Impact of COVID-19 prevention and control on tuberculosis and scarlet fever in China's Guizhou.

China has implemented a series of long-term measures to control the spread of COVID-19, however, the effects of these measures on other chronic and acute respiratory infectious diseases remain unclear. Tuberculosis (TB) and scarlet fever (SF) serve as representatives of chronic and acute respiratory infectious diseases, respectively. In China's Guizhou province, an area with a high prevalence of TB and SF, approximately 40,000 TB cases and hundreds of SF cases are reported annually. To assess the impact of COVID-19 prevention and control on TB and SF in Guizhou, the exponential smoothing method was employed to establish a prediction model for analyzing the influence of COVID-19 prevention and control on the number of TB and SF cases. Additionally, spatial aggregation analysis was utilized to describe spatial changes in TB and SF before and after the COVID-19 outbreak. The parameters of the TB and SF prediction models are R2 = 0.856, BIC = 10.972 and R2 = 0.714, BIC = 5.325, respectively. TB and SF cases declined rapidly at the onset of COVID-19 prevention and control measures, with the number of SF cases decreasing for about 3-6 months and the number of TB cases remaining in decline for 7 months after the 11th month. The spatial aggregation of TB and SF did not change significantly before and after the COVID-19 outbreak but exhibited a marked decrease. These findings suggest that China's COVID-19 prevention and control measures also reduced the prevalence of TB and SF in Guizhou. These measures may have a long-term positive impact on TB, but a short-term effect on SF. Areas with high TB prevalence may continue to experience a decline due to the implementation of COVID-19 preventive measures in the future.

Machine learning-based CT radiomics model distinguishes COVID-19 from non-COVID-19 pneumonia.

BACKGROUND: To develop a machine learning-based CT radiomics model is critical for the accurate diagnosis of the rapid spreading coronavirus disease 2019 (COVID-19). METHODS: In this retrospective study, a total of 326 chest CT exams from 134 patients (63 confirmed COVID-19 patients and 71 non-COVID-19 patients) were collected from January 20 to February 8, 2020. A semi-automatic segmentation procedure was used to delineate the volume of interest (VOI), and radiomic features were extracted. The Support Vector Machine (SVM) model was built on the combination of 4 groups of features, including radiomic features, traditional radiological features, quantifying features, and clinical features. By repeating cross-validation procedure, the performance on the time-independent testing cohort was evaluated by the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. RESULTS: For the SVM model built on the combination of 4 groups of features (integrated model), the per-exam AUC was 0.925 (95% CI 0.856 to 0.994) for differentiating COVID-19 on the testing cohort, and the sensitivity and specificity were 0.816 (95% CI 0.651 to 0.917) and 0.923 (95% CI 0.621 to 0.996), respectively. As for the SVM models built on radiomic features, radiological features, quantifying features, and clinical features, individually, the AUC on the testing cohort reached 0.765, 0.818, 0.607, and 0.739, respectively, significantly lower than the integrated model, except for the radiomic model. CONCLUSION: The machine learning-based CT radiomics models may accurately classify COVID-19, helping clinicians and radiologists to identify COVID-19 positive cases.

Applications, phytochemistry, pharmacological effects, pharmacokinetics, toxicity of Scutellaria baicalensis Georgi. and its probably potential therapeutic effects on COVID-19: a review.

Scutellaria baicalensis Georgi. (SB) is a common heat-clearing medicine in traditional Chinese medicine (TCM). It has been used for thousands of years in China and its neighboring countries. Clinically, it is mostly used to treat diseases such as cold and cough. SB has different harvesting periods and processed products for different clinical symptoms. Botanical researches proved that SB included in the Chinese Pharmacopoeia (1st, 2020) was consistent with the medicinal SB described in ancient books. Modern phytochemical analysis had found that SB contains hundreds of active ingredients, of which flavonoids are its major components. These chemical components are the material basis for SB to exert pharmacological effects. Pharmacological studies had shown that SB has a wide range of pharmacological activities such as antiinflammatory, antibacterial, antiviral, anticancer, liver protection, etc. The active ingredients of SB were mostly distributed in liver and kidney, and couldn't be absorbed into brain via oral absorption. SB's toxicity was mostly manifested in liver fibrosis and allergic reactions, mainly caused by baicalin. The non-medicinal application prospects of SB were broad, such as antibacterial plastics, UV-resistant silk, animal feed, etc. In response to the Coronavirus Disease In 2019 (COVID-19), based on the network pharmacology research, SB's active ingredients may have potential therapeutic effects, such as baicalin and baicalein. Therefore, the exact therapeutic effects are still need to be determined in clinical trials. SB has been reviewed in the past 2 years, but the content of these articles were not comprehensive and accurate. In view of the above, we made a comprehensive overview of the research progress of SB, and expect to provide ideas for the follow-up study of SB.

Early chest CT features of patients with 2019 novel coronavirus (COVID-19) pneumonia: relationship to diagnosis and prognosis.

OBJECTIVE: To determine the consistency between CT findings and real-time reverse transcription-polymerase chain reaction (RT-PCR) and to investigate the relationship between CT features and clinical prognosis in COVID-19. METHODS: The clinical manifestations, laboratory parameters, and CT imaging findings were analyzed in 34 COVID-19 patients, confirmed by RT-PCR from January 20 to February 4 in Hainan Province. CT scores were compared between the discharged patients and the ICU patients. RESULTS: Fever (85%) and cough (79%) were most commonly seen. Ten (29%) patients demonstrated negative results on their first RT-PCR. Of the 34 (65%) patients, 22 showed pure ground-glass opacity. Of the 34 (50%) patients, 17 had five lobes of lung involvement, while the 23 (68%) patients had lower lobe involvement. The lesions of 24 (71%) patients were distributed mainly in the subpleural area. The initial CT lesions of ICU patients were distributed in both the subpleural area and centro-parenchyma (80%), and the lesions were scattered. Sixty percent of ICU patients had five lobes involved, while this was seen in only 25% of the discharged patients. The lesions of discharged patients were mainly in the subpleural area (75%). Of the discharged patients, 62.5% showed pure ground-glass opacities; 80% of the ICU patients were in the progressive stage, and 75% of the discharged patients were at an early stage. CT scores of the ICU patients were significantly higher than those of the discharged patients. CONCLUSION: Chest CT plays a crucial role in the early diagnosis of COVID-19, particularly for those patients with a negative RT-PCR. The initial features in CT may be associated with prognosis. KEY POINTS: • Chest CT is valuable for the early diagnosis of COVID-19, particularly for those patients with a negative RT-PCR. • The early CT findings of COVID-19 in ICU patients differed from those of discharged patients.