Objective To understand the changing trend of Internet outpatient visits in public hospitals,and provide support for the development planning of Internet hospitals.Methods Using the data of Internet outpatient visits in a public hos-pital from January 2021 to June 2023,the ARIMA model and GM(1,1)model were constructed respectively.The mean absolute error(MAE)and root mean square error(RMSE)were used to evaluate the fitting effect,and the Internet outpatient visits from July to December 2023 were predicted based on the dominance model.Results ARIMA(1,2,1)model and GM(1,1)model were used to predict the number of return visits of Internet outpatient service.The average absolute errors were 369.86 and 978.84,and the root-mean-square errors were 479.49 and 1444.83,respectively.The ARIMA(0,1,0)model and GM(1,1)model were used to predict the number of Internet outpatient consultations.The average absolute errors were 297.23 and 369.62,and the root-mean-square errors were 413.61 and 496.30,respectively,indicating that the ARIMA model has a good prediction effect.The forecast results show that the predicted value of Internet outpatient visits in December 2023 is 14,831 cases,and the predicted value of consultation visits is 7461 cases.Conclusion The number of Internet outpatient visits in a public hospital will continue to rise from 2021 to 2023.Therefore,hospitals should fully realize the importance of Internet medical services,take ac-tive measures to continuously optimize the medical service model,and provide patients with high-quality,efficient and convenient Internet medical services.

Objective:To develop and evaluate metal artifact removal systems (MARSs) based on deep learning to assess their effectiveness in removing artifacts caused by different thicknesses of metals in cone-beam CT (CBCT) images.Methods:A full-mouth standard model (60 mm×75 mm×110 mm) was three-dimensional (3D) printed using photosensitive resin. The model included a removable and replaceable target tooth position where cobalt-chromium alloy crowns with varying thicknesses were inserted to generate matched CBCT images. The artifacts resulting from cobalt-chromium alloys with different thicknesses were evaluated using the structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR). CNN-MARS and U-net-MARS were developed using a convolutional neural network and U-net architecture, respectively. The effectiveness of both MARSs were assessed through visualization and by measuring SSIM and PSNR values. The SSIM and PSNR values were statistically analyzed using one-way analysis of variance (α=0.05).Results:Significant differences were observed in the range of artifacts produced by different thicknesses of cobalt-chromium alloys (all P<0.05), with 1 mm resulting in the least artifacts. The SSIM values for specimens with thicknesses of 1.0 mm, 1.5 mm, and 2.0 mm were 0.916±0.019, 0.873±0.010, and 0.833±0.010, respectively ( F=447.89, P<0.001). The corresponding PSNR values were 20.834±1.176, 17.002±0.427, and 14.673±0.429, respectively ( F=796.51, P<0.001). After applying CNN-MARS and U-net-MARS to artifact removal, the SSIM and PSNR values significantly increased for images with the same thickness of metal (both P<0.05). When using the CNN-MARS for artifact removal, the SSIM values for 1.0, 1.5 and 2.0 mm were 0.938±0.023, 0.930±0.029, and 0.928±0.020 ( F=2.22, P=0.112), while the PSNR values were 30.938±1.495, 30.578±2.154 and 30.553±2.355 ( F=0.54, P=0.585). When using the U-net-MARS for artifact removal, the SSIM values for 1.0, 1.5 and 2.0 mm were 0.930±0.024, 0.932±0.017 and 0.930±0.012 ( F=0.24, P=0.788), and the PSNR values were 30.291±0.934, 30.351±1.002 and 30.271±1.143 ( F=0.07, P=0.929). No significant differences were found in SSIM and PSNR values after artifact removal using CNN-MARS and U-net-MARS for different thicknesses of cobalt-chromium alloys (all P>0.05). Visualization demonstrated a high degree of similarity between the images before and after artifact removal using both MARSs. However, CNN-MARS displayed clearer metal edges and preserved more tissue details when compared with U-net-MARS. Conclusions:Both the CNN-MARS and U-net-MARS models developed in this study effectively remove the metal artifacts and enhance the image quality. CNN-MARS exhibited an advantage in restoring tissue structure information around the artifacts compared to U-net-MARS.

