Immune response induced by novel coronavirus infection.

The coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus (SARS-CoV)-2 has been prominent around the world since it was first discovered, affecting more than 100 million people. Although the symptoms of most infected patients are not serious, there is still a considerable proportion of patients who need hospitalization and even develop fatal symptoms such as cytokine storms, acute respiratory distress syndrome and so on. Cytokine storm is usually described as a collection of clinical manifestations caused by overactivation of the immune system, which plays an important role in tissue injury and multiorgan failure. The immune system of healthy individuals is composed of two interrelated parts, the innate immune system and the adaptive immune system. Innate immunity is the body's first line of defense against viruses; it can quickly perceive viruses through pattern recognition receptors and activate related inflammatory pathways to clear pathogens. The adaptive immune system is activated by specific antigens and is mainly composed of CD4+ T cells, CD8+ T cells and B cells, which play different roles in viral infection. Here, we discuss the immune response after SARS-CoV-2 infection. In-depth study of the recognition of and response of innate immunity and adaptive immunity to SARS-CoV-2 will help to prevent the development of critical cases and aid the exploration of more targeted treatments.

Oral Fungal Alterations in Patients with COVID-19 and Recovered Patients.

The oral bacteriome, gut bacteriome, and gut mycobiome are associated with coronavirus disease 2019 (COVID-19). However, the oral fungal microbiota in COVID-19 remains unclear. This article aims to characterize the oral mycobiome in COVID-19 and recovered patients. Tongue coating specimens of 71 COVID-19 patients, 36 suspected cases (SCs), 22 recovered COVID-19 patients, 36 SCs who recovered, and 132 controls from Henan are collected and analyzed using internal transcribed spacer sequencing. The richness of oral fungi is increased in COVID-19 versus controls, and beta diversity analysis reveals separate fungal communities for COVID-19 and control. The ratio of Ascomycota and Basidiomycota is higher in COVID-19, and the opportunistic pathogens, including the genera Candida, Saccharomyces, and Simplicillium, are increased in COVID-19. The classifier based on two fungal biomarkers is constructed and can distinguish COVID-19 patients from controls in the training, testing, and independent cohorts. Importantly, the classifier successfully diagnoses SCs with positive specific severe acute respiratory syndrome coronavirus 2 immunoglobulin G antibodies as COVID-19 patients. The correlation between distinct fungi and bacteria in COVID-19 and control groups is depicted. These data suggest that the oral mycobiome may play a role in COVID-19.

Development of a model by LASSO to predict hospital length of stay (LOS) in patients with the SARS-Cov-2 omicron variant.

The length of stay (LOS) in hospital varied considerably in different patients with COVID-19 caused by SARS-CoV-2 Omicron variant. The study aimed to explore the clinical characteristics of Omicron patients, identify prognostic factors, and develop a prognostic model to predict the LOS of Omicron patients. This was a single center retrospective study in a secondary medical institution in China. A total of 384 Omicron patients in China were enrolled. According to the analyzed data, we employed LASSO to select the primitive predictors. The predictive model was constructed by fitting a linear regression model using the predictors selected by LASSO. Bootstrap validation was used to test performance and eventually we obtained the actual model. Among these patients, 222 (57.8%) were female, the median age of patients was 18 years and 349 (90.9%) completed two doses of vaccination. Patients on admission diagnosed as mild were 363 (94.5%). Five variables were selected by LASSO and a linear model, and those with P < 0.05 were integrated into the analysis. It shows that if Omicron patients receive immunotherapy or heparin, the LOS increases by 36% or 16.1%. If Omicron patients developed rhinorrhea or occur familial cluster, the LOS increased by 10.4% or 12.3%, respectively. Moreover, if Omicron patients' APTT increased by one unit, the LOS increased by 0.38%. Five variables were identified, including immunotherapy, heparin, familial cluster, rhinorrhea, and APTT. A simple model was developed and evaluated to predict the LOS of Omicron patients. The formula is as follows: Predictive LOS = exp(1*2.66263 + 0.30778*Immunotherapy + 0.1158*Familiar cluster + 0.1496*Heparin + 0.0989*Rhinorrhea + 0.0036*APTT).

Comparison of clinical characteristics between SARS-CoV-2 Omicron variant and Delta variant infections in China.

