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Background: Endoscopic assessment for the diagnosis of gastric cancer is limited by interoperator variability and lack of real-time capability. Recently, Raman spectroscopy-based artificial intelligence (AI) has been proposed as a solution to overcome these limitations. Objective: To compare the performance of the AI-enabled Raman spectroscopy with that of high-definition white light endoscopy (HD-WLE) for the risk classification of gastric lesions. Methods: This was a randomized double-arm feasibility proof-of-concept trial in which participants with suspected gastric neoplasia underwent endoscopic assessment using either the Raman spectroscopy-based AI (SPECTRA IMDx™) or HD-WLE performed by expert endoscopists. Identified lesions were classified in real time as having either low or high risk for neoplasia. Diagnostic outcomes were compared between the two groups using histopathology as the reference. Results: A total of 20 patients with 25 lesions were included in the study. SPECTRA, in real-time, performed at a statistically similar level to that of HD-WLE performed by expert endoscopists, achieving an overall sensitivity, specificity, and accuracy of 100%, 80%, and 89.0%, respectively, by patient; and 100%, 80%, and 92%, respectively, by lesion, while expert endoscopists using HD-WLE attained a sensitivity, specificity, and accuracy of 100%, 80%, and 90%, respectively, by patient; and 100%, 83.3%, and 91.7%, respectively, by lesion, in differentiating high-risk from low-risk gastric lesions. Conclusions: The SPECTRA’s comparable performance with that of HD-WLE suggests that it can potentially be a valuable adjunct for less experienced endoscopists to attain accurate and real-time diagnoses of gastric lesions. Larger-scale prospective randomized trials are recommended to validate these promising results further.

BackgroundA plethora of weight management apps are available, but many individuals, especially those living with overweight and obesity, still struggle to achieve adequate weight loss. An emerging area in weight management is the support for one’s self-regulation over momentary eating impulses. ObjectiveThis study aims to examine the feasibility and effectiveness of a novel artificial intelligence–assisted weight management app in improving eating behaviors in a Southeast Asian cohort. MethodsA single-group pretest-posttest study was conducted. Participants completed the 1-week run-in period of a 12-week app-based weight management program called the Eating Trigger-Response Inhibition Program (eTRIP). This self-monitoring system was built upon 3 main components, namely, (1) chatbot-based check-ins on eating lapse triggers, (2) food-based computer vision image recognition (system built based on local food items), and (3) automated time-based nudges and meal stopwatch. At every mealtime, participants were prompted to take a picture of their food items, which were identified by a computer vision image recognition technology, thereby triggering a set of chatbot-initiated questions on eating triggers such as who the users were eating with. Paired 2-sided t tests were used to compare the differences in the psychobehavioral constructs before and after the 7-day program, including overeating habits, snacking habits, consideration of future consequences, self-regulation of eating behaviors, anxiety, depression, and physical activity. Qualitative feedback were analyzed by content analysis according to 4 steps, namely, decontextualization, recontextualization, categorization, and compilation. ResultsThe mean age, self-reported BMI, and waist circumference of the participants were 31.25 (SD 9.98) years, 28.86 (SD 7.02) kg/m2, and 92.60 (SD 18.24) cm, respectively. There were significant improvements in all the 7 psychobehavioral constructs, except for anxiety. After adjusting for multiple comparisons, statistically significant improvements were found for overeating habits (mean –0.32, SD 1.16; P

Background: Endoscopic assessment for the diagnosis of gastric cancer is limited by interoperator variability and lack of real-time capability. Recently, Raman spectroscopy-based artificial intelligence (AI) has been proposed as a solution to overcome these limitations. Objective: To compare the performance of the AI-enabled Raman spectroscopy with that of high-definition white light endoscopy (HD-WLE) for the risk classification of gastric lesions. Methods: This was a randomized double-arm feasibility proof-of-concept trial in which participants with suspected gastric neoplasia underwent endoscopic assessment using either the Raman spectroscopy-based AI (SPECTRA IMDx™) or HD-WLE performed by expert endoscopists. Identified lesions were classified in real time as having either low or high risk for neoplasia. Diagnostic outcomes were compared between the two groups using histopathology as the reference. Results: A total of 20 patients with 25 lesions were included in the study. SPECTRA, in real-time, performed at a statistically similar level to that of HD-WLE performed by expert endoscopists, achieving an overall sensitivity, specificity, and accuracy of 100%, 80%, and 89.0%, respectively, by patient; and 100%, 80%, and 92%, respectively, by lesion, while expert endoscopists using HD-WLE attained a sensitivity, specificity, and accuracy of 100%, 80%, and 90%, respectively, by patient; and 100%, 83.3%, and 91.7%, respectively, by lesion, in differentiating high-risk from low-risk gastric lesions. Conclusions: The SPECTRA's comparable performance with that of HD-WLE suggests that it can potentially be a valuable adjunct for less experienced endoscopists to attain accurate and real-time diagnoses of gastric lesions. Larger-scale prospective randomized trials are recommended to validate these promising results further. [ABSTRACT FROM AUTHOR]

