The mental health of college students: After the reduction of COVID-19 pandemic restrictions (preprint)

Among adolescents and young adults, self-reported baseline psychological distress was quite high during the COVID-19 pandemic. As a vulnerable group, college students suffer from serious psychological problems such as anxiety and depression during the COVID-19 pandemic. Despite the fact that COVID-19 no longer constitutes a public health emergency of international concern and the reduction of COVID-19 pandemic restrictions in March 2023, the change of college students' mental health remains unclear. In March and June 2023, college students in Kunming, China, were investigated by the Self-Rating Anxiety Scale, Self-Rating Depression Scale, and Pittsburgh Sleep Quality Index. After the COVID-19 pandemic restrictions were reduced, the results of the scale indicated anxiety (-0.6%, 21.6%-21%), mild anxiety (+ 0.5%, 16.50%-17.0%), moderate anxiety (-0.7%, 3.94%-3.2%), and severe anxiety (-0.4%, 1.2%-0.8%). Depression (-8.1%, 37.2%-29.1%), mild depression (-5.2%, 19.4%-14.2%), moderate depression (-2.2%, 16.7%-14.5%), or severe depression (-0.7%, 1.1%-0.3%) among college students. Good sleep quality (0%, 72.3%-72.3%) and poor sleep quality (0%, 27.7%-27.7%); anxiety or depression (-6.5%, 39.7%-33.2%) among college students. The incidence of anxiety and depression in college students was reduced, after the reduction of COVID-19 pandemic restrictions.

Research progress of severe acute respiratory syndrome coronavirus 2 on aerosol collection and detection

In this review, research progress of severe acute respiratory syndrome coronavirus 2 on aerosol collection and detection are summarized, and the process of collecting and detecting is shown. Image, graphical abstract

Olfactory and gustatory dysfunctions of COVID-19 patients in China: A multicenter study.

Introduction: With the spread of the epidemic worldwide, an increasing number of doctors abroad have observed the following atypical symptoms of coronavirus disease 2019 (COVID-19): olfactory or taste disorders. Therefore, clarifying the incidence and clinical characteristics of olfactory and taste disorders in Chinese COVID-19 patients is of great significance and urgency. Materials and Methods: A retrospective study was conducted, which included 229 severe acute respiratory syndrome coronavirus 2 confirmed patients, through face-to-face interviews and telephone follow-up. Following the completion of questionnaires, the patients participating in the study, were categorized according to the degree of olfactory and taste disorders experienced, and the proportion of each clinical type of patient with olfactory and taste disorders and the time when symptoms appeared were recorded. Results: Among the 229 patients, 31 (13.54%) had olfactory dysfunction, and 44 (19.21%) had gustatory dysfunction. For the patients with olfactory dysfunction, 6 (19.35%) developed severe disease and became critically ill. Olfactory dysfunction appeared before the other symptoms in 21.43% of cases. The proportion of females with olfactory and gustatory dysfunction was higher than that of males (P < 0.001). Conclusions: The incidence of olfactory and gustatory dysfunction was much lower than that reported abroad; the prognosis of patients with olfactory dysfunction is relatively favorable; olfactory and gustatory dysfunction can be used as a sign for early screening; females are more prone to olfactory and gustatory dysfunction.

A novel cartridge for nucleic acid extraction, amplification and detection of infectious disease pathogens with the help of magnetic nanoparticles

Nucleic acid detection (NAD) based on real-time polymerase chain reaction (real-time PCR) is gold standard for infectious disease detection. Magnetic nanoparticles (MNPs) are widely used for nucleic acid extraction (NAE) because of their excellent properties. Microfluidic technology makes automated NAD possible. However, most of the NAD microfluidic chips are too complex to be applied to point-of-care (POC) testing. In this paper, a simple-structure cartridge was developed for POC detection of infectious diseases. This self-contained cartridge can be divided into a magnetic-controlled NAE part, a valve-piston combined fluidic control part and a PCR chip, which is able to extract nucleic acid from up to 500 μL of liquid samples by MNPs and finish the detection process from "sample in” to "answer out” automatically. Performance tests of the cartridges show that it met the demands of automated NAD. Results of on-cartridge detection of hepatitis B virus (HBV) demonstrated that this system has good uniformity and no cross-contamination between different cartridges, and the limit of detection (LOD) of this system for HBV in serum is 50 IU/mL. Multiplex detections of severe acute respiratory syndrome coronaviruses 2 (SARS-CoV-2) with a concentration of 500 copies/mL were carried out on the system and 100% positive detection rate was achieved.

