that was common across all these datasets. It is noteworthy that increased N-acetylneuraminate has been linked to increased heart disease risk . TogetheAs of now, the hamster model for SARS-CoV-2 has been described as a suitable model to study lung pathologies and their associated clinical parameters . However6–8-week-old female Golden Syrian hamsters were acclimatized in biosafety level-2 (BSL-2) for 1 week and then infected in Animal BSL3 (ABSL-3) institutional facility. The animals were maintained under 12 hr light and dark cycle and fed a standard pellet diet and water ad libitum. All experimental protocols involving the handling of virus cultures and animal infections were approved by RCGM, and the institutional biosafety and IAEC animal ethics committee.SARS-related coronavirus 2, isolate USA-WA1/2020 virus was used as challenge strain and was grown and titrated in Vero E6 cell line grown in Dulbecco’s modified Eagle medium (DMEM) complete media containing 4.5 g/l D-glucose, 100,000 U/l penicillin-streptomycin, 100 mg/l sodium pyruvate, 25 mM HEPES, and 2% FBS. The virus stocks were plaque purified and amplified at THSTI Infectious Disease Research Facility (biosafety level 3 facility) as described previously .4 PFU (100 μl) or 105 PFU (100 μl) of live SARS-CoV-2 or with DMEM mock control inside the ABSL3 facility. The remdesivir group received a subcutaneous (sc) injection of remdesivir at 15 mg/kg body weight 1 day before and 1 day post infection.Infection in hamsters was carried out as previously described . The ham50 determination. A section of the lung and intestine along with other organs was fixed in 10% formalin solution and used for histological studies. The spleen was strained through 40 µm cell strainer with the help of a syringe plunger and used for qPCR and immunophenotyping studies.All infected animals were housed for 14 days, and their body mass was monitored as previously described . Nine anGACCCCAAAATCAGCGAAAT-3′ (forward), 5′-TCTGGTTACTGCCAGTTGAATCTG-3′ (reverse), and 5′-FAM-ACCCCGCATTACGTTTGGTGGACC-BHQ1 -3′ (Probe) detection, and subgenomic RNA copy numbers were estimated by ΔΔCt method. Hypoxanthine-guanine phosphoribosyltransferase (HGPRT) gene was used as an endogenous control for normalization through quantitative RT-PCR. The region of N gene of SARS-CoV-2 starting from 28287 to 29230 was cloned into pGEM-T-Easy vector (Promega). This clone was linearized using SacII enzyme and in vitro transcribed using the SP6 RNA polymerase (Promega). The transcript was purified and used as a template for generating a standard curve to estimate the copy number of SARS-CoV-2 N RNA , and RNA isolation was carried out as per the manufacturer’s protocol. Copy number estimation of SARS-CoV-2 RNA has been described previously . Briefly-2 N RNA .50 determination, 50 µl of homogenized lung supernatant samples were incubated with confluent Vero-E6 cells in 96-well plates as described previously  were homogenized to a final 10% (w/v) suspension in DMEM medium with gentamicin . The tissue samples from 2 to 14 dpi were used to infect Vero E6 cell monolayers in 48 plates as described previously. Virus titers were expressed as TCID50/g of tissue.For TCIDeviously . Briefly2SO4, and the plates were read at 450 nm on a 96-well microtiter plate reader. Sera endpoint titers were calculated as the reciprocal of serum dilution giving OD 450 nm readings higher than the lowest dilution of the placebo or control arm + 2× standard deviations.The binding-antibody response to SARS CoV-2 post infection was measured using an ELISA-based platform as described earlier . The antIL-6 sandwich ELISA for hamster sera samples was performed according to the ELISA kit user manual . Briefly, serum samples (diluted 1:3 times) along with a standard control were added to the precoated wells and then incubated with secondary antibody and streptavidin-HRP. Substrate was then added to each well after washing, and the reaction was stopped by using stop solution. The OD for each well was recorded as 450 nm against a blank control.In the absence of crystal structures of hamster ACE2, the crystal structure of human ACE2 and RBD, PDB ids 6m17 and 6VW1, was considered as templates to generate a robust hamster model through homology modeling . The robRNA was isolated from lung and heart homogenate and spleen samples using Trizol chloroform as previously described . ThereafExcised tissues of animal organs were fixed in 10% formaline solution and processed for paraffin embedding. The paraffin blocks were cut into 3-µm-thick sections and then mounted on silane-coated glass slides. One section from each organ sample was stained with hematoxylin and eosin. Lung, heart, and colon samples were stained with toluidine blue, MT, and mucicarmine stains, respectively. Each stained section was analyzed and captured at ×10 and ×60 magnification. Heart images were also captured at ×1 magnification. Blinded assessment and scoring  of each sample section was performed by a trained pathologist.0.5 million RBC lysed splenocytes were used for intracellular cytokine staining by restimulation with PMA , ionomycin , and monensin  for 6 hr or with RBD protein (50 µg) for 72 hr. Cell surface staining with anti-mouse CD4 (GK1.5) PerCp (BioLegend) was carried out for 20 min in the dark at RT. Intracellular anti-mouse IFN-γ (XMG1.2) (BioLegend) staining was then carried out after fixing the cells in Cytofix solution and permeabilizing with 1X Perm/Wash Buffer using kit  for 20 min in dark at RT. Cells were then washed and acquired on FACS Canto II and were analyzed with FlowJo software (Tree Star) as previously described .Lipid extraction from serum samples was performed as previously described with some modifications . 0.3 ml LipidMatch Flow was utilized with default settings for peak picking (using mzmine), lipid annotation, blank filtration, and combining positive and negative data . ThereafMetabolites were extracted from 100 µl of serum samples by using 100% methanol. Thereafter, the mixture was vortexed for 1 min and kept on ice for protein precipitation. After centrifugation , the supernatant was collected and distributed in two tubes for polar and nonpolar metabolite analysis. The collected supernatants were then dried using a speed vacuum for 20–25 min at RT and stored at –80°C till further analysis. For the reverse phase, metabolites were dissolved in 15% methanol in water (v/v), and for polar metabolite analysis, samples were dissolved in 50% acetonitrile in water (v/v).Orbitrap Fusion mass spectrometer (Thermo Fisher Scientific) coupled with heated electrospray ion source was used for data acquisition. Data acquisition methods have been followed as per published protocols  with minAll LC/MS acquired data has been processed using the Progenesis QI for metabolomics (Waters Corporation) software using default settings. The untargeted workflow of Progenesis QI was used to perform retention time alignment, feature detection, deconvolution, and elemental composition prediction. Metascore plug of Progenesis QI has been used for the in-house library with accurate mass, fragmentation pattern, and retention time for database search. We have also used an online available spectral library for further confirmation of identification. The cutoff for retention time match was 0.5 min, and spectral similarity was more than 30% fragmentation match in Progenesis QI. Peaks that had a coefficient of variation (CV) less than 30% in pool QC samples were kept for the further analysis of data. Additionally, manual verification of each detected feature has been done for the selection of the right