ObjectiveTo study the local vascular remodeling, inflammatory response, and their correlations following acute spinal cord injury (SCI) with different grades, and to assess the histological changes in SCI rats.MethodsOne hundred and sixteen adult female Sprague Dawley rats were randomly divided into 4 groups (n=29). The rats in sham group were received laminectomy only. A standard MASCIS spinal cord compactor was applied with drop height of 12.5, 25.0, or 50.0 mm to establish the mild, moderate, or severe SCI model, respectively. Quantitative rat endothelial cell antigen 1 (RECA1) and CD68 positive areas and the correlations were studied by double immunofluorescent (DIF) staining at 12 hours, 24 hours, 3 days, 7 days, and 28 days following SCI. Moreover, qualitative neurofilament-H (NF-H) and glial fibrillary acidic protein (GFAP) positive glial cells were studied by DIF staining at 28 days. ELISA was used to detect the levels of tumor necrosis factor α (TNF-α), interleukin 1β (IL-1β), and IL-6 in spinal cord homogenates at 12 hours, 24 hours, and 3 days, and the correlations between TNF-α, IL-1β, or IL-6 levels and microvascular density (RECA1) were accordingly studied. Moreover, the neural tissue integrity and neuron damage were assessed by HE staining at 12 hours, 24 hours, 3 days, 7 days, and 28 days, and Nissl’s staining at 28 days following SCI, respectively.ResultsDIF staining revealed that the ratio of RECA1 positive area was the highest in moderate group, higher in mild and severe groups, and the lowest in sham group with significant differences between groups (P<0.05). The ratio of CD68 positive area was the highest in severe group, higher in moderate and mild groups, and the lowest in sham group with significant differences between groups (P<0.05), except the comparisons between mild and moderate groups at 24 hours and 28 days after SCI (P>0.05). There was no significant correlation between the RECA1 and CD68 expressions in sham group at different time points (P>0.05). At 12 and 24 hours after SCI, the RECA1 and CD68 expressions in mild and moderate groups showed significant positive correlations (P<0.05), while no significant correlation was found in severe group (P>0.05). No significant correlations between the RECA1 and CD68 expressions was shown in all SCI groups at 3 days and in severe group at 7 days (P>0.05), while the negative correlations were shown in mild and moderate groups at 7 days, and in all SCI groups at 28 days (P<0.05). In mild, moderate, and severe groups, the axons became disrupted, shorter and thicker rods-like, or even merged blocks with increased injury, while the astrocytes decreased in number, unorganized and condensed in appearance. ELISA studies showed that TNF-α, IL-1β, and IL-6 levels in sham group were significantly lower than those in other 3 groups at different time points (P>0.05). The differences in TNF-α, IL-1β, and IL-6 levels between SCI groups at different time points were sinificant (P<0.05), except IL-1β levels between the mild and moderate groups at 12 hours (P>0.05). Three inflammatory factors were all significantly correlated with the microvascular density grades (P<0.05). Histological analysis indicated that the damage to spinal cord tissue structure correlated with the extent of SCI. In severe group, local hemorrhage, edema, and infiltration of inflammatory cells were found the most drastic, the grey/white matter boundary was disappeared concurrently with the formation of cavity and shortage of normal neurons.ConclusionIn the acute stage following mild or moderate SCI, progressively aggravated injury result in higher microvessel density and increased inflammation. However, at the SCI region, the relation between microvessel density and inflammation inverse with time in the different grades of SCI. Accordingly, the destruction of neural structures positively relate to the grades of SCI and severity of inflammation.
ObjectiveTo explore the correlation between readiness for hospital discharge and short-term quality of life among colorectal cancer (CRC) patients following enhanced recovery after surgery (ERAS) mode.MethodsSurveys of 127 CRC patients following ERAS mode were conducted in the West China Hospital of Sichuan University. The Readiness for Hospital Discharge Scale and EORTC QLQ-C30 Scale were issued at the discharge and 1 month after the operation, respectively.ResultsThe total score of RHDS was 149.43±33.25. The score of global quality of life was 66.80±18.84. Correlation analysis showed that the total score of RHDS was positively correlated with the score of global quality of life (r=0.220, P=0.013), and negatively correlated with the scores of fatigue, nausea and vomiting, pain, loss of appetite (r=–0.304, P=0.001; r=–0.189, P=0.033; r=–0.257, P=0.004; r=–0.254, P=0.004). The score of personal status dimension were positively correlated with the score of global quality of life and emotional function (r=0.213, P=0.016; r=0.197, P=0.027), and negatively correlated with scores of fatigue, pain and insomnia (r=–0.311, P=0.000; r=–0.264, P=0.003; r=–0.257, P=0.004). The score of knowledge dimension was negatively correlated with nausea and vomiting, pain and loss of appetite (r=–0.212, P=0.017; r=–0.182, P=0.040; r=–0.239, P=0.007). The score of coping ability dimension was positively correlated with the score of global quality of life and physical function (r=0.204, P=0.021; r=0.204, P=0.021), while negatively correlated with scores of fatigue, pain, insomnia and loss of appetite (r=–0.349, P=0.000; r=–0.240, P=0.007; r=–0.202, P=0.022; r=–0.201, P=0.024). The score of expected support was positively correlated with the score of global quality of life (r=0.220, P=0.013), and negatively correlated with scores of fatigue and loss of appetite (r=–0.249, P=0.005; r=–0.227, P=0.010).ConclusionsThe short term quality of life among CRC patients following ERAS keeps at upper middle level, and positively correlated with the readiness for hospital discharge. It is suggested that discharge preparation service is of great significance to improve the quality of life of patients.
