Objective:To explore the structural characteristics of intestinal microflora in children with non-alcoholic fatty liver disease（NAFLD）and the relationship between intestinal microflora and the occurrence as well as development of NAFLD in children.Methods:Fifteen children with NAFLD diagnosed at the First People's Hospital of Yunnan Province from January 2022 to December 2022 were selected as subjects，and 15 healthy children who received routine physical examinations at the outpatient clinic during the same period were randomly selected as healthy control group.The height，weight，waist circumference，blood pressure，blood biochemistry of all children were collected.At the same time，the fresh fecal samples of all children were collected，and the biological information of intestinal flora was analyzed by 16S rRNA sequencing.Results:In the NAFLD group，there were eight males and seven females，with an average age of（11.13±1.77）years.In healthy control group，there were seven males and eight females，with an average age of（9.73±2.25）years.There were no significant differences in sex，age，blood pressure between two groups.Compared with the healthy control group，the levels of body mass index，waist circumference，waist-to-height ratio，alanine aminotransferase，aspartate transaminase，gamma-glutamyl transpeptidase，total bilirubin，direct bilirubin，unconjugated bilirubin，low density lipoprotein cholesterol，uric acid and serum insulin significantly increased and high density lipoprotein cholesterol significantly decreased in NAFLD group（ P<0.05）.The results of species diversity analysis showed that chaol index and observed-species index in NAFLD group were significantly higher than those in healthy control group（ P<0.05）.Species diversity analysis showed that the species with increased abundance in NAFLD group included：Proteobacteria，Gammaproteobacteria，Enterobacteriaceae，Klebsiella，Escherichia-Shigella，Escherichia-Shigella-unclassified.Differential species with increased abundance in the healthy control group included：Bifidobacterium species，Bifidobacterium，Bifidobacteriaceae，Bifidobacteriales，Actinobacteria，Actinobacteriota，Bacteroidia，Bacteroidales，Streptococcus-thermophilus. Conclusion:There are metabolic abnormalities and obvious changes in the structure of intestinal flora in children with NAFLD.Exogenous supplementation of Bifidobacterium，Streptococcus thermophilus and Bacteroides may prevent the occurrence of NAFLD，delay the progression of disease and improve fat deposition in the liver.

Objective:To explore the prevalence and risk factors of exit-site infection (ESI) in elderly peritoneal dialysis (PD) patients.Methods:The status of exit-site was evaluated in elderly PD patients (≥60 years) who had catheter insertion in our center between January 1, 2009 and December 31, 2013, with follow-up for 1 year or withdrawing from peritoneal dialysis in this period. The patients were divided into ESI and non-ESI group. The data was collected including demographics, clinical features, and nursing care methods of the exit-site.Results:A total of 247 patients were recruited in this study, aged (68.6±6.2) years, among whom there were 132 male (53.4%) and 119 diabetes (48.2%). Median follow-up time was 12.0 months. Thirty-two patients had 34 episodes of ESI with a rate of 82.5 patient-months per episode (0.15 episodes per year). Coagulase-negative Staphylococcus was the main pathogen, accounting for 35.3% of the ESI. No bacterial growth was found in 8.8%. The exit-site nursing care status included that poor compliance of exit-site care 23.5%, poor catheter immobilization 62.3%, history of catheter-pulling injury 9.7%, mechanical stress on exit-site 5.3%, improper frequency of nursing care 29.6%, mupirocin usage 13.8%, patients taking exit-site care 26.7%, exit-site caregiver instability 16.6%. There were no differences in demographic (such as age, gender, primary disease, etc) and laboratory data (hemoglobin, serum albumin, blood potassium, etc) between the ESI and non-ESI groups. Poor compliance with exit-site care ( HR=2.352, 95% CI 1.008-5.488, P=0.048), poor catheter immobilization ( HR=3.074, 95% CI 1.046-9.035, P=0.041) and exit-site caregiver instability ( HR=2.423, 95% CI 1.004-5.845, P=0.049) were significantly correlated with increased risk of ESI. Conclusions:The prevalence of ESI in elderly PD patients was 0.15 episodes per year. Educating PD patients to improve the compliance with exit-site care, maintain catheter immobilization and do exit-site care by a stable and trained caregiver may reduce ESI events in elderly PD patients.