Exploiting an early warning Nomogram for predicting the risk of ICU admission in patients with COVID-19: a multi-center study in China.

BACKGROUND: Novel coronavirus disease 2019 (COVID-19) is a global public health emergency. Here, we developed and validated a practical model based on the data from a multi-center cohort in China for early identification and prediction of which patients will be admitted to the intensive care unit (ICU). METHODS: Data of 1087 patients with laboratory-confirmed COVID-19 were collected from 49 sites between January 2 and February 28, 2020, in Sichuan and Wuhan. Patients were randomly categorized into the training and validation cohorts (7:3). The least absolute shrinkage and selection operator and logistic regression analyzes were used to develop the nomogram. The performance of the nomogram was evaluated for the C-index, calibration, discrimination, and clinical usefulness. Further, the nomogram was externally validated in a different cohort. RESULTS: The individualized prediction nomogram included 6 predictors: age, respiratory rate, systolic blood pressure, smoking status, fever, and chronic kidney disease. The model demonstrated a high discriminative ability in the training cohort (C-index = 0.829), which was confirmed in the external validation cohort (C-index = 0.776). In addition, the calibration plots confirmed good concordance for predicting the risk of ICU admission. Decision curve analysis revealed that the prediction nomogram was clinically useful. CONCLUSION: We established an early prediction model incorporating clinical characteristics that could be quickly obtained on hospital admission, even in community health centers. This model can be conveniently used to predict the individual risk for ICU admission of patients with COVID-19 and optimize the use of limited resources.

A miniaturized and integrated dual-channel fluorescence module for multiplex real-time PCR in the portable nucleic acid detection system.

A portable nucleic acid detection (PNAD) system based on real-time polymerase chain reaction (real-time PCR) has been developed for point-of-care testing (POCT) of infectious disease pathogens. In order to achieve "sample-in, result-out" while keeping the system compact, the hardware system integrates optical, thermal and motion control modules in a limited space for nucleic acid extraction, purification, amplification and detection. Among these hardware modules, the fluorescence module is one of the most important modules, because its performance directly affects the accuracy and sensitivity of the testing results. In this paper, a miniaturized, high-sensitivity and integrated dual-channel fluorescence module have been proposed for the homemade PNAD system. Based on the principle of confocal optical path, two group of excitation-emission optical paths of different wavelengths are integrated in a small space. In terms of circuitry, a current-light dual negative feedback light emitting diode (LED) drive circuit is applied to improve the stability of the excited light source. All optical and electronic components are integrated in a metal box of 55 mm × 45 mm × 15 mm, that helps miniaturize the detection system. Two different modules have been assembled to fit various fluorescent dyes or probes with the set of excitation and emission as follow: module 1#: 470 nm/525 nm, 570 nm/630 nm; module 2#: 520 nm/570 nm, 630 nm/690 nm. Finally, hepatitis B virus (HBV) concentration gradient detection and multiplex detection of different gene targets of SARS-CoV-2 are carried out on the PNAD system equipped with these two fluorescence modules for evaluating their performances. Compared with the commercial real-time PCR instrument, our fluorescence module has good stability and detection sensitivity.

A prediction model for major adverse cardiovascular events (MACE) in patients with coronavirus disease 2019 (COVID-19).

BACKGROUND: Emerging evidence shows that cardiovascular injuries and events in coronavirus disease 2019 (COVID-19) should be considered. The current study was conducted to develop an early prediction model for major adverse cardiovascular events (MACE) during hospitalizations of COVID-19 patients. METHODS: This was a retrospective, multicenter, observational study. Hospitalized COVID-19 patients from Wuhan city, Hubei Province and Sichuan Province, China, between January 14 and March 9, 2020, were randomly divided into a training set (70% of patients) and a testing set (30%). All baseline data were recorded at admission or within 24 h after admission to hospitals. The primary outcome was MACE during hospitalization, including nonfatal myocardial infarction, nonfatal stroke and cardiovascular death. The risk factors were selected by LASSO regression and multivariate logistic regression analysis. The nomogram was assessed by calibration curve and decision curve analysis (DCA). RESULTS: Ultimately, 1206 adult COVID-19 patients were included. In the training set, 48 (5.7%) patients eventually developed MACE. Six factors associated with MACE were included in the nomogram: age, PaO2/FiO2 under 300, unconsciousness, lymphocyte counts, neutrophil counts and blood urea nitrogen. The C indices were 0.93 (95% CI 0.90, 0.97) in the training set and 0.81 (95% CI 0.70, 0.93) in the testing set. The calibration curve and DCA demonstrated the good performance of the nomogram. CONCLUSIONS: We developed and validated a nomogram to predict the development of MACE in hospitalized COVID-19 patients. More prospective multicenter studies are needed to confirm our results.

