Objective To explore the influencing factors of coronary artery lesion severity in patients with acute coronary syndrome (ACS). Methods Clinical data of ACS patients admitted to Zhongshan Hospital, Fudan University from December 2017 to December 2019 were consecutively collected. The modified Gensini score was used to assess the severity of coronary artery lesions. Univariate and multivariate linear regression analyses were performed to identify independent factors associated with coronary artery lesion severity. Results A total of 1 689 ACS patients were included, with an average age of (64.04±11.45) years; 1 353 (80.11%) were male, and the mean modified Gensini score was (8.12±4.03). Multivariate linear regression analysis revealed that sex (β＝0.97, P＝0.001), age (β＝0.03, P＝0.021), estimated glomerular filtration rate (eGFR; β＝－0.03, P＜0.001), low-density lipoprotein cholesterol (LDL-C; β＝0.58, P＜0.001), apolipoprotein A1 (Apo A1; β＝－1.28, P＝0.012), lipoprotein(a) [Lp(a); β＝0.001, P＝0.033], and glycated hemoglobin A_1C (HbA_1C; β＝0.45, P＜0.001) were independent influencing factors of the modified Gensini score. Conclusions Metabolic indicators, including Apo A1, LDL-C, HbA_1C, and Lp(a), may serve as risk factors for coronary artery lesion severity in ACS patients, with Apo A1 demonstrating the strongest impact.

BACKGROUND: Salvianolate is a compound mainly composed of salvia magnesium acetate, which is extracted from the Chinese herb Salvia miltiorrhiza . In recent years, salvianolate injection has been widely used in the treatment of cardiovascular diseases, but the mechanism of how it can alleviate cardiotoxicity remains unclear. METHODS: The cardiac injury model was constructed by treatment with doxorubicin (Dox) or azithromycin (Azi) in zebrafish larvae. Heart phenotype, heart rate, and cardiomyocyte apoptosis were observed in the study. RNA-sequencing (RNA-seq) analysis was used to explore the underlying mechanism of salvianolate treatment. Moreover, cardiomyocyte autophagy was assessed by in situ imaging. In addition, the miR-30a/becn1 axis regulation by salvianolate was further investigated. RESULTS: Salvianolate treatment reduced the proportion of pericardial edema, recovered heart rate, and inhibited cardiomyocyte apoptosis in Dox/Azi-administered zebrafish larvae. Mechanistically, salvianolate regulated the lysosomal pathway and promoted autophagic flux in zebrafish cardiomyocytes. The expression level of becn1 was increased in Dox-induced myocardial tissue injury after salvianolate administration; overexpression of becn1 in cardiomyocytes alleviated the Dox/Azi-induced cardiac injury and promoted autophagic flux in cardiomyocytes, while becn1 knockdown blocked the effects of salvianolate. In addition, miR-30a, negatively regulated by salvianolate, partially inhibited the cardiac amelioration of salvianolate by targeting becn1  directly. CONCLUSION This study has proved that salvianolate reduces cardiomyopathy by regulating autophagic flux through the miR-30a/becn1 axis in zebrafish and is a potential drug for adjunctive Dox/Azi therapy.
Animals ; Zebrafish ; MicroRNAs/genetics* ; Autophagy/drug effects* ; Myocytes, Cardiac/metabolism* ; Cardiomyopathies/metabolism* ; Beclin-1/genetics* ; Apoptosis/drug effects* ; Plant Extracts/therapeutic use* ; Doxorubicin