peaks.For both metabolomics and lipidomics data, the data was cleaned up to remove zero intensity values. The average calculations were done with the remaining available intensity values. The fold change was calculated as the ratio of average intensity values of cases divided by the average intensity of controls as follows:m is the number of available nonzero intensity values for case, n is the number of available nonzero intensity values for control, Iki is the intensity of ith case sample for condition k, where k ∈ {2D, 4D, 7D, 14D post infection}, and Ij is the intensity of jth control sample.where FC is the fold change, t-test considering similar means of case and control population was used to calculate the p-values. The FC was transformed to log2 FC and a threshold was set to ±2 FC (±1 at log2 FC). The FDR was set at 0.05 for the identified lipids and metabolites. The significance threshold for differential expression was set using simultaneous threshold filters of the log2 FC of ±1 as well as a p-value threshold of ≤0.05. This was followed for both the metabolomics and the lipidomics data. The significantly expressed analytes (metabolites and lipids) are also represented on the volcano plots. For the volcano plots, the p-values were transformed into –log10 p-values for plotting. The significant values as described above were highlighted and labeled on the graph using Tableau (version 2020.3).A two-tailed 10 p-value vs. log2 FC was plotted on the volcano plot. Tableau (version 2020.3) was used to highlight and annotate some of the significant hits (metabolites/lipids). The points above or below ±1 log2 FC  and 1.33 on the –log10 p-value scale  were selected for annotation and were colored red for visual distinction. For the heatmap construction, we used log2 FCs for both the metabolites and lipids heatmaps. Gene-E software (https://software.broadinstitute.org/GENE-E/) was used to create the heatmaps. For the analytes (metabolite/lipid) with zero values in controls but a positive intensity reading value in cases, we considered them as overexpressed vs. control in that particular condition, and used a maximum log2 FC imputed manually. The value chosen for such cases was the maximum value from the data. For setting the color scale, we used blue to represent all values below –4 log2 FC (1/16 FC) and all values above +4 log2 FC were colored red (+16 FC).-logClustVis was used to generate the heatmap for the complete dataset for both metabolites and lipids.Raw values of the metabolites and lipids were taken for all nine samples. The web-based tool ClustVis was used to create the sample PCA plots to check the clustering of biological samples .The data from the article  was downHuman plasma samples were collected according to the recommended guidelines of the Institutional Ethics Committee (Human Research) of THSTI and ESIC Hospital, Faridabad (letter ref no: THS 1.8.1/ (97) dated July 7, 2020). Human blood samples were collected from COVID-19 patients and healthy individuals after the written informed consent. Individuals were enrolled in this study based on the inclusion/exclusion criteria prescribed by the Institutional Ethics Committee (Human Research) of THSTI.In the period between July 7 and September 4, 2020, we enrolled 31 COVID-19 patients that were asymptomatic or mildly symptomatic with COVID-19.g for 25 min, and the plasma was carefully separated and aliquoted and stored at –80°C until further use.Blood samples were collected from RT-PCR-positive COVID-19-infected asymptomatic or mildly symptomatic individuals at 0–4 days post-onset of symptoms (POS) and 14 days POS. The blood samples were collected in Sodium Heparin CPT tubes. Similarly, blood from RT-PCR-negative, healthy individuals was collected in CPT tubes. The tubes were centrifuged at 1500 × 50, and qPCR studies were compared and analyzed using one-way ANOVA or Student’s t-test. Metabolomics and lipidomics analyses were carried out with n = 9 serum samples per group. p<0.05 was considered as statistically significant.All the results were analyzed and plotted using GraphPad Prism 7.0 software. Body mass, lung mass, gene expression, FACS, ELISA, TCID This study is of broad interest since it does provide data on cardiovascular disease in a hamster model of SARS-CoV-2 disease. It also makes interesting novel connections to lipidomic and metabolomic profiles in the context of the complications of a virally induced disease. In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.Decision letter after peer review:[Editors’ note: the authors submitted for reconsideration following the decision after peer review. What follows is the decision letter after the first round of review.]eLife. Your initial submission has been assessed by a Senior Editor in consultation with a member of the Board of Reviewing Editors. Although the work is of interest, we regret to inform you that the findings at this stage are too preliminary for further consideration at eLife.Thank you for choosing to send your work, "Immunological and cardio-vascular pathologies associated with SARS-CoV-2 infection in golden syrian hamster", for consideration at There are many outstanding aspects of this work that the authors are commended for. The model clearly reproduces lung pathology attributed to this diseases and to this animal model, the immunological aspects are outstanding and the lipidomics and metabolomics-based studies are an outstanding and novel aspect, but on their own they do not yet tell a cohesive story or explain the histological findings that the authors need to stress..The main area of weakness, which would have represented the potentially most significant and novel aspect of this work, relates to the cardiac histology findings. The quality of the images does not lend itself to the interpretations currently made and many helpful suggestions have been made by the reviewers that could help the authors pursue this issue and this generate a very high quality contribution to the field that will be of very wide interest. So overall the reviewers feel this work has tremendous potentialReviewer #1:+ T cell responses which are important features of SARS CoV-2 in this model with translational potential if hamsters are used to test therapeutics aimed at preventing long COVID sequelae. However, the pathologic data that is presented to link both the lipidomics and metabolomics to extrapulmonary pathology suggestive of long COVID is weak. This study would greatly benefit from the addition of a treatment group or extended study timeline to support the claim that hamsters model key aspects of long COVID as seen in people. The lack of convincing pathology undermines the authors' model that lipidomic and metabolic changes are supportive of hamsters as a translational model of long COVID.In this study, the authors aim to show that hamsters may be a useful model of SARS CoV-2 long-term sequelae or "long COVID." The authors present useful data on many aspects of acute SARS CoV-2 in hamsters that are not well demonstrated in the literature such as gene expression of major regulators of inflammation, lipidomics, metabolomics, and antigen-specific CD4Overall-there is lack of description on scoring systems for histology. The methods state that pathology assessment was performed by a histologist (?)