Novel aerosol detection platform for SARS­CoV­2: Based on specific magnetic nanoparticles adsorption sampling and digital droplet PCR detection.

The SARS­CoV­2 virus is released from an infectious source (such as a sick person) and adsorbed on aerosols, which can form pathogenic microorganism aerosols, which can affect human health through airborne transmission. Efficient sampling and accurate detection of microorganisms in aerosols are the premise and basis for studying their properties and evaluating their hazard. In this study, we built a set of sub-micron aerosol detection platform, and carried out a simulation experiment on the SARS­CoV­2 aerosol in the air by wet-wall cyclone combined with immunomagnetic nanoparticle adsorption sampling and ddPCR. The feasibility of the system in aerosol detection was verified, and the influencing factors in the detection process were experimentally tested. As a result, the sampling efficiency was 29.77%, and extraction efficiency was 98.57%. The minimum detection limit per unit volume of aerosols was 250 copies (102 copies/mL, concentration factor 2.5).

Microbial diversity and the abundance of keystone species drive the response of soil multifunctionality to organic substitution and biochar amendment in a tea plantation

High nitrogen (N) fertilizer inputs accelerate soil acidification and degradation in tea plantations, thus posing a threat to soil microbial diversity, species composition, and ecosystem service functions. The effects of organic fertilizer and biochar applications on improving soil fertility have been extensively studied on cropland;however, little is known about their effectiveness in promoting soil multifunctionality on rapidly expanding acidic soils in tea plantations. In this study, we conducted a two‐year field experiment in a subtropical tea plantation to investigate the effects of organic fertilizer substitution and biochar amendment on soil microbial communities and multifunctionality. The results showed that soil multifunctionality was enhanced in plots amended with organic fertilizer and biochar. Soil multifunctionality was significantly and positively correlated with alpha‐diversity of bacteria but not fungi. We also found that organic fertilizer substitution and biochar amendment improved soil multifunctionality by altering the abundance of keystone species. The abundance of keystone species classified as module hubs in the bacterial co‐occurrence network contributed significantly and positively to soil multifunctionality. In contrast, the keystone species categorized as module hubs in the fungal co‐occurrence network negatively affected soil multifunctionality. Soil pH was a key driver of soil microbial community composition, indicating that the increase in soil pH under organic fertilizer and biochar amendment had a crucial role in biological processes. These results suggest that organic substitution and biochar amendment are beneficial in preventing soil degradation and maintaining soil multifunctionality in subtropical tea plantations.

Prognostic Value of Machine Learning in Patients with Acute Myocardial Infarction.

(1) Background: Patients with acute myocardial infarction (AMI) still experience many major adverse cardiovascular events (MACEs), including myocardial infarction, heart failure, kidney failure, coronary events, cerebrovascular events, and death. This retrospective study aims to assess the prognostic value of machine learning (ML) for the prediction of MACEs. (2) Methods: Five-hundred patients diagnosed with AMI and who had undergone successful percutaneous coronary intervention were included in the study. Logistic regression (LR) analysis was used to assess the relevance of MACEs and 24 selected clinical variables. Six ML models were developed with five-fold cross-validation in the training dataset and their ability to predict MACEs was compared to LR with the testing dataset. (3) Results: The MACE rate was calculated as 30.6% after a mean follow-up of 1.42 years. Killip classification (Killip IV vs. I class, odds ratio 4.386, 95% confidence interval 1.943-9.904), drug compliance (irregular vs. regular compliance, 3.06, 1.721-5.438), age (per year, 1.025, 1.006-1.044), and creatinine (1 µmol/L, 1.007, 1.002-1.012) and cholesterol levels (1 mmol/L, 0.708, 0.556-0.903) were independent predictors of MACEs. In the training dataset, the best performing model was the random forest (RDF) model with an area under the curve of (0.749, 0.644-0.853) and accuracy of (0.734, 0.647-0.820). In the testing dataset, the RDF showed the most significant survival difference (log-rank p = 0.017) in distinguishing patients with and without MACEs. (4) Conclusions: The RDF model has been identified as superior to other models for MACE prediction in this study. ML methods can be promising for improving optimal predictor selection and clinical outcomes in patients with AMI.

