PURPOSE: To judge the injury mode and injury severity of the real human body through the measured values of anthropomorphic test devices (ATD) injury indices, the mapping relationship of lumbar injury between ATD and human body model (HBM) was explored. METHODS: Through the ATD model and HBM simulation, the mapping relationship of lumbar injury between the 2 subjects was explored. The sled environment consisted of a semi-rigid seat with an adjustable seatback angle and a 3-point seat belt system with a seatback-mounted D-ring. Three seatback recline states of 25°, 45°, and 65° were designed, and the seat pan angle was maintained at 15°. A 23 g, 47 km/h pulse was used. The validity of the finite element model of the sled was verified by the comparison of ATD simulation and test results. ATD model was the test device for human occupant restraint for autonomous vehicles (THOR-AV) dummy model and HBM was the total human model for safety (THUMS) v6.1. The posture of the 2 models was adjusted to adapt to the 3 seat states. The lumbar response of THOR-AV and the mechanical and biomechanical data on L1 - L5 vertebrae of THUMS were output, and the response relationship between THOR-AV and THUMS was descriptive statistically analyzed. RESULTS: Both THOR-AV and THUMS were submarined in the 65° seatback angle case. With the change of seatback angle, the lumbar spine axial compression force (F_z) of THOR-AV and THUMS changed in the similar trend. The maximum F_z ratio of THOR-AV to THUMS at 25° and 45° seatback angle cases were 1.6 and 1.7. The flexion moment (M_y) and the time when the maximum M_y occurred in the 2 subjects were very different. In particular, the form of moment experienced by the L1 - L5 vertebrae of THUMS also changed. The changing trend of M_y measured by THOR-AV over time can reflect the changing trend of maximum stress of L1 and L2 of THUMS. CONCLUSION The F_z of ATD and HBM presents a certain proportional relationship, and there is a mapping relationship between the 2 subjects on F_z. The mapping function can be further clarified by applying more pulses and adopting more seatback angles. It is difficult to map M_y directly because they are very different in ATD and HBM. The M_y of ATD and stress of HBM lumbar showed a similar change trend over time, and there may be a hidden mapping relationship.
Humans ; Lumbar Vertebrae/injuries* ; Finite Element Analysis ; Biomechanical Phenomena ; Manikins ; Spinal Injuries/physiopathology*

Creutzfeldt-Jakob disease(CJD)is a rare neurodegenerative disorder characterized by abnor-malities in the prion protein(PrP),the most common form of human prion disease.Although Genome-Wide Association Studies(GWAS)have identified numerous risk genes for CJD,the mechanisms under-lying these risk loci remain poorly understood.This study aims to elucidate novel genetically prioritized candidate proteins associated with CJD in the human brain through an integrative analytical pipeline.Uti-lizing datasets from Protein Quantitative Trait Loci(pQTL)(NpQTL1=152,NpQTL2=376),expres-sion QTL(eQTL)(N=452),and the CJD GWAS(NCJD=4 110,NControls=13 569),we imple-mented a systematic analytical pipeline.This pipeline included Proteome-Wide Association Study(PWAS),Mendelian randomization(MR),Bayesian colocalization,and Transcriptome-Wide Associa-tion Study(TWAS)to identify novel genetically prioritized candidate proteins implicated in CJD patho-genesis within the brain.Through PWAS,we identified that the altered abundance of six brain proteins was significantly associated with CJD.Two genes,STX6 and PDIA4,were established as lead causal genes for CJD,supported by robust evidence(False Discovery Rate＜0.05 in MR analysis;PP4/(PP3+PP4)≥0.75 in Bayesian colocalization).Specifically,elevated levels of STX6 and PDIA4 were asso-ciated with an increased risk of CJD.Additionally,TWAS demonstrated that STX6 and PDIA4 were asso-ciated with CJD at the transcriptional level.

