Association of remnant cholesterol and newly diagnosed early-onset type 2 diabetes mellitus in Chinese population: A retrospective cross-sectional study.

BACKGROUND: With the increasing incidence of diabetes worldwide, patients diagnosed with diabetes has been getting younger. Previous studies have shown that high remnant cholesterol (RC) level leads to an increased risk of cardiovascular disease events. However, the relationship between RC levels and newly diagnosed early-onset type 2 diabetes mellitus (T2DM) is unknown. This study aimed to explore the association between RC and newly diagnosed early-onset T2DM. METHODS: A total of 606 patients newly diagnosed with early-onset T2DM and 619 gender-matched subjects with normal blood glucose levels were retrospectively enrolled in this study. All T2DM patients showed onset age of 18-40 years. Binary logistic regression analysis was performed to analyze independent risk factors and receiver operating characteristic (ROC) analysis was used to explore the predictive value of RC and other unconventional lipids. Moreover, the correlation between RC and insulin resistance in patients with newly diagnosed early-onset T2DM was also examined with binary logistic regression analysis and Spearman correlation analysis. RESULTS: Increased RC level was an independent risk factor for early-onset T2DM (p < .05). The area under the curve on ROC analysis of RC was 0.805, 95% confidence interval (CI) was 0.781 ~ 0.826, sensitivity was 82.18% and specificity was 66.24%, which showed higher predictive value than those of triglyceride/high-density lipoprotein cholesterol (TG/HDL-C) ratio and total cholesterol (TC)/HDL-C ratio. Cutoff value of RC was 0.32 mmol/L. Level of RC in early-onset T2DM patients with moderate or severe insulin resistance was significantly higher than that in patients with mild insulin resistance (p < .0001). No difference in RC levels was found between patients with moderate and severe insulin resistance (p > .05). RC was still correlated with insulin resistance after adjusting the conventional lipid parameters (TG, TC, HDL-C, and low-density lipoprotein cholesterol) using partial correlation analysis. CONCLUSION: RC level was higher in patients with early-onset T2DM and was correlated to the degree of insulin resistance as well. Patients aged 18-40 years with RC >0.32 mmol/L showed an increased risk of developing T2DM.

Design and implementation of mechanical property testing equipment for thoracic aortic stent grafts.

The mechanical properties of the stent graft are important factors influencing the outcome of TEVAR treatment and the occurrence of postoperative complications. The aim of this study is to improve and design a mechanical performance testing equipment for thoracic aortic stent grafts. The mechanical performance testing equipment consists of a radial force testing equipment of the stent graft designed by the wire compression grip method and a dynamic straightening force testing device with stable and controllable test conditions and continuously variable test angles. By constructing the testing equipment to physically measure the stent specimen, the experimental results reflect the trend of change and the simulation results are basically consistent, i.e. the mechanical properties of the thoracic aortic stent designed in this study is feasible and the measured data are valid. The testing equipment can provide the basis and reference direction for the quality testing of stent graft products, optimisation of mechanical properties of stent grafts and R&D innovation.

Clinical characteristics of patients with early-onset diabetes mellitus: a single-center retrospective study.

