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 The finite element pelvis model with detailed anatomical structures which meets the Chinese human 95th percentile characteristics is developed,and the influence of cortical bone modeling method on the biomechanical response of the real pelvis is explored.Methods Based on the pelvic medical images of a 95th percentile male volunteer,two finite element pelvis models with real hip bone cortical bone thickness(REA-M)and 2 mm uniform cortical bone thickness(CON-M)dominated by hexahedral elements were constructed.Using the simulation method to reconstruct the loading conditions of cadaver experiments,the validation of models was verified by comparing the cadaver experimental results and simulation results,and biomechanical response differences of two models under different working conditions were discussed.Results The simulation data showed that there was a strong correlation between the overall biomechanical responses of two pelvic models and the cadaver experiment,and the mechanical response difference between two models was mostly within 8％,and the correlation score difference between two models was smaller than 2％.Conclusions The validation of two pelvic models established in this study is verified by rebuilding multiple simulation experiments.Although the biomechanical responses of CON-M and REA-M models were different,the difference was small.From the perspective of model simplification,the CON-M model can be used to study the biomechanical response of the pelvis.

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.

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.

Objective The finite element pelvis model with detailed anatomical structures which meets the Chinese human 95th percentile characteristics is developed,and the influence of cortical bone modeling method on the biomechanical response of the real pelvis is explored.Methods Based on the pelvic medical images of a 95th percentile male volunteer,two finite element pelvis models with real hip bone cortical bone thickness(REA-M)and 2 mm uniform cortical bone thickness(CON-M)dominated by hexahedral elements were constructed.Using the simulation method to reconstruct the loading conditions of cadaver experiments,the validation of models was verified by comparing the cadaver experimental results and simulation results,and biomechanical response differences of two models under different working conditions were discussed.Results The simulation data showed that there was a strong correlation between the overall biomechanical responses of two pelvic models and the cadaver experiment,and the mechanical response difference between two models was mostly within 8％,and the correlation score difference between two models was smaller than 2％.Conclusions The validation of two pelvic models established in this study is verified by rebuilding multiple simulation experiments.Although the biomechanical responses of CON-M and REA-M models were different,the difference was small.From the perspective of model simplification,the CON-M model can be used to study the biomechanical response of the pelvis.

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 expressions of H3.3G34W,p63 and SATB2 in giant cell tumor of bone(GCTB)and the effect and value of their combined application in the diagnosis of GCTB.Methods We collected the samples and medical records of 54 cases of GCTB and 83 cases of non-giant cell tumor of bone(14 cases of aneurysmal bone cyst,16 cases of chondroblastoma and 53 cases of non-ossifying fibroma)diagnosed between 2020 and 2022 in the Department of Pathology of Honghui Hospital Affiliated to Xi'an Jiaotong University.The expressions of H3.3G34W,p63 and SATB2 were detected by EliVision immunohistochemical method.X2 test was used to determine whether there are significant differences in the positive rates of H3.3G34W,p63 and SATB2 among all the groups.The combined diagnostic model including H3.3G34W,p63 and SATB2 was established by Logistic regression analysis,and the diagnostic value of the model was evaluated by ROC curve analysis.Results The positive rates of H3.3G34W,p63 and SATB2 in GCTB group were 81.5％,90.7％and 92.6％,respectively;the positive rates in NGCTB group were 2.4％,28.9％and 62.7％.Compared with NGCTB group,the age of GCTB group was significantly older[(41.222±14.849)vs.(16.566±9.439),P＜0.001],and the prevalence was higher in women than in men(51.9％vs.48.1％,P＜0.001).In addition,compared with the NGCTB group,the positive rates of H3.3G34W(81.5％vs.2.4％,P＜0.001),p63(90.7％vs.28.9％,P＜0.001)and SATB2(92.6％vs.62.7％,P＜0.001)were significantly higher in the GCTB group.Univariate regression analysis built a univariate prediction modeland ROC curve analysis showed that age(AUC=92.9％,P＜0.001),sex(AUC=64.5％,P=0.004),H3.3G34W positive rate(AUC=89.5％,P＜0.001),p63 positive rate(AUC=80.9％,P＜0.001)and SATB2 positive rate(AUC=65.0％,P=0.003)were independent predictors of diagnosis of giant cell tumor of bone.Multivariate regression analysis(Logistic)constructed a hybrid prediction model.ROC curve analysis suggested that the hybrid model showed better prediction value than the single factor model(AUC=98.4％,P＜0.001).Conclusion H3.3G34W,p63 and SATB2 are effective molecular markers for the diagnosis of GCTB,and their combined application can improve the prediction efficiency of the diagnosis of GCTB.

Objective To construct the theoretical framework of rehabilitation big data based on International Classification of Func-tioning,Disability and Health(ICF). Methods Drawing upon international rehabilitation policy documents,such as the World Health Organization's Rehabili-tation in health systems;Rehabilitation in health systems:guide for action;Rehabilitation indicator menu:a tool accompanying the Framework for Rehabilitation Monitoring and Evaluation(FRAME);Template for Rehabilita-tion Information Collection(TRIC):a tool accompanying the Systematic Assessment of Rehabilitation Situation(STARS);and Framework and Standards for Country Health Information Systems;this study examined the com-position and function of rehabilitation big data.The content structure of the rehabilitation big data domain was an-alyzed using the World Health Organization Family of International Classifications(WHO-FICs).Furthermore,the generation patterns of rehabilitation big data was constructed drawing on the Health Metrics Network and big data hierarchical classification. Results Within the six primary elements of the health service system,the information system element was particularly significant,encompassing a substantial branch known as rehabilitation big data.There were three components of rehabilitation big data:health condition,health-related factors and health services.The content framework for this data was derived from the WHO-FICs framework,which covered three dimensions:health and function,dis-ease and function,and disease,function and intervention.A comprehensive model for generating and applying re-habilitation big data in rehabilitation services was developed in line with the requirements for constructing big da-ta architectures.The sources of this data included population censuses,social registration information,population surveys,resources,services and personal records.The result chain of rehabilitation big data encompassed five major processes:input,process,output,outcome and impact.The processing and utilization of this data involved collection,storage,management,analysis and application. Conclusion A theoretical framework for rehabilitation big data has been constructed based on the ICF theory.