Background: The continued 'evolution' of Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to the emergence of the Omicron variant after the Delta variant, resulting in a significant increase in the number of people with COVID-19. This increase in the number of cases continues to have a significant impact on lives. Therefore, a more detailed understanding of the clinical characteristics of Omicron infection is essential. Methods: Using medical charts, we extracted clinical information for 384 patients infected with the Omicron variant in Anyang City, Henan Province, China. Epidemiology and clinical characteristics were compared with a cohort of people infected with the Delta variant in Zhengzhou in 2021. Findings: Common initial symptoms at onset of illness were cough [240 (63%)], expectoration [112 (29%)], fever [96 (25%)], nasal congestion [96 (25%)] and myalgia or fatigue [30 (6%)]. In patients with the Omicron variant, levels of total cholesterol, low-density lipoprotein and creatinine increased in 52 (14%), 36 (9%) and 58 (15%) patients, respectively, compared with patients with the Delta variant [one (1%), one (1%) and two (2%)]. Levels of triglyceride and high-density lipoprotein also increased. In patients with the Omicron variant, the levels of specific gravity and the erythrocyte sedimentation rate were increased in 115 (30%) and 81 (21%) patients, and serum levels of complement 3 decreased in 93 (41%). Results: Compared with patients infected with Delta, no major differences in initial clinical symptoms were identified in patients infected with Omicron. However, dyslipidemia and kidney injury were much more severe in patients with the Omicron variant, and the erythrocyte sedimentation rate was increased. Due to decreased levels of complement 3, the immunity of patients with the Omicron variant was weak.

Comparison of clinical characteristics between SARS-CoV-2 Omicron variant and Delta variant infections in China

Background The continued ‘evolution’ of Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to the emergence of the Omicron variant after the Delta variant, resulting in a significant increase in the number of people with COVID-19. This increase in the number of cases continues to have a significant impact on lives. Therefore, a more detailed understanding of the clinical characteristics of Omicron infection is essential. Methods Using medical charts, we extracted clinical information for 384 patients infected with the Omicron variant in Anyang City, Henan Province, China. Epidemiology and clinical characteristics were compared with a cohort of people infected with the Delta variant in Zhengzhou in 2021. Findings Common initial symptoms at onset of illness were cough [240 (63%)], expectoration [112 (29%)], fever [96 (25%)], nasal congestion [96 (25%)] and myalgia or fatigue [30 (6%)]. In patients with the Omicron variant, levels of total cholesterol, low-density lipoprotein and creatinine increased in 52 (14%), 36 (9%) and 58 (15%) patients, respectively, compared with patients with the Delta variant [one (1%), one (1%) and two (2%)]. Levels of triglyceride and high-density lipoprotein also increased. In patients with the Omicron variant, the levels of specific gravity and the erythrocyte sedimentation rate were increased in 115 (30%) and 81 (21%) patients, and serum levels of complement 3 decreased in 93 (41%). Results Compared with patients infected with Delta, no major differences in initial clinical symptoms were identified in patients infected with Omicron. However, dyslipidemia and kidney injury were much more severe in patients with the Omicron variant, and the erythrocyte sedimentation rate was increased. Due to decreased levels of complement 3, the immunity of patients with the Omicron variant was weak.

Characterization of oral and gut microbiome and plasma metabolomics in COVID-19 patients after 1-year follow-up.

BACKGROUND: Due to the outbreak and rapid spread of coronavirus disease 2019 (COVID-19), more than 160 million patients have become convalescents worldwide to date. Significant alterations have occurred in the gut and oral microbiome and metabonomics of patients with COVID-19. However, it is unknown whether their characteristics return to normal after the 1-year recovery. METHODS: We recruited 35 confirmed patients to provide specimens at discharge and one year later, as well as 160 healthy controls. A total of 497 samples were prospectively collected, including 219 tongue-coating, 129 stool and 149 plasma samples. Tongue-coating and stool samples were subjected to 16S rRNA sequencing, and plasma samples were subjected to untargeted metabolomics testing. RESULTS: The oral and gut microbiome and metabolomics characteristics of the 1-year convalescents were restored to a large extent but did not completely return to normal. In the recovery process, the microbial diversity gradually increased. Butyric acid-producing microbes and Bifidobacterium gradually increased, whereas lipopolysaccharide-producing microbes gradually decreased. In addition, sphingosine-1-phosphate, which is closely related to the inflammatory factor storm of COVID-19, increased significantly during the recovery process. Moreover, the predictive models established based on the microbiome and metabolites of patients at the time of discharge reached high efficacy in predicting their neutralizing antibody levels one year later. CONCLUSIONS: This study is the first to characterize the oral and gut microbiome and metabonomics in 1-year convalescents of COVID-19. The key microbiome and metabolites in the process of recovery were identified, and provided new treatment ideas for accelerating recovery. And the predictive models based on the microbiome and metabolomics afford new insights for predicting the recovery situation which benefited affected individuals and healthcare.

