A new method for computing the uniaxial modulus of articular cartilages using modified inhomogeneous triphasic model

It is well known that subtle changes in structure and tissue composition of articular cartilage can lead to its degeneration. The present paper puts forward a modified layered inhomogeneous triphasic model with four parameters based on the inhomogeneous triphasic model proposed by Narmoneva et al. Incorporating a piecewise fitting optimization criterion, the new model was used to obtain the uniaxial modulus Ha, and predict swelling pattern for the articular cartilage based on ultrasound-measured swelling strain data. The results show that the new method can be used to provide more accurate estimation on the uniaxial modulus than the inhomogeneous triphasic model with three parameters and the homogeneous mode, and predict effectively the swell- ing strains of highly nonuniform distribution of degenerated articular cartilages. This study can provide supplementary information for exploring mechanical and material properties of the cartilage, and thus be helpful for the diagnosis of osteoarthritis-related diseases.

Exploring the link between brain topological resilience and cognitive performance in the context of aging and vascular risk factors:A cross-ethnicity population-based study

Brain aging is typically associated with a significant decline in cognitive performance.Vascular risk factors(VRF)and subsequent atherosclerosis(AS)play a major role in this process.Brain resilience reflects the brain’s ability to withstand external perturbations,but the relationship of brain resilience with cognition during the aging process remains unclear.Here,we investigated how brain topological resilience(BTR)is associated with cognitive performance in the face of aging and vascular risk factors.We used data from two cross-ethnicity community cohorts,PolyvasculaR Evaluation for Cognitive Impairment and Vascular Events(PRECISE,n=2220)and Sydney Memory and Ageing Study(MAS,n=246).We conducted an attack simulation on brain structural networks based on k-shell decomposition and node degree centrality.BTR was defined based on changes in the size of the largest subgroup of the network during the simulation process.Subsequently,we explored the negative correlations of BTR with age,VRF,and AS,and its positive correlation with cognitive performance.Furthermore,using structural equation modeling(SEM),we constructed path models to analyze the directional dependencies among these variables,demonstrating that aging,AS,and VRF affect cognition by disrupting BTR.Our results also indicated the specificity of this metric,independent of brain volume.Overall,these findings underscore the supportive role of BTR on cognition during aging and highlight its potential application as an imaging marker for objective assessment of brain cognitive performance.

A mutli-scale spatial-temporal convolutional neural network with contrastive learning for motor imagery EEG classification

Motor imagery(MI)based Brain-computer interfaces(BCIs)have a wide range of applications in the stroke rehabilitation field.However,due to the low signal-to-noise ratio and high cross-subject variation of the electroencephalogram(EEG)signals generated by motor imagery,the classification performance of the existing methods still needs to be improved to meet the need of real practice.To overcome this problem,we propose a multi-scale spatial-temporal convolutional neural network called MSCNet.We introduce the contrastive learning into a multi-temporal convolution scale backbone to further improve the robustness and discrimination of embedding vectors.Experimental results of binary classification show that MSCNet outperforms the state-of-theart methods,achieving accuracy improvement of 6.04%,3.98%,and 8.15%on BCIC IV 2a,SMR-BCI,and OpenBMI datasets in subject-dependent manner,respectively.The results show that the contrastive learning method can significantly improve the classification accuracy of motor imagery EEG signals,which provides an important reference for the design of motor imagery classification algorithms.

Preliminary animal studies on observation of injured spinal cord with intraoperative ultrasound backscatter microscopy

Many studies have shown that strategies of nerve regeneration and cell-based transplantation are valid based on animal models of spinal cord injury (SCI).To apply these strategies and bridge spinal cord defects,the identification and precise localization of lesions during spinal cord surgery is necessary.The aim of the present experiment was to evaluate the capabilities of ultrasound backscatter microscopy (UBM) in identifying morphologic changes after SCI.After laminectomy,high-resolution ultrasound images of the spinal cord were obtained in one normal and seven spinal cord-injured adult Wistar rats using a UBM system with a 55-MHz center frequency scanner.Comparison between histoanatomic and UBM images was also performed.The results showed that UBM can identify cysts after the experimental SCI is removed in adult rats.In addition,the glial scar formed in secondary injury showed obvious hyperechoic speckle in the UBM image and correlated with the histoanatomic image.UBM has obvious clinical value in nerve regeneration and cell-based transplantation strategies in injured spinal cords.