Creutzfeldt-Jakob disease(CJD)is a rare neurodegenerative disorder characterized by abnor-malities in the prion protein(PrP),the most common form of human prion disease.Although Genome-Wide Association Studies(GWAS)have identified numerous risk genes for CJD,the mechanisms under-lying these risk loci remain poorly understood.This study aims to elucidate novel genetically prioritized candidate proteins associated with CJD in the human brain through an integrative analytical pipeline.Uti-lizing datasets from Protein Quantitative Trait Loci(pQTL)(NpQTL1=152,NpQTL2=376),expres-sion QTL(eQTL)(N=452),and the CJD GWAS(NCJD=4 110,NControls=13 569),we imple-mented a systematic analytical pipeline.This pipeline included Proteome-Wide Association Study(PWAS),Mendelian randomization(MR),Bayesian colocalization,and Transcriptome-Wide Associa-tion Study(TWAS)to identify novel genetically prioritized candidate proteins implicated in CJD patho-genesis within the brain.Through PWAS,we identified that the altered abundance of six brain proteins was significantly associated with CJD.Two genes,STX6 and PDIA4,were established as lead causal genes for CJD,supported by robust evidence(False Discovery Rate＜0.05 in MR analysis;PP4/(PP3+PP4)≥0.75 in Bayesian colocalization).Specifically,elevated levels of STX6 and PDIA4 were asso-ciated with an increased risk of CJD.Additionally,TWAS demonstrated that STX6 and PDIA4 were asso-ciated with CJD at the transcriptional level.

Objective:To develop an ultrasound multi-view fusion recognition model and evaluate its clinical value in diagnosing fetal conotruncal defects (CTD).Methods:This prospective study collected cardiac ultrasound images from fetuses at 20-32 weeks of gestation undergoing prenatal ultrasound at Dongguan Maternal and Child Health Hospital between September 2022 and May 2024. The case group comprised fetuses diagnosed with CTD, while controls with normal cardiac structures were collected at a 1∶2 ratio. Both groups were divided into modeling training and validation sets at a 3∶1 ratio. One optimal standard image each from the four-chamber view, left ventricular outflow tract view, right ventricular outflow tract view, and three vessels and trachea view was included per fetus. A deep learning-based multi-view fusion recognition model was developed to differentiate normal conotruncal anatomy from CTD. Model performance was validated against post-abortion pathology or postnatal echocardiography results. SAS software was used for statistical analysis to calculate the sensitivity and specificity of three fusion models (based on positivity in any two, three, or four views, and were designated as Fusion Model 1, Fusion Model 2, and Fusion Model 3, respectively), with the optimal model determined by the maximum Youden index. Senior, intermediate, and junior prenatal sonologists independently diagnosed cases in the validation set under blinding conditions. Their diagnostic results were compared with those of the optimal model. Paired Chi-square test (Cochran's Q test) was employed to compare the differences between the diagnostic accuracy rates of sonologists at different experience levels and the sensitivity of the optimal model, thereby analyzing the auxiliary diagnostic value of the multi-view fusion recognition model. Results:The study included 88 CTD cases, excluding six cases (non-CTD diagnosed by post-abortion pathology or postnatal echocardiography or poor image quality), divided into 60 training and 22 validation cases (12 tetralogy of Fallot, four double outlet right ventricle, three transposition of great arteries, three persistent truncus arteriosus). The control group included 176 cases, excluding 15 cases (other cardiac abnormalities confirmed postnatally or poor image quality after re-evaluation), divided into 120 training and 41 validation cases. The sensitivities of Fusion Model 1, Fusion Model 2, and Fusion Mudel 3 were 0.86, 0.64, and 0.27, while their specificities were 0.76, 0.95, and 1.00, respectively. Fusion Model 1 demonstrated the highest Youden index (0.62) and was selected as optimal. Its diagnostic sensitivity showed no significant difference from senior sonologists [86% vs. 91% (20/22), Bonferroni-corrected P>0.999], but was significantly higher than intermediate [55% (12/22), Bonferroni-corrected P=0.049] and junior sonologists [32% (7/22), Bonferroni-corrected P=0.003]. Conclusion:The deep learning multi-view fusion model achieved diagnostic performance comparable to senior sonologists, demonstrating potential value in assisting CTD diagnosis, training less experienced sonologists, and supporting research and education.