BACKGROUND: The prevalence of diabetes mellitus (DM) is dramatically increasing around the world, and patients are getting younger with changes in living standards and lifestyle. This study summarized and analyzed the clinical characteristics of different types of newly diagnosed diabetes mellitus patients with an onset age between 18 and 40 years to provide clinical evidence for the early diagnosis and treatment of diabetes, reduce short-term and long-term complications and offer scientific and personalized management strategies. METHODS: A total of 655 patients newly diagnosed with early-onset diabetes mellitus in the Department of Endocrinology, the First Medical Center of PLA General Hospital from January 2012 to December 2022 were retrospectively enrolled in this study, with an onset age of 18-40 years. Their clinical data were collected and investigated. All patients were divided into two groups according to whether they presented with diabetic microangiopathy. Similarly, patients with early-onset type-2 diabetes were grouped in accordance with whether they had ketosis at the time of diagnosis. Binary logistic regression analysis was performed to analyze risk factors, and receiver-operating characteristic (ROC) analysis was used to explore the predictive value of significant risk factors. RESULTS: The findings were as follows: (1) Of 655 enrolled patients, 477 (72.8%) were male and 178 (27.1%) were female, with a mean age of onset of was 29.73 years ± 0.24 SD. (2) The prevalence of early-onset diabetes was gradually increasing. Type-2 diabetes was the most common type of early-onset diabetes (491, 75.0%). The ages of onset of early-onset type-1 diabetes, type-2 diabetes and LADA were mainly 18-24 years, 25-40 years and 33-40 years, respectively. (3) Initial clinical manifestations of early-onset diabetes were classic diabetes symptoms (361, 55.1%), followed by elevated blood glucose detected through medical examination (207, 31.6%). (4) Binary logistic regression analysis suggested that high serum uric acid (UA), a high urinary albumin-to-creatinine ratio (UACR) and diabetic peripheral neuropathy (DPN) were risk factors for microangiopathy in early-onset diabetes patients (P < 0.05). The area under the curve (AUC) on ROC analysis of the combination of UA, UACR and DPN was 0.848, 95% CI was 0.818 ~ 0.875, sensitivity was 73.8% and specificity was 85.9%, which had higher predictive value than those of UA, UACR and DPN separately. (5) Weight loss, high glycosylated hemoglobin (HbA1c) and young onset age were risk factors for ketosis in patients with early-onset type-2 diabetes (P < 0.05). CONCLUSION: (1) Men were more likely to have early-onset diabetes than women. (2) Early-onset diabetes patients with high serum uric acid levels, high UACRs and peripheral neuropathy were prone to microangiopathy. Comprehensive evaluation of these risk factors could have higher predictive value in the prediction, diagnosis and treatment of microvascular lesions. (3) Patients with weight loss at onset, high HbA1c and young onset age were more likely to develop ketosis. Attention should be given to the metabolic disorders of these patients.

[Segmentation of prostate region in magnetic resonance images based on improved V-Net].

Magnetic resonance (MR) imaging is an important tool for prostate cancer diagnosis, and accurate segmentation of MR prostate regions by computer-aided diagnostic techniques is important for the diagnosis of prostate cancer. In this paper, we propose an improved end-to-end three-dimensional image segmentation network using a deep learning approach to the traditional V-Net network (V-Net) network in order to provide more accurate image segmentation results. Firstly, we fused the soft attention mechanism into the traditional V-Net's jump connection, and combined short jump connection and small convolutional kernel to further improve the network segmentation accuracy. Then the prostate region was segmented using the Prostate MR Image Segmentation 2012 (PROMISE 12) challenge dataset, and the model was evaluated using the dice similarity coefficient (DSC) and Hausdorff distance (HD). The DSC and HD values of the segmented model could reach 0.903 and 3.912 mm, respectively. The experimental results show that the algorithm in this paper can provide more accurate three-dimensional segmentation results, which can accurately and efficiently segment prostate MR images and provide a reliable basis for clinical diagnosis and treatment.

Metformin regulates chondrocyte senescence and proliferation through microRNA-34a/SIRT1 pathway in osteoarthritis.

BACKGROUND: Osteoarthritis (OA) is the most common degenerative disease in joints among elderly patients. Senescence is deeply involved in the pathogenesis of osteoarthritis. Metformin is widely used as the first-line drug for Type 2 diabetes mellitus (T2DM), and has great potential for the treatment of other aging-related disorders, including OA. However, the role of metformin in OA is not fully elucidated. Therefore, our aim here was to investigate the effects of metformin on human chondrocytes. METHODS: After metformin treatment, expression level of microRNA-34a and SIRT1 in chondrocyte were detected with quantitative real-time PCR and immunofluorescence staining. Then, microRNA-34a mimic and small interfering RNA (siRNA) against SIRT1 (siRNA-SIRT1) were transfected into chondrocyte. Senescence-associated ß-galactosidase (SA-ß-gal) staining was performed to assess chondrocyte senescence. Chondrocyte viability was illustrated with MTT and colony formation assays. Western blot was conducted to detect the expression of P16, IL-6, matrix metalloproteinase-13 (MMP-13), Collagen type II (COL2A1) and Aggrecan (ACAN). RESULTS: We found that metformin treatment (1 mM) inhibited microRNA-34a while promoted SIRT1 expression in OA chondrocytes. Both miR-34a mimics and siRNA against SIRT1 inhibited SIRT1 expression in chondrocytes. SA-ß-gal staining assay confirmed that metformin reduced SA-ß-gal-positive rate of chondrocytes, while transfection with miR-34a mimics or siRNA-SIRT1 reversed it. MTT assay and colony formation assay showed that metformin accelerated chondrocyte proliferation, while miR-34a mimics or siRNA-SIRT1 weakened this effect. Furthermore, results from western blot demonstrated that metformin suppressed expression of senescence-associated protein P16, proinflammatory cytokine IL-6 and catabolic gene MMP-13 while elevated expression of anabolic proteins such as Collagen type II and Aggrecan, which could be attenuated by transfection with miR-34a mimics. CONCLUSION: Overall, our data suggest that metformin regulates chondrocyte senescence and proliferation through microRNA-34a/SIRT1 pathway, indicating it could be a novel strategy for OA treatment.

