Severity-associated markers and assessment model for predicting the severity of COVID-19: a retrospective study in Hangzhou, China.

BACKGROUND: The severity of COVID-19 associates with the clinical decision making and the prognosis of COVID-19 patients, therefore, early identification of patients who are likely to develop severe or critical COVID-19 is critical in clinical practice. The aim of this study was to screen severity-associated markers and construct an assessment model for predicting the severity of COVID-19. METHODS: 172 confirmed COVID-19 patients were enrolled from two designated hospitals in Hangzhou, China. Ordinal logistic regression was used to screen severity-associated markers. Least Absolute Shrinkage and Selection Operator (LASSO) regression was performed for further feature selection. Assessment models were constructed using logistic regression, ridge regression, support vector machine and random forest. The area under the receiver operator characteristic curve (AUROC) was used to evaluate the performance of different models. Internal validation was performed by using bootstrap with 500 re-sampling in the training set, and external validation was performed in the validation set for the four models, respectively. RESULTS: Age, comorbidity, fever, and 18 laboratory markers were associated with the severity of COVID-19 (all P values < 0.05). By LASSO regression, eight markers were included for the assessment model construction. The ridge regression model had the best performance with AUROCs of 0.930 (95% CI, 0.914-0.943) and 0.827 (95% CI, 0.716-0.921) in the internal and external validations, respectively. A risk score, established based on the ridge regression model, had good discrimination in all patients with an AUROC of 0.897 (95% CI 0.845-0.940), and a well-fitted calibration curve. Using the optimal cutoff value of 71, the sensitivity and specificity were 87.1% and 78.1%, respectively. A web-based assessment system was developed based on the risk score. CONCLUSIONS: Eight clinical markers of lactate dehydrogenase, C-reactive protein, albumin, comorbidity, electrolyte disturbance, coagulation function, eosinophil and lymphocyte counts were associated with the severity of COVID-19. An assessment model constructed with these eight markers would help the clinician to evaluate the likelihood of developing severity of COVID-19 at admission and early take measures on clinical treatment.

Spatiotemporal characteristics and the epidemiology of tuberculosis in China from 2004 to 2017 by the nationwide surveillance system.

BACKGROUND: China has always been one of the countries with the most serious Tuberculosis epidemic in the world. Our study was to observe the Spatial-temporal characteristics and the epidemiology of Tuberculosis in China from 2004 to 2017 with Joinpoint regression analysis, Seasonal Autoregressive integrated moving average (SARIMA) model, geographic cluster, and multivariate time series model. METHODS: The data of TB from January 2004 to December 2017 were obtained from the notifiable infectious disease reporting system supplied by the Chinese Center for Disease Control and Prevention. The incidence trend of TB was observed by the Joinpoint regression analysis. The Seasonal autoregressive integrated moving average (SARIMA) model was used to predict the monthly incidence. Geographic clusters was employed to analyze the spatial autocorrelation. The relative importance component of TB was detected by the multivariate time series model. RESULTS: We included 13,991,850 TB cases from January 2004 to December 2017, with a yearly average morbidity of 999,417 cases. The final selected model was the 0 Joinpoint model (P = 0.0001) with an annual average percent change (AAPC) of - 3.3 (95% CI: - 4.3 to - 2.2, P < 0.001). A seasonality was observed across the 14 years, and the seasonal peaks were in January and March every year. The best SARIMA model was (0, 1, 1) X (0, 1, 1)12 which can be written as (1-B) (1-B12) Xt = (1-0.42349B) (1-0.43338B12) εt, with a minimum AIC (880.5) and SBC (886.4). The predicted value and the original incidence data of 2017 were well matched. The MSE, RMSE, MAE, and MAPE of the modelling performance were 201.76, 14.2, 8.4 and 0.06, respectively. The provinces with a high incidence were located in the northwest (Xinjiang, Tibet) and south (Guangxi, Guizhou, Hainan) of China. The hotspot of TB transmission was mainly located at southern region of China from 2004 to 2008, including Hainan, Guangxi, Guizhou, and Chongqing, which disappeared in the later years. The autoregressive component had a leading role in the incidence of TB which accounted for 81.5-84.5% of the patients on average. The endemic component was about twice as large in the western provinces as the average while the spatial-temporal component was less important there. Most of the high incidences (> 70 cases per 100,000) were influenced by the autoregressive component for the past 14 years. CONCLUSION: In a word, China still has a high TB incidence. However, the incidence rate of TB was significantly decreasing from 2004 to 2017 in China. Seasonal peaks were in January and March every year. Obvious geographical clusters were observed in Tibet and Xinjiang Province. The relative importance component of TB driving transmission was distinguished from the multivariate time series model. For every provinces over the past 14 years, the autoregressive component played a leading role in the incidence of TB which need us to enhance the early protective implementation.

Risk prediction models for hepatocellular carcinoma in chronic hepatitis B patients on antiviral therapy: A meta-analysis.

