ObjectivesTo analyze the balance of medical human resource allocation in Chengdu and to discuss the impact of human resource allocation structure on the hospital’s medical service capabilities, so as to provide a theoretical basis for the hospital to optimize the talent team structure.MethodsThe Moran’s index and Gini coefficient were used to evaluate the spatial aggregation and grade distribution difference of human resources allocation, respectively. The case mix index (CMI), the length of hospital stay, and the difficulty of surgery were used as outcome variables, and a multiple regression analysis model was established to explore the impact of human resource indicators on the hospital’s medical service capabilities.ResultsThe distribution of doctors showed an obvious spatial aggregation in Chengdu, and there was a positive spatial correlation (Moran’s Idoctor=0.290); the distribution of nurses had no obvious spatial aggregation (Moran’s Inurse=0.102). Under different medical service capacity segments, the Gini coefficient of doctors was 0.518, and the Gini coefficient of nurses was 0.576, both exceeding 0.5, indicating that the distribution of medical human resources in different levels of hospitals was considerably different. The regression results showed that the expansion of the quantity of senior title doctors and the proportion of medical care both could improve the hospital’s CMI. The increase in the quantity of doctors with senior titles also had a certain impact on improving the hospital’s surgical capabilities. The expansion of the proportion of medical care could lead to a slight increase in the length of patients' hospital stay.ConclusionsOptimizing the allocation structure of medical human resources in different regions and hospitals with different levels is an important task in the construction of a hierarchical diagnosis and treatment system. It is necessary to further integrate medical human resources throughout the whole city and strengthen personnel training and team building in primary health institutions. Hospitals at different levels should implement their own development positioning and further optimize their human resource allocation structure according to development needs. Tertiary hospitals should pay more attention to the cultivation of senior professional doctors, optimize the proportion of medical care, and improve the hospital’s medical service capabilities. The primary health institutions should pay attention to the comprehensive construction of medical personnel, and strengthen the development of the team of general practitioners, so as to achieve a comprehensive improvement of the city’s medical service capabilities.
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.
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.
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 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.
Brain-computer interface (BCI) based on functional near-infrared spectroscopy (fNIRS) is a new-type human-computer interaction technique. To explore the separability of fNIRS signals in different motor imageries on the single limb, the study measured the fNIRS signals of 15 subjects (amateur football fans) during three different motor imageries of the right foot (passing, stopping and shooting). And the correlation coefficient of the HbO signal during different motor imageries was extracted as features for the input of a three-classification model based on support vector machines. The results found that the classification accuracy of the three motor imageries of the right foot was 78.89%±6.161%. The classification accuracy of the two-classification of motor imageries of the right foot, that is, passing and stopping, passing and shooting, and stopping and shooting was 85.17%±4.768%, 82.33%±6.011%, and 89.33%±6.713%, respectively. The results demonstrate that the fNIRS of different motor imageries of the single limb is separable, which is expected to add new control commands to fNIRS-BCI and also provide a new option for rehabilitation training and control peripherals for unilateral stroke patients. Besides, the study also confirms that the correlation coefficient can be used as an effective feature to classify different motor imageries.
In order to improve the accuracy of classification with small amount of motor imagery training data on the development of brain-computer interface (BCI) systems, we proposed an analyzing method to automatically select the characteristic parameters based on correlation coefficient analysis. Throughout the five sample data of dataset Ⅳa from 2005 BCI Competition, we utilized short-time Fourier transform (STFT) and correlation coefficient calculation to reduce the number of primitive electroencephalogram dimension, then introduced feature extraction based on common spatial pattern (CSP) and classified by linear discriminant analysis (LDA). Simulation results showed that the average rate of classification accuracy could be improved by using correlation coefficient feature selection method than those without using this algorithm. Comparing with support vector machine (SVM) optimization features algorithm, the correlation coefficient analysis can lead better selection parameters to improve the accuracy of classification.
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.
Objective To investigate the current status of disease uncertainty and alexithymia in elderly hospitalized patients with chronic obstructive pulmonary disease (COPD). Methods By using the convenience sampling method, a total of 165 elderly patients with COPD were investigated by using general information questionnaire, Mishel's uncertainty in illness Scale (MUIS) and Toronto alexithymia scale (TAS-20). Results The disease course of the elderly hospitalized patients with COPD was mostly 3 - 6 years (40.0%), and most of them had 1 - 2 chronic diseases (59.4%). More than half of the elderly had a history of smoking and drinking. The severity of COPD in the elderly was moderate (57.0%), and the number of hospitalization in the year was more than 2 times (58.8%). The score of disease uncertainty in the elderly hospitalized patients with COPD was 89.49±9.45, and the score of uncertainty was the highest (36.59±4.08), followed by the lack of information (18.51±1.86). The score of alexithymia in the elderly hospitalized patients with COPD was 55.32±6.37, and the score of all dimensions was the highest (21.87±2.93), followed by affective recognition disorder (18.27±2.55). The results of correlation analysis showed that the total score and scores of each dimension were positively correlated (P<0.01). The results of multi-factor analysis showed that age and course of disease and severity of COPD were the main influencing factors of disease uncertainty in elderly hospitalized patients with COPD (P<0.05). Conclusions The elderly hospitalized patients with COPD have a moderate level of disease uncertainty and a high degree of alexithymia. Besides, the greater the disease uncertainty is, the more serious the alexithymia. Therefore, clinical doctors and nurses should pay more attention to give emotional and psychological support and education guidance to the elderly patients with COPD, in order to improve their clinical efficacy and quality of life.
Attention can concentrate our mental resources on processing certain interesting objects, which is an important mental behavior and cognitive process. Recognizing attentional states have great significance in improving human’s performance and reducing errors. However, it still lacks a direct and standardized way to monitor a person’s attentional states. Based on the fact that visual attention can modulate the steady-state visual evoked potential (SSVEP), we designed a go/no-go experimental paradigm with 10 Hz steady state visual stimulation in background to investigate the separability of SSVEP features modulated by different visual attentional states. The experiment recorded the EEG signals of 15 postgraduate volunteers under high and low visual attentional states. High and low visual attentional states are determined by behavioral responses. We analyzed the differences of SSVEP signals between the high and low attentional levels, and applied classification algorithms to recognize such differences. Results showed that the discriminant canonical pattern matching (DCPM) algorithm performed better compared with the linear discrimination analysis (LDA) algorithm and the canonical correlation analysis (CCA) algorithm, which achieved up to 76% in accuracy. Our results show that the SSVEP features modulated by different visual attentional states are separable, which provides a new way to monitor visual attentional states.