Objective:To develop and evaluate metal artifact removal systems (MARS) based on deep learning to assess their effectiveness in removing artifacts caused by different thicknesses of metals in cone-beam CT (CBCT) images.Methods:A full-mouth standard model (60 mm×75 mm×110 mm) was three-dimensional (3D) printed using photosensitive resin. The model included a removable and replaceable target tooth position where cobalt-chromium alloy crowns with varying thicknesses were inserted to generate matched CBCT images. The artifacts resulting from cobalt-chromium alloys with different thicknesses were evaluated using the structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR). CNN-MARS and U-net-MARS were developed using a convolutional neural network and U-net architecture, respectively. The effectiveness of both MARSs were assessed through visualization and by measuring SSIM and PSNR values. The SSIM and PSNR values were statistically analyzed using one-way analysis of variance (α=0.05).Results:Significant differences were observed in the range of artifacts produced by different thicknesses of cobalt-chromium alloys (all P<0.05), with 1 mm resulting in the least artifacts. The SSIM values for specimens with thicknesses of 1.0, 1.5, and 2.0 mm were 0.916±0.019, 0.873±0.010, and 0.833±0.010, respectively ( F=447.89, P<0.001). The corresponding PSNR values were 20.834±1.176, 17.002±0.427, and 14.673±0.429, respectively ( F=796.51, P<0.001). After applying CNN-MARS and U-net-MARS to artifact removal, the SSIM and PSNR values significantly increased for images with the same thickness of metal (both P<0.05). When using the CNN-MARS for artifact removal, the SSIM values for 1.0, 1.5 and 2.0 mm were 0.938±0.023, 0.930±0.029, and 0.928±0.020 ( F=2.22, P=0.112), while the PSNR values were 30.938±1.495, 30.578±2.154 and 30.553±2.355 ( F=0.54, P=0.585). When using the U-net-MARS for artifact removal, the SSIM values for 1.0, 1.5 and 2.0 mm were 0.930±0.024, 0.932±0.017 and 0.930±0.012 ( F=0.24, P=0.788), and the PSNR values were 30.291±0.934, 30.351±1.002 and 30.271±1.143 ( F=0.07, P=0.929). No significant differences were found in SSIM and PSNR values after artifact removal using CNN-MARS and U-net-MARS for different thicknesses of cobalt-chromium alloys (all P>0.05). Visualization demonstrated a high degree of similarity between the images before and after artifact removal using both MARS. However, CNN-MARS displayed clearer metal edges and preserved more tissue details when compared with U-net-MARS. Conclusions:Both the CNN-MARS and U-net-MARS models developed in this study effectively remove the metal artifacts and enhance the image quality. CNN-MARS exhibited an advantage in restoring tissue structure information around the artifacts compared to U-net-MARS.

Rapid turnover of the intestinal epithelium is a critical strategy to balance the uptake of nutrients and defend against environmental insults, whereas inappropriate death promotes the spread of inflammation. PPARα is highly expressed in the small intestine and regulates the absorption of dietary lipids. However, as a key mediator of inflammation, the impact of intestinal PPARα signaling on cell death pathways is unknown. Here, we show that Pparα deficiency of intestinal epithelium up-regulates necroptosis signals, disrupts the gut vascular barrier, and promotes LPS translocation into the liver. Intestinal Pparα deficiency drives age-related hepatic steatosis and aggravates hepatic fibrosis induced by a high-fat plus high-sucrose diet (HFHS). PPARα levels correlate with TRIM38 and MLKL in the human ileum. Inhibition of PPARα up-regulates necroptosis signals in the intestinal organoids triggered by TNF-α and LPS stimuli via TRIM38/TRIF and CREB3L3/MLKL pathways. Butyric acid ameliorates hepatic steatosis induced by intestinal Pparα deficiency through the inhibition of necroptosis. Our data suggest that intestinal PPARα is essential for the maintenance of microenvironmental homeostasis and the spread of inflammation via the gut-liver axis.

