OBJECTIVE: To investigate the feasibility of MRI three-dimensional (3D) reconstruction model in quantifying glenoid bone defect by comparing with CT 3D reconstruction model measurement. METHODS: Forty patients with shoulder anterior dislocation who met the selection criteria between December 2021 and December 2022 were admitted as study participants. There were 34 males and 6 females with an average age of 24.8 years (range, 19-32 years). The injury caused by sports injury in 29 cases and collision injury in 6 cases, and 5 cases had no obvious inducement. The time from injury to admission ranged from 4 to 72 months (mean, 28.5 months). CT and MRI were performed on the patients' shoulder joints, and a semi-automatic segmentation of the images was done with 3D slicer software to construct a glenoid model. The length of the glenoid bone defect was measured on the models by 2 physicians. The intra-group correlation coefficient ( ICC) was used to evaluate the consistency between the 2 physicians, and Bland-Altman plots were constructed to evaluate the consistency between the 2 methods. RESULTS: The length of the glenoid bone defects measured on MRI 3D reconstruction model was (3.83±1.36) mm/4.00 (0.58, 6.13) mm for physician 1 and (3.91±1.20) mm/3.86 (1.39, 5.96) mm for physician 2. The length of the glenoid bone defects measured on CT 3D reconstruction model was (3.81±1.38) mm/3.80 (0.60, 6.02) mm for physician 1 and (3.99±1.19) mm/4.00 (1.68, 6.38) mm for physician 2. ICC and Bland-Altman plot analysis showed good consistency. The ICC between the 2 physicians based on MRI and CT 3D reconstruction model measurements were 0.73 [95% CI (0.54, 0.85)] and 0.80 [95% CI (0.65, 0.89)], respectively. The 95% CI of the difference between the two measurements of physicians 1 and 2 were (-0.46, 0.49) and (-0.68, 0.53), respectively. CONCLUSION The measurement of glenoid bone defect based on MRI 3D reconstruction model is consistent with that based on CT 3D reconstruction model. MRI can be used instead of CT to measure glenoid bone defects in clinic, and the soft tissue of shoulder joint can be observed comprehensively while reducing radiation.
Male ; Female ; Humans ; Young Adult ; Adult ; Imaging, Three-Dimensional/methods* ; Tomography, X-Ray Computed/methods* ; Joint Instability ; Shoulder Joint/diagnostic imaging* ; Shoulder Dislocation ; Magnetic Resonance Imaging/methods*

The gait acquisition system can be used for gait analysis. The traditional wearable gait acquisition system will lead to large errors in gait parameters due to different wearing positions of sensors. The gait acquisition system based on marker method is expensive and needs to be used by combining with the force measurement system under the guidance of rehabilitation doctors. Due to the complex operation, it is inconvenient for clinical application. In this paper, a gait signal acquisition system that combines foot pressure detection and Azure Kinect system is designed. Fifteen subjects are organized to participate in gait test, and relevant data are collected. The calculation method of gait spatiotemporal parameters and joint angle parameters is proposed, and the consistency analysis and error analysis of the gait parameters of proposed system and camera marking method are carried out. The results show that the parameters obtained by the two systems have good consistency (Pearson correlation coefficient r ≥ 0.9, P < 0.05) and have small error (root mean square error of gait parameters is less than 0.1, root mean square error of joint angle parameters is less than 6). In conclusion, the gait acquisition system and its parameter extraction method proposed in this paper can provide reliable data acquisition results as a theoretical basis for gait feature analysis in clinical medicine.
Humans ; Biomechanical Phenomena ; Gait ; Lower Extremity ; Foot ; Gait Analysis ; Reproducibility of Results

Transfer learning is provided with potential research value and application prospect in motor imagery electroencephalography (MI-EEG)-based brain-computer interface (BCI) rehabilitation system, and the source domain classification model and transfer strategy are the two important aspects that directly affect the performance and transfer efficiency of the target domain model. Therefore, we propose a parameter transfer learning method based on shallow visual geometry group network (PTL-sVGG). First, Pearson correlation coefficient is used to screen the subjects of the source domain, and the short-time Fourier transform is performed on the MI-EEG data of each selected subject to acquire the time-frequency spectrogram images (TFSI). Then, the architecture of VGG-16 is simplified and the block design is carried out, and the modified sVGG model is pre-trained with TFSI of source domain. Furthermore, a block-based frozen-fine-tuning transfer strategy is designed to quickly find and freeze the block with the greatest contribution to sVGG model, and the remaining blocks are fine-tuned by using TFSI of target subjects to obtain the target domain classification model. Extensive experiments are conducted based on public MI-EEG datasets, the average recognition rate and Kappa value of PTL-sVGG are 94.9% and 0.898, respectively. The results show that the subjects' optimization is beneficial to improve the model performance in source domain, and the block-based transfer strategy can enhance the transfer efficiency, realizing the rapid and effective transfer of model parameters across subjects on the datasets with different number of channels. It is beneficial to reduce the calibration time of BCI system, which promote the application of BCI technology in rehabilitation engineering.
Algorithms ; Brain-Computer Interfaces ; Electroencephalography/methods* ; Humans ; Imagination ; Machine Learning