Ventilation Strategies for Mitigation of Infection Disease Transmission in an Indoor Environment: A Case Study in Office

During the normalization phase of the COVID-19 epidemic, society has gradually reverted to using building space, especially for public buildings, e.g., offices. Prevention of airborne pollutants has emerged as a major challenge. Ventilation strategies can contribute to mitigating the spread of airborne disease in an indoor environment, including increasing supply air rate, modifying ventilation mode, etc. The larger ventilation rate can inevitably lead to high energy consumption, which may be also ineffective in reducing infection risk. As a critical factor affecting the spread of viral contaminant, the potential of ventilation modes for control of COVID-19 should be explored. This study compared several ventilation strategies in the office, including mixing ventilation (MV), zone ventilation (ZV), stratum ventilation (SV) and displacement ventilation (DV), through analyzing ventilation performance and infection risk for the optimal one. By using ANSYS Fluent, the distributions of airflow and pollutant were simulated under various ventilation modes and infected occupants. The SV showed greater performance in mitigating infection disease spread than MV, ZV and DV, with an air distribution performance index (ADPI) of 90.5% and minimum infection risk of 13%. This work can provide a reference for development of ventilation strategies in public space oriented the prevention of COVID-19.

Communication Pattern Changes Along With Declined IGF1 of Immune Cells in COVID-19 Patients During Disease Progression.

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes the coronavirus disease 2019 (COVID-19) pandemic, represents a global crisis. Most patients developed mild/moderate symptoms, and the status of immune system varied in acute and regulatory stages. The crosstalk between immune cells and the dynamic changes of immune cell contact is rarely described. Here, we analyzed the features of immune response of paired peripheral blood mononuclear cell (PBMC) samples from the same patients during acute and regulatory stages. Consistent with previous reports, both myeloid and T cells turned less inflammatory and less activated at recovery phase. Additionally, the communication patterns of myeloid-T cell and T-B cell are obviously changed. The crosstalk analysis reveals that typical inflammatory cytokines and several chemokines are tightly correlated with the recovery of COVID-19. Intriguingly, the signal transduction of metabolic factor insulin-like growth factor 1 (IGF1) is altered at recovery phase. Furthermore, we confirmed that the serum levels of IGF1 and several inflammatory cytokines are apparently dampened after the negative conversion of SARS-CoV-2 RNA. Thus, these results reveal several potential detection and therapeutic targets that might be used for COVID-19 recovery.

Antimalarial chloroquine: a powerful weapon for viruses? [Chinese]

The treatment of 2019 novel coronavirus (COVID-19) pneumonia is imminent. What is worrying is that there is currently no clear drug on the market that can prevent or treat it. The antimalarial drug chloroquine has been used clinically for more than 70 years with high safety. Recent studies have shown that chloroquine can inhibit the activity of a variety of viruses, and is likely to become a potential drug for the treatment of COVID-19 pneumonia. Based on the PubMed database, here is a summary of the antiviral research of chloroquine in recent years and a discussion about the feasibility and strategy of using chloroquine against COVID-19, with an overview of the current development of new anti-COVID-19 drugs or the clinical treatment of COVID-19 pneumonia strategies that could provide useful help.

A Promising and Challenging Approach: Radiologists' Perspective on Deep Learning and Artificial Intelligence for Fighting COVID-19.

Chest X-rays (CXR) and computed tomography (CT) are the main medical imaging modalities used against the increased worldwide spread of the 2019 coronavirus disease (COVID-19) epidemic. Machine learning (ML) and artificial intelligence (AI) technology, based on medical imaging fully extracting and utilizing the hidden information in massive medical imaging data, have been used in COVID-19 research of disease diagnosis and classification, treatment decision-making, efficacy evaluation, and prognosis prediction. This review article describes the extensive research of medical image-based ML and AI methods in preventing and controlling COVID-19, and summarizes their characteristics, differences, and significance in terms of application direction, image collection, and algorithm improvement, from the perspective of radiologists. The limitations and challenges faced by these systems and technologies, such as generalization and robustness, are discussed to indicate future research directions.