Objective:To evaluate the value of 99Tc m-methoxyisobutylisonitrile(MIBI) SPECT/CT imaging for the identification of dystonic muscles in patients with primary cervical dystonia (PCD). Methods:A total of 10 patients with PCD (3 males, 7 females, age (47.3±9.9) years) and 10 healthy subjects (4 males, 6 females, age (43.5±9.4) years; control group) between August 2019 and October 2021 in China-Japan Friendship Hospital were enrolled prospectively. All subjects underwent 99Tc m-MIBI SPECT/CT scan. The SUV max of 8 bilateral representative muscles, including rectus capitis posterior major, obliquus capitis inferior, splenius capitis, semispinalis, sternocleidomastoid, trapezius, musculus scalenus muscle and levator scapulae were evaluated in control group. In PCD group, muscles with abnormal uptake were determined. ROI was drawn and SUV max was measured. Independent-sample t test was used to analyze the differences of SUV max between normal and abnormal muscles. The detecting rates of neck MRI and SPECT/CT for abnormal muscles were analyzed by χ2 test. Results:Normal muscles of healthy subjects showed mild symmetrical radioactivity distribution, with the SUV max of 1.10±0.19. A total of 60 muscles with abnormal uptake in 10 patients were found, including 7 rectus capitis posterior major, 10 obliquus capitis inferior, 8 splenius capitis, 8 semispinalis, 10 sternocleidomastoid, 5 trapezius, 3 musculus scalenus muscle and 9 levator scapulae. The SUV max of muscles with abnormal uptake was 1.81±0.43, which was higher than that of normal muscles ( t=17.05, P<0.001). Only 30 pieces abnormal hypertrophy muscle were found by neck MRI, and the detecting rate was much lower than that of SPECT/CT (18.75%(30/160) vs 37.50%(60/160); χ2=28.03, P<0.001). Conclusion:99Tc m-MIBI SPECT/CT may be a useful method for identifying dystonic muscles and a guide to precision therapy in patients with PCD.

The potential medicinal value of Ma bamboo (Dendrocalamus latiflorus), one of the most popular and economically important bamboo species in China, has been underestimated. In the present study, we found that D. latiflorus leaf extract (DLE) reduced fasting blood glucose levels, body weight, and low-density lipoprotein cholesterol with low liver toxicity in db/db mice. In addition, gene expression profiling was performed and pathway enrichment analysis showed that DLE affected metabolic pathways. Importantly, DLE activated the AKT signaling pathway and reduced glucose production by downregulating glucose-6-phosphatase (G6PC) and phosphoenolpyruvate carboxykinase 1 (PCK1) expression. Moreover, network pharmacology analysis identified rutin as an active component in DLE through targeting insulin growth factor 1 receptor (IGF1R), an upstream signaling transducer of AKT. Due to its hypoglycemic effects and low toxicity, DLE may be considered an adjuvant treatment option for type 2 diabetes patients.

Objective:To analyze the progress and promotion effect of the national multidisciplinary team(MDT) pilot project of digestive system tumor diagnosis and treatment, for the reference in promoting the popularition of tumor MDT model.Methods:The data of MDT project evaluation forms of 231 digestive system tumor MDT pilot hospitals in 2018(July 2017 to June 2018), 2019(July 2018 to June 2019)and 2020(July 2018 to June 2019)were obtained. The MDT of digestive system tumors, the development of outpatient and inpatient MDT, the distribution of cases, and the management, charging and regional radiation of MDT in the pilot hospital were analyzed. Descriptive analysis and frequency analysis were used for all the data.Results:With pilot hospitals of missing data excluded, the number of pilot hospitals included in the analysis from 2018 to 2020 was 227, 224 and 224, respectively.The number of pilot hospitals carrying out digestive system tumor MDT increased from 174 in 2018 to 222 in 2020, the number of outpatient and inpatient MDT cases increased from 48 332 and 61 823 to 72 493 and 106 899 respectively, and the proportion of pilot hospitals implementing the MDT management system increased from 159 to 214. In 2020, the average expenses of outpatient and inpatient MDT were mainly 200-500 yuan, and 135(60.3%) pilot hospitals became the leading MDT hospitals in the region.Conclusions:The MDT pilot project of digestive system tumors in China has achieved remarkable results.For example, the number of pilot hospitals carrying out MDT keeps increasing year by year, and the pilot hospitals have played a leading role in the region. In order to accelerate the coverage of the tumor MDT model, the authors suggested that the hospitals should optimize MDT in terms of patient accessibility, optimize management mode, promote the medical insurance reimbursement, and strengthen regional influence.

