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Background: Long non-coding RNAs (lncRNAs) are known to be involved in tumorigenesis. The functions of LINC00511 in gastric cancer are poorly understood. Methods: Quantitative RT-PCR was performed to investigate the levels of LINC00511 in gastric cancer tissues and cell lines. CCK-8, flow cytometry, wound-healing and Transwell assays were performed to examine cellular functions. The underlying regulatory mechanisms of LINC00511 in gastric cancer progression were determined using luciferase reporter and RIP assays. Results: LINC00511 levels were significantly higher in gastric cancer tissues and cell lines than in normal samples. The high expression of LINC00511 in gastric cancer patient samples positively correlated with advanced clinical characters and poor prognosis. Depleting LINC00511 reduced tumor cell proliferation, migration and invasion, slowed tumor growth, and accelerated cell apoptosis. Our mechanistic study results indicated that LINC00511 promotes gastric cancer progression in a miR-515-5p-dependent manner. Conclusion: We established that LINC00511 may contribute to the proliferation and invasion of gastric cancer cells by modulating miR-515-5p, indicating that LINC00511 may be a potential molecular target for the development of anti-cancer drugs.

BACKGROUND Detection of atrial fibrillation (AF) occurrence over a long duration has been a challenge in the screening and follow-up of AF patients. Wearable devices may be an ideal solution.OBJECTIVE The purpose of this study was to measure the sensitivity, specificity, and accuracy of a recently developed smart wristband device that is equipped with both photoplethysmographic (PPG) and single-channel electrocardiogram (ECG) systems and an AF-identifying, artificial intelligence (AI) algorithm, used in the short term.METHODS Use of the Amazfit Health Band 1S, which records both PPG and single-channel ECG data, was assessed in 401 patients (251 normal individuals and 150 ECG-diagnosed AF patients).RESULTS ECG and PPG readings could not be judged in 15 and 18 subjects, respectively. Subjects who were unable to be judged were defined as either false negative or false positive. The sensitivity, specificity, and accuracy of wristband PPG readings were 88.00%, 96.41%, and 93.27%, respectively, and those of wristband ECG readings were 87.33%, 99.20%, and 94.76%, respectively. When the original wristband ECG records were judged by physicians, the sensitivity, specificity, and accuracy were 96.67%, 98.01%, and 97.51%, respectively.CONCLUSION This promising new combination of PPG, ECG, and AI algorithm has the potential to facilitate AF detection.

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