Automatic diagnosis of pneumothorax with M-mode ultrasound images based on D-MPL.

PURPOSE: To address the difficulties of M-mode ultrasound images classification in pneumothorax diagnosis and the shortcomings of existing neural network algorithms in this field, we proposed an M-mode ultrasound images classification model based on Disturbed Meta-Pseudo-Labels (D-MPL). METHODS: An M-mode ultrasound image augmentation system was designed to make the model more robust and generalizable. In D-MPL, teacher-generated pseudo-labeling was first taught to students through a soft mask, and additional disturbance data were added to the teacher network. As the loss of the teacher network continues to decline, disturbance data were injected to improve the generalization of the model to cope with image differences across patients in clinical settings. RESULTS: We compared the proposed model with four commonly used models, including MPL, EfficientnetB2, Inception V3, and Resnet101, in order to confirm its efficacy. Our model has an average specificity of 98.28%, sensitivity of 98.22%, F1-score of 98.23%, and AUC of 98.10%, according to the experiment findings, and its comprehensive performance is better than the above four models. CONCLUSION: The results demonstrated our model's superiority over the competition and its greater. The model proposed in this study is expected to assist doctors in the diagnosis of pneumothorax as an auxiliary mean.

Design of non-equal-strut stent hoops for structural optimization of thoracic aortic stent-grafts.

BACKGROUND: Endovascular aortic aneurysm repair (EVAR) with stent-grafts is used widely for the treatment of thoracic aortic aneurysms (TAA). Inappropriate design of stent-grafts may lead to complications such as endoleak, stent-graft migration and new entries, causes of which may be inappropriate radial support force or insufficient longitudinal flexibility of the stent-grafts. MATERIAL AND METHODS: To improve the mechanical performance of the stent-grafts, a type of non-equal-strut stent hoops was proposed, and the influence of structural parameters on the mechanical performance was studied. RESULTS: Results of numerical simulation and physical experiments show that by using the proposed non-equal-strut stent hoops, radial support force and longitudinal flexibility of stent-grafts can be reconciled and balanced. CONCLUSION: Results of this study could be used to facilitate radial force control and longitudinal flexibility enhancement in the design of aortic stent-grafts.

Improving Performance of Breast Cancer Risk Prediction by Incorporating Optical Density Image Feature Analysis: An Assessment.

RATIONALE AND OBJECTIVES: The purpose of this study is to improve accuracy of near-term breast cancer risk prediction by applying a new mammographic image conversion method combined with a two-stage artificial neural network (ANN)-based classification scheme. MATERIALS AND METHODS: The dataset included 168 negative mammography screening cases. In developing and testing our new risk model, we first converted the original grayscale value (GV)-based mammographic images into optical density (OD)-based images. For each case, our computer-aided scheme then computed two types of image features representing bilateral asymmetry and the maximum of the image features computed from GV and OD images, respectively. A two-stage classification scheme consisting of three ANNs was developed. The first stage included two ANNs trained using features computed separately from GV and OD images of 138 cases. The second stage included another ANN to fuse the prediction scores produced by two ANNs in the first stage. The risk prediction performance was tested using the rest 30 cases. RESULTS: With the two-stage classification scheme, the computed area under the receiver operating characteristic curve (AUC) was 0.816 ± 0.071, which was significantly higher than the AUC values of 0.669 ± 0.099 and 0.646 ± 0.099 achieved using two ANNs trained using GV features and OD features, respectively (P < .05). CONCLUSION: This study demonstrated that applying an OD image conversion method can acquire new complimentary information to those acquired from the original images. As a result, fusion image features computed from these two types of images yielded significantly higher performance in near-term breast cancer risk prediction.