Purpose To explore factors influencing vaginal delivery during the first stage of labor using intrapartum ultrasound and to construct predictive models for delivery decision-making.Materials and Methods A total of 473 nulliparous women admitted to Heping Hospital,Northern Theater General Hospital from July to December 2021 were prospectively enrolled as the training set.Clinical data on admission and fetal biometric parameters(biparietal diameter,femur length,head circumference and abdominal circumference)measured within one week before delivery were collected.Ultrasound assessments of fetal position,angle of progression(AOP)and head-perineum distance(HPD)were performed during the first stage of labor.The latent phase group(n=255)was subdivided into vaginal delivery group(n=186)and cesarean section group(n=69);the active phase group(n=218)was divided into vaginal delivery group(n=168)and cesarean section group(n=50).The associations between fetal position,AOP,HPD and vaginal delivery were analyzed,and predictive models were constructed for the latent phase(model 1)and active phase(model 2).A validation set of 547 women from January to September 2022 was used to evaluate model performance via area under the curve(AUC),calibration curves and decision curve analysis.Results In the latent phase,multivariate regression identified maternal height(OR=3.970,P=0.002),pre-pregnancy body mass index(OR=0.893,P=0.036),labor onset type(OR=2.415,P=0.045),neonatal birth weight(OR=3.728,P=0.002),AOP(OR=11.649,P<0.001)and HPD(OR=4.240,P=0.004)as significant predictors.The training and validation sets showed AUCs of 0.917 and 0.869,respectively.Goodness-of-fit tests indicated excellent model calibration(χ2=3.437,P=0.904;χ2=10.877,P=0.209).Decision curve analysis demonstrated strong clinical utility.For the active phase,significant predictors included maternal height(OR=6.532,P<0.001),neonatal birth weight(OR=11.890,P<0.001),fetal position(OR=4.600,P=0.003),AOP(OR=7.229,P<0.001)and HPD(OR=4.722,P=0.005).AUCs were 0.943(training)and 0.906(validation),with good calibration(χ2=4.340,P=0.740;χ2=9.836,P=0.277)and clinical applicability.Conclusion First stage ultrasound assessment of fetal position,AOP and HPD correlates with delivery outcomes.The developed nomogram models combining these parameters with clinical factors provide valuable guidance for delivery decision-making.

Purpose To explore factors influencing vaginal delivery during the first stage of labor using intrapartum ultrasound and to construct predictive models for delivery decision-making.Materials and Methods A total of 473 nulliparous women admitted to Heping Hospital,Northern Theater General Hospital from July to December 2021 were prospectively enrolled as the training set.Clinical data on admission and fetal biometric parameters(biparietal diameter,femur length,head circumference and abdominal circumference)measured within one week before delivery were collected.Ultrasound assessments of fetal position,angle of progression(AOP)and head-perineum distance(HPD)were performed during the first stage of labor.The latent phase group(n=255)was subdivided into vaginal delivery group(n=186)and cesarean section group(n=69);the active phase group(n=218)was divided into vaginal delivery group(n=168)and cesarean section group(n=50).The associations between fetal position,AOP,HPD and vaginal delivery were analyzed,and predictive models were constructed for the latent phase(model 1)and active phase(model 2).A validation set of 547 women from January to September 2022 was used to evaluate model performance via area under the curve(AUC),calibration curves and decision curve analysis.Results In the latent phase,multivariate regression identified maternal height(OR=3.970,P=0.002),pre-pregnancy body mass index(OR=0.893,P=0.036),labor onset type(OR=2.415,P=0.045),neonatal birth weight(OR=3.728,P=0.002),AOP(OR=11.649,P<0.001)and HPD(OR=4.240,P=0.004)as significant predictors.The training and validation sets showed AUCs of 0.917 and 0.869,respectively.Goodness-of-fit tests indicated excellent model calibration(χ2=3.437,P=0.904;χ2=10.877,P=0.209).Decision curve analysis demonstrated strong clinical utility.For the active phase,significant predictors included maternal height(OR=6.532,P<0.001),neonatal birth weight(OR=11.890,P<0.001),fetal position(OR=4.600,P=0.003),AOP(OR=7.229,P<0.001)and HPD(OR=4.722,P=0.005).AUCs were 0.943(training)and 0.906(validation),with good calibration(χ2=4.340,P=0.740;χ2=9.836,P=0.277)and clinical applicability.Conclusion First stage ultrasound assessment of fetal position,AOP and HPD correlates with delivery outcomes.The developed nomogram models combining these parameters with clinical factors provide valuable guidance for delivery decision-making.

