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Background: Few studies have examined the association between treatment given time and clinical outcomes, which is indeed of great importance to clinical management of coronavirus disease 2019 (COVID-19). We performed this study to explore whether early treatment brings favorable clinical outcomes. Methods: In this retrospective multicenter study, we included patients aged 18 to 87 years with confirmed COVID-19 on admission from 54 hospitals in nine provinces of China from 21 January to 10 March, 2020. Final date of follow-up was March 17, 2020. All patients were treated by Lung cleansing & detoxifying decoction combined with western medicine. Patients were divided into four groups according to the interval from the first date of onset of symptoms to the date of starting a treatment, i.e., ≤1 week group (≤7 days), 1-2 weeks group (>7 days and ≤14 days), 2-3 weeks group (>14 days and ≤21 days) and >3 weeks group (>21 days). Multivariable Cox proportional hazard ratio (HR) models were used to estimate unadjusted and adjusted HRs and 95% confidence intervals (CIs) for the association between the treatment given time and clinical outcomes (time to recovery, days of viral shedding, duration of hospital stay, course of disease, fever and CT images). Findings: Of the 782 patients (median age was 46 years old, and 405 (52%) were male), there were 321 (41%) patients in ≤1 week group, 221 (28%) in 1-2 weeks group, 123 (16%) in 2-3 weeks group and 117 (15%) in >3 weeks group. Compared to patients in later treatment group (greater than 3 weeks), patients in earlier treatment groups of less than 1 week, 1 to 2 weeks, or 2 to 3 weeks had higher likelihood of recovery, with adjusted HR (95% CI) of 3.81 (2.65-5.48), 2.63 (1.86-3.73) and 1.92 (1.34-2.75), respectively. The median days of viral shedding was 13 days and 12 days in 2-3 weeks group and 3 weeks group (P=0.0137). The median course of disease decreased from 34 days to 24 days, 21 days and 18 days when treatment was given every one week in advance compared to that was given later than 3 weeks from the onset of symptoms (P

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.

Graphical abstract, 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

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.