BACKGROUND AND AIMS: The risk prediction of hepatocellular carcinoma (HCC) in patients with chronic hepatitis B (CHB) is a challenge especially in the era of antiviral therapy. The aim of this meta-analysis was to comprehensively evaluate the performance of existing HCC prediction scores in HCC prediction on antivirals. METHODS: We searched PubMed, Web of Science and Cochrane Library for relevant prospective studies from the inception to August 24, 2021. The areas under the receiver operating characteristics (AUROCs) and their relevant 95% confidence intervals (CIs) of the risk prediction models were calculated. RESULTS: Nine eligible articles with 21561 patients (HCC developed in 947patients, 4.39%; mean follow-up duration: 5 years) and 14 predictive risk scores were included. The pooled AUROC of all included scores for 3-year and 5-year prediction of HCC was 0.72 (95%CI 0.68-0.76) and 0.80 (95%CI 0.76-0.83), with the corresponding sensitivity of 0.84 (95% CI 0.71-0.92) and 0.91(95% CI 0.86-0.95) and specificity of 0.46 (95% CI 0.30-0.63) and 0.48 (95% CI 0.37-0.59), respectively. All the 14 prediction models, as a whole, performed well in different populations, whether they include factor cirrhotic status or not; while those integrated viral load were less accurate (sensitivity 0.78, specificity of 0.57). CONCLUSIONS: In patients with CHB on antivirals, the scores included in our meta-analysis have been proven to be useful for mid-long term HCC prediction. Viral load seems not useful, whereas cirrhosis and its objective surrogates remain the predominant components. These models are expected to translate clinical benefits if used in complementarity with regular HCC surveillance.

Diagnostic of FibroTouch and six serological models in assessing the degree of liver fibrosis among patients with chronic hepatic disease: A single-center retrospective study.

BACKGROUND AND AIMS: The aim of this study was to evaluate the diagnostic value of FibroTouch and serological models on staging hepatic fibrosis in chronic liver diseases. METHODS: We recruited 850 patients undergoing liver biopsy and received FibroTouch test before or after liver biopsy within one week, blood was taken for the routine inspection before the operation within one week. The serological models were calculated by the blood results and routine clinical information. The diagnostic value of FibroTouch and six serological models was analyzed by receiver operating characteristic curve (ROC). RESULTS: Patients with severe liver fibrosis had significantly higher AST, ALT, GGT, RDW, ALP, and FT-LSM. The area under the receiver operating characteristic curve (AUROC) of FT-LSM for the liver diagnosis of S≥2, S≥3 and S = 4 was 0.75(95% confidence interval [CI]:0.72-0.78), 0.83(95% CI: 0.80-0.86), and 0.85 (95% CI: 0.81-0.89), respectively. The optimal cut-off of FT-LSM for diagnosing S≥2, S≥3 and S = 4 was 8.7, 10.7, and 12.3, respectively. CONCLUSIONS: Our study showed the FibroTouch has a higher diagnostic value compared with the non-invasive serological models in staging the fibrosis stage. The cut-off of FibroTouch and five serological models (APRI, FIB-4, S-index, Forns, and PRP) increased with the severe of fibrosis stage.

Triptolide inhibits epithelial­mesenchymal transition and induces apoptosis in gefitinib­resistant lung cancer cells.

The epidermal growth factor receptor­tyrosine kinase inhibitor (EGFR­TKI), gefitinib, is used widely to treat non­small cell lung cancer (NSCLC) with EGFR­activating mutations. Unfortunately, the acquired drug resistance promoted by epithelial­mesenchymal transition (EMT) markedly limits the clinical effects and remains a major barrier to a cure. Our previous isobaric tags for relative and absolute quantitation­based proteomics analysis revealed that the E­cadherin protein level was markedly upregulated by triptolide (TP). The present study aimed to determine whether TP reverses the gefitinib resistance of human lung cancer cells by regulating EMT. It was revealed that TP combined with gefitinib synergistically inhibited the migration and invasion of lung adenocarcinoma cell line A549; the combination treatment had a significantly better outcome than that of TP and gefitinib alone. Moreover, TP effectively increased the sensitivity of drug resistant A549 cells to gefitinib by upregulating E­cadherin protein expression and downregulating the MMP9, SNAIL, and vimentin expression levels. The dysregulated E­cadherin expression of gefitinib­sensitive cells induced gefitinib resistance, which could be overcome by TP. Finally, TP combined with gefitinib significantly inhibited the growth of xenograft tumors induced using gefitinib­resistant A549 cells, which was associated with EMT reversal and E­cadherin signaling activation in vivo. The present results indicated that the combination of TP and TKIs may be a promising therapeutic strategy to treat patients with NSCLCs harboring EGFR mutations.

Diagnostic value and dynamic variance of serum antibody in coronavirus disease 2019.

OBJECTIVE: To investigate the diagnostic value of serological testing and dynamic variance of serum antibody in coronavirus disease 2019 (COVID-19). METHODS: This study retrospectively included 43 patients with a laboratory-confirmed infection and 33 patients with a suspected infection, in whom the disease was eventually excluded. The IgM/IgG titer of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was measured by chemiluminescence immunoassay analysis. RESULTS: Compared to molecular detection, the sensitivities of serum IgM and IgG antibodies to diagnose COVID-19 were 48.1% and 88.9%, and the specificities were 100% and 90.9%, respectively.In the COVID-19 group, the IgM-positive rate increased slightly at first and then decreased over time; in contrast, the IgG-positive rate increased to 100% and was higher than IgM at all times. The IgM-positive rate and titer were not significantly different before and after conversion to virus-negative. The IgG-positive rate was up to 90% and not significantly different before and after conversion to virus-negative. However, the median IgG titer after conversion to virus-negative was double that before, and the difference was significant. CONCLUSIONS: Viral serological testing is an effective means of diagnosis for SARS-CoV-2 infection. The positive rate and titer variance of IgG are higher than those of IgM in COVID-19.