This paper realized a portable brain-computer interface (BCI) system tailored for smart healthcare. Through the decoding of steady-state visual evoked potential (SSVEP), this system can rapidly and accurately identify the intentions of subjects, thereby meeting the practical demands of daily medical scenarios. Firstly, an SSVEP stimulation interface and an electroencephalogram (EEG) signal acquisition software were designed, which enable the system to execute multi-target and multi-task operations while also incorporating data visualization functionality. Secondly, the EEG signals recorded from the occipital region were decomposed into eight sub-frequency bands using filter bank canonical correlation analysis (FBCCA). Subsequently, the similarity between each sub-band signal and the reference signals was computed to achieve efficient SSVEP decoding. Finally, 15 subjects were recruited to participate in the online evaluation of the system. The experimental results indicated that in real-world scenarios, the system achieved an average accuracy of 85.19% in identifying the intentions of the subjects, and an information transfer rate (ITR) of 37.52 bit/min. This system was awarded third prize in the Visual BCI Innovation Application Development competition at the 2024 World Robot Contest, validating its effectiveness. In conclusion, this study has developed a portable, multifunctional SSVEP online decoding system, providing an effective approach for human-computer interaction in smart healthcare.
Impedance cardiography (ICG) is essential in evaluating cardiac function in patients with cardiovascular diseases. Aiming at the problem that the measurement of ICG signal is easily disturbed by motion artifacts, this paper introduces a de-noising method based on two-step spectral ensemble empirical mode decomposition (EEMD) and canonical correlation analysis (CCA). Firstly, the first spectral EEMD-CCA was performed between ICG and motion signals, and electrocardiogram (ECG) and motion signals, respectively. The component with the strongest correlation coefficient was set to zero to suppress the main motion artifacts. Secondly, the obtained ECG and ICG signals were subjected to a second spectral EEMD-CCA for further denoising. Lastly, the ICG signal is reconstructed using these share components. The experiment was tested on 30 subjects, and the results showed that the quality of the ICG signal is greatly improved after using the proposed denoising method, which could support the subsequent diagnosis and analysis of cardiovascular diseases.
For coronary artery diseases, imaging diagnosis is usually used to guide the treatment. However, it can only reflect the geometric characteristics of the disease but does not determine the hemodynamically significant stenosis. This study was aimed to investigate the relationship between angiographic and functional severity of coronary artery stenosis and to improve the diagnostic value of imaging. 39 patients with 55 stenosis vessels were included in this study. The correlation between FFR and stenosis rate was analyzed with the medical statistical analysis method, and the influence of the position of stenosis and coronary dominant type on the correlation was discussed. By regression analysis, the stenosis rate of left anterior descending artery of right dominant type showed a significant correlation with FFR value (r≈0.79, P < 0.000 1) after grouping with position and the dominant type. Due to the significance of a value of the FFR < 0.80 in determining inducible ischemia, the diagnostic accuracy of myocardial ischemia by the stenosis rate increased from 70.9% to 82.8% after grouping. Sensitivity (from 72.2% to 78.6%) and specificity (from 70.3% to 86.7%) were also significantly improved. This study indicates that the position of stenosis and the coronary dominant type are significant influence factors on the correlation between FFR and stenosis rate. Consideration of these two factors in the diagnosis of myocardial ischemia by imaging will be helpful to improve the effectiveness of diagnosis.
Mental rotation cognitive tasks based on motor imagery (MI) have excellent predictability for individual’s motor imagery ability. In order to explore the relationship between motor imagery and behavioral data, in this study, we asked 10 right-handed male subjects to participate in the experiments of mental rotation tasks based on corresponding body parts pictures, and we therefore obtained the behavioral effects according to their reaction time (RT) and accuracy (ACC). Later on, we performed Pearson correlation analysis between the behavioral data and the scores of the Movement Imagery Questionnaire-Revised(MIQ-R). For each subject, the results showed significant angular and body location effect in the process of mental rotation. For all subjects, the results showed that there were correlations between the behavioral data and the scores of MIQ-R. Subjects who needed the longer reaction time represented lower motor imagery abilities in the same test, and vice versa. This research laid the foundation for the further study on brain electrophysiology in the process of mental rotation based on MI.
