GlPS1 overexpression accumulates coumarin secondary metabolites in transgenic Arabidopsis.

The dried root of Glehnia littoralis is a traditional Chinese herbal medicine mainly used to treat lung diseases and plays an important role in fighting coronavirus disease 2019 pneumonia in China. This study focused on the key enzyme gene GlPS1 for furanocoumarin synthesis in G. littoralis. In the 35S:GlPS1 transgenic Arabidopsis study, the Arabidopsis thaliana-overexpressing GlPS1 gene was more salt-tolerant than Arabidopsis in the blank group. Metabolomics analysis showed 30 differential metabolites in Arabidopsis, which overexpressed the GlPS1 gene. Twelve coumarin compounds were significantly upregulated, and six of these coumarin compounds were not detected in the blank group. Among these differential coumarin metabolites, isopimpinellin and aesculetin have been annotated by the Kyoto Encyclopedia of Genes and Genomes and isopimpinellin was not detected in the blank group. Through structural comparison, imperatorin was formed by dehydration and condensation of zanthotoxol and a molecule of isoprenol, and the difference between them was only one isoprene. Results showed that the GlPS1 gene positively regulated the synthesis of coumarin metabolites in A. thaliana and at the same time improved the salt tolerance of A. thaliana. Supplementary Information: The online version contains supplementary material available at 10.1007/s11240-022-02427-w.

Study on the correlations of different clinical types with imaging findings at initial diagnosis and clinical laboratory indexes in COVID-19 patients.

Objectives: To investigate the correlations of initial lab and imaging findings in COVID-19 patients of different clinical types. Methods: We retrospective analyzed patients confirmed with COVID-19 in the Fifth Medical Center of the People's Liberation Army (PLA) General Hospital between February to April 2020, selected a total of 58 (N) patients with lab and imaging examinations that met the study criteria, using Artificial intelligence (AI) software to calculate the percentage of COVID-19 lesions in the volume of the whole lung, then the correlations of general information, initial chest CT examination after admission and laboratory examinations were analyzed. Results: The 58 (N) COVID-19 patients were divided into mild group [41(n) cases]: and severe group [17(n) cases]: according to patient's condition. CT findings of the severe group and mild group mainly included single or multiple ground glass opacity (GGO), with lesions mainly distributed in the periphery of lungs or GGO mixed with consolidation, with lesions involved in peripheral and central areas of both lungs, accompanied other signs. A significant difference in CRP, IL-6, D-D, GGT was observed between the two groups (p < 0.05). The ratios regarding lymphocyte abnormality and neutrophil abnormality in the severe group were higher than those in the mild group (p < 0.05). Conclusion: The CT features at initial diagnosis of COVID-19 were mainly characterized by multiple GGO with or without partial consolidation in both lungs, with the lesions mainly distributed at the subpleural regions. Some lab test indexes were correlated with the clinical types of COVID-19.

Development and validation of a clinical prediction model to estimate the risk of critical patients with COVID-19.

The outbreak of coronavirus disease 2019 (COVID-19) has globally strained medical resources and caused significant mortality. This study was aimed to develop and validate a prediction model based on clinical features to estimate the risk of patients with COVID-19 at admission progressing to critical patients. Patients admitted to the hospital between January 16, 2020, and March 10, 2020, were retrospectively enrolled, and they were observed for at least 14 days after admission to determine whether they developed into severe pneumonia. According to the clinical symptoms, all patients were divided into four groups: mild, normal, severe, and critical. A total of 390 patients with COVID-19 pneumonia were identified, including 212 severe patients and 178 nonsevere patients. The least absolute shrinkage and selection operator (LASSO) regression reduced the variables in the model to 6, which are age, number of comorbidities, computed tomography severity score, lymphocyte count, aspartate aminotransferase, and albumin. The area under curve of the model in the training set is 0.898, and the specificity and sensitivity were 89.7% and 75.5%. The prediction model, nomogram might be useful to access the onset of severe and critical illness among COVID-19 patients at admission, which is instructive for clinical diagnosis.

