IntroductionHuman exhaled volatile organic compounds (VOCs) are being extensively studied for the purposes of noninvasive cancer diagnoses. This article was primarily to assess the feasibility of utilizing exhaled VOCs analysis for gastrointestinal cancer (GIC) diagnosis.MethodsPRISMA-based system searches were conducted for related studies of exhaled VOCs in GIC diagnosis based on predetermined criteria. Relevant articles on colorectal cancer and gastroesophageal cancer were summarized, and meta analysis was performed on articles providing sensitivity and specificity data.ResultsFrom 2,227 articles, 14 were found to meet inclusion criteria, six of which were on colorectal cancer (CRC) and eight on Gastroesophageal cancer(GEC). Five articles could provide specific data of sensitivity and specificity in GEC, which were used for meta-analysis. The pooled sensitivity, specificity, diagnostic odds ratio (DOR), and area under the curve (AUC) were calculated based on the combination of these data, and were 85.0% [95% confidence interval (CI): 79.0%–90.0%], 89.0% (95%CI: 86.0%–91.0%), 41.30 (21.56–79.10), and 0.93, respectively.ConclusionVOCs can distinguish gastrointestinal cancers from other gastrointestinal diseases, opening up a new avenue for the diagnosis and identification of gastrointestinal cancers, and the analysis of VOCs in exhaled breath has potential clinical application in screening. VOCs are promising tumor biomarkers for GIC diagnosis. Furthermore, limitations like the heterogeneity of diagnostic VOCs between studies should be minded.

INTRODUCTION: Patients with atrophic gastritis (AG) or gastric intestinal metaplasia (GIM) have elevated risk of gastric adenocarcinoma. Endoscopic screening and surveillance have been implemented in high incidence countries. The study aimed to evaluate the accuracy of a deep convolutional neural network (CNN) for simultaneous recognition of AG and GIM. METHODS: Archived endoscopic white light images with corresponding gastric biopsies were collected from 14 hospitals located in different regions of China. Corresponding images by anatomic sites containing AG, GIM, and chronic non-AG were categorized using pathology reports. The participants were randomly assigned (8:1:1) to the training cohort for developing the CNN model (TResNet), the validation cohort for fine-tuning, and the test cohort for evaluating the diagnostic accuracy. The area under the curve (AUC), sensitivity, specificity, and accuracy with 95% confidence interval (CI) were calculated. RESULTS: A total of 7,037 endoscopic images from 2,741 participants were used to develop the CNN for recognition of AG and/or GIM. The AUC for recognizing AG was 0.98 (95% CI 0.97–0.99) with sensitivity, specificity, and accuracy of 96.2% (95% CI 94.2%–97.6%), 96.4% (95% CI 94.8%–97.9%), and 96.4% (95% CI 94.4%–97.8%), respectively. The AUC for recognizing GIM was 0.99 (95% CI 0.98–1.00) with sensitivity, specificity, and accuracy of 97.9% (95% CI 96.2%–98.9%), 97.5% (95% CI 95.8%–98.6%), and 97.6% (95% CI 95.8%–98.6%), respectively. DISCUSSION: CNN using endoscopic white light images achieved high diagnostic accuracy in recognizing AG and GIM.

No abstract

Pseudomonas aeruginosa is a common pathogen of infection in burn and trauma patients, and multi-drug resistant P. aeruginosa has become an increasingly important pathogen. Essential genes are key to the development of novel antibiotics. The PA0715 gene is a novel unidentified essential gene that has attracted our interest as a potential antibiotic target. Our study aims to determine the exact role of PA0715 in cell physiology and bacterial pathogenicity, providing important clues for antibiotic development.Patients and Methods: The shuttle vector pHERD20T containing an arabinose inducible promoter was used to construct the CRISPRi system. Alterations in cellular physiology and bacterial pathogenicity of P. aeruginosa PAO1 after PA0715 inhibition were characterized. High-throughput RNA-seq was performed to gain more insight into the mechanisms by which PA0715 regulates the vital activity of P. aeruginosa. Results: We found that down-regulation of PA0715 significantly reduced PAO1 growth rate, motility and chemotaxis, antibiotic resistance, pyocyanin and biofilm production. In addition, PA0715 inhibition reduced the pathogenicity of PAO1 to the greater galleria mellonella larvae. Transcriptional profiling identified 1757 genes including those related to amino acid, carbohydrate, ketone body and organic salt metabolism, whose expression was directly or indirectly controlled by PA0715. Unexpectedly, genes involved in oxidative phosphorylation also varied with PA0715 levels, and these findings support a hitherto unrecognized critical role for PA0715 in oxidative respiration in P. aeruginosa. Conclusion:We identified PA0715 as a global regulator of the metabolic network that is indispensable for the survival and reproduction of P. aeruginosa. Our results provide a basis for future studies of potential antibiotic targets for P. aeruginosa and offer new ideas for P. aeruginosa infection control.