Activity

The development of biomechanical models of the torso and the spine opens the door to computational solutions for the design of braces for adolescent idiopathic scoliosis. However, the design of such biomechanical models faces several unknowns, such as the correct identification of relevant mechanical elements, or the required accuracy of model parameters. The objective of this study was to design a methodology for the identification of the aforementioned elements, with the purpose of creating personalized models suited for patient-specific brace design and the definition of parameter estimation criteria.

We have developed a comprehensive model of the torso, including spine, ribcage and soft tissue, and we have developed computational tools for the analysis of the model parameters. With these tools, we perform an analysis of the model under typical loading conditions of scoliosis braces.

We present a complete sensitivity analysis of the models mechanical parameters and a comparison between a reference het the axial rotation of the spine also requires careful modeling.

The size, shape, and position of the pancreas are affected by the patient characteristics such as age, sex, adiposity. Owing to more complex anatomical structures (size, shape, and position) of the pancreas, specialists have some difficulties in the analysis of pancreatic diseases (diabetes, pancreatic cancer, pancreatitis). Therefore, the treatment of the disease requires enormous time and depends on the experience of specialists. In order to decrease the rate of pancreatic disease deaths and to assist the specialist in the analysis of pancreatic diseases, automatic pancreas segmentation techniques have been actively developed in the research article for many years.

Although the rapid growth of deep learning and proving satisfactory performance in many medical image processing and computer vision applications, the maximum Dice Similarity Coefficients (DSC) value of these techniques related to automatic pancreas segmentation is only around 85% due to complex structure of the pancreas and other factors. Coacy (ACC), Specificity (SPE), Receiver Operating Characteristics (ROC) and Area under ROC curve (AUC). The average values of DSC, JI, REC and ACC are computed as 86.15%, 75.93%, 86.27%, 86.27% and 99.95% respectively, which are the best values among well-established studies for automatic pancreas segmentation.

It is demonstrated with qualitative and quantitative results that our suggested two-phase approach creates more favorable results than other existing approaches.

It is demonstrated with qualitative and quantitative results that our suggested two-phase approach creates more favorable results than other existing approaches.A novel approach of using two stage anaerobic digestion coupled with electrodialysis technology has been investigated. This approach was used to improving bio hydrogen and methane yields from food waste while simultaneously producing a green chemical feedstock. The first digester was used for hydrogen production and the second digester was used for methane production. The first digester was combined with continuous separation of volatile fatty acids using electrodialysis. The concentrations of carbohydrates, proteins and fats in the prepared food waste were 22.7%, 5.7% and 5.2% respectively. Continuous removal of volatile fatty acids during fermentation in the hydrogen digester not only increased hydrogen yields but also increased the production rate of volatile fatty acids. As a result of continuous VFA separation, hydrogen yields increased from 17.3 mL H2/g VS fermenter to 33.68 mL H2/g VS fermenter. Methane yields also increased from 28.94 mL CH4/g VS fermenter to 43.94 mL CH4/g VS fermenter. This represents a total increase in bio-energy yields of 77.1%. COD reduced by 73% after using two stage anaerobic digestion, however, this reduction increased to 86.7% after using electrodialysis technology for separation of volatile fatty acids. Electrodialysis technology coupled with anaerobic digestion improved substrate utilization, increased bioenergy yields and looks to be promising for treating complex wastes such as food waste.The history of low-dose total-body irradiation (LTBI) as a means of radiotherapy for treating malignant tumors can be traced back to the 1920s. Despite this very low total dose, LTBI can induce long-term remissions. Tumor cells are known to change and maintain their own survival and development conditions through autocrine and paracrine signaling. LTBI can change the tumor microenvironment, enhance the infiltration of activated T cells, and trigger inflammatory processes. LTBI-mediated immune response can exert systemic long-term anti-tumor effects, and can induce tumor regression at the primary site and metastatic sites. With a continuous improvement in the anti-tumor immune microenvironment in the field of tumor therapy, LTBI provides more choices to comprehensively treat of tumors. The present study aimed to explore the experimental research mechanism of LTBI and immune microenvironment, and discuss the difficulties and development prospects of applying LTBI to tumor treatment.

To develop a nomogram for predicting the prognosis of T1 esophageal squamous cell carcinoma (ESCC) patients with positive lymph node.

T1 ESCC patients with lymph node metastasis diagnosed between 2010 and 2015 were selected from the Surveillance, Epidemiology, and Final Results (SEER) database. The entire cohort was randomly divided in the ratio of 73 into a training group (n=457) and validation group (n=192), respectively. Prognostic factors were identified by univariate and multivariate Cox regression models. Harrell’s concordance index (C-index), receiver operating characteristic (ROC) curve, and calibration curve were used to evaluate the discrimination and calibration of the nomogram. H3B-120 cell line The accuracy and clinical net benefit of the nomogram compared with the 7

AJCC staging system were evaluated using net reclassification improvement (NRI), integrated discrimination improvement (IDI), and decision curve analysis (DCA).

The nomogram consisted of eight factors insurance, T stage, summary stage, primary site, radiation code, chemotherapy, surgery, and radiation sequence with surgery.