Objective:To develop an ultrasound multi-view fusion recognition model and evaluate its clinical value in diagnosing fetal conotruncal defects (CTD).Methods:This prospective study collected cardiac ultrasound images from fetuses at 20-32 weeks of gestation undergoing prenatal ultrasound at Dongguan Maternal and Child Health Hospital between September 2022 and May 2024. The case group comprised fetuses diagnosed with CTD, while controls with normal cardiac structures were collected at a 1∶2 ratio. Both groups were divided into modeling training and validation sets at a 3∶1 ratio. One optimal standard image each from the four-chamber view, left ventricular outflow tract view, right ventricular outflow tract view, and three vessels and trachea view was included per fetus. A deep learning-based multi-view fusion recognition model was developed to differentiate normal conotruncal anatomy from CTD. Model performance was validated against post-abortion pathology or postnatal echocardiography results. SAS software was used for statistical analysis to calculate the sensitivity and specificity of three fusion models (based on positivity in any two, three, or four views, and were designated as Fusion Model 1, Fusion Model 2, and Fusion Model 3, respectively), with the optimal model determined by the maximum Youden index. Senior, intermediate, and junior prenatal sonologists independently diagnosed cases in the validation set under blinding conditions. Their diagnostic results were compared with those of the optimal model. Paired Chi-square test (Cochran's Q test) was employed to compare the differences between the diagnostic accuracy rates of sonologists at different experience levels and the sensitivity of the optimal model, thereby analyzing the auxiliary diagnostic value of the multi-view fusion recognition model. Results:The study included 88 CTD cases, excluding six cases (non-CTD diagnosed by post-abortion pathology or postnatal echocardiography or poor image quality), divided into 60 training and 22 validation cases (12 tetralogy of Fallot, four double outlet right ventricle, three transposition of great arteries, three persistent truncus arteriosus). The control group included 176 cases, excluding 15 cases (other cardiac abnormalities confirmed postnatally or poor image quality after re-evaluation), divided into 120 training and 41 validation cases. The sensitivities of Fusion Model 1, Fusion Model 2, and Fusion Mudel 3 were 0.86, 0.64, and 0.27, while their specificities were 0.76, 0.95, and 1.00, respectively. Fusion Model 1 demonstrated the highest Youden index (0.62) and was selected as optimal. Its diagnostic sensitivity showed no significant difference from senior sonologists [86% vs. 91% (20/22), Bonferroni-corrected P>0.999], but was significantly higher than intermediate [55% (12/22), Bonferroni-corrected P=0.049] and junior sonologists [32% (7/22), Bonferroni-corrected P=0.003]. Conclusion:The deep learning multi-view fusion model achieved diagnostic performance comparable to senior sonologists, demonstrating potential value in assisting CTD diagnosis, training less experienced sonologists, and supporting research and education.

Objective To investigate the changes in plasma amyloid-β (Aβ) level and their relationship with white matter microstructure in the patients with amnesic mild cognitive impairment(aMCI) and vascular mild cognitive impairment (vMCI).Methods A total of 36 aMCI patients,20 vMCI patients,and 34 sex and age matched healthy controls (HC) in the outpatient and inpatient departments of the First Affiliated Hospital of Anhui Medical University were enrolled in this study.Neuropsychological scales,including the Mini-Mental State Examination,the Montreal Cognitive Assessment,and the Activity of Daily Living Scale,were employed to assess the participants.Plasma samples of all the participants were collected for the measurement of Aβ42 and Aβ40 levels.All the participants underwent magnetic resonance scanning to obtain diffusion tensor imaging (DTI) data.The DTI indexes of 48 white matter regions of each individual were measured (based on the ICBM-DTI-81 white-matter labels atlas developed by Johns Hopkins University),including fractional anisotropy (FA) and mean diffusivity (MD).The cognitive function,plasma Aβ42,Aβ40,and Aβ42/40 levels,and DTI index were compared among the three groups.The correlations between the plasma Aβ42/40 levels and DTI index of aMCI and vMCI patients were analyzed.Results The Mini-Mental State Examination and the Montreal Cognitive Assessment scores of aMCI and vMCI groups were lower than those of the HC group (all P<0.001).There was no significant difference in the Activity of Daily Living Scale score among the three groups (P=0.654).The plasma Aβ42 level showed no significant difference among the three groups (P=0.227).The plasma Aβ40 level in the vMCI group was higher than that in the HC group (P=0.014),while it showed no significant difference between aMCI and HC groups (P=1.000).The plasma Aβ42/40 levels in aMCI and vMCI groups showed no significant differences from that in the HC group (P=1.000,P=0.105),while the plasma Aβ42/40 level was lower in the vMCI group than in the aMCI group (P=0.016).The FA value of the left anterior limb of internal capsule in the vMCI group was lower than those in HC and aMCI groups (all P=0.001).The MD values of the left superior corona radiata,left external capsule,left cingulum (cingulate gyrus),and left superior fronto-occipital fasciculus in the vMCI group were higher than those in HC (P=0.024,P=0.001,P=0.003,P<0.001) and aMCI (P=0.015,P=0.004,P=0.019,P=0.001) groups,while the MD values of the right posterior limb of internal capsule (P=0.005,P=0.001) and left cingulum (hippocampus) (P=0.017,P=0.031) in the aMCI and vMCI groups were higher than those in the HC group.In the aMCI group,plasma Aβ42/40 level was positively correlated with FA of left posterior limb of internal capsule (r=0.403,P=0.015) and negatively correlated with MD of the right fonix (r=-0.395,P=0.017).In the vMCI group,plasma Aβ42/40 level was positively correlated with FA of the right superior cerebellar peduncle and the right anterior limb of internal capsule (r=0.575,P=0.008;r=0.639,P=0.002),while it was negatively correlated with MD of the right superior cerebellar peduncle and the right anterior limb of internal capsule (r=-0.558,P=0.011;r=-0.626,P=0.003).Conclusions Plasma Aβ levels vary differently in the patients with aMCI and vMCI.The white matter regions of impaired microstructural integrity differ in the patients with different dementia types in the early stage.The plasma Aβ levels in the patients with aMCI and vMCI are associated with the structural integrity of white matter,and there is regional specificity between them.
Humans ; Diffusion Tensor Imaging ; White Matter/diagnostic imaging* ; Cognitive Dysfunction ; Outpatients ; Cognition ; Amyloid beta-Peptides

