[Advances in epidemiology, etiology, and treatment of community-acquired pneumonia].

Community-acquired pneumonia (CAP) is the third leading cause of death worldwide and one of the most commonly infectious diseases. Its epidemiological characteristics vary with host and immune status, and corresponding pathogen spectrums migrate over time and space distribution. Meanwhile, with the outbreak of COVID-19, some unconventional treatment strategies are on the rise. This article reviewed the epidemiological characteristics, pathogen spectrum and treatment direction of CAP in China over the years, and aimed to provide guidance for the diagnosis and treatment of CAP in clinical practice.

[Advances in SARS-CoV-2 diagnostic strategies during the later COVID-19 era].

Based on natural infection or vaccination, the protective barrier for population has been preliminarily established. However, with constant appearances of SARS-CoV-2 variants, breakthrough infection events cannot be completely avoided, and thus the diagnostic strategy is still the key to discovering epidemic sources and blocking the transmission chain. Currently, SARS-CoV-2 diagnosis technologies based on nucleic acid, antigen and antibody detections have developed and extended in diversity. Under the background of work resumption and epidemic-prevention normalization during the later COVID-19 era, it is necessary for us to choose appropriate detection methods to satisfy the need of epidemic prevention and control in various scenarios. We summarized the principles and applicable characteristics of existing SARS-CoV-2 detection technologies in this paper, aimed to provide guidance for clinical and public health personnel to make targeted decisions.

Risk Assessment Using Early Quantitative Chest CT Parameters for the Severity of COVID-19

Background: Today, the outbreak of coronavirus disease 2019 (COVID-19) is known as a public health emergency by the World Health Organization (WHO). Therefore, risk assessment is necessary for making a correct decision in disease management. Objectives: This study aimed to assess the risk of progression to the critical stage in COVID-19 patients, based on the early quantitative chest computed tomography (CT) parameters. Patients and Methods: In this case-control study, 39 laboratory-confirmed critical or expired COVID-19 cases (critical group), as well as 117 laboratory-confirmed COVID-19 patients including mild, moderate, and severe cases (non-critical group), were enrolled. Seven quantitative CT parameters, representing the lung volume percentages at different density intervals, were automatically calculated, using the artificial intelligence (AI) algorithms. Multivariable-adjusted logistic regression models, based on the quantitative CT parameters, were established to predict the adverse outcomes (critical vs. non-critical). The predictive performance was estimated using the receiver operating characteristic (ROC) curve analysis and by measuring the area under the ROC curve (AUC). The quantitative CT parameters in different stages were compared between the two groups. Results: No significant differences were found between the two groups regarding the lung volume percentages at different density intervals within 0 - 4 days (P= 0.596-0.938);however, this difference began to become significant within 5 -9 days and persisted even after one month. Overall, the quantitative CT parameters could well predict the severity of COVID-19. The lung volume percentage of -7 Hounsfield units (-7 HUs) had the largest crude odds ratio (OR:1.999;95% CI,1.453 2.750;P < 0.001) and adjusted OR(adjusted OR: 1.768;95% CI, 1.114 2.808;P = 0.016). The lung volume percentage of -6 HU showed the best predictive performance with the largest AUC of 0.808;the cutoff value of 5.93% showed 71.79% sensitivity and 84.62% specificity. Conclusion: Early quantitative chest CT parameters can be measured to assess the risk of progression to the critical stage of COVID19;this is of critical importance in the clinical management of this disease.

[Analysis of the clinical characteristics of COVID-19 reinfection cases].

Objective: To explore the epidemiological and clinical characteristics of COVID-19 reinfection cases. Methods: The published COVID-19 reinfection cases were reviewed and the relevant data were extracted, including the baseline characteristics of patients, the results of antibody tests, and the whole-genome sequencing results of the viral strains. Results: We reviewed 29 reinfection cases in 20 reports from 14 countries. The age of re-infected patients ranged from 21 to 90 years (median 53 years), and there was no significant difference in gender distribution. Among the 29 patients, 11 were health care workers, 6 received immunosuppressive drugs (including glucocorticoids), 17 presented more severe symptoms than their primary infections and 5 (all aged over 80 years) died. The interval of the two infections was usually less than 60 days when the patients were infected by the same viral strain,while the interval was much longer (median 78.5 days) when the patients were infected by different viral strains. Nine patients had negative antibody test results or low antibody titers when the reinfections were confirmed, and 5 of them had negative antibody test results even during the initial infection. Conclusions: Virus-specific antibodies had a protective effect against COVID-19 reinfection for the majority of the population, however, this effect may decrease over time. Occupational exposure, low levels of antibodies, or an inability to produce antibodies may be the main risk factors for reinfection. Advanced age was a major risk factor for a poor prognosis. Effective personal prevention and social distancing were still essential for the prevention of reinfection.