In order to realize the quantitative assessment of muscle strength in hand function rehabilitation and then formulate scientific and effective rehabilitation training strategies, this paper constructs a multi-scale convolutional neural network (MSCNN) - convolutional block attention module (CBAM) - bidirectional long short-term memory network (BiLSTM) muscle strength prediction model to fully explore the spatial and temporal features of the data and simultaneously suppress useless features, and finally achieve the improvement of the accuracy of the muscle strength prediction model. To verify the effectiveness of the model proposed in this paper, the model in this paper is compared with traditional models such as support vector machine (SVM), random forest (RF), convolutional neural network (CNN), CNN - squeeze excitation network (SENet), MSCNN-CBAM and MSCNN-BiLSTM, and the effect of muscle strength prediction by each model is investigated when the hand force application changes from 40% of the maximum voluntary contraction force (MVC) to 60% of the MVC. The research results show that as the hand force application increases, the effect of the muscle strength prediction model becomes worse. Then the ablation experiment is used to analyze the influence degree of each module on the muscle strength prediction result, and it is found that the CBAM module plays a key role in the model. Therefore, by using the model in this article, the accuracy of muscle strength prediction can be effectively improved, and the characteristics and laws of hand muscle activities can be deeply understood, providing assistance for further exploring the mechanism of hand functions .
Neural Networks, Computer ; Humans ; Hand Strength/physiology* ; Support Vector Machine ; Muscle Strength/physiology* ; Hand/physiology* ; Convolutional Neural Networks

BackgroundSibling relationships play a critical role in shaping anxiety symptoms in firstborn children. Anxiety symptoms often originate in early childhood and can persist into adolescence and adulthood. However， there is insufficient research on anxiety symptoms in preschool children， especially firstborn preschool children. ObjectiveTo explore the influencing factors of anxiety symptoms among firstborn preschool children， so as to provide references for the intervention of anxiety symptom for children in families with multiple children. MethodsFrom October to December 2021， a total of 8 449 children from 234 kindergartens in Longhua District of Shenzhen were included using a cluster sampling method. Sibling Inventory of Behavior （SIB） and Spence Preschool Anxiety Scale （SPAS） were used to investigate. Logistic regression analysis was used to identify influencing factors of anxiety symptoms in firstborn preschool children. ResultsA total of 8 419 （99.64%） valid questionnaires were collected. Anxiety symptoms were detected in 344（4.09%） firstborn preschool children. Statistically significant differences were observed between anxiety group and non-anxiety group in terms of household registration， monthly family income， maternal age， maternal education level， paternal education level， family living conditions and whether they are left-behind children （χ²/t=9.906， 33.490， 5.136， 13.485， 9.690， 17.332， 21.975， P<0.05 or 0.01）. Compared with non-anxiety group， children in the anxiety group scored higher on the SIB dimensions of rivalry， aggression and avoidance （t=165.322， 74.471， 286.419， P<0.01）， and lower on companionship， empathy and teaching （t=59.133， 42.417， 39.112， P<0.01）. Risk factors for anxiety symptoms in firstborn preschool children included left-behind children， as well as negative sibling relationships characterized by rivalry and avoidance （OR=1.195， 1.143， 1.260， P<0.05 or 0.01）. ConclusionFirstborn preschool children who are left-behind are more susceptible to anxiety symptoms. Negative sibling relationships， characterized by competition and avoidance， may also contribute to the emergence of anxiety symptoms in firstborn preschool children.