Background & Aims: RUNX transcription factors play pivotal roles in embryonic development and neoplasia. We previously identified the single missense mutation R122C in RUNX3 from human gastric cancer. However, how RUNX3R122C mutation disrupts stem cell homeostasis and promotes gastric carcinogenesis remained unclear. Methods: To understand the oncogenic nature of this mutation in vivo, we generated the RUNX3R122C knock-in mice. Stomach tissues were harvested, followed by histologic and immunofluorescence staining, organoid culture, flow cytometry to isolate gastric corpus isthmus and nonisthmus epithelial cells, and RNA extraction for transcriptomic analysis. Results: The corpus tissue of RUNX3R122C/R122C homozygous mice showed a precancerous phenotype such as spasmolytic polypeptide-expressing metaplasia. We observed mucous neck cell hyperplasia; massive reduction of pit, parietal, and chief cell populations; as well as a dramatic increase in the number of rapidly proliferating isthmus stem/progenitor cells in the corpus of RUNX3R122C/R122C mice. Transcriptomic analyses of the isolated epithelial cells showed that the cell-cycle–related MYC target gene signature was enriched in the corpus epithelial cells of RUNX3R122C/R122C mice compared with the wild-type corpus. Mechanistically, RUNX3R122C mutant protein disrupted the regulation of the restriction point where cells decide to enter either a proliferative or quiescent state, thereby driving stem cell expansion and limiting the ability of cells to terminally differentiate. Conclusions: RUNX3R122C missense mutation is associated with the continuous cycling of isthmus stem/progenitor cells, maturation arrest, and development of a precancerous state. This work highlights the importance of RUNX3 in the prevention of metaplasia and gastric cancer.

Context Polyphenols are plant-based compounds with potential anti-inflammatory, antioxidant, and anti-obesogenic properties. However, their effects on health outcomes remain unclear. Objective To evaluate the effects of polyphenols on anthropometric and cardiometabolic markers. Data Sources Six electronic databases—namely, EMBASE, CINAHL, PubMed, Scopus, The Cochrane Library (reviews only), and Web of Science—were searched for relevant systematic reviews with meta-analyses (SRMAs). Data Extraction Three reviewers performed the data extraction via a data-extraction Microsoft Excel spreadsheet. Data Analysis An umbrella review and meta-analysis of existing SRMAs was conducted. Eighteen SRMAs published from 2015 to 2023, representing 445 primary studies and 838 unique effect sizes, were identified. Meta-analyses were conducted using random-effects models with general inverse variance. Polyphenol-containing foods were found to significantly improve weight (-0.36 kg; 95% confidence interval [CI]: −0.62, 0.77 kg; P  < 0.01, I 2 = 64.9%), body mass index (−0.25 kg/m2; 95% CI: −0.34, −0.17 kg/m2; P  < 0.001, I 2 = 82.4%), waist circumference (−0.74 cm; 95% CI: −1.34, −0.15 cm; P  < 0.01, I 2 = 99.3%), low-density-lipoprotein cholesterol (−1.75 mg/dL; 95% CI: −2.56, −0.94; P  < 0.001, I 2 = 98.6%), total cholesterol (−1.23 mg/dL; 95% CI: −2.00, −0.46; P  = 0.002, I 2 = 94.6%), systolic blood pressure (−1.77 mmHg; 95% CI: −1.77, −0.93 mmHg; P  < 0.001, I 2 = 72.4%), diastolic blood pressure (−1.45 mmHg; 95% CI: −2.09, −0.80 mmHg; P  < 0.001, I 2 = 61.0%), fat percentage (−0.70%; 95% CI: −1.03, −0.36%; P  < 0.001, I 2 = 52.6%), fasting blood glucose (−0.18 mg/dL; 95% CI: −0.35, −0.01 mg/dL; P  = 0.04, I 2 = 62.0%), and C-reactive protein (CRP; including high-sensitivity-CRP [hs-CRP]) (−0.2972 mg/dL; 95% CI: −0.52, −0.08 mg/dL; P  = 0.01, I 2 = 87.9%). No significant changes were found for high-density-lipoprotein cholesterol (−0.12 mg/dL; 95% CI: −1.44, 0.69; P  = 0.67, I 2 = 89.4%) and triglycerides (−1.29 mg/dL; 95% CI: −2.74, 0.16; P  = 0.08, I 2 = 85.4%). Between-study heterogeneity could be explained by polyphenol subclass differences. Conclusion The findings of this umbrella review support the beneficial effects of polyphenols on anthropometric and metabolic markers, but discretion is warranted to determine the clinical significance of the magnitude of the biomarker improvements. Systematic Review Registration International Prospective Register of Systematic Reviews no. CRD42023420206. [ABSTRACT FROM AUTHOR]