Under the"New Medicine"context,how to integrate medical engineering technology into modern teaching methods has become a key approach to enhancing the quality and efficiency of medical education.This paper aims to advance medical education and teaching reforms,promote the comprehensive improvement in the quality of higher education,and focus on enhancing the quality of medical talent cultivation.For this purpose,the authors proposed an innovative intelligent classroom teaching model,based on artificial intelligence technology.This model utilized advanced camera technology to capture students'facial expressions in real-time,combining artificial intelligence algorithms to analyze students'understanding of medical concepts,and assisted teachers in adjusting classroom teaching strategies in real-time to improve learning effectiveness.This approach has significantly enhanced the overall quality and efficiency of medical education and is of great significance for cultivating versatile medical professionals with high levels of expertise and practical skills.

In response to the limitation of some medical image segmentation models for rectal cancer auxiliary diagnosis that are only applicable to single-modality images,a medical image segmentation method based on a cross attention mechanism that is applicable to both CT and MRI modalities is presented.Considering the different feature representations of CT and MRI images,a cross attention mechanism is proposed to unify the feature representations of the two types of images.In view of the small lesions on rectal cancer images,an improved Swin Transformer segmentation network with 3 branches is established,and the cross attention mechanism is incorporated into it,enabling the model to segment lesion areas in both types of images.The proposed method is validated using CT and MRI image data from patients with rectal cancer.Compared with ADDA,CycleGAN,and SIFA methods,the proposed method improves the accuracy by 2.94%,3.05%,0.71%on CT images,and 3.31%,4.55%,1.76%on MRI images,respectively,demonstrating its superior segmentation performance for both types of images.

Objective To analyze the molecular characteristics and genetic origin of a novel avian influenza A H7N4 virus casuing a case of human infection in China. Methods Specimens were collected from the patient and chickens and ducks kept by the patient and neighbours and then detected by real-time quantitative PCR. The original specimens and virus isolates were analyzed by next-generation sequencing technology to obtain viral whole-genome sequences. Pairwise sequence alignments and phylogenetic analysis were performed by BLASTs,ClustalX and MEGA 6. 1 softwares. Results In January 2018, a human case infected with avian influenza A H7N4 virus was confirmed. Seven H7N4 viruses were isolated from speci-mens collected from chicken and ducks kept in the patient`s backyard. H7N4 virus was a novel reassortant vi-rus with all eight gene fragments derived from wild waterfowl in Eurasia. HA protein contained a single basic amino acid residue R in cleavage site, suggesting that H7N4 virus was low pathogenic. The receptor-binding sites of HA had QSG at 226-228 residues, which indicated that the virus retained avian-type receptor speci-ficity (SAα2-3Gal). Different from H7N4 viruses in avian, the virus isolated from the patient had substitu-tion at position 627 ( E→K) in PB2 protein, which might increase its adaptation in human host. Conclusion This study reported a case of human infection with a novel reassortant avian influenza A H7N4 virus, which revealed that the traditional backyard breeding models might facilitate cross-species transmission of avian in-fluenza viruses in southern China.