--pathology should be reviewed by a blinded veterinary pathologist. The data presented to show cardiac hypertrophy, fibrosis, and inflammation is not convincing. The pathology images are very low resolution and the fibrosis and inflammation cannot be observed clearly (if present). Control  and age matched tissue specimens are not shown. Organ weights for spleen and heart are not shown (preferably need to see organ/body weight measurements). Heart weight is the gold standard for cardiac hypertrophy-without this measure cardiac hypertrophy cannot be diagnosed; at a minimum measurement of the interventricular septum should be shown-the images do not convincingly show differences over time.Ln 155: The images of mast cells are not clear-how was this scored (counts/high power field?). Please show control animals (not just time 0).Likewise increased mucin staining in the GI tract is also not convincing. Please show controls over time.Reviewer #2:There are many published studies in which golden Syrian hamsters (GSH) are used as a small animal model for human SARS-CoV-2 infection and associated pathology. In this study, Rizvi, Zaigham Abbas et al. investigated several aspects of SARS-CoV-2 infection of GSHs, with an emphasis on the usefulness of the model to study cardiovascular complications in human COVID-19. From in silico analyses, they confirm with more detail than previous reports that hamster angiotensin converting enzyme-2 (ACE-2) interactions with SARS-CoV-2 spike protein are predicted to be very similar to human ACE-2 interactions, and they show novel data that predict hamster neuropilin-1 (NRP-1) interaction with spike protein are similar to human NRP-1 interactions. A shortcoming of this analysis is lack of biochemical or blocking studies, either in vivo or in vitro to establish of NP is an important receptor for viral infection of hamster cells. The authors histological and gene expression analysis of pulmonary pathology convincingly recapitulates the severe but resolving inflammatory lung disease in the hamsters that has been described in other publications, validating to relevance of the model in their hands before exploring extrapulmonary manifestations. The lung studies here also added some limited new findings that type 2 inflammation with mast cell activation and eotaxin are prominent in the hamster, which has also been implicated in some human studies.+ IFN-γ+ T cell numbers, and splenocyte mRNA expression all demonstrated a vigorous systemic immune/inflammatory responses, with most parameters peaking by day 2 after intranasal infection, and returning to baseline by day 14, while the serum anti-viral IgG titers showed continuous elevation from d2 to d14. Although the interrogation of immunological responses was limited in scope, especially with respect to local tissue responses, and lack of single cell omic analyses, the results are sufficient to establish the similarity of the post infection disease in the authors hands with previous hamster reports and to compare with comparable human parameters, again validating the relevance of looking at metabolic and cardiovascular parameters not previously explored.Analyses of serum antibodies specific for viral RBD, spike and N proteins, spleen size, splenic CD4A major strength of this study is the intent to study cardiovascular complications in the hamster model, and to this end the authors focused on heart histopathology and serum lipids that are known to associate with human cardiovascular disease. The analysis of heart tissue was limited to H&E and Mason Trichome stained sections, to assess morphologic changes in myocardium and intramyocardial blood vessels, myocardial inflammation, and fibrosis. The interpretation that there is myocardial hypertrophy, increased coronary fibrosis, and increased inflammation in infected mice at days 7 and 14 is questionable based on the images shown in Figure 4, which appear to represent one section of one heart for each time point. Quantitative image analyses of the histopathogical parameters, including the hearts from the 9 animals sacrificed at each time point would help is assessing the significance of the findings. The images shown suggest some perivascular fibrosis, even at day 0, and mild perivascular inflammation, although the resolution of the images limits interpretation. Additional data showing cardiac inflammation and myocardial dysfunction by other methods would be helpful, such as transcriptional analysis, immunohistochemistry, or flow-cytometry of cardiac cell suspensions. Each or all could help substantiate the authors conclusions. A relevant but unanswered question is if there is viral infection of cardiac cells in their experimental animals, as myocyte SARS-CoV-2 infection has been reported in a few recent papers. The acute changes in blood cholesterol, lipoproteins, and triglycerides only seen at day 4, shown in Figure 4, are unlikely to have any consequences on long term vascular pathology or any myocardial pathology that may be present at day 7 or 14. Extremely elevated cholesterol levels over many weeks can lead to arterial atherosclerosis in some experimental rodent models, rarely resulting in myocardial ischemic injury, but there is no evidence of atherosclerosis or ischemic myocardial injury in this study.The extensive blood lipidomic and metabolic analyses on the hamsters at different time points after infection, and comparison of with lipid and metabolite changes associated with human COVD-19 patients, is a major strength and novel aspect of this study. The value of these types of data in developing biomarkers of disease severity and risk for long term sequalae to infection remains to be determined, but the findings here help establish the value of the hamster model for such purposes. Nonetheless, these data are descriptive, and the causal connection of changes in any of the lipids or metabolites with putative cardiovascular pathologies is tenuous.Overall, the authors succeeded in showing how the hamster model can be useful to study systemic extra -pulmonic pathologies and systemic metabolic changes associated with SARS-CoV-2 infection. The data shown do not make a compelling case for the hamster model to study long term effects of infection outside the lung, given that the longest time point examined was d14, and the cardiac/vascular data were not robust. Furthermore, most of the cardiovascular complications that have been reported in COVD patients concern thrombosis, yet this report does not show any evidence of thrombotic complications in the hamsters.The possibility that cardiac cells in experimental animals are infected by SARS-CoV-2 should be tested. Recent reports that cardiac myocytes can be infected with SARS-CoV-2, including hamster myocytes :478-492. doi: 10.1016/j.stemcr.2021.02.008.)Cardiac hypertrophy is best assessed by heart weight, and a second best way is to do careful quantitative analysis of left ventricular wall thickness on cross sections from all animals in each group. The photographs of two hearts.Myocardial histopathology must be assessed and reported in quantitative fashion.In Figure S4 the images labelled Kidney are Liver, and vice versa. The kidney images are of poor resolution, and no glomeruli can made out in the d2 section. Sections from d7 and 14 should also be shown.If there are activating anti-hamster CD3 reagents available to activate T cells, that would be a better  way for activating spleen T cells than PMA/ionomyicn.Reviewer #3:In this work the authors examine changes in the lungs and extrapulmonary organs in the setting of SARS-CoV-2 infection using the golden Syrian hamster model, with emphasis on cardiac changes. This is a well-established model to study SARS-CoV-2 infection. The authors also examine changes in splenic inflammatory related molecules, serum lipid molecules and serum metabolites.The major strength of the work is the attempt to perform detailed analyses