Based on CT scan data,a bionic model of lumbar spine injuries with high biofidelity is developed and validated through cadaver experiments.Decoupling the constraint system that affects occupants during collisions due to inertial forces and the subsequent pressure exerted by the seat upon returning to position,a simulated fall experiment is designed.The simulated outcomes are trained and predicted using deep learning algorithms,and the accuracy of the trained neural network prediction model is verified.Key parameters are analyzed for correlation using principal component analysis and cross-reverse methods.The results shows that the predicted lumbar spine injury model obtained from training has high reliability(R2>0.9).Comprehensive analysis reveals that after experiencing axial impact,the L4 vertebral body bears the highest impact load and can be used as a representative measure of lumbar spine injury.Among the environmental variables,the axial force on the L4 lumbar spine is mainly affected by torso mass and fall height,both of which have positive correlations.Torso mass,fall height,and posture angle all have positive effects on internal energy.Conversely,torso mass and fall height have negative correlations with stress.These research findings provide a scientific basis for further elucidating lumbar spine injury mechanisms in intelligent cockpit environments,devising corresponding safety protection measures,and evaluating occupant safety in automobiles.

Objective To investigate the risk of thoracoabdominal injuries in six-year-old child occupants in a reclined seating posture during frontal collisions,and provide a reference for developing child restraint systems(CRS).Methods Three validated biomechanical models of six-year-old child occupants in different seating postures with detailed anatomical structures were used.The acceleration curve from a sport utility vehicle crash test was applied to analyze the effects of seating posture on thoracic motion trajectory,chest acceleration,thoracoabdominal compression,viscous criterion(VC)of the chest and abdomen,internal organ strain,and spinal stress.Results Thoracic motion trajectories varied in the Z-direction under three seating postures.As the upper torso angle increased,thoracoabdominal kinematic injury parameters showed an upward trend.The thoracic and abdominal VC under 120° and 135° posture increased by 67％and 113％,10.7％and 25％compared with that under 105° standard sitting posture.The risk of thoracic internal organ injury was inversely related to the seating angle,while the risk of abdominal internal organ injury was positively related to the seating angle.The primary spinal injury mechanism was compression-flexion.Conclusions CRS protection evaluation should comprehensively consider thoracoabdominal kinematic parameters,internal organ biomechanics,and spinal injury risk.These findings have important implications for CRS development in intelligent driving systems and occupant protection strategy formulation.

Abnormal deployment of the airbag during a frontal car collision can cause injuries to the upper extremity of drivers with non-standard driving postures.Finite element simulation offers an effective approach for evaluating such injury risks.In this study,a biomechanical finite element model of the upper limb of the 95th percentile human body with detailed anatomical structures was developed.The validity of the upper extremity-airbag collision model was confirmed by reconstructing the cadaveric forearm and airbag impact experiments.Based on the validated model,the influence of factors such as airbag mass rate parameters,upper limb grip angle,and grip force on upper limb injuries in frontal collisions was investigated.The results indicate that variations in these three parameters have a significant influence on upper extremity injury,and these factors should be considered in the assessment of upper extremity injuries during car collision.

Objective To investigate the risk of thoracoabdominal injuries in six-year-old child occupants in a reclined seating posture during frontal collisions,and provide a reference for developing child restraint systems(CRS).Methods Three validated biomechanical models of six-year-old child occupants in different seating postures with detailed anatomical structures were used.The acceleration curve from a sport utility vehicle crash test was applied to analyze the effects of seating posture on thoracic motion trajectory,chest acceleration,thoracoabdominal compression,viscous criterion(VC)of the chest and abdomen,internal organ strain,and spinal stress.Results Thoracic motion trajectories varied in the Z-direction under three seating postures.As the upper torso angle increased,thoracoabdominal kinematic injury parameters showed an upward trend.The thoracic and abdominal VC under 120° and 135° posture increased by 67％and 113％,10.7％and 25％compared with that under 105° standard sitting posture.The risk of thoracic internal organ injury was inversely related to the seating angle,while the risk of abdominal internal organ injury was positively related to the seating angle.The primary spinal injury mechanism was compression-flexion.Conclusions CRS protection evaluation should comprehensively consider thoracoabdominal kinematic parameters,internal organ biomechanics,and spinal injury risk.These findings have important implications for CRS development in intelligent driving systems and occupant protection strategy formulation.

Based on CT scan data,a bionic model of lumbar spine injuries with high biofidelity is developed and validated through cadaver experiments.Decoupling the constraint system that affects occupants during collisions due to inertial forces and the subsequent pressure exerted by the seat upon returning to position,a simulated fall experiment is designed.The simulated outcomes are trained and predicted using deep learning algorithms,and the accuracy of the trained neural network prediction model is verified.Key parameters are analyzed for correlation using principal component analysis and cross-reverse methods.The results shows that the predicted lumbar spine injury model obtained from training has high reliability(R2>0.9).Comprehensive analysis reveals that after experiencing axial impact,the L4 vertebral body bears the highest impact load and can be used as a representative measure of lumbar spine injury.Among the environmental variables,the axial force on the L4 lumbar spine is mainly affected by torso mass and fall height,both of which have positive correlations.Torso mass,fall height,and posture angle all have positive effects on internal energy.Conversely,torso mass and fall height have negative correlations with stress.These research findings provide a scientific basis for further elucidating lumbar spine injury mechanisms in intelligent cockpit environments,devising corresponding safety protection measures,and evaluating occupant safety in automobiles.

Abnormal deployment of the airbag during a frontal car collision can cause injuries to the upper extremity of drivers with non-standard driving postures.Finite element simulation offers an effective approach for evaluating such injury risks.In this study,a biomechanical finite element model of the upper limb of the 95th percentile human body with detailed anatomical structures was developed.The validity of the upper extremity-airbag collision model was confirmed by reconstructing the cadaveric forearm and airbag impact experiments.Based on the validated model,the influence of factors such as airbag mass rate parameters,upper limb grip angle,and grip force on upper limb injuries in frontal collisions was investigated.The results indicate that variations in these three parameters have a significant influence on upper extremity injury,and these factors should be considered in the assessment of upper extremity injuries during car collision.

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.

PCOS is a highly prevalent disease in modern women of gestational age, characterized by infertility. Prevention before onset has been a key focus of national efforts in recent years. This article explored the prevention and treatment plan for polycystic ovary syndrome based on the three-level prevention theory of Academician Wang Qi. Primary prevention: control pathogenic risk factors; secondary prevention: precise screening and life intervention to prevent the formation of dangerous constitution; third level prevention: differentiation of body-differentiation of disease-differentiation of syndrome to achieve the goal of three-level prevention. In the prevention and treatment of PCOS, pre-disease prevention, post disease prevention and cure are tried to achieve, which could provide a truly effective, easy to operate, and applicable three-level prevention and treatment plan for a large population in clinical response to PCOS.

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.