OBJECTIVE: To compare the efficacy of plerixafor (PXF) combined with granulocyte colony-stimulating factor (G-CSF) (PXF+G-CSF) and cyclophosphamide (Cy) combined with G-CSF (Cy+G-CSF) in the mobilization of peripheral blood stem cells (PBSCs) in patients with multiple myeloma (MM). METHODS: The clinical data of 41 MM patients who underwent PBSC mobilization using PXF+G-CSF (18 cases) or Cy+G-CSF (23 cases) in Shanxi Bethune Hospital from January 2019 to December 2021 were retrospectively analyzed, including the count of collected CD34⁺ cells, acquisition success rate, failure rate, and optimal rate. The correlation of sex, age, disease type, DS staging, ISS staging, number of chemotherapy cycle, disease status before mobilization, and mobilization regimen with the collection results was analyzed, and the adverse reactions, length of hospital stay, and hospitalization costs were compared between the two mobilization regimens. RESULTS: The 41 patients underwent 97 mobilization collections, and the median number of CD34⁺ cells collected was 6.09 (0-34.07)×10⁶/kg. The acquisition success rate, optimal rate, and failure rate was 90.2%, 56.1%, and 9.8%, respectively. Univariate analysis showed that sex, age, disease type, and disease stage had no significant correlation with the number of CD34⁺ cells collected and acquisition success rate (P >0.05), but the patients with better disease remission than partial remission before mobilization were more likely to obtain higher CD34⁺ cell count (P <0.05). The PXF+G-CSF group had a larger number of CD34⁺ cells and higher acquisition success rate in the first collection than Cy+G-CSF group (both P <0.05), and had lower infection risk and shorter length of hospital stay during mobilization (both P <0.05), but the economic burden increased (P <0.05). CONCLUSION PXF+G-CSF used for PBSC mobilization in MM patients has high first acquisition success rate, large number of CD34⁺ cells, less number of collection times, and short length of hospital stay, but the economic cost is heavy.
Humans ; Antigens, CD34/metabolism* ; Cyclophosphamide/therapeutic use* ; Granulocyte Colony-Stimulating Factor/therapeutic use* ; Hematopoietic Stem Cell Mobilization/methods* ; Hematopoietic Stem Cell Transplantation ; Heterocyclic Compounds/therapeutic use* ; Multiple Myeloma/drug therapy* ; Peripheral Blood Stem Cells/metabolism* ; Retrospective Studies

Aim To study the mechanism of anti-plate- let aggregation of sorghum root active parts. Methods The effects of active fraction (WEAE-M 30%) from sorghum roots on platelet aggregation induced by collagen, thrombin and adenosine diphosphate were investigated in vitro. Western blot, enzyme-linked immunoas-say, flow cytometry and fluorescence techniques were used to explore the mechanism of the antiplatelet aggregation effect of WEAE-M 30% . Results WEAE-M 30% had a significant inhibitory effect on platelet aggregation induced by the three agonists mentioned above. The inhibitory effect on platelet aggregation induced by collagen was the most significant, with an inhibitory rate of (72. 91 ±2. 42)%. It was found that WEAE-M 30% had a significant inhibitory effect on the collagen- mediated platelet (IPVI signaling pathway protein Src, MAPK signaling pathway protein p38 and ERK phosphorylation. It also significantly inhibited the levels of ATP, P-selection and Ca2+ in platelets. Conclusions It is suggested that the mechanism of WE-AE-M 30% antiplatelet aggregation may be related to the inhibition of platelet activation pathway GPV1, MAPK and the release of typical platelet representative particles.