of the extrapulmonary organs combined with splenic inflammatory changes, metabolomics and lipidomics in the setting of acute SARS-CoV-2 infection in an animal model.The major weakness of this work is that often the histologic changes being reported, particularly in the heart, are not well supported by the data provided. In addition, more rigorous statistical approaches could be employed, and there is no clear mechanism proposed to link the changes in the splenic inflammation, serum lipidomics, and serum metabolomics with the proposed cardiac changes.1. There are numerous studies now on SARS-CoV-2 in the hamster. There should be much better effort relating this work to the published literature, particularly stressing what is novel and what is not. It is not clear if any of the pulmonary studies being reported here are actually novel.2. The authors claim: "Strikingly, SARS-CoV2 infection in the hamster leads to significant interstitial coronary fibrosis on 7 and 14 dpi characterized by thickening of ventricular walls interventricular septum." and later: "The ventricular walls thickening at 7 and 14 dpi with SARS-CoV2 infection in GSH was marked by increased inflammation surrounding the coronary artery and elevated interstitial coronary fibrosis ." However, the cardiac changes being reported are simply not supported by the images provided. Heart weights and wall thicknesses need to be measured and compared across the groups using statistics. Also, cardiac fibrosis and inflammatory cell infiltration should be quantified and comparted across the groups using statistics. Better images showing clear changes correlating with the quantitative analyses would be required.3. The authors state "…we reasoned that cardio-vascular pathologies of SARS-CoV2 in GSH may be related to changes in circulating lipid molecules . Indeed, serum lipid profile showed elevated cholesterol, TGs,HDL, LDL and VLDL levels at 4 dpi ." However, the changes in serum lipids were very transient and unlikely to rapidly cause cardiac fibrosis. There is no clear logical mechanism being present linking the serum lipid levels with the proposed cardiac changes.4. For all of the studies performed including the lipidomics and metabolomic studies the authors simply us a P-value of less than 0.05 as being statistically significant with no adjustment for multiple assessments or consideration of FDR. A more sophisticated statical approach is needed.5. The spleens need to be weighed and the weights compared across the groups using statistics.6. There are frequent statements in the discussion that simply do not match the data shown. For example, the authors state: "Our data show that lung pathologies long persists after the virus clearance from the lung since we observed a high pathological score on 7 dpi." However, the authors own data show that the virus is still present at 7 dpi.7. All figure panels need proper labels, particularly both axes of any plots.8. In Figure S4, the degree of mucin staining in the images does not appear to match that shown in the bar graphs. The scale for the scale bars is not legible. For Panel B the images marked "liver" are not liver, and the images marked "kidney" are not kidney.9. There should be some rational mechanism linking the splenic inflammatory cell changes, lipidomics and metabolomics with the proposed cardiac changes.eLife. Your revised article has been evaluated by Balram Bhargava (Senior Editor) and a Reviewing Editor.Thank you for resubmitting your work entitled "Golden Syrian hamster as a model to study cardiovascular complications associated with SARS-CoV2 infection" for further consideration by This report suggests that the Syrian hamster has the potential to be a good model to examine the metabolic changes following heart injury caused by SARS-CoV-2.The manuscript has been improved but there are some remaining issues that need to be addressed, as outlined below:1) Revise statistical analyses using non-parametric approaches as suggested.2) Minimize or remove connections made to long COVID- the evidence is not strong enough that this is a model.3) Include recent references as suggested.4) Extensive editing of grammar and syntax is absolutely necessary.5) Make MS readouts legible.Reviewer #1:This manuscript nicely highlights SARS CoV-2 effects on the heart in the Syrian hamster model, particularly from the virologic, transcriptional, and metabolic standpoint. The data clearly highlights metabolic changes that support the exploration of using the hamster model for cardiovascular complications of COVID. The data for histopathologic evidence for cardiovascular changes remain unconvincing at the timepoints shown. I believe a longer-term study following hamsters for greater than 14 days would be more biologically relevant for histopathologic changes. Regardless, the biochemical and immunologic data support the use of this model for studying cardiovascular changes.The authors have addressed all the concerns raised. Unfortunately, the histopathologic data, although shown as significant via scoring, is still not biologically convincing. The heart weight to body weight index tracks for infected animals only. The ventricular width should have been done quantitatively and not semi quantitatively (scoring) to be convincing. The appropriate statistical comparison would be heart mass/body mass of infected to uninfected animals at each time point assayed. Also, the semiquantitative scoring of inflammation and fibrosis does not match up with what can be seen by eye. Lastly, the scoring metric for pathology should be clearly outlined in the figure legend . What is the total score possible?Regardless--the cytokine, lipid, and metabolic profiling and viral load data from the heart is very interesting and supports the further exploration of this model for studying cardiovascular effects of COVID.There is still substantial copy editing needed for this manuscript. Many sentences do not make sense and grammar needs substantial improvement prior to publication.Reviewer #2:The authors have studied SARS-CoV-2 infection in golden hamsters. Evidence is provided for viral RNA in the heart and for many alterations in the circulation, discovered by metabolomics and lipidomics, including many that have been linked to cardiovascular disease in humans. The novel aspect of the studies presented are the studies utilizing lipidomics and metabolomics and the abrogation of heart disease after Remdesevir. This is a revised manuscript and most of the concerns of the previous reviewers have been largely addressed. However, there is at least one other relatively recent study that the authors have failed to make note of, (and that was published in July 2021), that has also observed myocardial inflammation and evidence for viral RNA in the heart. So, some of the findings have been independently confirmed and that is viewed as a strength rather than as a limitation. The other study made simple, direct links between the presence of viral RNA in a tissue and the events that lead to cardiac inflammation.The authors have made some important improvements to the first submission especially in terms of better documenting the cardiac involvement in the model.At this point in time changes like the increase in circulating sialic acids are interesting but most of the metabolic and lipidomic changes are best attributed to systemic inflammation as well as to heart damage. Given the time frame, it is difficult to consider these alterations as causal for the heart inflammation seen but these changes could be of relevance when one considers the cardiovascular complications of