Detection of microaneurysms and hemorrhages based on improved Hessian matrix.

PURPOSE: Knowing the early lesion detection of fundus images is very important to prevent blindness, and accurate lesion segmentation can provide doctors with diagnostic evidence. This study proposes a method based on improved Hessian matrix eigenvalue analysis to detect microaneurysms and hemorrhages in the fundus images of diabetic patients. METHODS: A two-step method including identification of lesion candidate regions and classification of candidate regions is adopted. In the first step, the method of eigenvalue analysis based on the improved hessian matrix was applied to enhance the image preprocessed. A dual-threshold method was used for segmentation. Then, blood vessels were gradually removed to obtain the lesion candidate regions. In the second step, all candidates were classified into three categories: microaneurysms, hemorrhages and the others. RESULTS: The proposed method has achieved a better performance compared with the existing algorithms on accuracy rates. The classification accuracy rates of microaneurysms and hemorrhages obtained by using our method were 94.4% and 94.0%, respectively, while the classification accuracy rates obtained by using Frangi's filter based on the Hessian matrix to enhance the image were 90.9% and 92.1%. CONCLUSION: This study demonstrated a methodology for enhancing images by using eigenvalue analysis based on the improved Hessian matrix and segmentation by using double thresholds. The proposed method is beneficial to improve the detection accuracy of microaneurysms and hemorrhages in fundus images.

Dual-functional neural network for bilateral hippocampi segmentation and diagnosis of Alzheimer's disease.

PURPOSE: Knowing the course of Alzheimer's disease is very important to prevent the deterioration of the disease, and accurate segmentation of sensitive lesions can provide a visual basis for the diagnosis results. This study proposes an improved end-to-end dual-functional 3D convolutional neural network for segmenting bilateral hippocampi from 3D brain MRI scans and diagnosing AD progression states simultaneously. METHODS: The proposed neural network is based on the V-Net and adopts an end-to-end structure. In order to relieve the excessive amount of convolutional parameters at the bottom of the V-Net, we change them to bottleneck architecture. Based on the segmentation network, we establish a classification network for diagnosing pathological states of brain. In order to balance the two tasks of hippocampi segmentation and brain pathological states diagnosis, we designed a unique loss function. This study included 132 samples, of which 100 were selected as training, and the remaining 32 were used to test the performance of our model. During training, we adopted fivefold cross-validation method. RESULTS: We selected the intersection over union and dice coefficient to evaluate the hippocampus segmentation performance, while the brain pathological states diagnosis performance was evaluated by accuracy, specificity, sensitivity, precision and F1 score. By using the proposed neural network, the left hippocampi segmentation Iou and dice coefficient reach 0.8240 ± 0.020 and 0.9035 ± 0.020, respectively. The right hippocampi Iou and dice coefficient reach 0.8454 ± 0.023 and 0.9162 ± 0.023, respectively. The accuracy, specificity, sensitivity, precision and F1 score of three-category classification of brain pathology are 84%, 92%, 84%, 86% and 85%, respectively. CONCLUSION: The proposed neural network has two functions of brain pathological states diagnosis and bilateral hippocampi segmentation with higher robustness and accuracy, respectively. The segmented bilateral hippocampi can be used as a reference for clinical decision making or intervention.

[Meticulous Management of the Whole Life Cycle of Medical Equipment in Hospital].

OBJECTIVE: Aiming at the different characteristics of the various stages of medical equipment life cycle in hospital, research on the targeted and meticulous management mode. METHODS: Divides the whole life cycle of medical equipment in hospital into four phases, which are the selection demonstration period, purchase acceptance period, maintenance period, and retirement disposal period, and comparison with human fetal period, infant stage, adult stage and old age. RESULTS: With the meticulous management mode, the service quality of medical equipment in hospital has been improved, and the service benefits have been enhanced. CONCLUSIONS: According to the respective characteristics of different stages, the corresponding meticulous management mode is implemented to make the management more scientific and standardized, and the operation is safer and more reliable, which escorts the whole life cycle of medical equipment in hospital.