Thermal Perception and Physiological Responses under Different Protection States in Indoor Crowded Spaces during the COVID-19 Pandemic in Summer

Currently, people in crowded indoor spaces are required to wear a variety of personal protective equipment to curb the spread of COVID-19. This study aimed to investigate the effects of wearing four types of personal protective equipment (unprotected, wearing masks, wearing face shield and wearing medical protective clothing) on human thermal perception and physiological responses in indoor crowded spaces in summer. The experiment was conducted in a climate chamber designed to simulate the indoor crowded spaces. Environmental parameters of climate chamber (air temperature, relative humidity and wind speed), physiological parameters of subjects (wrist skin temperature and pulse rate), and subjective perceptions (thermal sensation and thermal comfort) were collected during the experiment. The experimental results showed that medical protective clothing has the most obvious blocking effect on heat exchange between human and environment. Thermal sensation in state 4 (wearing medical protective clothing) was significantly (p < 0.05) higher than that in other states. The study of physiological parameters showed that the wrist skin temperature and pulse rate under different protection states increased with the increase of room temperature. Through regression analysis, the thermal sensation estimation model of protective personnel in indoor crowded spaces based on wrist skin temperature and pulse rate was established. The adjusted R2 and RMSE of all models were above 82% and less than 1, indicating that the established thermal sensation model had a good prediction effect.

Potential Associations Between Microbiome and COVID-19.

The coronavirus disease 2019 (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has plunged the world into a major crisis. The disease is characterized by strong infectivity, high morbidity, and high mortality. It is still spreading in some countries. Microbiota and their metabolites affect human physiological health and diseases by participating in host digestion and nutrition, promoting metabolic function, and regulating the immune system. Studies have shown that human microecology is associated with many diseases, including COVID-19. In this research, we first reviewed the microbial characteristics of COVID-19 from the aspects of gut microbiome, lung microbime, and oral microbiome. We found that significant changes take place in both the gut microbiome and airway microbiome in patients with COVID-19 and are characterized by an increase in conditional pathogenic bacteria and a decrease in beneficial bacteria. Then, we summarized the possible microecological mechanisms involved in the progression of COVID-19. Intestinal microecological disorders in individuals may be involved in the occurrence and development of COVID-19 in the host through interaction with ACE2, mitochondria, and the lung-gut axis. In addition, fecal bacteria transplantation (FMT), prebiotics, and probiotics may play a positive role in the treatment of COVID-19 and reduce the fatal consequences of the disease.

Characterization of the Human Oropharyngeal Microbiomes in SARS-CoV-2 Infection and Recovery Patients.

Respiratory tract microbiome is closely related to respiratory tract infections, while characterization of oropharyngeal microbiome in recovered coronavirus disease 2019 (COVID-19) patients is not studied. Herein, oropharyngeal swabs are collected from confirmed cases (CCs) with COVID-19 (73 subjects), suspected cases (SCs) (36), confirmed cases who recovered (21), suspected cases who recovered (36), and healthy controls (Hs) (140) and then completed MiSeq sequencing. Oropharyngeal microbial α-diversity is markedly reduced in CCs versus Hs. Opportunistic pathogens are increased, while butyrate-producing genera are decreased in CCs versus Hs. The classifier based on eight optimal microbial markers is constructed through a random forest model and reached great diagnostic efficacy in both discovery and validation cohorts. Notably, the classifier successfully diagnosed SCs with positive IgG antibody as CCs and is demonstrated from the perspective of the microbiome. Importantly, several genera with significant differences gradually increase and decrease along with recovery from COVID-19. Forty-four oropharyngeal operational taxonomy units (OTUs) are closely correlated with 11 clinical indicators of SARS-CoV-2 infection and Hs based on Spearman correlation analysis. Together, this research is the first to characterize oropharyngeal microbiota in recovered COVID-19 cases and suspected cases, to successfully construct and validate the diagnostic model for COVID-19 and to depict the correlations between microbial OTUs and clinical indicators.

Alterations in the human oral and gut microbiomes and lipidomics in COVID-19.