clinical COVID-19 that fall into the PASC spectrum.The authors should more comprehensively reference earlier papers on the use of hamsters as a model for SARS-CoV-2 including studies in 2020 from Rosenke et al. (PMID 33251966) and Boudewijns et al. (PMID 33203860). However, they should, at least during this submission, have quoted a manuscript showing cardiovascular disease in a similar model that was published in July 2021. This study published about four months ago from Francis and colleagues. (PMID 34265022) should certainly have been discussed because it in part supports the authors' studies and demonstrated inflammation in the heart. In the studies of Francis et al., there was viral RNA in the heart though live virus was not recovered from this organ, and there was evidence provided for Type I interferon dysregulation, eosinophilic myocarditis, inflammatory cytokines, T cell infiltration and activation, though the evidence suggested the absence of TH2 cells and T regs.There remain issues related to the authors' lack of care with syntax and grammar that still need to be taken care of and careful proofreading of the manuscript should be undertaken.Reviewer #3:– A central issue is that depending on the dose, SARS-CoV-2 is cleared in 5-6 days in the hamster model. Authors need to defend and define how can this is appropriate for modeling late phase effects of COVID-19 in humans.– Throughout their figures the authors indicate that one-way ANOVA is used to compare the groups but do not specify the post-hoc test being used to compare the individual groups. E.g. Dunnett's test would be the most appropriate for comparing each group to the control (e.g. time zero). However, ANOVA with post-hoc should be applied only to parametric data . However, the scoring data will be non-parametric and will need to be compared using a non-parametric test.– In their results (line 158), the authors equate recovery with elevated antibodies at day 14. Elevated is antibodies is expected (e.g. would get same result in vaccination) and should not be taken as a proxy for recovery.– Lines 187-189. authors contend that a cytokine storm is in operation during acute infection. Circulating levels of IL-6 is one of the key cytokines that predicts storms in human COVID-19. Why was this not measured? IL-6 mRNA was measured later, but the authors should measure the actual level of IL-6 in circulation.– It seems that in the biochemical analysis vs LC-MS/MS analysis paint different pictures regarding the time course of lipid biosynthesis. It seems that in the latter case, the authors are more interested in reporting changes, rather than the trajectory of those changes . This paints a confusing picture. It also doesn't help that the text on Figure 4F is too small to read.– Use Kruskal-Wallis test for comparing non-parametric score/count data.– Would suggest disentangling the proposed parallels between late stage effects seen him COVID-19 in humans with SARS-CoV-2 infection in hamsters, unless longer term effects well outside the two week sampling window can be demonstrated. [Editors’ note: the authors resubmitted a revised version of the paper for consideration. What follows is the authors’ response to the first round of review.]Reviewer #1:+ T cell responses which are important features of SARS CoV-2 in this model with translational potential if hamsters are used to test therapeutics aimed at preventing long COVID sequelae. However, the pathologic data that is presented to link both the lipidomics and metabolomics to extrapulmonary pathology suggestive of long COVID is weak. This study would greatly benefit from the addition of a treatment group or extended study timeline to support the claim that hamsters model key aspects of long COVID as seen in people. The lack of convincing pathology undermines the authors' model that lipidomic and metabolic changes are supportive of hamsters as a translational model of long COVID.In this study, the authors aim to show that hamsters may be a useful model of SARS CoV-2 long-term sequelae or "long COVID." The authors present useful data on many aspects of acute SARS CoV-2 in hamsters that are not well demonstrated in the literature such as gene expression of major regulators of inflammation, lipidomics, metabolomics, and antigen-specific CD4We are grateful to the in-depth review of our manuscript and critical comments to improve the manuscript. In line with the reviewers suggested, we have now included new experimental data from remdesivir therapeutic group . Remdesivir is a previously known anti-viral drug that shows good efficacy in hamster. In line, we observed significant decrease in heart viral load in remdesivir treated challenged hamsters as compared to no remdesivir group. The serum cardiac troponin I level, which is a biomarker for CVC also was downregulated in remdesivir group. In addition, we have also included the ventricular mass/ body mass ratio as per the suggestion of the reviewers . The assessment score for the histopathologies have been included in Figure 3. Wherever possible, we have now included blinded scores by trained pathologist for quantitation of the histological images.Overall-there is lack of description on scoring systems for histology. The methods state that pathology assessment was performed by a histologist (?)--pathology should be reviewed by a blinded veterinary pathologist. The data presented to show cardiac hypertrophy, fibrosis, and inflammation is not convincing. The pathology images are very low resolution and the fibrosis and inflammation cannot be observed clearly (if present). Control  and age matched tissue specimens are not shown. Organ weights for spleen and heart are not shown (preferably need to see organ/body weight measurements). Heart weight is the gold standard for cardiac hypertrophy-without this measure cardiac hypertrophy cannot be diagnosed; at a minimum measurement of the interventricular septum should be shown-the images do not convincingly show differences over time.We are thankful for the critical comments. We have now added a comprehensive statement on histological scoring system in the methodology section (Page 30 line number 573 to 575). Briefly, the blinded assessment and scoring  to 5 (highest score)) of each sample sections were performed by a trained pathologist.Appropriate experimental control for Toulidine blue  and MT staining  has been added in the supplementary section along with histological scores for each pathological parameters has now been included as bar graph in the main figure panel .We have included the results from our new experiments showing spleen weight/ body weight  and ventricular weight/body weight data  in the manuscript. Moreover, we have also evaluated viral load in the heart and cardiac troponin-I serum levels which is a biomarker for cardiovascular diseases . The new data has been described in the result section and Discussion section of the manuscript to support our findings.Ln 155: The images of mast cells are not clear-how was this scored (counts/high power field?). Please show control animals (not just time 0).Likewise increased mucin staining in the GI tract is also not convincing. Please show controls over time.Thank you for the comment. We have included an experimental healthy control Toluidine blue staining image in the supplementary figures . Moreover, the statement defining the scoring for mast cells has been included in the figure legend and further described in the methodology section.The GI tract mucin data has been removed from the manuscript since we found that the data was out of place and did not contribute to the