Clinical outcomes and risk factors in patients with cervical metastatic spinal cord compression after posterior decompressive and spinal stabilization surgery.

PURPOSE: The aim of this study was to investigate the clinical results of surgery for cervical spine metastasis and identify clinical risk factors affecting postoperative survival and neurological outcome. PATIENTS AND METHODS: A retrospective analysis of medical records was performed on 19 patients who had undergone decompressive surgery and spine stabilization due to metastatic spinal cord compression in the cervical spine. All patients had severe pain before surgery. Worst pain, average pain, and pain interference were evaluated using the visual analog scale (range, 0-10) for each patient at baseline and following surgery. Neurological recovery was assessed using the Japanese Orthopaedic Association Score (JOAS). In addition, associations between ten characteristics and postoperative survival and neurological outcomes were analyzed in the study. RESULTS: The mean worst pain score in a 24-hour period was 8.6 before the operation. At 1 day, 1, 3, 6, and 12 months after the operation, the mean worst pain scores decreased to 5.6, 4.5, 3.8, 2.6, and 2.4 (all P<0.001 vs baseline), respectively. Similar decreases in average pain and pain interference were also observed. The median JOAS in a 24-hour period was 11.0 before the operation. At 1 day, 1, 3, 6, and 12 months after the operation, the median JOAS increased to 12.0 (P=0.469), 13.0 (P=0.010), 14.0 (P<0.001), 15.0 (P<0.001), and 14.0 (P<0.001), respectively. According to the multivariate analysis, postoperative survival was significantly associated with the type of primary tumor (P=0.033), preoperative ambulatory status (P=0.004), extra-spinal bone metastasis (P=0.021), 125I seed brachytherapy (P=0.014), and complication status (P=0.009). Better neurological outcome was found to be correlated with higher JOAS (P=0.013). Surgery-related complications occurred in 26.3% of patients. CONCLUSION: Posterior decompression and spine stabilization for painful cervical spine metastasis resulting from spinal cord compression were found to be effective for neurological recovery and pain control with a tolerable rate of complications.

A robust method for segmenting pectoral muscle in mediolateral oblique (MLO) mammograms.

PURPOSE: Accurately detecting and removing pectoral muscle areas depicting on mediolateral oblique (MLO) view mammograms are an important step to develop a computer-aided detection scheme to assess global mammographic density or tissue patterns. This study aims to develop and test a new fully automated, accurate and robust method for segmenting pectoral muscle in MLO mammograms. METHODS: The new method includes the following steps. First, a small rectangular region in the top-left corner of the MLO mammogram which may contain pectoral muscle is captured and enhanced by the fractional differential method. Next, an improved iterative threshold method is applied to segment a rough binary boundary of the pectoral muscle in the small region. Then, a rough contour is fitted with the least squares method on the basis of points of the rough boundary. Last, the fitting contour is subjected to local active contour evolution to obtain the final pectoral muscle segmentation line. The method has been tested on 720 MLO mammograms. RESULTS: The segmentation results generated using the new scheme were evaluated by two expert mammographic radiologists using a 5-scale rating system. More than 65% were rated above scale 3. When assessing the segmentation results generated using Hough transform, morphologic thresholding methods and Unet-based model, less than 20%, 35% and 47% of segmentation results were rated above scale 3 by two radiologists, respectively. Quantitative data analysis results show that the Dice coefficient of 0.986 ± 0.005 is obtained. In addition, the mean rate of errors and Hausdorff distance between the contours detected by automated and manual segmentation are FP = 1.71 ± 3.82%, FN = 5.20 ± 3.94% and 2.75 ± 1.39 mm separately. CONCLUSION: The proposed method can be used to segment the pectoral muscle in MLO mammograms with higher accuracy and robustness.

Incorporating automatically learned pulmonary nodule attributes into a convolutional neural network to improve accuracy of benign-malignant nodule classification.

