Glycosylation editing: an innovative therapeutic opportunity in precision oncology.

Cancer is still one of the most arduous challenges in the human society, even though humans have found many ways to try to conquer it. With our incremental understandings on the impact of sugar on human health, the clinical relevance of glycosylation has attracted our attention. The fact that altered glycosylation profiles reflect and define different health statuses provide novel opportunities for cancer diagnosis and therapeutics. By reviewing the mechanisms and critical enzymes involved in protein, lipid and glycosylation, as well as current use of glycosylation for cancer diagnosis and therapeutics, we identify the pivotal connection between glycosylation and cellular redox status and, correspondingly, propose the use of redox modulatory tools such as cold atmospheric plasma (CAP) in cancer control via glycosylation editing. This paper interrogates the clinical relevance of glycosylation on cancer and has the promise to provide new ideas for laboratory practice of cold atmospheric plasma (CAP) and precision oncology therapy.

Identifying pathological slices of gastric cancer via deep learning.

BACKGROUND: The accuracy of histopathology diagnosis largely depends on the pathologist's experience. It usually takes over 10 years to cultivate a senior pathologist, and small numbers of them lead to a high workload for those available. Meanwhile, inconsistent diagnostic results may arise among different pathologists, especially in complex cases, because diagnosis based on morphology is subjective. Computerized analysis based on deep learning has shown potential benefits as a diagnostic strategy. METHODS: This research aims to automatically determine the location of gastric cancer (GC) in the images of GC slices through artificial intelligence. We use image data from a regional teaching hospital in Taiwan for training. We collect images of patients diagnosed with GC from January 1, 2019 to December 31, 2020. In this study, scanned images are used to dissect 13,600 images from 50 different patients with GC sections whose GC sections are stained with hematoxylin and eosin (H&E stained) through a whole slide scanner, the scanned images from 50 different GC slice patients are divided into 80% training combinations, 2200 images of 40 patients are trained. The remaining 20%, totaling 10 people, are validated from a test set of 550 images. RESULTS: The validation results show that 91% of the correct rates are interpreted as GC images through deep learning. The sensitivity, specificity, PPV, and NPV were 84.9%, 94%, 87.7%, and 92.5%, respectively. After creating a 3D model through the grayscale value, the position of the GC is completely marked by the 3D model. The purpose of this research is to use artificial intelligence (AI) to determine the location of the GC in the image of GC slice. CONCLUSION: In patients undergoing pancreatectomy for pancreatic cancer, intraoperative infusion of lidocaine did not improve overall or disease-free survival. Reduced formation of circulating NETs was absent in pancreatic tumour tissue. CONCLUSION: For AI to assist pathologists in daily practice, to help a pathologist making a definite diagnosis is not the prime purpose at present time. The benefits could come from cancer screening and double-check quality control in a heavy workload which could distract the attention of pathologist during the time constraint examination process. We propose a two-steps method to identify cancerous areas in endoscopic gastric biopsy slices via deep learning. Then a 3D model is used to further mark all the positions of GC in the picture, and the model overcomes the problem that deep learning can't catch all GC.