Objective: To examine the accuracy of computer-aided intraoperative navigation (Ci-Navi) in bimaxillary orthognathic surgery by comparing preoperative planning and postoperative outcome. Methods: The study comprised 45 patients with congenital dentomaxillofacial deformities who were scheduled to undergo bimaxillary orthognathic surgery. Virtual bimaxillary orthognathic surgery was simulated using Mimics software. Intraoperatively, a Le Fort I osteotomy of the maxilla was performed using osteotomy guide plates. After the Le Fort I osteotomy and bilateral sagittal split ramus osteotomy of the mandible, the mobilized maxilla and the distal mandibular segment were fixed using an occlusal splint, forming the maxillomandibular complex (MMC). Realtime Ci-Navi was used to lead the MMC in the designated direction. Osteoplasty of the inferior border of the mandible was performed using Ci-Navi when facial symmetry and skeletal harmony were of concern. Linear and angular distinctions between preoperative planning and postoperative outcomes were calculated. Results: The mean linear difference was 0.79 mm (maxilla: 0.62 mm, mandible: 0.88 mm) and the overall mean angular difference was 1.20°. The observed difference in the upper incisor point to the Frankfort horizontal plane, midfacial sagittal plane, and coronal plane was < 1 mm in 40 cases. Conclusions This study demonstrates the role of Ci-Navi in the accurate positioning of bone segments during bimaxillary orthognathic surgery. Ci-Navi was found to be a reliable method for the accurate transfer of the surgical plan during an operation.

Objective: To examine the accuracy of computer-aided intraoperative navigation (Ci-Navi) in bimaxillary orthognathic surgery by comparing preoperative planning and postoperative outcome. Methods: The study comprised 45 patients with congenital dentomaxillofacial deformities who were scheduled to undergo bimaxillary orthognathic surgery. Virtual bimaxillary orthognathic surgery was simulated using Mimics software. Intraoperatively, a Le Fort I osteotomy of the maxilla was performed using osteotomy guide plates. After the Le Fort I osteotomy and bilateral sagittal split ramus osteotomy of the mandible, the mobilized maxilla and the distal mandibular segment were fixed using an occlusal splint, forming the maxillomandibular complex (MMC). Realtime Ci-Navi was used to lead the MMC in the designated direction. Osteoplasty of the inferior border of the mandible was performed using Ci-Navi when facial symmetry and skeletal harmony were of concern. Linear and angular distinctions between preoperative planning and postoperative outcomes were calculated. Results: The mean linear difference was 0.79 mm (maxilla: 0.62 mm, mandible: 0.88 mm) and the overall mean angular difference was 1.20°. The observed difference in the upper incisor point to the Frankfort horizontal plane, midfacial sagittal plane, and coronal plane was < 1 mm in 40 cases. Conclusions This study demonstrates the role of Ci-Navi in the accurate positioning of bone segments during bimaxillary orthognathic surgery. Ci-Navi was found to be a reliable method for the accurate transfer of the surgical plan during an operation.

The phase lock value(PLV) is an effective method to analyze the phase synchronization of the brain, which can effectively separate the phase components of the electroencephalogram (EEG) signal and reflect the influence of the signal intensity on the functional connectivity. However, the traditional locking algorithm only analyzes the phase component of the signal, and can't effectively analyze characteristics of EEG signal. In order to solve this problem, a new algorithm named amplitude locking value (ALV) is proposed. Firstly, the improved algorithm obtained intrinsic mode function using the empirical mode decomposition, which was used as input for Hilbert transformation (HT). Then the instantaneous amplitude was calculated and finally the ALV was calculated. On the basis of ALV, the instantaneous amplitude of EEG signal can be measured between electrodes. The data of 14 subjects under different cognitive tasks were collected and analyzed for the coherence of the brain regions during the arithmetic by the improved method. The results showed that there was a negative correlation between the coherence and cognitive activity, and the central and parietal areas were most sensitive. The quantitative analysis by the ALV method could reflect the real biological information. Correlation analysis based on the ALV provides a new method and idea for the research of synchronism, which offer a foundation for further exploring the brain mode of thinking.

Focused on the world-wide issue of improving the accuracy of emotion recognition, this paper proposes an electroencephalogram (EEG) signal feature extraction algorithm based on wavelet packet energy entropy and auto-regressive (AR) model. The auto-regressive process can be approached to EEG signal as much as possible, and provide a wealth of spectral information with few parameters. The wavelet packet entropy reflects the spectral energy distribution of the signal in each frequency band. Combination of them gives a better reflect of the energy characteristics of EEG signals. Feature extraction and fusion are implemented based on kernel principal component analysis. Six emotional states from a public multimodal database for emotion analysis using physiological signals (DEAP) are recognized. The results show that the recognition accuracy of the proposed algorithm is more than 90%, and the highest recognition accuracy is 99.33%. It indicates that this algorithm can extract the feature of EEG emotion well, and it is a kind of effective emotion feature extraction algorithm, providing support to emotion recognition.

