Automatic detection of small bowel lesions with different bleeding risks based on deep learning models.

Background: Deep learning provides an efficient automatic image recognition method for small bowel (SB) capsule endoscopy (CE) that can assist physicians in diagnosis. However, the existing deep learning models present some unresolved challenges.

Aim: To propose a novel and effective classification and detection model to automatically identify various SB lesions and their bleeding risks, and label the lesions accurately so as to enhance the diagnostic efficiency of physicians and the ability to identify high-risk bleeding groups.

[Comparison of Rhesus boxes in Hans and Uighurs].

Objective: To study the difference and similarity between Hans and Uighurs in regard to Rhesus box and its significance.

Methods: The sequence specific primers of upstream, downstream and hybrid Rhesus boxes were designed on the basis of RHD gene sequence. The upstream, downstream and hybrid Rhesus boxes were determined by polymerase chain reaction-sequence specific primer(PCP-SSP) and mismatched PCR.