Tricyclic Aza-Andrographolide Derivatives from Late-Stage Hydroamination and Their Anti-human Coronavirus (Anti-HCoV) Activity.

A late-stage functionalization (LSF) of the natural product andrographolide for the efficient assembly of a range of structurally interesting and diverse tricyclic-aza derivatives was developed. The key to the diversification is a photo-catalyzed intramolecular hydroamination reaction, and acridinium derivatives were demonstrated to be the optimal catalysts. Additionally, the synthesized tricyclic aza-andrographolide derivatives were found to inhibit human coronavirus with high potency.

Prediction of disease progression in patients with COVID-19 by artificial intelligence assisted lesion quantification.

To investigate the value of artificial intelligence (AI) assisted quantification on initial chest CT for prediction of disease progression and clinical outcome in patients with coronavirus disease 2019 (COVID-19). Patients with confirmed COVID-19 infection and initially of non-severe type were retrospectively included. The initial CT scan on admission was used for imaging analysis. The presence of ground glass opacity (GGO), consolidation and other findings were visually evaluated. CT severity score was calculated according to the extent of lesion involvement. In addition, AI based quantification of GGO and consolidation volume were also performed. 123 patients (mean age: 64.43 ± 14.02; 62 males) were included. GGO + consolidation was more frequently revealed in progress-to-severe group whereas pure GGO was more likely to be found in non-severe group. Compared to non-severe group, patients in progress-to-severe group had larger GGO volume (167.33 ± 167.88 cm3 versus 101.12 ± 127 cm3, p = 0.013) as well as consolidation volume (40.85 ± 60.4 cm3 versus 6.63 ± 14.91 cm3, p < 0.001). Among imaging parameters, consolidation volume had the largest area under curve (AUC) in discriminating non-severe from progress-to-severe group (AUC = 0.796, p < 0.001) and patients with or without critical events (AUC = 0.754, p < 0.001). According to multivariate regression, consolidation volume and age were two strongest predictors for disease progression (hazard ratio: 1.053 and 1.071, p: 0.006 and 0.008) whereas age and diabetes were predictors for unfavorable outcome. Consolidation volume quantified on initial chest CT was the strongest predictor for disease severity progression and larger consolidation volume was associated with unfavorable clinical outcome.

Incorporation of Urinary Neutrophil Gelatinase-Associated Lipocalin and Computed Tomography Quantification to Predict Acute Kidney Injury and In-Hospital Death in COVID-19 Patients.

BACKGROUND: The prevalence of acute kidney injury (AKI) in COVID-19 patients is high, with poor prognosis. Early identification of COVID-19 patients who are at risk for AKI and may develop critical illness and death is of great importance. OBJECTIVE: The aim of this study was to develop and validate a prognostic model of AKI and in-hospital death in patients with COVID-19, incorporating the new tubular injury biomarker urinary neutrophil gelatinase-associated lipocalin (u-NGAL) and artificial intelligence (AI)-based chest computed tomography (CT) analysis. METHODS: A single-center cohort of patients with COVID-19 from Wuhan Leishenshan Hospital were included in this study. Demographic characteristics, laboratory findings, and AI-assisted chest CT imaging variables identified on hospital admission were screened using least absolute shrinkage and selection operator (LASSO) and logistic regression to develop a model for predicting the AKI risk. The accuracy of the AKI prediction model was measured using the concordance index (C-index), and the internal validity of the model was assessed by bootstrap resampling. A multivariate Cox regression model and Kaplan-Meier curves were analyzed for survival analysis in COVID-19 patients. RESULTS: One hundred seventy-four patients were included. The median (±SD) age of the patients was 63.59 ± 13.79 years, and 83 (47.7%) were men.u-NGAL, serum creatinine, serum uric acid, and CT ground-glass opacity (GGO) volume were independent predictors of AKI, and all were selected in the nomogram. The prediction model was validated by internal bootstrapping resampling, showing results similar to those obtained from the original samples (i.e., 0.958; 95% CI 0.9097-0.9864). The C-index for predicting AKI was 0.955 (95% CI 0.916-0.995). Multivariate Cox proportional hazards regression confirmed that a high u-NGAL level, an increased GGO volume, and lymphopenia are strong predictors of a poor prognosis and a high risk of in-hospital death. CONCLUSIONS: This model provides a useful individualized risk estimate of AKI in patients with COVID-19. Measurement of u-NGAL and AI-based chest CT quantification are worthy of application and may help clinicians to identify patients with a poor prognosis in COVID-19 at an early stage.