Existing deep-learning-based pulmonary nodule classification models usually use images and benign-malignant labels as inputs for training. Image attributes of the nodules, as human-nameable high-level semantic labels, are rarely used to build a convolutional neural network (CNN). In this paper, a new method is proposed to combine the advantages of two classifications, which are pulmonary nodule benign-malignant classification and pulmonary nodule image attributes classification, into a deep learning network to improve the accuracy of pulmonary nodule classification. For this purpose, a unique 3D CNN is built to learn image attribute and benign-malignant classification simultaneously. A novel loss function is designed to balance the influence of two different kinds of classifications. The CNN is trained by a publicly available lung image database consortium (LIDC) dataset and is tested by a cross-validation method to predict the risk of a pulmonary nodule being malignant. This proposed method generates the accuracy of 91.47%, which is better than many existing models. Experimental findings show that if the CNN is built properly, the nodule attributes classification and benign-malignant classification can benefit from each other. By using nodule attribute learning as a control factor in a deep learning scheme, the accuracy of pulmonary nodule classification can be significantly improved by using a deep learning scheme.

Applying a new maximum local asymmetry feature analysis method to improve near-term breast cancer risk prediction.

Quantitative assessment of mammographic asymmetry has been investigated for breast cancer risk prediction. A new asymmetry feature extraction method was proposed in this study to enhance the risk prediction accuracy of near-term breast cancer. Breast areas in each pair of bilateral mammographic images were divided into several pairs of matched local annular regions and the maximum local asymmetry features (MLAF) were extracted from these regions. Radial basis function network (RBFN) was used to merge these features for breast cancer risk prediction. The dataset included 560 negative subjects. The risk prediction performance was tested using a leave-one-case-out (LOCO) cross-validation method. Area under the receiver operating characteristic curve (AUC) was used as the risk prediction performance evaluation index. AUC = 0.898 ± 0.013 was obtained by using the MLAFs extracted from the annular regions, which was significantly higher than the AUC value of 0.505 ± 0.025 achieved by using global asymmetry features computed from the whole breast regions (p < 0.05, DeLong's test) and much higher than the AUC values of 0.825 ± 0.017 and 0.717 ± 0.021 achieved by using MLAFs extracted from horizontal strip regions and vertical strip regions. The study demonstrated that near-term breast cancer risk prediction could be improved by using the proposed feature extraction method.

Applying a new bilateral mammographic density segmentation method to improve accuracy of breast cancer risk prediction.

PURPOSE: How to optimally detect bilateral mammographic asymmetry and improve risk prediction accuracy remains a difficult and unsolved issue. Our aim was to find an effective mammographic density segmentation method to improve accuracy of breast cancer risk prediction. METHODS: A dataset including 168 negative mammography screening cases was used. We applied a mutual threshold to bilateral mammograms of left and right breasts to segment the dense breast regions. The mutual threshold was determined by the median grayscale value of all pixels in both left and right breast regions. For each case, we then computed three types of image features representing asymmetry, mean and the maximum of the image features, respectively. A two-stage classification scheme was developed to fuse the three types of features. The risk prediction performance was tested using a leave-one-case-out cross-validation method. RESULTS: By using the new density segmentation method, the computed area under the receiver operating characteristic curve was 0.830 ± 0.033 and overall prediction accuracy was 81.0%, significantly higher than those of 0.633 ± 0.043 and 57.1% achieved by using the previous density segmentation method ([Formula: see text], t-test). CONCLUSIONS: A new mammographic density segmentation method based on a bilateral mutual threshold can be used to more effectively detect bilateral mammographic density asymmetry and help significantly improve accuracy of near-term breast cancer risk prediction.

A new approach to develop computer-aided diagnosis scheme of breast mass classification using deep learning technology.

