Area of research
Cancer Research · Molecular Biology
Research interest
Research topics from publications: Diagnosing chronic atrophic gastritis by gastroscopy using artificial intelligence; Radiosensitizer-based injectable hydrogel for enhanced radio-chemotherapy of TNBC; A two-emitting dye-embedded fluorescent sensor based on zirconium MOF for effective detection of fluoride. Representative work: BACKGROUND: The sensitivity of endoscopy in diagnosing chronic atrophic gastritis is only 42%, and multipoint biopsy, despite being more accurate, is not always available. AIMS: This study aimed to construct a convolutional neural network to improve the diagnostic rate of chronic atrophic gastritis. METHODS: We collected 5470 images of the gastric antrums of 1699 patients and labeled them with their pathological findings. Of these, 3042 images depicted atrophic gastritis and 2428 did not. We designed and trained a convolutional neural network-chronic atrophic gastritis model to diagnose atrophic gastritis accurately, verified by five-fold cross-validation. Moreover, the diagnoses of the deep Radionuclide therapy (RNT) stands out as a highly effective method for treating solid tumors. However, its therapeutic efficiency faces challenges due to the radioresistance of tumors, the limited penetration depth and intracellular deposition of rays in tumor tissue, which causes residue of living cancer cells. Herein, we report a novel approach by utilizing radionuclide 131I-labelled polydopamine encapsulated gold nanoparticle co-loaded with the classical anticancer drug gemcitabine within a hydrogel formed from oxidized glucan and chitosan hydrochloride, combining RNT with chemotherapy for cancer treatment. Au, as a high Z element, is able to interact with short-range β-rays to emit brems