Objective To establish a machine learning radiomics model that can accurately predict MRI enhancement patterns of glioma based on T2 fluid attenuated inversion recovery(T2-FLAIR)images for optimizing the workflow of magnetic resonance imaging(MRI)examinations of glioma patients.Methods We retrospectively collected preoperative MR T2-FLAIR images from 385 patients with pathologically confirmed glioma,who were divided into enhancing and non-enhancing groups according to the enhancement pattern.Predictive radiomics models were established using Gaussian Process,Linear Regression,Linear Regression-Least absolute shrinkage and selection operator,Support Vector Machine,Linear Discriminant Analysis or Naive Bayes as the classifiers in the training cohort(n=201)and tested both in the internal(n=85)and external validation cohorts(n=99).The receiver-operating characteristic curve was used to assess the predictive performance of the models.Results The predictive model constructed based on 15 radiomics features using Gaussian Process as the classifier had the best predictive performance in both the training cohort and the internal validation cohort,with areas under the curve(AUC)of 0.88(95%CI:0.81-0.94)and 0.80(95%CI:0.71-0.88),respectively.In the external validation cohort,the model showed an AUC of 0.81(95%CI:0.71-0.90)with sensitivity,specificity,positive predictive value and negative predictive value of 0.98,0.61,0.76 and 0.96,respectively.Conclusion The T2-FLAIR-based machine learning radiomics model can accurately predict the enhancement pattern of gliomas on MRI.

Objective To establish a machine learning radiomics model that can accurately predict MRI enhancement patterns of glioma based on T2 fluid attenuated inversion recovery(T2-FLAIR)images for optimizing the workflow of magnetic resonance imaging(MRI)examinations of glioma patients.Methods We retrospectively collected preoperative MR T2-FLAIR images from 385 patients with pathologically confirmed glioma,who were divided into enhancing and non-enhancing groups according to the enhancement pattern.Predictive radiomics models were established using Gaussian Process,Linear Regression,Linear Regression-Least absolute shrinkage and selection operator,Support Vector Machine,Linear Discriminant Analysis or Naive Bayes as the classifiers in the training cohort(n=201)and tested both in the internal(n=85)and external validation cohorts(n=99).The receiver-operating characteristic curve was used to assess the predictive performance of the models.Results The predictive model constructed based on 15 radiomics features using Gaussian Process as the classifier had the best predictive performance in both the training cohort and the internal validation cohort,with areas under the curve(AUC)of 0.88(95%CI:0.81-0.94)and 0.80(95%CI:0.71-0.88),respectively.In the external validation cohort,the model showed an AUC of 0.81(95%CI:0.71-0.90)with sensitivity,specificity,positive predictive value and negative predictive value of 0.98,0.61,0.76 and 0.96,respectively.Conclusion The T2-FLAIR-based machine learning radiomics model can accurately predict the enhancement pattern of gliomas on MRI.

The study presents a method for the personalized biomechanical modeling of the human head and validates the generated model.Based on the TUST 50th percentile head biomechanical model,the method utilizes head CT data of the target model,and employs three-dimensional point cloud registration and free-form deformation techniques to rapidly develop a personalized head finite element model with detailed brain tissue structures.By reconstructing classic cadaver tests,it is found that the personalized head biomechanical model created by the proposed method shows a good consistency with the results of cadaver tests in kinematic and biomechanical responses.Furthermore,no significant differences are observed when compared with the head biomechanical model developed using reverse engineering method,thus verifying the effectiveness of the developed model.Consequently,the proposed method can be used to quickly construct personalized head biomechanical models with detailed anatomical structures,providing a fundamental computational analysis tool for researches in injury biomechanics,clinical medicine,and forensic identification.

