Association between early treatment with Qingfei Paidu decoction and favorable clinical outcomes in patients with COVID-19: A retrospective multicenter cohort study.

The coronavirus disease 2019 (COVID-19) epidemic has been almost controlled in China under a series of policies, including "early diagnosis and early treatment". This study aimed to explore the association between early treatment with Qingfei Paidu decoction (QFPDD) and favorable clinical outcomes. In this retrospective multicenter study, we included 782 patients (males, 56 %; median age 46) with confirmed COVID-19 from 54 hospitals in nine provinces of China, who were divided into four groups according to the treatment initiation time from the first date of onset of symptoms to the date of starting treatment with QFPDD. The primary outcome was time to recovery; days of viral shedding, duration of hospital stay, and course of the disease were also analyzed. Compared with treatment initiated after 3 weeks, early treatment with QFPDD after less than 1 week, 1-2 weeks, or 2-3 weeks had a higher likelihood of recovery, with adjusted hazard ratio (HR) (95 % confidence interval [CI]) of 3.81 (2.65-5.48), 2.63 (1.86-3.73), and 1.92 (1.34-2.75), respectively. The median course of the disease decreased from 34 days to 24 days, 21 days, and 18 days when treatment was administered early by a week (P < 0.0001). Treatment within a week was related to a decrease by 1-4 days in the median duration of hospital stay compared with late treatment (P<0.0001). In conclusion, early treatment with QFPDD may serve as an effective strategy in controlling the epidemic, as early treatment with QFPDD was associated with favorable outcomes, including faster recovery, shorter time to viral shedding, and a shorter duration of hospital stay. However, further multicenter, prospective studies with a larger sample size should be conducted to confirm the benefits of early treatment with QFPDD.

The Efficacy and Toxicity of Gefitinib in Treating Non-small Cell Lung Cancer: A Meta-analysis of 19 Randomized Clinical Trials.

Background: This meta-analysis evaluated the efficacy and toxicity of gefitinib with other commonly used drugs in different treatment settings and epidermal growth factor receptor (EGFR) mutation status. Methods: Nineteen randomize clinical trials (RCTs) of 6,554 patients with NSCLC were pooled in this meta-analysis by random-effects or fixed-effects model, whichever is proper. Results: In first-line therapy, gefitinib showed higher odds than chemotherapy (OR = 2.19, 95% CI: 1.20-4.01), but less than other targeted therapies (OR = 0.58, 95% CI: 0.38-0.88). As non-first-line therapy, the overall survival (OS) and progression-free survival (PFS) were similar between gefitinib and controls (HR = 1.00, 95% CI: 0.93-1.08; HR = 0.91, 95% CI: 0.72-1.15), respectively. With the regard to toxicity, the incidences of dry skin, rash and pruritus were higher in gefitinib compared with controls, while gefitinib significantly reduced the incidence of hematologic toxicity. Conclusion: Gefitinib might be more efficient than chemotherapy, but less efficient than other targeted therapies in ORR, especially in EGFR mutation-positive patients. Gefitinib can decrease the odds of hematologic toxicity compared to controls. Future studies, especially those with EGFR mutation-positive patients, will be needed to confirm our findings.

Comparison of dimension reduction-based logistic regression models for case-control genome-wide association study: principal components analysis vs. partial least squares.

With recent advances in biotechnology, genome-wide association study (GWAS) has been widely used to identify genetic variants that underlie human complex diseases and traits. In case-control GWAS, typical statistical strategy is traditional logistical regression (LR) based on single-locus analysis. However, such a single-locus analysis leads to the well-known multiplicity problem, with a risk of inflating type I error and reducing power. Dimension reduction-based techniques, such as principal component-based logistic regression (PC-LR), partial least squares-based logistic regression (PLS-LR), have recently gained much attention in the analysis of high dimensional genomic data. However, the performance of these methods is still not clear, especially in GWAS. We conducted simulations and real data application to compare the type I error and power of PC-LR, PLS-LR and LR applicable to GWAS within a defined single nucleotide polymorphism (SNP) set region. We found that PC-LR and PLS can reasonably control type I error under null hypothesis. On contrast, LR, which is corrected by Bonferroni method, was more conserved in all simulation settings. In particular, we found that PC-LR and PLS-LR had comparable power and they both outperformed LR, especially when the causal SNP was in high linkage disequilibrium with genotyped ones and with a small effective size in simulation. Based on SNP set analysis, we applied all three methods to analyze non-small cell lung cancer GWAS data.

[Application of gene-based logistic kernel-machine regression model on studies related to the genome-wide association].

To explore the gene-based logistic kernel-machine regression model and its application in genome-wide association study(GWAS). Using the simulated genome-wide single-nucleotide polymorphism(SNPs)genotypes data, we proposed a practical statistical analysis strategy-named 'the logistic kernel-machine regression model', based on the gene levels to assess the association between genetic variations and complex diseases. The results from simulation showed that the P value of genes in related diseases was the smallest among all the genes. The results of simulation indicated that not only it could borrow information from different SNPs that were grouped in genes and reducing the degree of freedom through hypothesis testing, but could also incorporate the covariate effects and the complex SNPs interactions. The gene-based logistic kernel-machine regression model seemed to have certain statistical power for testing the association between genetic genes and diseases in GWAS.

[Gene-based principal component logistic regression model and its application on genome-wide association study].

To explore the gene-based principal component logistic regression model and its application in genome-wide association study. Using the simulated genome-wide single nucleotide polymorphism (SNPs) genotypes data, we proposed a practical statistical analysis strategy-'the principal component logistic regression model', based on the gene levels to assess the association between genetic variations and complex diseases. The simulation results showed that the P value of genes in related diseases was the smallest among the results from all the genes. The results of simulation indicated that not only it could reduce the degree of freedom through hypothesis testing but could also better understand the correlations between SNPs. The gene-based principal component logistic regression model seemed to have certain statistical power for testing the association between genetic genes and diseases in the genome-wide association studies.