Blood Urea Nitrogen to Serum Albumin Ratio (BAR) Predicts Critical Illness in Patients with Coronavirus Disease 2019 (COVID-19).

PURPOSE: We sought to explore the prognostic value of blood urea nitrogen (BUN) to serum albumin ratio (BAR) and further develop a prediction model for critical illness in COVID-19 patients. PATIENTS AND METHODS: This was a retrospective, multicenter, observational study on adult hospitalized COVID-19 patients from three provinces in China between January 14 and March 9, 2020. Primary outcome was critical illness, including admission to the intensive care unit (ICU), need for invasive mechanical ventilation (IMV), or death. Clinical data were collected within 24 hours after admission to hospitals. The predictive performance of BAR was tested by multivariate logistic regression analysis and receiver operating characteristic (ROC) curve and then a nomogram was developed. RESULTS: A total of 1370 patients with COVID-19 were included and 113 (8.2%) patients eventually developed critical illness in the study. Baseline age (OR: 1.031, 95% CI: 1.014, 1.049), respiratory rate (OR: 1.063, 95% CI: 1.009, 1.120), unconsciousness (OR: 40.078, 95% CI: 5.992, 268.061), lymphocyte counts (OR: 0.352, 95% CI: 0.204, 0.607), total bilirubin (OR: 1.030, 95% CI: 1.001, 1.060) and BAR (OR: 1.319, 95% CI: 1.183, 1.471) were independent risk factors for critical illness. The predictive AUC of BAR was 0.821 (95% CI: 0.784, 0.858; P<0.01) and the optimal cut-off value of BAR was 3.7887 mg/g (sensitivity: 0.690, specificity: 0.786; positive predictive value: 0.225, negative predictive value: 0.966; positive likelihood ratio: 3.226, negative likelihood ratio: 0.394). The C index of nomogram including above six predictors was 0.9031125 (95% CI: 0.8720542, 0.9341708). CONCLUSION: Elevated BAR at admission is an independent risk factor for critical illness of COVID-19. The novel predictive nomogram including BAR has superior predictive performance.

Health Protection of CT Radiographers During the Outbreak of COVID-19: Application of Automatic Positioning Technology for Relocatable CT in the Fang Cang Hospital.

Background: To investigate the value of automatic positioning technology in improving the protection of radiographers in the relocatable CT room of a Fang Cang hospital during the outbreak of coronavirus disease 2019 (COVID-19). Methods: The National Emergency Medical Team of our hospital assumed command of Wuchang Fang Cang Hospital and treated confirmed COVID-19 patients with mild symptoms. Relocatable CT was used to examine patients in this hospital. Automatic positioning technology was applied to avoid close contact between medical staff and patients and to protect medical staff more effectively. Results: Seven hundred lung CT scans acquired from 269 patients were completed from February 17 to 26, 2020 with automatic positioning technology for relocatable CT in a Fang Cang hospital. All scans were conducted successfully using automatic positioning technology. All patients entered the scanning room from a separate door. All the position lines were accurate, and all images met the requirement for diagnosis of COVID-19, with satisfied quality. None of our medical staff had any close contact with patients. Conclusion: Automatic positioning technology applied to relocatable CT can minimize the close contact between technologists and patients and effectively improve the protection of medical staff without sacrificing image quality.

The point-of-care-testing of nucleic acids by chip, cartridge and paper sensors

ABSTRACT Point-of-care nucleic acid testing (POCNAT) has played an important role in the outbreak of infectious diseases (e.g., COVID-19) over recent years. POCNAT aims to realize the rapid, simple and automatic detection of nucleic acid. Thanks to the development of manufacturing technology, electronic information technology, artificial intelligence technology, and biological information technology in recent years, the development of the POCNAT device has led to significant advancement. Instead of the normal nucleic acid detection methods used in the laboratory, some novel experimental carriers have been applied, such as chips, cartridges and papers. The application of these experimental carriers has realized the automation and integration of nucleic acid detection. The entire process of nucleic acid detection is normally divided into three steps (nucleic acid extraction, target amplification and signal detection). All of the reagents required by the process can be pre-stored on these experimental carriers, without unnecessary manual operation. Furthermore, all of the processes are carried out in this experimental carrier, with the assistance of a specific control device. Although they are complicated to manufacture and precise in design, their application provides a significant step forwards in nucleic acid detection and realizes the integration of nucleic acid detection. This technology has great potential in the field of point-of-care molecular diagnostics in the future. This paper focuses on the relevant content of these experimental carriers.