Objective To investigate the injury mechanisms of three-year-old child occupants by reconstructing a real traffic accident.Methods A traffic accident case from the CIREN database was reconstructed using a vehicle finite element model and a three-year-old child occupant injury bionic model(TUST IBMs 3YO-O).The Δv,mass of the vehicle,and deformation energy were comprehensively analyzed to calculate the collision velocity of the vehicle.This accident was simulated to present injuries to a child occupant,and the injury mechanisms were analyzed in depth.Results The TUST IBMs 3YO-O fully reconstructed the injuries of the child occupant in this case.The kinematic and biomechanical responses of the children's heads differed.The biomechanical response of the internal tissues and organs in the chest cavity showed no injury,however,the result ant chest acceleration at 3 ms reached 54 g,which exceeded the threshold.Conclusions In the future,it will be necessary to adopt biomechanical parameters for occupant safety evaluations.The application of human biomechanical models with high biofidelity to reconstruct occupant injuries in traffic accidents can not only be used to observe the kinematic responses of the occupant in the accident and analyze the injury mechanisms in depth,but also to provide references for virtual testing,as well as for the research and development of child occupant protection devices and the formulation of safety regulations.

Acyl-CoA synthetase long chain family member 5 (ACSL5), is a member of the acyl-CoA synthetases (ACSs) family that activates long chain fatty acids by catalyzing the synthesis of fatty acyl-CoAs. The dysregulation of ACSL5 has been reported in some cancers, such as glioma and colon cancers. However, little is known about the role of ACSL5 in acute myeloid leukemia (AML). We found that the expression of ACSL5 was higher in bone marrow cells from AML patients compared with that from healthy donors. ACSL5 level could serve as an independent prognostic predictor of the overall survival of AML patients. In AML cells, the ACSL5 knockdown inhibited cell growth both in vitro and in vivo. Mechanistically, the knockdown of ACSL5 suppressed the activation of the Wnt/β-catenin pathway by suppressing the palmitoylation modification of Wnt3a. Additionally, triacsin c, a pan-ACS family inhibitor, inhibited cell growth and robustly induced cell apoptosis when combined with ABT-199, the FDA approved BCL-2 inhibitor for AML therapy. Our results indicate that ACSL5 is a potential prognosis marker for AML and a promising pharmacological target for the treatment of molecularly stratified AML.
Humans ; Antineoplastic Agents/therapeutic use* ; Apoptosis ; beta Catenin/metabolism* ; Biomarkers, Tumor/metabolism* ; Cell Line, Tumor ; Coenzyme A Ligases/metabolism* ; Leukemia, Myeloid, Acute/metabolism* ; Lipoylation ; Prognosis ; Wnt Signaling Pathway

Objective To study influencing factors of renal blunt impact injury by using finite element (FE) method. Methods Based on CT images of the kidney, the kidney FE models for different age groups were constructed. The renal blunt impact test was reconstructed, and the influence of kidney material constitutive parameters, kidney tissue structure, kidney size, impact position and impact velocity on injury severity were analyzed. Results Under the same impact condition, the stress of renal cortex decreased with the kidney mass increasing, and increased with the impact velocity of the hammer increasing. The renal capsule had a certain energy absorption effect, so as to reduce the kidney stress. When the kidney was impacted, the stress of renal cortex under side impact was significantly higher than that under frontal impact. Conclusions Compared with viscoelastic constitutive model, Mooney Rivlin material constitutive model is more suitable for FE evaluation on renal injury severity. The renal injury decreases with the kidney mass increasing. The increase of impact velocity will intensify the renal injury severity. Renal capsule will reduce renal injury to a certain extent, so the existence of renal capsule structure must be considered in FE modeling of the kidney. Compared with frontal and rear impact, the renal injury severity is greater when the kidney is impacted from the lateral side.

Objective To predict and assess biomechanical responses and injury mechanisms of the thorax and abdomen for small-sized females in vehicle collisions. Methods The accurate geometric model of the thorax and abdomen was constructed based on CT images of Chinese 5th percentile female volunteers. A thoracic-abdominal finite element model of Chinese 5th percentile female with detailed anatomical structure was developed by using the corresponding software. The model was validated by reconstructing three groups of cadaver experiments (namely, test of blunt anteroposterior impact on the thorax, test of bar anteroposterior impact on the abdomen, test of blunt lateral impact on the chest and abdomen). Results The force-deformation curves and injury biomechanical responses of the organs from the simulations were consistent with the cadaver experiment results, which validated effectiveness of the model. Conclusions The model can be used for studying injury mechanisms of the thorax and abdomen for small-sized female, as well as developing small-sized occupant restraint systems and analyzing the forensic cases, which lays foundation for developing the whole body finite element model of Chinese 5th percentile female.