Objective:To investigate a time series deep learning model for respiratory motion prediction.Methods:Eighty pieces of respiratory motion data from lung cancer patients were used in this study. They were divided into a training set and a test set at a ratio of 8∶2. The Informer deep learning network was employed to predict the respiratory motions with a latency of about 600 ms. The model performance was evaluated based on normalized root mean square errors (nRMSEs) and relative root mean square errors (rRMSEs).Results:The Informer model outperformed the conventional multilayer perceptron (MLP) and long short-term memory (LSTM) models. The Informer model yielded an average nRMSE and rRMSE of 0.270 and 0.365, respectively, at a prediction time of 423 ms, and 0.380 and 0.379, respectively, at a prediction time of 615 ms.Conclusions:The Informer model performs well in the case of a longer prediction time and has potential application value for improving the effects of the real-time tracking technology.

Objective:To investigate the pseudo-CT generation from cone beam CT (CBCT) by a deep learning method for the clinical need of adaptive radiotherapy.Methods:CBCT data from 74 prostate cancer patients collected by Varian On-Board Imager and their simulated positioning CT images were used for this study. The deformable registration was implemented by MIM software. And the data were randomly divided into the training set ( n=59) and test set ( n=15). U-net, Pix2PixGAN and CycleGAN were employed to learn the mapping from CBCT to simulated positioning CT. The evaluation indexes included mean absolute error (MAE), structural similarity index (SSIM) and peak signal to noise ratio (PSNR), with the deformed CT chosen as the reference. In addition, the quality of image was analyzed separately, including soft tissue resolution, image noise and artifacts, etc. Results:The MAE of images generated by U-net, Pix2PixGAN and CycleGAN were (29.4±16.1) HU, (37.1±14.4) HU and (34.3±17.3) HU, respectively. In terms of image quality, the images generated by U-net and Pix2PixGAN had excessive blur, resulting in image distortion; while the images generated by CycleGAN retained the CBCT image structure and improved the image quality.Conclusion:CycleGAN is able to effectively improve the quality of CBCT images, and has potential to be used in adaptive radiotherapy.

Objective:To investigate the method of simulating low-dose CT (LDCT) images using routine dose level scanning mode to generate LDCT images with correspondence to the routine dose CT (RDCT) images in the training sets for deep learning model, which would be used for LDCT noise reduction.Methods:The CT images reconstructed by different algorithms in Philips CT Big Core had different noise levels, where the noise was larger with iDose 4 algorithm and lower with IMR(knowledge-based iterative model reconstruction)algorithm. A new method of replacing LDCT image with noise equivalent reconstructed image was proposed. The uniform module of CTP712 was scanned with the exposure of 250 mAs for RDCT, 35 mAs for LDCT. The images were reconstructed using IMR algorithm for LDCT images and iDose 4 algorithm at multiple noise reduction levels for RDCT images, respectively. The noise distribution of each image set was analyzed to find the noise equivalent images of LDCT. Then, RDCT images, those selected images were used for training cycle-consistent adversarial networks (CycleGAN)model, and the noise reduction ability of the proposed method on real LDCT images of phantom was tested. Results:The RDCT images generated with iDose 4 level 1 could substitute the LDCT images reconstructed with IMR algorithm. The radiation dose was reduced by 86% in low dose scanning. Using CycleGAN model, the noise reduction degree was 45% for uniform module, and 50%, 13%, 7% for CIRS-SBRT 038 phantom in the specific regions of brain, spinal cord, bone, respectively. Conclusions:Equivalent noise level reconstructed images could potentially serve as the alternative of LDCT images for deep learning network training to avoid additional radiation dose. The generated CT images had substantially reduced noise relative to that of LDCT.

