简介：AbstractImportance:LIM domain only 1 (LMO1) gene polymorphisms were previously found to be implicated in the risk of several cancers. No available studies were performed regarding the predisposing effect of LMO1 gene single nucleotide polymorphisms (SNPs) on central nervous system (CNS) tumor risk.Objective:We aimed to determine whether the LMO1 gene SNPs were associated with the risk of CNS tumor by applying a case-control study with 191 cases and 248 controls in China.Methods:The contributions of LMO1 gene SNPs to the risk of CNS tumor was evaluated by multinomial logistic regression.Results:Based on the calculations of odds ratio (OR) and 95% confidence interval (CI), we failed to detect a significant relationship between each LMO1 gene SNP (rs110419 A>G, rs4758051 G>A, rs10840002 A>G, rs204938 A>G, and rs2168101 G>T) and CNS tumor risk, respectively. A negative association was also found in the combined effects on these five SNPs and CNS tumor risk. The stratification analysis further demonstrated the individuals with rs204938 AG/GG genotype confer to increased risk of CNS tumor compared with those with an AA genotype in males (OR: 1.74, 95% CI: 1.01-2.98, P = 0.046).Interpretation:We concluded that LMO1 gene SNPs may not strong enough to influence the risk of CNS tumor in Chinese children. More studies are required to verify this association.

简介：AbstractCurrently, the diagnosis of tuberculosis (TB) is mainly based on the comprehensive consideration of the patient's symptoms and signs, laboratory examinations and chest radiography (CXR). CXR plays a pivotal role to support the early diagnosis of TB, especially when used for TB screening and differential diagnosis. However, high cost of CXR hardware and shortage of certified radiologists poses a major challenge for CXR application in TB screening in resource limited settings. The latest development of artificial intelligence (AI) combined with the accumulation of a large number of medical images provides new opportunities for the establishment of computer-aided detection (CAD) systems in the medical applications, especially in the era of deep learning (DL) technology. Several CAD solutions are now commercially available and there is growing evidence demonstrate their value in imaging diagnosis. Recently, WHO published a rapid communication which stated that CAD may be used as an alternative to human reader interpretation of plain digital CXRs for screening and triage of TB.

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简介：AbstractObjective:Overweight and obesity are increasingly epidemic and negatively related to reproductive outcome. The aim of this study was to investigate the advantages of a modified ultra-long downregulation protocol on pregnancy outcomes of patients with high body mass index (BMI) undergoing in vitro fertilization (IVF)/intracytoplasmic sperm injection (ICSI), compared to the long protocol (LP).Methods:We retrospectively analyzed the clinical data of 3,920 infertile patients at the Reproductive and Genetic Hospital of Citic-Xiangya from January 2012 to December 2017 by propensity score matching (PSM). Patients were divided into two groups: modified ultra-LP (MULP) (n = 1,960) and LP (n = 1,960).Results:In the MULP group, live birth rate (52.65% vs. 46.79%, P < 0.001, odds ratio [OR]: 1.784, 95% confidence interval [CI]: 1.563-2.036), clinical pregnancy rate (62.50% vs. 57.91%, P = 0.003, OR: 1.211, 95% CI: 1.066-1.377), and implantation rate (53.24% vs. 49.65%, P = 0.004, OR: 1.155, 95% CI: 1.048-1.272) were statistically significantly higher than those of the LP group. Moreover, the cycle cancellation rates (12.70% vs. 15.15%, P = 0.027, OR: 0.815, 95% CI: 0.68-0.977), abortion rates (12% vs. 14.8%, P = 0.046, OR: 0.785, 95% CI: 0.619-0.996), and ectopic pregnancy rates (1.06% vs. 2.11%, P = 0.04, OR: 0.497, 95% CI: 0.252-0.98) were lower than those in the LP group.Conclusion:The modified ultra-long downregulation protocol improved the pregnancy outcomes in patients with high BMI undergoing IVF/ICSI treatment, providing a potential option for physicians when deciding an optimized ovary stimulation protocol for high BMI patients.