Objective To probe into the preventive effect of Xinhuang tablet dissolved in vinegar on phlebitis caused by Doxorubicin? Methods The self-control study was performed in 30 patients treated by doxorubicin? In the initial course,33%magnesium sulfate by wet dressings was externally applied on the skin along the vein until completion of transfusion of chemotherapeutic drugs? In the second course,Xinhuang tablet dissolved in vinegar was used on the skin along the vein until completion of transfusion?The phlebitis rate by magnesium sulfate by wet dressings was compared to that by Xinhuang tablet dissolved in vinegar? Result The phlebitis rate in the initial course was 83?3% while 36?7% in the second course(P<0?01)? Conclusions Xinhuang tablet dissolved in vinegar applied on the skin may be of use for the prevention of phlebitis? It may reduce the damage and improve the quality of the life?

Objective To investigate the effects of simvastatin on the production of interleukin (IL)-17and B-cell activating transcription factor (BATF) in the peripheral blood mononuclear cells (PBMCs) of rheumatoid arthritis (RA) patients and healthy individuals.Methods PBMCs were isolated from heparinized blood of healthy donors or RA patients using Ficoll-Hypaque density gradient centrifugation.The cells were stimulated by PMA and ionomycin in the absence or presence of simvastatin or MVA at 37 ℃ 5％CO2.The mRAN level of IL-17,BATF and GAPDH was detected by RT-PCR; the protein level of IL-17 in supernatants was assayed by ELISA kit; and the protein level of BATF was detected by Western Blotting.The comparison between the two groups was carried out by paired-t test and Chi-square test was used for muhi-group comparison.Results PBMCs of healthy donors [(69.2±12.2) vs (8.1±2.2) pg/ml,P＜0.05; (76.6±14.7) vs (10.2±7.2) pg/ml,P＜0.05] and RA patients [(79.6±12.7) vs (15.8±5.8) pg/ml,P＜0.05; (90.3±9.7) vs (12.9±7.9) pg/ml,P＜0.05] were stimulated with PMA and ionomycin to produce high levels of IL-17.After treatment with simvastatin,the expression and secretion level of IL-17 in healthy controls and RA PBMCs were markedly decreased.The inhibition of simvastatin on the production of IL-17 was reversed by mevalonic acid (MVA),but no significant changes of BATF after treating with simvastatin.Conclusion Simvastatin inhibits the production of IL-17 in the PBMCs at gene and protein levels,which is not targeted at suppressing the expression of IL-17 transcription factor BATF.

To solve the problem of cardiac arrhythmias classification, we proposed a novel algorithm based on the multi-feature fusion and support vector machines (SVM). Kernel independent component analysis (KICA) was used to extract nonlinear features and wavelet transform (WT) was used to extract time-frequency features. Combining these features could include more information about the disease. We designed the classification model based on SVM combined with error correcting output codes (ECOC). Receiver operating characteristic curve (ROC) and Area Under the ROC curve (AUC) value were used to assess the classification model. The value of AUC is 0.956 against MIT-BIH arrhythmia database. Experimental results showed effectiveness of the proposed method.
Algorithms ; Area Under Curve ; Arrhythmias, Cardiac ; classification ; diagnosis ; Electrocardiography ; methods ; Humans ; Principal Component Analysis ; ROC Curve ; Signal Processing, Computer-Assisted ; Support Vector Machine

This review systematically introduces the functional connections among cardiovascular centers from spinal cord to cortex, and the mechanisms underlying pressor or depressor response of these cardiovascular centers, including the pathways, transmitters and receptors involved. The pressor or depressor response of these cardiovascular centers is mainly mediated by RVLM-sympathetic vasoconstrictor nerve system.
Blood Pressure ; physiology ; Central Nervous System ; physiology ; Cerebral Cortex ; physiology ; Humans ; Hypothalamus ; physiology ; Medulla Oblongata ; physiology ; Spinal Cord ; physiology