Prognostic Factors for COVID-19 Hospitalized Patients with Preexisting Type 2 Diabetes.

BACKGROUND: Type 2 diabetes (T2D) as a worldwide chronic disease combined with the COVID-19 pandemic prompts the need for improving the management of hospitalized COVID-19 patients with preexisting T2D to reduce complications and the risk of death. This study aimed to identify clinical factors associated with COVID-19 outcomes specifically targeted at T2D patients and build an individualized risk prediction nomogram for risk stratification and early clinical intervention to reduce mortality. METHODS: In this retrospective study, the clinical characteristics of 382 confirmed COVID-19 patients, consisting of 108 with and 274 without preexisting T2D, from January 8 to March 7, 2020, in Tianyou Hospital in Wuhan, China, were collected and analyzed. Univariate and multivariate Cox regression models were performed to identify specific clinical factors associated with mortality of COVID-19 patients with T2D. An individualized risk prediction nomogram was developed and evaluated by discrimination and calibration. RESULTS: Nearly 15% (16/108) of hospitalized COVID-19 patients with T2D died. Twelve risk factors predictive of mortality were identified. Older age (HR = 1.076, 95% CI = 1.014-1.143, p=0.016), elevated glucose level (HR = 1.153, 95% CI = 1.038-1.28, p=0.0079), increased serum amyloid A (SAA) (HR = 1.007, 95% CI = 1.001-1.014, p=0.022), diabetes treatment with only oral diabetes medication (HR = 0.152, 95%CI = 0.032-0.73, p=0.0036), and oral medication plus insulin (HR = 0.095, 95%CI = 0.019-0.462, p=0.019) were independent prognostic factors. A nomogram based on these prognostic factors was built for early prediction of 7-day, 14-day, and 21-day survival of diabetes patients. High concordance index (C-index) was achieved, and the calibration curves showed the model had good prediction ability within three weeks of COVID-19 onset. CONCLUSIONS: By incorporating specific prognostic factors, this study provided a user-friendly graphical risk prediction tool for clinicians to quickly identify high-risk T2D patients hospitalized for COVID-19.

Risk Factors Associated With Clinical Outcomes in 323 Coronavirus Disease 2019 (COVID-19) Hospitalized Patients in Wuhan, China.

BACKGROUND: With evidence of sustained transmission in more than 190 countries, coronavirus disease 2019 (COVID-19) has been declared a global pandemic. Data are urgently needed about risk factors associated with clinical outcomes. METHODS: A retrospective review of 323 hospitalized patients with COVID-19 in Wuhan was conducted. Patients were classified into 3 disease severity groups (nonsevere, severe, and critical), based on initial clinical presentation. Clinical outcomes were designated as favorable and unfavorable, based on disease progression and response to treatments. Logistic regression models were performed to identify risk factors associated with clinical outcomes, and log-rank test was conducted for the association with clinical progression. RESULTS: Current standard treatments did not show significant improvement in patient outcomes. By univariate logistic regression analysis, 27 risk factors were significantly associated with clinical outcomes. Multivariate regression indicated age >65 years (P < .001), smoking (P = .001), critical disease status (P = .002), diabetes (P = .025), high hypersensitive troponin I (>0.04 pg/mL, P = .02), leukocytosis (>10 × 109/L, P < .001), and neutrophilia (>75 × 109/L, P < .001) predicted unfavorable clinical outcomes. In contrast, the administration of hypnotics was significantly associated with favorable outcomes (P < .001), which was confirmed by survival analysis. CONCLUSIONS: Hypnotics may be an effective ancillary treatment for COVID-19. We also found novel risk factors, such as higher hypersensitive troponin I, predicted poor clinical outcomes. Overall, our study provides useful data to guide early clinical decision making to reduce mortality and improve clinical outcomes of COVID-19.