The aim of this updated guideline is to provide comprehensive recommendations for the management of gout in patients with common comorbidities, such as chronic kidney disease(CKD), cardiovascular disease(CVD), diabetes, osteoarthritis(OA), and gastrointestinal disorders. This guideline was developed by a multidisciplinary expert panel consisting of specialists in endocrinology, rheumatology, nephrology, cardiology, gastroenterology, and methodology. The development process adhered to standard methodologies, including PICO(population, intervention, comparator, and outcomes) question deconstruction, systematic literature review, the Grading of Recommendations Assessment, Development and Evaluation(GRADE) for evidence and recommendation evaluation, Delphi voting, and expert consensus. The guideline presents 26 evidence-based recommendations addressing 7 clinical questions for patients with hyperuricemia and gout in the context of comorbidities. Key recommendations include the maintenance of strict serum urate targets, particularly for patients with CKD stage≥3, chronic gouty arthritis, and OA, in order to prevent disease progression. In patients with CVD or diabetes, intra-articular triamcinolone is preferred over systemic glucocorticoids. Prioritized anti-inflammatory treatments for patients with CKD, gastrointestinal diseases and OA are recommended. The guideline also introduces emerging therapies, such as interleukin-1 inhibitors and selective urate transport inhibitors, as potential treatment options for refractory cases. The update offers a comprehensive, patient-centered approach to managing gout, particularly in individuals with associated comorbidities. Multidisciplinary collaboration and emerging new treatments and evidence ensure the optimization of the recommendations.

The aim of this updated guideline is to provide comprehensive recommendations for the management of gout in patients with common comorbidities, such as chronic kidney disease(CKD), cardiovascular disease(CVD), diabetes, osteoarthritis(OA), and gastrointestinal disorders. This guideline was developed by a multidisciplinary expert panel consisting of specialists in endocrinology, rheumatology, nephrology, cardiology, gastroenterology, and methodology. The development process adhered to standard methodologies, including PICO(population, intervention, comparator, and outcomes) question deconstruction, systematic literature review, the Grading of Recommendations Assessment, Development and Evaluation(GRADE) for evidence and recommendation evaluation, Delphi voting, and expert consensus. The guideline presents 26 evidence-based recommendations addressing 7 clinical questions for patients with hyperuricemia and gout in the context of comorbidities. Key recommendations include the maintenance of strict serum urate targets, particularly for patients with CKD stage≥3, chronic gouty arthritis, and OA, in order to prevent disease progression. In patients with CVD or diabetes, intra-articular triamcinolone is preferred over systemic glucocorticoids. Prioritized anti-inflammatory treatments for patients with CKD, gastrointestinal diseases and OA are recommended. The guideline also introduces emerging therapies, such as interleukin-1 inhibitors and selective urate transport inhibitors, as potential treatment options for refractory cases. The update offers a comprehensive, patient-centered approach to managing gout, particularly in individuals with associated comorbidities. Multidisciplinary collaboration and emerging new treatments and evidence ensure the optimization of the recommendations.

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.

Objective:To investigate the correlation of serum high-sensitivity cardiac troponin T (hs-cTnT) level with major adverse cardiovascular events (MACE) in patients with chronic coronary syndrome (CCS) undergoing percutaneous coronary intervention (PCI) and to explore its predictive value.Methods:It was a case-control study. Clinical data of 731 patients with CCS who underwent PCI in the Affiliated Zhongshan Hospital of Fudan University between May 2019 and April 2020 were retrospectively analyzed. Baseline clinical characteristics and pre/postoperative laboratory results were gathered, and patients were followed up and the incidence of MACE was documented. The correlation of serum hs-cTnT levels with MACE was analyzed, and the threshold of hs-cTnT for predicting the occurrence of MACE was determined.Results:Among 731 patients there were 560 males (76.61%) with the age of (64.05±9.48) years. Patients were followed up for 29.9 (18.8, 35.3) months, and MACE occurred in 216 cases (MACE group), and did not occur in 515 cases (control group). The X-tile software analysis showed that the optimal cutoff value of post-PCI hs-cTnT was 4.17×upper reference limit (URL) for predicting MACE ( P=0.033). Multivariate Cox regression analysis revealed that postoperative cTnT>6×URL was an independent risk factor for MACE in CCS patients after PCI ( HR=1.87, 95% CI: 1.19-2.94, P=0.007). The net reclassification index pairwise comparison results indicated that hs-cTnT>6×URL had the better predictive performance for MACE in CCS patients after PCI compared to 7×URL, 8×URL, 9×URL, 10×URL and 15×URL (all P<0.05). Conclusion:Postoperative hs-cTnT>6×URL is an independent risk factor for MACE in CCS patients after PCI, and hs-cTnT>6×URL is the optimal threshold for predicting the risk of MACE.

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 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 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.