We proposed a multi-resolution-wavelet-transform based method to extract brainstem auditory evoked potential (BAEP) from the background noise and then to identify its characteristics correctly. Firstly we discussed the mother wavelet and wavelet transform algorithm and proved that bi-orthogonal wavelet bior5.5 and stationary discrete wavelet transform (SWT) were more suitable for BAEP signals. The correlation analysis of D6 scale wavelet coefficients between single trails and the ensemble average of all trails showed that the trails with good correlation (> 0.4) had higher signal-to-noise ratio, so that we could get a clear BAEP from a few trails by an average and wavelet filter method. Finally, we used this method to select desirable trails, extracted BAEP from every 10 trails and calculated theⅠ-Ⅴinter-waves' latency. The results showed that this strategy of trail selection was efficient. This method can not only achieve better de-noising effect, but also greatly reduce the stimulation time needed as well.
ObjectiveTo study the effects of visceral adipose tissue area (VTA) and subcutaneous adipose tissue area (STA) on pulmonary ventilation function (PVF), and then to evaluate the impact of abdominal fat distribution on PVF.Methods Patients who underwent both PVF examination and abdominal CT between January 1st and December 31st, 2017 were selected from the electronic medical record system of West China Hospital of Sichuan University. The demographic data and PVF indexes [vital capacity (VC), forced vital capacity (FVC), forced expiratory volume in one second (FEV1), and 1 s rate (FEV1/FVC)] were collected. VTA and STA were obtained by abdominal CT measurement. The correlations between PVF indexes and VTA or STA were compared. Results A total of 224 patients were included. According to the VTA/STA ratio, there were 92 cases (41.07%) in group VTA/STA<1 and 132 cases (58.93%) in group VTA/STA≥1. VTA was not correlated with FVC (rs=?0.078, P=0.244), but negatively correlated with VC (rs=?0.138, P=0.040), FEV1 (rs=?0.141, P=0.034) and FEV1/FVC (rs=?0.137, P=0.041); STA had no correlation with VC, FVC, FEV1 or FEV1/FVC (P>0.05). VTA/STA was negatively correlated with VC (rs=?0.220, P=0.001), FEV1 (rs=?0.273, P<0.001) and FEV1/FVC (rs=?0.380, P<0.001), but it had no correlation with FVC (rs=?0.083, P=0.214). In group VTA/STA<1, VTA/STA was negatively correlated with FEV1 (rs =?0.205, P=0.050) and FEV1/FVC (rs=?0.317, P=0.002), but it had no correlation with VC or FVC (P>0.05). In group VTA/STA≥1, VTA/STA was negatively correlated with VC, FVC, FEV1 and FEV1/FVC (P<0.05). Conclusions VTA and STA are negatively correlated with PVF. The ratio of VTA/STA can be used as an index to evaluate the effect of abdominal fat distribution on lung function.
Objective To explore the association between behavioral, emotional problems and life events among adolescents, and to determine which factors of life events correlate most highly with the behavioral, emotional problems. Method A total of 1 325 adolescents were investigated with Youth Self-Report (YSR) of Achenbach’s behavior checklist and Adolescent Self-Rating Life Events Checklist (ASLEC), and the data were analyzed with canonical correlation analysis. Results Canonical correlation was statistically significant. The correlation coefficients of the first pair of canonical variables in the male and female group were 0.631 3 and 0.621 1, respectively, and the cumulative proportion of the first two pairs of canonical variables was above 0.95. In the first pair of canonical variables, the loadings of anxious/depressed, interpersonal sensitivity and study pressure were higher, while in the second pair, withdrawal and punishment were the most important factors. Conclusions The effects of life events on emotional problems mainly contributed to interpersonal sensitivity and study pressure.
Current studies on electroencephalogram (EEG) emotion recognition primarily concentrate on discrete stimulus paradigms under controlled laboratory settings, which cannot adequately represent the dynamic transition characteristics of emotional states during multi-context interactions. To address this issue, this paper proposes a novel method for emotion transition recognition that leverages a cross-modal feature fusion and global perception network (CFGPN). Firstly, an experimental paradigm encompassing six types of emotion transition scenarios was designed, and EEG and eye movement data were simultaneously collected from 20 participants, each annotated with dynamic continuous emotion labels. Subsequently, deep canonical correlation analysis integrated with a cross-modal attention mechanism was employed to fuse features from EEG and eye movement signals, resulting in multimodal feature vectors enriched with highly discriminative emotional information. These vectors are then input into a parallel hybrid architecture that combines convolutional neural networks (CNNs) and Transformers. The CNN is employed to capture local time-series features, whereas the Transformer leverages its robust global perception capabilities to effectively model long-range temporal dependencies, enabling accurate dynamic emotion transition recognition. The results demonstrate that the proposed method achieves the lowest mean square error in both valence and arousal recognition tasks on the dynamic emotion transition dataset and a classic multimodal emotion dataset. It exhibits superior recognition accuracy and stability when compared with five existing unimodal and six multimodal deep learning models. The approach enhances both adaptability and robustness in recognizing emotional state transitions in real-world scenarios, showing promising potential for applications in the field of biomedical engineering.