Global burden of esophageal cancer attributable to high BMI in 204 countries and territories: 1990-2019.

BACKGROUND: Esophageal cancer (EC), a common and fatal disease, includes two histological subtypes; esophageal squamous cell carcinoma (ESCC) and esophageal adenocarcinoma (ECA). To aid policymakers in the allocation of resources for the prevention and treatment of EC, updated data on EC deaths and disability-adjusted life years (DALYs) attributable to high body mass index (BMI) are necessary. The objective of this study was to identify trends in EC associated with high BMI between 1990 and 2019 using 2019 Global Burden of Disease data. METHODS: In this observational population-based study, epidemiological data on the association between high BMI and EC were obtained from GBD 2019. The age-standardized mortality rate (ASMRs) and disability-adjusted life year rate (ASDRs) attributable to high BMI-related EC were stratified by year, age, country, and sociodemographic index (SDI). The estimated annual percentage change (EAPC) was calculated to evaluate the temporal trends of the ASMRs and ASDRs between 1990 and 2019. RESULTS: In 2019, the proportion of EC deaths and DALYs attributed to high BMI was 18.1% and 18.9%, respectively, resulting in 89 904 (95% confidence interval [CI]: 27 879-171 255) deaths and 2 202 314 (95% CI: 681 901-4 173 080) DALYs. High BMI-related deaths and DALYs showed a strong upward trend, increasing by more than two-fold since 1990. East Asia and Western Europe showed the highest risk of EC mortality and DALYs attributable to high BMI; China and the USA bear the greatest burden. The ASMR and ASDR increased in five SDI regions. CONCLUSIONS: The incidence of EC is increasing, particularly in developing nations, which may be attributed to the prevalence of high BMI. To mitigate the impact of high BMI on the incidence of EC, it is important to increase awareness of its deleterious effects, which may alleviate the burden of this disease.

Olmesartan Attenuates Single-Lung Ventilation Induced Lung Injury via Regulating Pulmonary Microbiota.

Single-lung ventilation (SLV) associated acute lung injury is similar to ischemia reperfusion (IR) injury which is usually occurred during lung surgery. Olmesartan (Olm), a novel angiotensin receptor blocker (ARB), has been reported to ameliorate organ IR injury. Several recent studies have shown that lung microbiota may be involved in pulmonary diseases, but the effect of pulmonary microbiota in SLV-induced lung injury has not been reported. This study aims to determine the mechanism of how Olm attenuates SLV induced lung injury. Our data showed that 7 days Olm treatment before modeling markedly alleviated SLV-induced lung injury by suppressing inflammation and reactive oxygen species. Bronchoalveolar lavage fluid samples from the injured side were collected for 16S rRNA gene-based sequencing analysis and 53 different bacteria at the genus and species levels were identified. Furthermore, the injured lung samples were collected for metabolomics analysis using liquid chromatography-mass spectrometry analyses to explore differential metabolites. The Kyoto Encyclopedia of Genes and Genomes (KEGG) was applied to analyze the correlation between differential metabolites and lung microbiota. A total of 38 pathways were identified according to differential metabolites and 275 relevant pathways were enriched via analyzing the microbial community, 24 pathways were both identified by analyzing either metabolites or microbiota, including pyrimidine metabolism, purine metabolism, aminoacyl-tRNA biosynthesis and ATP-binding cassette transporter. Besides classical blockage of the renin-angiotensin II system, Olm could also alleviate SLV-induced lung injury by rewiring the interaction between pulmonary microbiota and metabolites.

Classification of Metastatic and Non-Metastatic Thoracic Lymph Nodes in Lung Cancer Patients Based on Dielectric Properties Using Adaptive Probabilistic Neural Networks.

OBJECTIVE: Dielectric properties can be used in normal and malignant tissue identification, which requires an effective classifier because of the high throughput nature of the data. With easy training and fast convergence, probabilistic neural networks (PNNs) are widely applied in pattern classification problems. This study aims to propose a classifier to identify metastatic and non-metastatic thoracic lymph nodes in lung cancer patients based on dielectric properties. METHODS: The dielectric properties (permittivity and conductivity) of lymph nodes were measured using an open-ended coaxial probe. The Synthetic Minority Oversampling Technique method was adopted to modify the dataset. Feature parameters were scored to select the appropriate feature vector using a Statistical Dependency algorithm. The dataset was classified using adaptive PNNs with an optimized smooth factor using the simulated annealing PNN (SA-PNN). The results were compared with traditional Probabilistic, Support Vector Machines, k-Nearest Neighbor and the Classify functions in MATLAB. RESULTS: The conductivity frequencies of 3959, 3958, 3960, 3978, 3510, 3889, 3888, and 3976 MHz were selected as the feature vectors for 219 lymph nodes (178 non-metastatic and 41 metastatic). Compared with the other methods, SA-PNN achieved the highest classification accuracy (92.92%) and the corresponding specificity and sensitivity were 94.72% and 91.11%, respectively. CONCLUSIONS: Compared with the other methods, the SA-PNN proposed in the present study achieved a higher classification accuracy, which provides a new scheme for classification of metastatic and non-metastatic thoracic lymph nodes in lung cancer patients based on dielectric properties.