Feasibility of Shengmai powder in convalescent patients with novel coronavirus pneumonia

As the number of discharged patients with novel coronavirus pneumonia (COVID-19) increased, TCM treatment received more attention. Some COVID-19 diagnosis and treatment plans issued by national health commission and local government recorded the syndrome differentiation and classification of TCM in the convalescent period and the treatment prescriptions. The clinical characteristics and TCM classification of convalescent period were also reported in recent literatures. Deficiency of Qi and Yin is the main syndrome in recovery period, and the Shengmai Powder (SMS) is the representative prescription. This paper reviews the studies of SMS in the treatment of deficiency of Qi and Yin, pulmonary fibrosis and vascular endothelial cell injury. The feasibility of SMS for the discharged patients with COVID-19 was discussed. This review will provide reference for clinical doctors and patients in the recovery period of TCM treatment.

D-Dimer and Prothrombin Time Are the Significant Indicators of Severe COVID-19 and Poor Prognosis.

OBJECTIVE: To investigate the value of coagulation indicators D-dimer (DD), prothrombin time (PT), activated partial thromboplastin time (APTT), thrombin time (TT), and fibrinogen (Fg) in predicting the severity and prognosis of COVID-19. METHODS: A total of 115 patients with confirmed COVID-19, who were admitted to Tianyou Hospital of Wuhan University of Science and Technology between January 18, 2020, and March 5, 2020, were included. The dynamic changes of DD, PT, APTT, and Fg were tested, and the correlation with CT imaging, clinical classifications, and prognosis was studied. RESULTS: Coagulation disorder occurred at the early stage of COVID-19 infection, with 50 (43.5%) patients having DD increased and 74 (64.3%) patients having Fg increased. The levels of DD and Fg were correlated with clinical classification. Among 23 patients who deceased, 18 had DD increased at the first lab test, 22 had DD increased at the second and third lab tests, and 18 had prolonged PT at the third test. The results from ROC analyses for mortality risk showed that the AUCs of DD were 0.742, 0.818, and 0.851 in three times of test, respectively; PT was 0.643, 0.824, and 0.937. In addition, with the progression of the disease, the change of CT imaging was closely related to the increase of the DD value (P < 0.01). CONCLUSIONS: Coagulation dysfunction is more likely to occur in severe and critically ill patients. DD and PT could be used as the significant indicators in predicting the mortality of COVID-19.

Serum Amyloid A is a biomarker of severe Coronavirus Disease and poor prognosis.

BACKGROUND: To explore the significance of SAA in evaluating the severity and prognosis of COVID-19. METHODS: A total of 132 patients with confirmed COVID-19 who were admitted to a designated COVID-19 hospital in Wuhan, China from January 18, 2020 to February 26, 2020 were collected. The dynamic changes of blood SAA, CRP, PCT, WBC, Lymphocyte (L), PLT, CT imaging, and disease progression were studied. All patients completed at least twice laboratory data collection and clinical condition assessment at three time points indicated for this study; The length of hospital stay was longer than 14 days prior to February 26, 2020. RESULTS: COVID-19 patients had significantly increased SAA and CRP levels, while L count decreased, and PCT, WBC, and PLT were in the normal range. As disease progressed from mild to critically severe, SAA and CRP gradually increased, while L decreased, and PLT, WBC, and PCT had no significant changes; ROC curve analysis suggests that SAA/L, CRP, SAA, and L count are valuable in evaluating the severity of COVID-19 and distinguishing critically ill patients from mild ones; Patients with SAA consistently trending down during the course of disease have better prognosis, compared with the patients with SAA continuously rising; The initial SAA level is positively correlated with the dynamic changes of the serial CT scans. Patient with higher initial SAA level are more likely to have poor CT imaging. CONCLUSIONS: SAA and L are sensitive indicators in evaluating the severity and prognosis of COVID-19. Monitoring dynamic changes of SAA, combined with CT imaging could be valuable in diagnosis and treatment of COVID-19.