Standardized training of newly recruited nurses is crucial for enhancing their clinical competencies and facilita-ting rapid adaptation to clinical environments.This approach aims to develop nursing professionals with advanced clinical skills and expertise.This paper reviews and analyzes the training paradigms for new nurses globally,focusing on the challenges faced in the standardized training of new nurses in China,to provide insights and references for future training programs.

Objective To evaluate the effectiveness of WeChat group integrating with BOPPPS instructional model in the standardized training of nurses in an orthopedics department.Methods A total of 56 nurses in orthopedical standardized training from a hospital were selected and divided into a control group(28 nurses from September 2022 to August 2023)and an interven-tion group(28 nurses trained via WeChat groups and the BOPPPS model from September 2023 to August 2024).The two groups were compared in terms of their final assessment scores,critical thinking,and self-directed learning capabilities.Results After training,the intervention group had significantly higher scores in the final assessment,all items of the California Critical Thinking Disposition Inventory,and all dimensions of the Learning Ability Assessment Scale compared to the control group(all P＜0.05).Conclusion WeChat groups combined with the BOPPPS teaching mode effectively improves the self-directed learning ability,critical thinking skills,and assessment results of orthopedic nurses.

Objective To synthesize high-quality synthetic CT (sCT) images from cone beam CT (CBCT) by learning lung CT domain image features with a deep learning algorithm. Methods A sCT generation algorithm which employs perceptual loss-based cyclic generative adversarial network model (CycleGAN) and iterative registration was presented. CycleGAN model was trained to generate high-quality sCT images by combining perceptual loss and cycle consistency loss;and Elastix was used to register the generated sCT image and the planned CT (pCT) image,and iterate CycleGAN generator model. Results Experiments were conducted on the obtained pCT and CBCT data of 70 patients with lung tumors. From a quantitative perspective,the SSIM between sCT generated by the proposed algorithm and pCT was improved by 11.9％ as compared with that between CBCT and pCT,increasing from 0.825 to 0.923;additionally,RMSE dropped from 110.97 HU to 78.62 HU,PSNR increased from 32.21 dB to 34.74 dB,and mutual information increased from 1.187 to 1.418. The visual evaluation revealed that the proposed algorithm greatly eliminated the scattering artifacts of CBCT slices,highlighted the bone structure,and repaired the soft tissue structure. The comparisons with U-CycleGAN,R-CycleGAN and CUT models confirmed the effectiveness of the proposed algorithm. Conclusion Using the proposed algorithm for sCT images generation can effectively reduce the dose error and structural error between CBCT and pCT,making it possible to apply the proposed algorithm to accurate dose calculations and assist doctors in clinical diagnosis.

Standardized training of newly recruited nurses is crucial for enhancing their clinical competencies and facilita-ting rapid adaptation to clinical environments.This approach aims to develop nursing professionals with advanced clinical skills and expertise.This paper reviews and analyzes the training paradigms for new nurses globally,focusing on the challenges faced in the standardized training of new nurses in China,to provide insights and references for future training programs.

Objective To evaluate the effectiveness of WeChat group integrating with BOPPPS instructional model in the standardized training of nurses in an orthopedics department.Methods A total of 56 nurses in orthopedical standardized training from a hospital were selected and divided into a control group(28 nurses from September 2022 to August 2023)and an interven-tion group(28 nurses trained via WeChat groups and the BOPPPS model from September 2023 to August 2024).The two groups were compared in terms of their final assessment scores,critical thinking,and self-directed learning capabilities.Results After training,the intervention group had significantly higher scores in the final assessment,all items of the California Critical Thinking Disposition Inventory,and all dimensions of the Learning Ability Assessment Scale compared to the control group(all P＜0.05).Conclusion WeChat groups combined with the BOPPPS teaching mode effectively improves the self-directed learning ability,critical thinking skills,and assessment results of orthopedic nurses.

Objective To synthesize high-quality synthetic CT (sCT) images from cone beam CT (CBCT) by learning lung CT domain image features with a deep learning algorithm. Methods A sCT generation algorithm which employs perceptual loss-based cyclic generative adversarial network model (CycleGAN) and iterative registration was presented. CycleGAN model was trained to generate high-quality sCT images by combining perceptual loss and cycle consistency loss;and Elastix was used to register the generated sCT image and the planned CT (pCT) image,and iterate CycleGAN generator model. Results Experiments were conducted on the obtained pCT and CBCT data of 70 patients with lung tumors. From a quantitative perspective,the SSIM between sCT generated by the proposed algorithm and pCT was improved by 11.9％ as compared with that between CBCT and pCT,increasing from 0.825 to 0.923;additionally,RMSE dropped from 110.97 HU to 78.62 HU,PSNR increased from 32.21 dB to 34.74 dB,and mutual information increased from 1.187 to 1.418. The visual evaluation revealed that the proposed algorithm greatly eliminated the scattering artifacts of CBCT slices,highlighted the bone structure,and repaired the soft tissue structure. The comparisons with U-CycleGAN,R-CycleGAN and CUT models confirmed the effectiveness of the proposed algorithm. Conclusion Using the proposed algorithm for sCT images generation can effectively reduce the dose error and structural error between CBCT and pCT,making it possible to apply the proposed algorithm to accurate dose calculations and assist doctors in clinical diagnosis.

Adrenal vein sampling is required for the staging diagnosis of primary aldosteronism,and the frames in which the adrenal veins are presented are called key frames.Currently,the selection of key frames relies on the doctor's visual judgement which is time-consuming and laborious.This study proposes a key frame recognition algorithm based on deep learning.Firstly,wavelet denoising and multi-scale vessel-enhanced filtering are used to preserve the morphological features of the adrenal veins.Furthermore,by incorporating the self-attention mechanism,an improved recognition model called ResNet50-SA is obtained.Compared with commonly used transfer learning,the new model achieves 97.11%in accuracy,precision,recall,F1,and AUC,which is superior to other models and can help clinicians quickly identify key frames in adrenal veins.

Objective To detect Chlamydia trachomatis phage Vp1 gene in clinical swab specimens and anti-Vp1 antibodies in serum specimens.MethodsCervical and urethral swab as well as serum specimens were collected from attendees to the sexually transmitted disease(STD) clinic in the Tianjin Institute of STD,during March 2008 to March 2011.PCR was conducted to detect chlamydial phage Vp1 gene in swab samples,enzyme linked immunosorbent assay(ELISA) and Western blot to detect anti-Vp1 antibody in sera.The swab specimens positive for Vp1 gene were subjected to cell culture followed by the detection of Vp1 protein with an immunofluorescence-based method.ResultsTotally,36 out of 1542 swab specimens turned out to be positive for Vp1 gene,and 23 out of 453 serum specimens for anti-Vp1 antibody.No positive results were obtained in the Vp1 gene-positive swab specimens by cell culture and immunofluorescence-based assay.ConclusionThe Vp1 gene of Chlamydial trachomatis phage and anti-Vp1 antibody are successfully detected from clinical swab and serum specimens respectively.