The radial artery pulse wave contains a wealth of physiological and pathological information about the human body, and non-invasive studies of the radial artery pulse wave can assess arterial vascular elasticity in different age groups.The piezoelectric pulse wave transducers were used to non-invasively acquire radial artery pulse waves at different contact pressures in young and middle-aged and elderly populations. The radial artery waveforms were decomposed using a triangular blood flow model fitting method to obtain forward and reflected waves and calculate reflection parameters. Finally a correlation analysis and regression analysis of the contact pressure P_sensor with the reflection parameters was carried out. The results showed that the reflection parameters RM, RI and R_d had a strong negative correlation with P_sensor in both types of subjects, and the correlation coefficients and slopes of the regression curves were significantly different between the two types of subjects (P<0.05). Based on the results of this study, excessive contact pressure on the transducer should be avoided when detecting radial artery reflection waves in clinical practice. The results also show that the magnitude of the slope of the regression curve between the reflection parameters and the transducer contact pressure may be a potentially useful indicator for quantifying the elastic properties of the vessel.
Middle Aged ; Aged ; Humans ; Blood Pressure/physiology* ; Arteries ; Blood Flow Velocity/physiology* ; Elasticity ; Pulse Wave Analysis ; Radial Artery/physiology*

<p>OBJECTIVETo study the molecular basis of O04 allele expression of weak A antigen at the ABO locus.p><p>METHODSSerological assays were performed to characterize the erythrocyte phenotype of a proband. Mutations of the ABO gene were screened with polymerase chain reaction (PCR), reverse-transcription PCR (RT-PCR) and DNA sequencing.p><p>RESULTSThe proband was identified as weak AB phenotype by serological assays. A B101 allele and an O04 allele were identified by gene sequencing, gene cloning and RT-PCR.p><p>CONCLUSIONA B101 allele and an O04 allele were confirmed by the sequencing based typing in a suspicious ABO blood group. The O04 allele is characterized by expression of weak A antigen.p>
ABO Blood-Group System ; genetics ; Adult ; Base Sequence ; China ; Exons ; Female ; Humans ; Molecular Sequence Data ; Young Adult

High-frequency steady-state asymmetric visual evoked potential (SSaVEP) provides a new paradigm for designing comfortable and practical brain-computer interface (BCI) systems. However, due to the weak amplitude and strong noise of high-frequency signals, it is of great significance to study how to enhance their signal features. In this study, a 30 Hz high-frequency visual stimulus was used, and the peripheral visual field was equally divided into eight annular sectors. Eight kinds of annular sector pairs were selected based on the mapping relationship of visual space onto the primary visual cortex (V1), and three phases (in-phase[0º, 0º], anti-phase [0º, 180º], and anti-phase [180º, 0º]) were designed for each annular sector pair to explore response intensity and signal-to-noise ratio under phase modulation. A total of 8 healthy subjects were recruited in the experiment. The results showed that three annular sector pairs exhibited significant differences in SSaVEP features under phase modulation at 30 Hz high-frequency stimulation. And the spatial feature analysis showed that the two types of features of the annular sector pair in the lower visual field were significantly higher than those in the upper visual field. This study further used the filter bank and ensemble task-related component analysis to calculate the classification accuracy of annular sector pairs under three-phase modulations, and the average accuracy was up to 91.5%, which proved that the phase-modulated SSaVEP features could be used to encode high- frequency SSaVEP. In summary, the results of this study provide new ideas for enhancing the features of high-frequency SSaVEP signals and expanding the instruction set of the traditional steady state visual evoked potential paradigm.
Humans ; Evoked Potentials, Visual ; Brain-Computer Interfaces ; Healthy Volunteers ; Signal-To-Noise Ratio