main theme of the manuscript.Reviewer #2:[…]Overall, the authors succeeded in showing how the hamster model can be useful to study systemic extra -pulmonic pathologies and systemic metabolic changes associated with SARS-CoV-2 infection. The data shown do not make a compelling case for the hamster model to study long term effects of infection outside the lung, given that the longest time point examined was d14, and the cardiac/vascular data were not robust. Furthermore, most of the cardiovascular complications that have been reported in COVD patients concern thrombosis, yet this report does not show any evidence of thrombotic complications in the hamsters.We are grateful for the in-depth review of the manuscript and critical comments for improving the quality of the manuscript.The possibility that cardiac cells in experimental animals are infected by SARS-CoV-2 should be tested. Recent reports that cardiac myocytes can be infected with SARS-CoV-2, including hamster myocytes :478-492. Doi: 10.1016/j.stemcr.2021.02.008.)Thank you for indicating an important gap in our study regarding viral load quantitation in the heart samples. We have now included a heart viral load data in the main figure in support of our cardiovascular findings. Our heart viral load data suggests presence of virus in the cardiomyocytes of infected hamsters on day 2 and 4 post infection .Cardiac hypertrophy is best assessed by heart weight, and a second best way is to do careful quantitative analysis of left ventricular wall thickness on cross sections from all animals in each group. The photographs of two hearts.Thank you for suggesting us to evaluate the heart weight/ body weight ratio as an indicator for ventricular hypertrophy. We have now included data from new experimental sets indicating changes in heart weight to body weight ratio in the supplementary figure . We have also evaluated serum cardiac troponin-I levels in uninfected, infected and remdesivir group. Our results include elevated levels of cTnI levels in the serum of infected hamsters as compared to uninfected hamsters .Myocardial histopathology must be assessed and reported in quantitative fashion.Thank you for suggesting us to evaluate the histogical data. We have now included a blinded score in the main figure panel for our histological images including the myocardial histopathology . The methodology for scoring has been included in the methodology section.In Figure S4 the images labelled Kidney are Liver, and vice versa. The kidney images are of poor resolution, and no glomeruli can made out in the d2 section. Sections from d7 and 14 should also be shown.Thank you for pointing out this error in labelling. The other extra-pulmonary organ data including the GI tract histological data has been removed from the manuscript as we feel that this did not fit with the cardiovascular theme of the manuscript.If there are activating anti-hamster CD3 reagents available to activate T cells, that would be a better  way for activating spleen T cells than PMA/odelling.Thank you for suggesting to use T cell specific mitogen for activating splenocytes. Indeed, it would have shed more light on T cell clonal activation than using a heterogeneous activation by PMA/ Ionomycin. However, we did not have anti-hamster CD3 especially due to limitation in antibody resources for hamster study. We acknowledge this limitation in our study.Reviewer #3:In this work the authors examine changes in the lungs and extrapulmonary organs in the setting of SARS-CoV-2 infection using the golden Syrian hamster model, with emphasis on cardiac changes. This is a well-established model to study SARS-CoV-2 infection. The authors also examine changes in splenic inflammatory related molecules, serum lipid molecules and serum metabolites.The major strength of the work is the attempt to perform detailed analyses of the extrapulmonary organs combined with splenic inflammatory changes, metabolomics and lipidomics in the setting of acute SARS-CoV-2 infection in an animal model.The major weakness of this work is that often the histologic changes being reported, particularly in the heart, are not well supported by the data provided. In addition, more rigorous statistical approaches could be employed, and there is no clear mechanism proposed to link the changes in the splenic inflammation, serum lipidomics, and serum metabolomics with the proposed cardiac changes.We are grateful to the reviewer for the critically reviewing our manuscript and helping us improve the quality of the manuscript. We have now incorporated histological assessment data in the manuscript to support histology readout. We have also performed new experiments and evaluated cardiovascular markers and heart viral load to strengthen our findings.1. There are numerous studies now on SARS-CoV-2 in the hamster. There should be much better effort relating this work to the published literature, particularly stressing what is novel and what is not. It is not clear if any of the pulmonary studies being reported here are actually novel.Thank you for asking this question. It is true that SARS-CoV2 associated lung pathologies have been earlier reported in human and hamsters. However, in the present study we have tried to evaluate the other factors which are associated with lung injury such as Eotaxin, AGAR which have been previously shown to contribute to lung pathologies . We have also evaluated the expression of mast cell functionality and it was found to be upregulated at the peak of infection. To our best of knowledge, these parameters have not been reported before for the hamsters SARS-CoV2 challenge study.2. The authors claim: “Strikingly, SARS-CoV2 infection in the hamster leads to significant interstitial coronary fibrosis on 7 and 14 dpi characterized by thickening of ventricular walls interventricular septum.” And later: “The ventricular walls thickening at 7 and 14 dpi with SARS-CoV2 infection in GSH was marked by increased inflammation surrounding the coronary artery and elevated interstitial coronary fibrosis .” However, the cardiac changes being reported are simply not supported by the images provided. Heart weights and wall thicknesses need to be measured and compared across the groups using statistics. Also, cardiac fibrosis and inflammatory cell infiltration should be quantified and comparted across the groups using statistics. Better images showing clear changes correlating with the quantitative analyses would be required.We are grateful to the reviewer for asking us to evaluate the heart weight/ body weight ratio which is a standard method for evaluating ventricular hypertrophy. We have now included new sets of data from recently completed experiment and have incorporated new data on heart viral load, heart weight/body weight ratio and serum cardiac troponin-I levels. Furthermore, we have included the histological assessment data in the form of blinded scoring by trained pathologist. The method of scoring has also been described in the methodology section.3. The authors state “…we reasoned that cardio-vascular pathologies of SARS-CoV2 in GSH may be related to changes in circulating lipid molecules . Indeed, serum lipid profile showed elevated cholesterol, TGs,HDL, LDL and VLDL levels at 4 dpi .” However, the changes in serum lipids were very transient and unlikely to rapidly cause cardiac fibrosis. There is no clear logical mechanism being present linking the serum lipid levels with the proposed cardiac changes.We are grateful for the valuable suggestion. We have now incorporated new data set from recent studies in which we have evaluated both viral load and