Abivertinib, a third-generation tyrosine kinase inhibitor, is originally designed to target epidermal growth factor receptor (EGFR)-activating mutations. Previous studies have shown that abivertinib has promising antitumor activity and a well-tolerated safety profile in patients with non-small-cell lung cancer. However, abivertinib also exhibited high inhibitory activity against Bruton's tyrosine kinase and Janus kinase 3. Given that these kinases play some roles in the progression of megakaryopoiesis, we speculate that abivertinib can affect megakaryocyte (MK) differentiation and platelet biogenesis. We treated cord blood CD34⁺ hematopoietic stem cells, Meg-01 cells, and C57BL/6 mice with abivertinib and observed megakaryopoiesis to determine the biological effect of abivertinib on MK differentiation and platelet biogenesis. Our in vitro results showed that abivertinib impaired the CFU-MK formation, proliferation of CD34⁺ HSC-derived MK progenitor cells, and differentiation and functions of MKs and inhibited Meg-01-derived MK differentiation. These results suggested that megakaryopoiesis was inhibited by abivertinib. We also demonstrated in vivo that abivertinib decreased the number of MKs in bone marrow and platelet counts in mice, which suggested that thrombopoiesis was also inhibited. Thus, these preclinical data collectively suggested that abivertinib could inhibit MK differentiation and platelet biogenesis and might be an agent for thrombocythemia.
Acrylamides/pharmacology* ; Animals ; Blood Platelets/drug effects* ; Cell Differentiation ; Megakaryocytes/drug effects* ; Mice ; Mice, Inbred C57BL ; Piperazines/pharmacology* ; Pyrimidines/pharmacology*

The finite element method is a new method to study the mechanism of brain injury caused by blunt instruments. But it is not easy to be applied because of its technology barrier of time-consuming and strong professionalism. In this study, a rapid and quantitative evaluation method was investigated to analyze the craniocerebral injury induced by blunt sticks based on convolutional neural network and finite element method. The velocity curve of stick struck and the maximum principal strain of brain tissue (cerebrum, corpus callosum, cerebellum and brainstem) from the finite element simulation were used as the input and output parameters of the convolutional neural network The convolutional neural network was trained and optimized by using the 10-fold cross-validation method. The Mean Absolute Error (MAE), Mean Square Error (MSE), and Goodness of Fit ( R ²) of the finally selected convolutional neural network model for the prediction of the maximum principal strain of the cerebrum were 0.084, 0.014, and 0.92, respectively. The predicted results of the maximum principal strain of the corpus callosum were 0.062, 0.007, 0.90, respectively. The predicted results of the maximum principal strain of the cerebellum and brainstem were 0.075, 0.011, and 0.94, respectively. These results show that the research and development of the deep convolutional neural network can quickly and accurately assess the local brain injury caused by the sticks blow, and have important application value for understanding the quantitative evaluation and the brain injury caused by the sticks struck. At the same time, this technology improves the computational efficiency and can provide a basis reference for transforming the current acceleration-based brain injury research into a focus on local brain injury research.
Brain ; Brain Injuries ; Computer Simulation ; Finite Element Analysis ; Humans ; Neural Networks, Computer

Objective To explore the influence of child head injury under different impact angles by applying the finite element model of six-year-old child pedestrian as specified in the European New Car Assessment Programme (Euro NCAP). Methods Based on the finite element model of 6-year-old pedestrian with detailed anatomical structure as specified by the Euro NCAP (TB024), four groups of simulation experiments were set up to explore the mechanism of head injury in children under different impact angles. The initial position for head mass center was on the longitudinal center line of the car. The initial speed of the car was 40 km/h. The car contacted with the model from the direction of the right (0°), the front (90°), the left (180°) and the back (270°). The kinematics differences and head impact responses were compared, and injuries of the facial bone and skull were analyzed. Results Through the analysis of head contact force, acceleration of head mass center, resultant velocity of head mass center with the vehicle, head injury criterion (HIC15), facial bone fracture and skull stress distribution, it was found that the risk of head fracture and brain contusion under back impact and front impact was higher than that under side impact. The risk of head fracture and brain contusion was highest under back impact, while the lowest under side impact. Conclusions Child pedestrian head injury was the largest under back impact. The results have important application values for the assessment and development of car-pedestrian collision protection device.