After decades of development,protein and peptide drugs have now grown into a major drug class in the marketplace.Target identification and validation are crucial for the discovery of protein and peptide drugs,and bioinformatics prediction of targets based on the characteristics of known target proteins will help improve the efficiency and success rate of target selection.However,owing to the developmental history in the pharmaceutical industry,previous systematic exploration of the target spaces has mainly focused on traditional small-molecule drugs,while studies related to protein and peptide drugs are lacking.Here,we systematically explore the target spaces in the human genome specifically for protein and peptide drugs.Compared with other proteins,both suc-cessful protein and peptide drug targets have many special characteristics,and are also significantly different from those of small-molecule drugs in many aspects.Based on these features,we develop separate effective genome-wide target prediction models for protein and peptide drugs.Finally,a user-friendly web server,Predictor Of Protein and Peptide drugs'therapeutic Targets(POPPIT)(http://poppit.ncpsb.org.cn/),is established,which provides not only target prediction specifically for protein and peptide drugs but also abundant annotations for predicted targets.

Objective:To evaluate the clinical value of 18F-fluorodexoyglucose (FDG) PET/CT in distinguishing benign from malignant tumors in patients with cardiac tumors. Methods:Between January 2015 and September 2018, 18F-FDG PET/CT was performed in 3 678 patents in Beijing Anzhen Hospital, and 51 of them (51/3 678, 1.39%) were diagnosed as cardiac tumors. Finally, 28 patients (10 males, 18 females; mean age (52±14) years, age range: 18-84 years) with pathological results were included. According to pathological results, patients were divided into 4 groups: group 1 with primary benign cardiac tumor ( n=9), group 2 with primary malignant cardiac tumor ( n=9), group 3 with lymphoma ( n=6) and group 4 with secondary malignant cardiac tumor ( n=4). All patients underwent early (60 min) 18F-FDG PET/CT imaging and 22 patients (6, 7, 6, 3 patients in group 1, group 2, group 3, group 4 respectively) underwent delayed (120 min) imaging. The maximum standardized uptake value (SUV max) and target/backgroud ratio (TBR) of 4 groups in early imaging and delayed imaging were calculated and compared with one-way analysis of viariace and Scheffe Post-hoc test. TBR were calcualted as SUV max/mean standardized uptake value (SUV mean) in the liver. Receiver operating characteristic (ROC) curve analysis was also performed. Results:SUV max during early imaging, defined SUV max(early), was 2.6±1.5, 9.9±4.0, 20.5±6.1, 9.2±5.8 in group 1-4 respectively ( F=21.39, P<0.01), the value of group 1 was lower than that of group 2 and 3, and the value of group 3 was the highest (all P<0.005). TBR early was 1.1±0.6, 4.1±1.6, 9.4±2.6, 3.7±2.0 in the 4 groups ( F=29.15, P<0.01), the value of group 1 was lower than that of group 2 and 3, and the value of group 3 was the highest (all P<0.005). SUV max in delayed imaging (SUV max(delay)) was 2.4±1.2, 11.0±5.9, 25.8±7.7, 13.7±7.7 respectively in the 4 groups ( F=16.01, P<0.01). TBR delay was also significantly different among the 4 groups (1.3±0.7, 5.5±2.9, 14.4±4.9, 7.9±5.0; F=14.78, P<0.01), the value of group 3 was higher than that of group 1 and 2 (all P<0.05). ROC curve analysis showed optimal cut-off values for indicating malignancy were: SUV max(early)=4.2, TBR early=1.6, SUV max(delay)=4.6, TBR delay=1.9. The corresponding sensitivities, specificities, accuracies were 19/19, 8/9, 96.4%(27/28); 19/19, 7/9, 92.9%(26/28); 16/16, 6/6, 100%(22/22); 16/16, 5/6, 95.5%(21/22), respectively. Conclusions:18F-FDG PET/CT imaging can accurately diagnose malignant cardiac tumors. Delayed imaging can further improve the accuracy for diagnosis of malignant cardiac tumors.

The fourth affiliated hospital of Zhejiang university is responsible for training general practitioners in view of the basics of local general practitioners and the characteristics of chronic and frequently-occurring diseases in Yiwu city. This program is carried out relying on the high-quality faculty of medical alliances within Zhejiang university, and funded by the municipal government of Yiwu. The "4+6"training program is designed to elevate the competence of primary general practitioners. Such training fit the overall medical level of general practitioners, enhances people′s trust of general practitioners and the rate of diagnosis at primary level.