serum cardiac troponin-I level in circulation that could be an valuable markers for validating cardiovascular pathologies in SARS-CoV2 infected hamsters. Furthermore, we have also added a histological assessment data based on blinded random scoring by trained pathologist to support the quantitative assessment of cardio-vascular histopathologies.4. For all of the studies performed including the lipidomics and metabolomic studies the authors simply us a P-value of less than 0.05 as being statistically significant with no adjustment for multiple assessments or consideration of FDR. A more sophisticated statical approach is needed.We appreciate the concern raised by the reviewers. The FDR correction has been incorporated for the omnics data and a statement describing FDR correction has been incorporated in the methodology section .5. The spleens need to be weighed and the weights compared across the groups using statistics.Thank you for this critical suggestion. We have not included the spleen weight/ body weight data in the main figure panel to support the splenomegaly condition arising due to SARS-CoV2 infection .6. There are frequent statements in the discussion that simply do not match the data shown. For example, the authors state: “Our data show that lung pathologies long persists after the virus clearance from the lung since we observed a high pathological score on 7 dpi.” However, the authors own data show that the virus is still present at 7 dpi.We regret for the ambiguity in the statement. We have now corrected the statement to make it simpler and unambiguous.7. All figure panels need proper labels, particularly both axes of any plots.Thank you for raising this concern. We have rechecked the figures of the manuscript and have added the axis wherever it was missing.8. In Figure S4, the degree of mucin staining in the images does not appear to match that shown in the bar graphs. The scale for the scale bars is not legible. For Panel B the images marked “liver” are not liver, and the images marked “kidney” are not kidney.Thank you for pointing out the limitations of the study. Since we have now revised the manuscript focusing on the cardiovascular and lipido-metabolomics changes we feel that other extra-pulmonary pathologies do not fit with the main theme of the study. Therefore, we have removed histology data for the other organs from the manuscript.9. There should be some rational mechanism linking the splenic inflammatory cell changes, lipidomics and metabolomics with the proposed cardiac changes.Thank you for your comment. We have improved the manuscript in line with the suggestions made and have tried to connect the immunological and cardiovascular changes with other parameters studied. In line with this, we have improved the introduction and Discussion section focusing on the pathological changes associated with SARS-CoV2 infection and molecular factors that maybe involved in the pathologies. For example, inflammatory cytokines contribute to lung pathologies and further IL6 and some other cytokines and chemokines have also been described as contributors for CVC. CVC is usually associated with injuries to cardiomyocytes which may happen due to accumulation of cholesterol, LDLs and LCFAs in the circulation. Furthermore, N-acetylneuraminate metabolite levels have been previously linked with CVC condition in clinical settings.[Editors’ note: what follows is the authors’ response to the second round of review.]The manuscript has been improved but there are some remaining issues that need to be addressed, as outlined below:1) Revise statistical analyses using non-parametric approaches as suggested.2) Minimize or remove connections made to long COVID- the evidence is not strong enough that this is a model.3) Include recent references as suggested.4) Extensive editing of grammar and syntax is absolutely necessary.5) Make MS readouts legible.Thank you for your valuable comments which will help in improving the quality of the manuscript. We have now made necessary revisions throughout the manuscript in line with the reviewers comments. Briefly, the statistical analysis of the data now shows non-parameteric analysis and the exact p values of the data has been shown in the figure legends. We have revised or removed long-COVID term wherever applicable throughout the manuscript. All the recent references suggested by the reviewers has been cited in the manuscript. Moreover, we have thoroughly worked on the grammar and syntax of the manuscript and have tried to improve it to our best capabilities.Reviewer #1:This manuscript nicely highlights SARS CoV-2 effects on the heart in the Syrian hamster model, particularly from the virologic, transcriptional, and metabolic standpoint. The data clearly highlights metabolic changes that support the exploration of using the hamster model for cardiovascular complications of COVID. The data for histopathologic evidence for cardiovascular changes remain unconvincing at the timepoints shown. I believe a longer-term study following hamsters for greater than 14 days would be more biologically relevant for histopathologic changes. Regardless, the biochemical and immunologic data support the use of this model for studying cardiovascular changes.Thank you very much for providing in-depth review and helping in improving the quality of the manuscript. It would have been indeed interesting to do a longitudinal study for SARS-CoV-2 infection in hamster looking at cardiovascular changes beyond 14 days window. We accept this as a limitation to our study. Nonetheless, we believe that the findings of the manuscript would be crucial for constructing further studies related to cardio-vascular pathologies in hamsters.The authors have addressed all the concerns raised. Unfortunately, the histopathologic data, although shown as significant via scoring, is still not biologically convincing. The heart weight to body weight index tracks for infected animals only. The ventricular width should have been done quantitatively and not semi quantitatively (scoring) to be convincing. The appropriate statistical comparison would be heart mass/body mass of infected to uninfected animals at each time point assayed. Also, the semiquantitative scoring of inflammation and fibrosis does not match up with what can be seen by eye. Lastly, the scoring metric for pathology should be clearly outlined in the figure legend . What is the total score possible?Regardless—the cytokine, lipid, and metabolic profiling and viral load data from the heart is very interesting and supports the further exploration of this model for studying cardiovascular effects of COVID.There is still substantial copy editing needed for this manuscript. Many sentences do not make sense and grammar needs substantial improvement prior to publication.Thank you for your valuable suggestions. We have carefully revised the manuscript wherever possible in line with the suggestions made. We have thoroughly worked on the grammar and have improved it to the best of our capabilities. At this point of time, we do not have quantitative evaluation of ventricular wall thickening. However, our ventricular mass/ body mass data suggests that the day 7 and day 14 ventricular mass/ body mass ratio was significantly higher than that of the healthy control indicating ventricular hypertrophy . We have also improved the methodology section on the semi-quantitative scoring method of the histological sections (Histology section of methodology). The histological score for respective pathologies were given on the scale of 0-5, where score 0 indicated the absence of pathological signs, while