PURPOSE: To develop and test a deep learning based computer-aided diagnosis (CAD) scheme of mammograms for classifying between malignant and benign masses. METHODS: An image dataset involving 560 regions of interest (ROIs) extracted from digital mammograms was used. After down-sampling each ROI from 512×512 to 64×64 pixel size, we applied an 8 layer deep learning network that involves 3 pairs of convolution-max-pooling layers for automatic feature extraction and a multiple layer perceptron (MLP) classifier for feature categorization to process ROIs. The 3 pairs of convolution layers contain 20, 10, and 5 feature maps, respectively. Each convolution layer is connected with a max-pooling layer to improve the feature robustness. The output of the sixth layer is fully connected with a MLP classifier, which is composed of one hidden layer and one logistic regression layer. The network then generates a classification score to predict the likelihood of ROI depicting a malignant mass. A four-fold cross validation method was applied to train and test this deep learning network. RESULTS: The results revealed that this CAD scheme yields an area under the receiver operation characteristic curve (AUC) of 0.696±0.044, 0.802±0.037, 0.836±0.036, and 0.822±0.035 for fold 1 to 4 testing datasets, respectively. The overall AUC of the entire dataset is 0.790±0.019. CONCLUSIONS: This study demonstrates the feasibility of applying a deep learning based CAD scheme to classify between malignant and benign breast masses without a lesion segmentation, image feature computation and selection process.

Ginsenoside Rh2 enhances the antitumor immunological response of a melanoma mice model.

The treatment of malignant tumors following surgery is important in preventing relapse. Among all the post-surgery treatments, immunomodulators have demonstrated satisfactory effects on preventing recurrence according to recent studies. Ginsenoside is a compound isolated from panax ginseng, which is a famous traditional Chinese medicine. Ginsenoside aids in killing tumor cells through numerous processes, including the antitumor processes of ginsenoside Rh2 and Rg1, and also affects the inflammatory processes of the immune system. However, the role that ginsenoside serves in antitumor immunological activity remains to be elucidated. Therefore, the present study aimed to analyze the effect of ginsenoside Rh2 on the antitumor immunological response. With a melanoma mice model, ginsenoside Rh2 was demonstrated to inhibit tumor growth and improved the survival time of the mice. Ginsenoside Rh2 enhanced T-lymphocyte infiltration in the tumor and triggered cytotoxicity in spleen lymphocytes. In addition, the immunological response triggered by ginsenoside Rh2 could be transferred to other mice. In conclusion, the present study provides evidence that ginsenoside Rh2 treatment enhanced the antitumor immunological response, which may be a potential therapy for melanoma.

[CCR7 silence by siRNA inhibits proliferation, invasion and promotes apoptosis of human MG63 osteosarcoma cells].

Objective To investigate the effect of siRNA-mediated chemokine receptor 7 (CCR7) silence on the proliferation, migration, invasion and apoptosis of human MG-63 osteosarcoma cells. Methods The study designed and synthesized siRNA targeting CCR7 (CCR7-siRNA). After MG63 cells were transfected with CCR7-siRNA, the expression of CCR7 was identified by Western blotting; cell apoptosis was detected by annexinV-FITC/PI double staining combined with flow cemetery; cell proliferation was tested by MTT assay; and cell migration and invasion abilities were examined by TranswellTM migration/invasion assays. Results CCR7 expression in MG63 cells was significantly inhibited after transfected with CCR7-siRNA. At the same time, cell proliferation, migration and invasion abilities were distinctly suppressed, and cell apoptosis rate increased. Conclusion Down-regulating CCR7 expression in MG63 cells could apparently inhibit cell proliferation, migration and invasion abilities of MG63 cells, and also induce cell apoptosis.

[Design and In Vitro Experimental Study of an Endoscopic Multiple-clip Applier].

Considering the problems such as reposition limited, easily detached and singly fired of the existing clip products, we developed an endoscopic multiple-clip applier which can apply 4 clips fired successively at a time. Th instrument also equipped with an independent grasper which can be used to clamp target tissues. In order to explor its feasibility and effectiveness of endoluminal closure of gastric perforation, 22 pig stomachs were making a 1 cm full-thickness incision from outside and closed by multiple-clip applier (n = 12) in vitro. Outcome was measured by bursting pressure and compared with negative control (n = 5) and hand suture (n = 5). We set a threshold pressure value (10 mm Hg) for a secure closure. Except 2 cases of invalid data, the mean bursting pressures of negative control, multiple-clip applier, hand suture were (1.5 ± 0.3) mm Hg, (46.0 ± 7.1) mm Hg, and (72.5 ± 7.7) mm Hg, respectively. The results showed that bursting pressure of multiple-clip applier was significantly higher than that of negative control (P < 0.05) and threshold value. Multiple-clip applier can be served as an effective and safe device to perform the endoluminal closure of gastric perforation.