Epidemiological analysis of Group A streptococcus infection diseases among children in Beijing, China under COVID-19 pandemic

Background Group A streptococcus is human-restricted gram-positive pathogen, responsible for various clinical presentations from mild epidermis infections to life threatened invasive diseases. Under COVID-19 pandemic,. the characteristics of the epidemic strains of GAS could be different. Purpose To investigate epidemiological and molecular features of isolates from GAS infections among children in Beijing, China between January 2020 and December 2021. Antimicrobial susceptibility profiling was performed based on Cinical Laboratory Sandards Institute. Distribution of macrolide-resistance genes, emm types, and superantigens was examined by polymerase chain reaction. Results 114 GAS isolates were collected which were frequent resistance against erythromycin (94.74%), followed by clindamycin (92.98%), tetracycline (87.72%). Emm12 (46.49%), emm1 (25.44%) were dominant emm types. Distribution of ermB, ermA, and mefA gene was 93.85%, 2.63%, and 14.04%, respectively. Frequent superantigenes identified were smeZ (97.39%), speG (95.65%), and speC (92.17%). Emm1 strains possessed smeZ, ssa, and speC, while emm12 possessed smeZ, ssa, speG, and speC. Erythromycin resistance was predominantly mediated by ermB. Scarlet fever strains harbored smeZ (98.81%), speC (94.05%). Impetigo strains harbored smeZ (88.98%), ssa (88.89%), and speC (88.89%). Psoriasis strains harbored smeZ (100%). Conclusions Under COVID-19 pandemic, our collections of GAS infection cutaneous diseases decreased dramatically. Epidemiological analysis of GAS infections among children during COVID-19 pandemic was not significantly different from our previous study. There was a correlation among emm, superantigen gene and disease manifestations. Long-term surveillance and investigation of emm types and superantigens of GAS prevalence are imperative.

Epidemiological analysis of Group A streptococcus infection diseases among children in Beijing, China under COVID-19 pandemic.

BACKGROUND: Group A streptococcus is human-restricted gram-positive pathogen, responsible for various clinical presentations from mild epidermis infections to life threatened invasive diseases. Under COVID-19 pandemic,. the characteristics of the epidemic strains of GAS could be different. PURPOSE: To investigate epidemiological and molecular features of isolates from GAS infections among children in Beijing, China between January 2020 and December 2021. Antimicrobial susceptibility profiling was performed based on Cinical Laboratory Sandards Institute. Distribution of macrolide-resistance genes, emm types, and superantigens was examined by polymerase chain reaction. RESULTS: 114 GAS isolates were collected which were frequent resistance against erythromycin (94.74%), followed by clindamycin (92.98%), tetracycline (87.72%). Emm12 (46.49%), emm1 (25.44%) were dominant emm types. Distribution of ermB, ermA, and mefA gene was 93.85%, 2.63%, and 14.04%, respectively. Frequent superantigenes identified were smeZ (97.39%), speG (95.65%), and speC (92.17%). Emm1 strains possessed smeZ, ssa, and speC, while emm12 possessed smeZ, ssa, speG, and speC. Erythromycin resistance was predominantly mediated by ermB. Scarlet fever strains harbored smeZ (98.81%), speC (94.05%). Impetigo strains harbored smeZ (88.98%), ssa (88.89%), and speC (88.89%). Psoriasis strains harbored smeZ (100%). CONCLUSIONS: Under COVID-19 pandemic, our collections of GAS infection cutaneous diseases decreased dramatically. Epidemiological analysis of GAS infections among children during COVID-19 pandemic was not significantly different from our previous study. There was a correlation among emm, superantigen gene and disease manifestations. Long-term surveillance and investigation of emm types and superantigens of GAS prevalence are imperative.

Risk Factors for COVID-19 in Patients with Hypertension.

BACKGROUND: Hypertension, as the most common comorbidity for patients with coronavirus disease 19 (COVID-19), has resulted in cases with more severe symptoms and higher mortality. The risk factors associated with COVID-19 in patients with hypertension are unknown. METHODS: All the available and confirmed patients with COVID-19 from February 3 to March 10, 2020, were enrolled from Huoshenshan Hospital, Wuhan, China. The demographic characteristics, clinical manifestations, laboratory data, radiological assessments, and treatments on admission were extracted and compared. Univariate and multivariate logistic regression methods were used to explore risk factors associated with COVID-19 in patients with hypertension and the severity of the cohort. RESULTS: A total of 430 available patients with COVID-19 were enrolled in the study, including 151 eligible patients with COVID-19 and hypertension. After PSM analysis, 141 patients without hypertension and 141 cases with hypertension were well matched. Compared with cases without hypertension, patients with hypertension were more severe (28.4% vs. 12.1%, p=0.001). In multivariate analysis, we found that neutrophil count (OR: 1.471; p=0.001), coronary heart disease (OR: 5.281; p=0.011), and the level of K+ (OR: 0.273; p < 0.001) were associated with patients with hypertension. In addition, the percentage of pulmonary infection volume was larger in cases with hypertension (4.55 vs. 5.8, p=0.017) and was a high risk factor for severe COVID-19 in patients with hypertension (OR: 1.084; p < 0.001). CONCLUSION: On admission, coronary heart disease, neutrophil count, and the level of K+ were associated with COVID-19 patients with hypertension. The percentage of the pulmonary infection volume was significantly larger in COVID-19 patients with hypertension and was a risk factor for COVID-19 severity of the cohort.

Automated detection and quantification of COVID-19 pneumonia: CT imaging analysis by a deep learning-based software.

BACKGROUND: The novel coronavirus disease 2019 (COVID-19) is an emerging worldwide threat to public health. While chest computed tomography (CT) plays an indispensable role in its diagnosis, the quantification and localization of lesions cannot be accurately assessed manually. We employed deep learning-based software to aid in detection, localization and quantification of COVID-19 pneumonia. METHODS: A total of 2460 RT-PCR tested SARS-CoV-2-positive patients (1250 men and 1210 women; mean age, 57.7 ± 14.0 years (age range, 11-93 years) were retrospectively identified from Huoshenshan Hospital in Wuhan from February 11 to March 16, 2020. Basic clinical characteristics were reviewed. The uAI Intelligent Assistant Analysis System was used to assess the CT scans. RESULTS: CT scans of 2215 patients (90%) showed multiple lesions of which 36 (1%) and 50 patients (2%) had left and right lung infections, respectively (> 50% of each affected lung's volume), while 27 (1%) had total lung infection (> 50% of the total volume of both lungs). Overall, 298 (12%), 778 (32%) and 1300 (53%) patients exhibited pure ground glass opacities (GGOs), GGOs with sub-solid lesions and GGOs with both sub-solid and solid lesions, respectively. Moreover, 2305 (94%) and 71 (3%) patients presented primarily with GGOs and sub-solid lesions, respectively. Elderly patients (≥ 60 years) were more likely to exhibit sub-solid lesions. The generalized linear mixed model showed that the dorsal segment of the right lower lobe was the favoured site of COVID-19 pneumonia. CONCLUSION: Chest CT combined with analysis by the uAI Intelligent Assistant Analysis System can accurately evaluate pneumonia in COVID-19 patients.