score 5 is indicative the highest pathological changes at given time points. This information now been included in the figure legends 1 and 3 as per the suggestions.Reviewer #2:The authors have studied SARS-CoV-2 infection in golden hamsters. Evidence is provided for viral RNA in the heart and for many alterations in the circulation, discovered by metabolomics and lipidomics, including many that have been linked to cardiovascular disease in humans. The novel aspect of the studies presented are the studies utilizing lipidomics and metabolomics and the abrogation of heart disease after Remdesevir. This is a revised manuscript and most of the concerns of the previous reviewers have been largely addressed. However, there is at least one other relatively recent study that the authors have failed to make note of, (and that was published in July 2021), that has also observed myocardial inflammation and evidence for viral RNA in the heart. So, some of the findings have been independently confirmed and that is viewed as a strength rather than as a limitation. The other study made simple, direct links between the presence of viral RNA in a tissue and the events that lead to cardiac inflammation.The authors have made some important improvements to the first submission especially in terms of better documenting the cardiac involvement in the model.At this point in time changes like the increase in circulating sialic acids are interesting but most of the metabolic and lipidomic changes are best attributed to systemic inflammation as well as to heart damage. Given the time frame, it is difficult to consider these alterations as causal for the heart inflammation seen but these changes could be of relevance when one considers the cardiovascular complications of clinical COVID-19 that fall into the PASC spectrum.Thank you very much for providing in-depth review and helping in improving the quality of the manuscript.The authors should more comprehensively reference earlier papers on the use of hamsters as a model for SARS-CoV-2 including studies in 2020 from Rosenke et al. (PMID 33251966) and Boudewijns et al. (PMID 33203860). However, they should, at least during this submission, have quoted a manuscript showing cardiovascular disease in a similar model that was published in July 2021. This study published about four months ago from Francis and colleagues. (PMID 34265022) should certainly have been discussed because it in part supports the authors’ studies and demonstrated inflammation in the heart. In the studies of Francis et al., there was viral RNA in the heart though live virus was not recovered from this organ, and there was evidence provided for Type I interferon dysregulation, eosinophilic myocarditis, inflammatory cytokines, T cell infiltration and activation, though the evidence suggested the absence of TH2 cells and T regs.Thank you very much for providing in-depth review and helping in improving the quality of the manuscript. We have tried our best to address the concerns in the manuscript and have updated the references list as suggested.There remain issues related to the authors’ lack of care with syntax and grammar that still need to be taken care of and careful proofreading of the manuscript should be undertaken.We are grateful for the insightful review of our study and helping in improving the quality of the manuscript. We have thoroughly worked on the grammar of the manuscript and have addressed the points raised along with other grammatical corrections to the best of our capabilities.Reviewer #3:– A central issue is that depending on the dose, SARS-CoV-2 is cleared in 5-6 days in the hamster model. Authors need to defend and define how can this is appropriate for odelling late phase effects of COVID-19 in humans.Thank you for raising this concern. We agree with the comment that further longitudinal studies would be required for better correlating the cardio-vascular pathology in hamster with the long-COVID symptoms in human. We have therefore, decided to remove the use of long-COVID usage from the manuscript to make it more coherent for the audience.– Throughout their figures the authors indicate that one-way ANOVA is used to compare the groups but do not specify the post-hoc test being used to compare the individual groups. E.g. Dunnett’s test would be the most appropriate for comparing each group to the control (e.g. time zero). However, ANOVA with post-hoc should be applied only to parametric data . However, the scoring data will be non-parametric and will need to be compared using a non-parametric test.Thank you for your comments and helping us improve the quality of the manuscript. We have revised the statement on statistics throughout the manuscript with disclosure on parameteric/ non-parametric analysis in the figure legends of the main panel as well as supplementary panel. Furthermore, we have mentioned the statistical test used for the analysis as suggested.– In their results (line 158), the authors equate recovery with elevated antibodies at day 14. Elevated is antibodies is expected (e.g. would get same result in vaccination) and should not be taken as a proxy for recovery.Thank you for raising this important point. Indeed, neutralizing antibody response is better correlated with the protective immunity against SARS-CoV-2 infection and poorly correlated with the recovery response against COVID-19. In light of the suggestions, we have accordingly revised the manuscript and removed the ambiguous statement.– Lines 187-189. authors contend that a cytokine storm is in operation during acute infection. Circulating levels of IL-6 is one of the key cytokines that predicts storms in human COVID-19. Why was this not measured? IL-6 mRNA was measured later, but the authors should measure the actual level of IL-6 in circulation.Thank you for pointing this out. We have now included additional IL-6 ELISA data in the main figure  and supplementary figure  to support our findings.– It seems that in the biochemical analysis vs LC-MS/MS analysis paint different pictures regarding the time course of lipid biosynthesis. It seems that in the latter case, the authors are more interested in reporting changes, rather than the trajectory of those changes . This paints a confusing picture. It also doesn't help that the text on Figure 4F is too small to read.– Use Kruskal-Wallis test for comparing non-parametric score/count data.We are grateful to the reviewer for making valuable suggestions, we made the changes in revised manuscript as suggested. We agree with the reviewers that the labels for figure 4F is too small to read, this is due to higher numbers of lipid molecules showing significant downregulation. To accommodate reviewer’s suggestions, we have re-created the figures with updated colour schemes for better clarity in the changes in lipid profile observed. We have also provided a higher resolution heatmap (full figure) as separate supplementary material for better clarity and readability.– Would suggest disentangling the proposed parallels between late stage effects seen him COVID-19 in humans with SARS-CoV-2 infection in hamsters, unless longer term effects well outside the two week sampling window can be demonstrated.We thank the reviewer for an insightful comment. We accept that longitudinal follow up of the hamsters beyond 14 days post infection would have added value in drawing parallel of late stage effects seen in COVID19 in humans especially in the context of cardio-vascular pathologies. We have therefore, decided to remove the term “long-COVID” usage from the manuscript to make it more coherent for the audience.