An increasing number of publications had reported the association between single‐nucleotide polymorphisms (SNPs) and esophageal cancer (EC) risk in the past decades. Results from these publications were controversial. We used PubMed, Medline, and Web of Science to identify meta‐analysis articles published before 30 July 2018, that summarize a comprehensive investigation for cumulative evidence of genetic polymorphisms of EC and its subtype risk. Two methods, Venice criteria and false‐positive report probability (FPRP) tests, were used to assess cumulative evidence of significant associations. At last, 107 meta‐analyses were considered to be in conformity with the inclusion criteria, yielding 51 variants associated with EC or esophageal squamous cell carcinoma (ESCC). Thirty‐eight variants were considered to be nominally significant associated with risk of EC or ESCC, whereas the rest showed non‐association. In additional, five variants on five genes were rated as strong cumulative epidemiological evidence for a nominally significant association with EC and ESCC risk, including CYP1A1 rs1048943, EGF rs444903, HOTAIR rs920778, MMP2 rs243865, and PLCE1 rs2274223, 10 variants were rated as moderate, and 18 variants were rated as weak. Additionally, 17 SNPs were verified noteworthy in six genomewide association studies (GWAS) using FPRP methods. Collectively, this review offered a comprehensively referenced information with cumulative evidence of associations between genetic polymorphisms and EC and ESCC risk.

Background and Aim:A large number of papers reporting the relationships between body mass index (BMI) and esophageal cancer (EC) risk have been published in the past few decades; however, these results are inconsistent. Therefore, we carried out meta-analyses to explore the relationships between BMI and the risk of EC (including esophageal squamous cell carcinoma [ESCC] and esophageal adenocarcinoma [EADC]). Methods: We used the Web of Science, PubMed, and Embase to identify all published/online articles before December 30, 2018, which yielded 25 articles eligible for data extraction (including 16,561 cases and 11,954,161 controls), and then pooled the relative risks (RRs) and corresponding 95% confidence intervals (CIs) using randomeffects model. Results: Our study presented that underweight had statistically significant association with the risk of EC (RR = 1.78, 95% CI = 1.48, 2.14, P < 0.001) and ESCC (RR = 1.57, 95% CI = 1.20, 2.06, P = 0.001) when compared with normal weight. Interestingly, both overweight and obesity could increase the risk of EADC (RR = 1.56, 95% CI = 1.42, 1.71, P < 0.001; RR = 2.34, 95% CI = 2.02, 2.70, P < 0.001) while decrease the risk of ESCC (RR = 0.71, 95% CI = 0.60, 0.84, P < 0.001; RR = 0.63, 95% CI = 0.60, 0.84, P = 0.002). Additionally, obesity could increase the risk of EC (RR = 1.51, 95% CI = 1.21, 1.89, P < 0.001). Conclusion: These meta-analyses provide a comprehensive and updated epidemiological evidence to confirm the associations between BMI and EC risk. These findings have public health implications with respect to better control bodyweight and then reduce the occurrence of EC (including ESCC and EADC).Body mass index and esophageal cancer risk J Tian et al. Figure 2The forest plot of overall effect for association between underweight subjects and esophageal cancer risk. Figure 3 The forest plot of overall effect for association between underweight subjects and esophageal squamous cell carcinoma risk. at wileyonlinelibrary.com] [Color figure can be viewed [Color figure can be viewed at wileyonlinelibrary.com] J Tian et al. Body mass index and esophageal cancer risk

Photothermal detectors have attracted tremendous research interest in uncooled infrared imaging technology but with a relatively slow response. Here, Si/SnSe-nanorod (Si/SnSe-NR) heterojunctions are fabricated as a photothermal detector to realize high-performance infrared response beyond the bandgap limitation. Vertically standing SnSe-NR arrays are deposited on Si by a sputtering method. Through manipulating the photoinduced thermoelectric (PTE) behavior along the c-axis, the Si/SnSe-NRs heterojunction exhibits a unique four-stage photoresponse with a high photoresponsivity of 106.3 V W–1 and high optical detectivity of 1.9 × 1010 cm Hz1/2 W–1 under 1342 nm illumination. Importantly, an ultrafast infrared photothermal response is achieved with the rise/fall time of 11.3/258.7 μs. Moreover, the coupling effect between the PTE behavior and external thermal excitation enables an improved response by 288.4%. The work not only offers a new strategy to develop high-speed photothermal detectors but also performs a deep understanding of the PTE behavior in a heterojunction system.

Objective In the past few decades, more than 500 reports have been published on the relationship between single nucleotide polymorphisms (SNPs) on candidate genes and gastric cancer (GC) risk. Previous findings have been disputed and are controversial. Therefore, we performed this article to summarize and assess the credibility and strength of genetic polymorphisms on the risk of GC. Methods We used Web of Science, PubMed, and Medline to identify meta-analyses published before July 30th, 2018 that assessed associations between variants on candidate genes and the risk of GC. Cumulative epidemiological evidence of statistical associations was assessed combining Venice criteria and a false-positive report probability (FPRP) test. Results Sixty-one variants demonstrated a significant association with GC risk, whereas 29 demonstrated no association. Nine variants on nine genes were rated as presenting strong cumulative epidemiological evidence for a nominally significant association with GC risk, including APE1 (rs1760944), DNMT1 (rs16999593), ERCC5 (rs751402), GSTT1 (null/presence), MDM2 (rs2278744), PPARG (rs1801282), TLR4 (rs4986790), IL-17F (rs763780), and CASP8 (rs3834129). Eleven SNPs were rated as moderate, and 33 SNPs were rated as weak. We also used the FPRP test to identify 13 noteworthy SNPs in five genome-wide association studies. Conclusions Sixty-one variants are significantly associated with GC risk, and 29 variants are not associated with GC risk; however, five variants on five genes presented strong evidence for an association upgraded from moderate. Further study of these variants may be needed in the future. Our study also provides referenced information for the genetic predisposition to GC.

Background: Brain metastasis (BM) causes high morbidity and mortality rates in lung cancer (LC) patients. The present study aims to develop models for predicting the development and prognosis of BM using a large LC cohort. Methods: A total of 266,522 LC cases diagnosed between 2010 and 2016 were selected from the Surveillance, Epidemiology, and End Results (SEER) Program cohort. Risk factors for developing BM and prognosis were calculated by univariable and multivariable logistic and Cox regression analysis, respectively, and nomograms were constructed based on risk factors. Nomogram performance was evaluated with receiver operating characteristics (ROC) curve, or C-index and calibration curve. Results: The prevalence of BM was 13.33%. Associated factors for developing BM include: advanced age;Asian or Pacific Islander race; uninsured status; primary tumor site; higher T stage; higher N stage; poorly differentiated grade; the presence of lung, liver, and bone metastases; and adenocarcinoma histology. Median overall survival (OS) was 4 months; associated prognosis factors were similar to risk factors plus female gender, unmarried status, and surgery. The calibration curve showed good agreement between predicted and actual probability, and the AUC/C-index was 73.1% (95% CI: 72.6-73.6%) and 0.88 (95% CI: 0.87-0.89) for risk and prognosis predictive models, respectively.Conclusions: BM was highly developed in LC patients, and homogeneous and heterogeneous factors were found between risk and prognosis for BM. The nomogram showed good performance in predicting BM development and prognosis.