Single-cell epigenomic landscape of peripheral immune cells reveals establishment of trained immunity in individuals convalescing from COVID-19.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection often causes severe complications and even death. However, asymptomatic infection has also been reported, highlighting the difference in immune responses among individuals. Here we performed single-cell chromatin accessibility and T cell-receptor analyses of peripheral blood mononuclear cells collected from individuals convalescing from COVID-19 and healthy donors. Chromatin remodelling was observed in both innate and adaptive immune cells in the individuals convalescing from COVID-19. Compared with healthy donors, recovered individuals contained abundant TBET-enriched CD16+ and IRF1-enriched CD14+ monocytes with sequential trained and activated epigenomic states. The B-cell lineage in recovered individuals exhibited an accelerated developmental programme from immature B cells to antibody-producing plasma cells. Finally, an integrated analysis of single-cell T cell-receptor clonality with the chromatin accessibility landscape revealed the expansion of putative SARS-CoV-2-specific CD8+ T cells with epigenomic profiles that promote the differentiation of effector or memory cells. Overall, our data suggest that immune cells of individuals convalescing from COVID-19 exhibit global remodelling of the chromatin accessibility landscape, indicative of the establishment of immunological memory.

Diagnostic Value of Hematological and Biochemical Parameters Combinations for Predicting Coronavirus Disease 2019 (COVID-19) in Suspected Patients.

BACKGROUND: The severe epidemiologic situation of COVID-19 due to the limited capacity of healthcare systems makes it necessary to improve the hospital management and early identification and stratification of patients. The aim of the study was to explore hematological and biochemical parameters at admission to the hospital as novel early predictors for diagnosis with coronavirus disease 2019 (COVID-19) among all suspected patients. METHODS: This was a retrospective, multicenter, observational study. The clinical data of all suspected patients were analyzed. The suspected patients with negative RT-PCR results were included as the control group, and compared with confirmed patients. Receiver- operating characteristic (ROC) curves and logistic regression analyses were used to evaluate the hematological indexes. RESULTS: In total, 326 confirmed COVID-19 patients and 116 control patients were included. The predictive ability of combinations of the hematological and biochemical parameters was significantly superior to that of a single parameter. The area under the ROC curve (AUC) of the aspartate aminotransferase (AST) to neutrophil ratio index (ANRI) and the AST to monocyte ratio index (AMRI) were 0.791 and 0.812, respectively. In the multivariate analysis, an ANRI ≥ 6.03(OR: 3.26, 95% CI: 1.02-10.40, P=0.046) and an AMRI ≥ 36.32(OR: 3.64. 95% CI: 1.24-10.68, P=0.02) at admission were independent risk factors related to the occurrence of COVID-19. CONCLUSIONS: We found two novel predictors with promising predictive capacities for COVID-19 among all suspected patients: ANRI and AMRI. Our findings need to be confirmed in further studies.

Integrated gut virome and bacteriome dynamics in COVID-19 patients.

SARS-CoV-2 is the cause of the current global pandemic of COVID-19; this virus infects multiple organs, such as the lungs and gastrointestinal tract. The microbiome in these organs, including the bacteriome and virome, responds to infection and might also influence disease progression and treatment outcome. In a cohort of 13 COVID-19 patients in Beijing, China, we observed that the gut virome and bacteriome in the COVID-19 patients were notably different from those of five healthy controls. We identified a bacterial dysbiosis signature by observing reduced diversity and viral shifts in patients, and among the patients, the bacterial/viral compositions were different between patients of different severities, although these differences are not entirely distinguishable from the effect of antibiotics. Severe cases of COVID-19 exhibited a greater abundance of opportunistic pathogens but were depleted for butyrate-producing groups of bacteria compared with mild to moderate cases. We replicated our findings in a mouse COVID-19 model, confirmed virome differences and bacteriome dysbiosis due to SARS-CoV-2 infection, and observed that immune/infection-related genes were differentially expressed in gut epithelial cells during infection, possibly explaining the virome and bacteriome dynamics. Our results suggest that the components of the microbiome, including the bacteriome and virome, are affected by SARS-CoV-2 infections, while their compositional signatures could reflect or even contribute to disease severity and recovery processes.