The study aimed to evaluate the safety and function of poly(lactic-acid-co-ε-caprolactone) (PLCL)/fibrinogen nanofibers (P/F-Ns), and provide theoretical basis for the clinical application. The surface morphology, mechanical properties, the hydrophilicity and the fibrinogen content of P/F-Ns were tested by scanning electron microscope, the material testing machine, the contact angle meter and the microplate reader, respectively. The cell adhesion, proliferation and ligament remodeling genes expression of Hig-82 cells on P/F-Ns were conducted through cell counting kit-8 (CCK-8) and real-time quantitative PCR analyses, respectively. The results showed that with the increase of the fibrinogen content, the pore sizes and hydrophilicity of three P/F-Ns increased, but the mechanical properties decreased. Cell adhesion and proliferation tests showed that P/F-N-2 held the best ability to promote cell adhesion and proliferation. The ligament remodeling genes expressions of Hig-82 cells on P/F-N-1, P/F-N-2 and P/F-N-3 were all up-regulated compared to P/F-N-0 on days 3 and 7. All the three P/F-Ns containing fibrinogen (P/F-N-1, P/F-N-2 and P/F-N-3) had better biocompatibility compared to P/F-N-0, and could be efficiently applied to the reconstruction of anterior cruciate ligament.
Anterior Cruciate Ligament Reconstruction ; Cell Adhesion ; Fibrinogen ; Materials Testing ; Nanofibers

The portable light-weight magnetic resonance imaging system can be deployed in special occasions such as Intensive Care Unit (ICU) and ambulances, making it possible to implement bedside monitoring imaging systems, mobile stroke units and magnetic resonance platforms in remote areas. Compared with medium and high field imaging systems, ultra-low-field magnetic resonance imaging equipment utilizes light-weight permanent magnets, which are compact and easy to move. However, the image quality is highly susceptible to external electromagnetic interference without a shielded room and there are still many key technical problems in hardware design to be solved. In this paper, the system hardware design and environmental electromagnetic interference elimination algorithm were studied. Consequently, some research results were obtained and a prototype of portable shielding-free 50 mT magnetic resonance imaging system was built. The light-weight magnet and its uniformity, coil system and noise elimination algorithm and human brain imaging were verified. Finally, high-quality images of the healthy human brain were obtained. The results of this study would provide reference for the development and application of ultra-low-field magnetic resonance imaging technology.
Humans ; Magnetic Resonance Imaging/methods* ; Magnetic Resonance Spectroscopy/methods* ; Head ; Equipment Design ; Magnets

OBJECTIVE: To review the research progress in the construction strategy and application of bone/cartilage immunomodulating hydrogels. METHODS: The literature related to bone/cartilage immunomodulating hydrogels at home and abroad in recent years was reviewed and summarized from the immune response mechanism of different immune cells, the construction strategy of immunomodulating hydrogels, and their practical applications. RESULTS: According to the immune response mechanism of different immune cells, the biological materials with immunoregulatory effect is designed, which can regulate the immune response of the body and thus promote the regeneration of bone/cartilage tissue. Immunomodulating hydrogels have good biocompatibility, adjustability, and multifunctionality. By regulating the physical and chemical properties of hydrogel and loading factors or cells, the immune system of the body can be purposively regulated, thus forming an immune microenvironment conducive to osteochondral regeneration. CONCLUSION Immunomodulating hydrogels can promote osteochondral repair by affecting the immunomodulation process of host organs or cells. It has shown a wide application prospect in the repair of osteochondral defects. However, more data support from basic and clinical experiments is needed for this material to further advance its clinical translation process.
Hydrogels ; Cartilage ; Bone and Bones ; Tissue Engineering/methods*

The electroencephalogram (EEG) signal is a general reflection of the neurophysiological activity of the brain, which has the advantages of being safe, efficient, real-time and dynamic. With the development and advancement of machine learning research, automatic diagnosis of Alzheimer's diseases based on deep learning is becoming a research hotspot. Started from feedforward neural networks, this paper compared and analysed the structural properties of neural network models such as recurrent neural networks, convolutional neural networks and deep belief networks and their performance in the diagnosis of Alzheimer's disease. It also discussed the possible challenges and research trends of this research in the future, expecting to provide a valuable reference for the clinical application of neural networks in the EEG diagnosis of Alzheimer's disease.
Humans ; Alzheimer Disease/diagnosis* ; Neural Networks, Computer ; Machine Learning ; Brain ; Electroencephalography