Objective:To develop a deep learning method to predict the anatomical images of nasopharyngeal carcinoma patients during the treatment course, which could detect the anatomical variation for specific patients in advance.Methods:Imaging data including planning CT (pCT) and cone-beam CT (CBCT) for each fraction of 230 patients with T 3-T 4 staging nasopharyngeal carcinoma who treated in Cancer Hospital Chinese Academy of Medical Sciences from January 1, 2020 to December 31, 2022 were collected. The anatomical images of week k+1 were predicted using a 3D Unet model with inputs of pCT, CBCT on days 1-3, and CBCT of weeks 2- k. In this experiment, we trained four models to predict anatomical images of weeks 3-6, respectively. The nasopharynx gross tumor volume (GTV nx) and bilateral parotid glands were delineated on the predicted and real images (ground truth). The performance of models was evaluated by the consistence of the delineation between the predicted and ground truth images. Results:The proposed method could predict the anatomical images over the radiotherapy course. The contours of interest in the predicted image were consistent with those in the real image, with Dice similarity coefficient of 0.96, 0.90, 0.92, mean Hausdorff distance of 3.28, 4.18 and 3.86 mm, and mean distance to agreement of 0.37, 0.70, and 0.60 mm, for GTV nx, left parotid, and right parotid, respectively. Conclusion:This deep learning method is an accurate and feasible tool for predicting the patient's anatomical images, which contributes to predicting and preparing treatment strategy in advance and achieving individualized treatment.

Objective To improve the performance of auto-segmentation of prostate target area and organs-at-risk in online magnetic resonance image and enhance the efficiency of magnetic resonance imaging-guided adaptive radiotherapy(MRIgART)for prostate cancer.Methods A retrospective study was conducted on 40 patients who underwent MRIgART for prostate cancer,including 25 in the training set,5 in the validation set,and 10 in the test set.The planning CT images and corresponding contours,along with online MR images,were registered and input into a deep learning network for online MR image auto-segmentation.The proposed method was compared with deformable image registration(DIR)method and single-MR-input deep learning(SIDL)method.Results The overall accuracy of the proposed method for auto-segmentation was superior to those of DIR and SIDL methods,with average Dice similarity coefficients of 0.896 for clinical target volume,0.941 for bladder,0.840 for rectum,0.943 for left femoral head and 0.940 for right femoral head,respectively.Conclusion The proposed method can effectively improve the accuracy and efficiency of auto-segmentation in MRIgART for prostate cancer.

Objective:To evaluate the feasibility of using MRI-only simulation images for dose calculation of both photon and proton radiotherapy for nasopharyngeal carcinoma cases.Methods:T 1-weighted MRI images and CT images of 100 patients with nasopharyngeal carcinoma treated with radiotherapy in Cancer Hospital of Chinese Academy of Medical Sciences from January 2020 to December 2021 were retrospectively analyzed. MRI images were converted to generate pseudo-CT images by using deep learning network models. The training set, validation set and test set included 70 cases, 10 cases and 20 cases, respectively. Convolutional neural network (CNN) and cycle-consistent generative adversarial neural network (CycleGAN) were exploited. Quantitative assessment of image quality was conducted by using mean absolute error (MAE) and structural similarity (SSIM), etc. Dose assessment was performed by using 3D-gamma pass rate and dose-volume histogram (DVH). The quality of pseudo-CT images generated was statistically analyzed by Wilcoxon signed-rank test. Results:The MAE of the CNN and CycleGAN was (91.99±19.98) HU and (108.30±20.54) HU, and the SSIM was 0.97±0.01 and 0.96±0.01, respectively. In terms of dosimetry, the accuracy of pseudo-CT for photon dose calculation was higher than that of the proton plan. For CNN, the gamma pass rate (3 mm/3%) of the photon radiotherapy plan was 99.90%±0.13%. For CycleGAN, the value was 99.87%±0.34%. The gamma pass rates of proton radiotherapy plans were 98.65%±0.64% (CNN, 3 mm/3%) and 97.69%±0.86% (CycleGAN, 3 mm/3%). For DVH, the dose calculation accuracy in the photon plan of pseudo-CT was better than that of the proton plan.Conclusions:The deep learning-based model generated accurate pseudo-CT images from MR images. Most dosimetric differences were within clinically acceptable criteria for photon and proton radiotherapy, demonstrating the feasibility of an MRI-only workflow for radiotherapy of nasopharyngeal cancer. However, compared with the raw CT images, the error of the CT value in the nasal cavity of the pseudo-CT images was relatively large and special attention should be paid during clinical application.