A data-mining study on the prediction of head injury in traffic accidents among vulnerable road users with varying body sizes and head anatomical characteristics.

Body sizes and head anatomical characteristics play the major role in the head injuries sustained by vulnerable road users (VRU) in traffic accidents. In this study, in order to study the influence mechanism of body sizes and head anatomical characteristics on head injury, we used age, gender, height, and Body Mass Index (BMI) as characteristic parameters to develop the personalized human body multi-rigid body (MB) models and head finite element (FE) models. Next, using simulation calculations, we developed the VRU head injury dataset based on the personalized models. In the dataset, the dependent variables were the degree of head injury and the brain tissue von Mises value, while the independent variables were height, BMI, age, gender, traffic participation status, and vehicle speed. The statistical results of the dataset show that the von Mises value of VRU brain tissue during collision ranges from 4.4 kPa to 46.9 kPa at speeds between 20 and 60 km/h. The effects of anatomical characteristics on head injury include: the risk of a more serious head injury of VRU rises with age; VRU with higher BMIs has less head injury in collision accidents; height has very erratic and nonlinear impacts on the von Mises values of the VRU's brain tissue; and the severity of head injury is not significantly influenced by VRU's gender. Furthermore, we developed the classification prediction models of head injury degree and the regression prediction models of head injury response parameter by applying eight different data mining algorithms to this dataset. The classification prediction models have the best accuracy of 0.89 and the best R2 value of 0.85 for the regression prediction models.

A fast methodology for generating skeletal FEM with detailed human geometric features based on CPD and RBF algorithms.

Due to the significant effects of the human anatomical characteristics on the injury mechanism of passenger in traffic accidents, it is necessary to develop human body FEM (Finite Element Model) with detailed anatomical characteristics. However, traditional development of a human body FEM is an extremely complicated process. In particular, the meshing of human body is a huge and time-consuming project. In this paper, a new fast methodology based on CPD (Coherent Point Drift) and RBF (Radial Basis Function) was proposed to achieve the rapid developing the FEM of human bone with detailed anatomical characteristics. In this methodology, the mesh morphing technology based the RBF was used to generate FEM mesh in the geometry extracted from the target CT (Computed Tomography) data. In order to further improve the accuracy and speed of mesh morphing, the target geometric feature points required in the mesh morphing process were realized via the rapid and automatic generation based on the point-cloud registration technology of the CPD algorithm. Finally, this new methodology was used to generate a 3-year-old ribcage FEM consisting of a total of 27,728 elements with mesh size 3-5 mm based on the THUMS (Total Human Model for Safety) adult model. In the entire process of generating this new ribcage model, it only took about 2.7 s. The average error between the new FEM and target geometries was only about 2.7 mm. This indicated that the new FEM well described the detailed anatomical characteristics of target geometry, thus importantly revealing that the mesh quality of the new FEM was basically similar to that of source FEM.

Combinatorial Antitumor Effect of Rapamycin and ß-Elemene in Follicular Thyroid Cancer Cells.

Background. mTOR signaling would be a promising target for thyroid cancer therapy. However, in clinical trials, objective response rate with mTOR inhibitor monotherapy in most cancer types was modest. A new focus on development of combinatorial strategies with rapalogs is increasing. Objective. Investigating the combinatorial antitumor effect of rapamycin and ß-elemene in follicular thyroid cancer cells. Methods. MTT assay was used to determine the FTC-133 cell proliferation after culturing with rapamycin and/or ß-elemene. To analyze their combinatorial effect, immunoblotting was performed to analyze the activation status of AKT. Moreover, ß-elemene attenuated rapamycin-induced immunosuppression was tested in mice. Results. Combination of rapamycin and ß-elemene exerted significant synergistic antiproliferative effects in FTC-133 cell lines in vitro, based on inhibiting the AKT feedback activation induced by rapamycin. In vivo, the ß-elemene could attenuate rapamycin-induced immunosuppression via reversing imbalance of Treg/Th17, with the underlying mechanism needed to be declared. Conclusions. We demonstrate that the novel combination of mTOR inhibitor with ß-elemene synergistically attenuates tumor cell growth in follicular thyroid cancer, which requires additional preclinical validation.