OBJECTIVE: To characterise the oral microbiome, gut microbiome and serum lipid profiles in patients with active COVID-19 and recovered patients; evaluate the potential of the microbiome as a non-invasive biomarker for COVID-19; and explore correlations between the microbiome and lipid profile. DESIGN: We collected and sequenced 392 tongue-coating samples, 172 faecal samples and 155 serum samples from Central China and East China. We characterised microbiome and lipid molecules, constructed microbial classifiers in discovery cohort and verified their diagnostic potential in 74 confirmed patients (CPs) from East China and 37 suspected patients (SPs) with IgG positivity. RESULTS: Oral and faecal microbial diversity was significantly decreased in CPs versus healthy controls (HCs). Compared with HCs, butyric acid-producing bacteria were decreased and lipopolysaccharide-producing bacteria were increased in CPs in oral cavity. The classifiers based on 8 optimal oral microbial markers (7 faecal microbial markers) achieved good diagnostic efficiency in different cohorts. Importantly, diagnostic efficacy reached 87.24% in the cross-regional cohort. Moreover, the classifiers successfully diagnosed SPs with IgG antibody positivity as CPs, and diagnostic efficacy reached 92.11% (98.01% of faecal microbiome). Compared with CPs, 47 lipid molecules, including sphingomyelin (SM)(d40:4), SM(d38:5) and monoglyceride(33:5), were depleted, and 122 lipid molecules, including phosphatidylcholine(36:4p), phosphatidylethanolamine (PE)(16:0p/20:5) and diglyceride(20:1/18:2), were enriched in confirmed patients recovery. CONCLUSION: This study is the first to characterise the oral microbiome in COVID-19, and oral microbiomes and lipid alterations in recovered patients, to explore their correlations and to report the successful establishment and validation of a diagnostic model for COVID-19.

Characteristic of 523 COVID-19 in Henan Province and a Death Prediction Model.

Certain high-risk factors related to the death of COVID-19 have been reported, however, there were few studies on a death prediction model. This study was conducted to delineate the clinical characteristics of patients with coronavirus disease 2019 (covid-19) of different degree and establish a death prediction model. In this multi-centered, retrospective, observational study, we enrolled 523 COVID-19 cases discharged before February 20, 2020 in Henan Province, China, compared clinical data, screened for high-risk fatal factors, built a death prediction model and validated the model in 429 mild cases, six fatal cases discharged after February 16, 2020 from Henan and 14 cases from Wuhan. Out of the 523 cases, 429 were mild, 78 severe survivors, 16 non-survivors. The non-survivors with median age 71 were older and had more comorbidities than the mild and severe survivors. Non-survivors had a relatively delay in hospitalization, with higher white blood cell count, neutrophil percentage, D-dimer, LDH, BNP, and PCT levels and lower proportion of eosinophils, lymphocytes and albumin. Discriminative models were constructed by using random forest with 16 non-survivors and 78 severe survivors. Age was the leading risk factors for poor prognosis, with AUC of 0.907 (95% CI 0.831-0.983). Mixed model constructed with combination of age, demographics, symptoms, and laboratory findings at admission had better performance (p = 0.021) with a generalized AUC of 0.9852 (95% CI 0.961-1). We chose 0.441 as death prediction threshold (with 0.85 sensitivity and 0.987 specificity) and validated the model in 429 mild cases, six fatal cases discharged after February 16, 2020 from Henan and 14 cases from Wuhan successfully. Mixed model can accurately predict clinical outcomes of COVID-19 patients.

A Randomized, Open-Label, Controlled Clinical Trial of Azvudine Tablets in the Treatment of Mild and Common COVID-19, a Pilot Study.

Coronavirus disease 2019 (COVID-19) has spread worldwide. To date, no specific drug for COVID-19 has been developed. Thus, this randomized, open-label, controlled clinical trial (ChiCTR2000029853) was performed in China. A total of 20 mild and common COVID-19 patients were enrolled and randomly assigned to receive azvudine and symptomatic treatment (FNC group), or standard antiviral and symptomatic treatment (control group). The mean times of the first nucleic acid negative conversion (NANC) of ten patients in the FNC group and ten patients in the control group are 2.60 (SD 0.97; range 1-4) d and 5.60 (SD 3.06; range 2-13) d, respectively (p = 0.008). The mean times of the first NANC of four newly diagnosed subjects in the FNC group and ten subjects in the control group are 2.50 (SD 1.00; range 2-4) d and 9.80 (SD 4.73; range 3-19) d, respectively (starting from the initial treatment) (p = 0.01). No adverse events occur in the FNC group, while three adverse events occur in the control group (p = 0.06). The preliminary results show that FNC treatment in the mild and common COVID-19 may shorten the NANC time versus standard antiviral treatment. Therefore, clinical trials of FNC treating COVID-19 with larger sample size are warranted.