ObjectiveTo summarize the effectiveness of nitinol memory alloy two foot fixator with autologous cancellous bone grafting in treating old scaphoid fracture and nonunion.MethodsBetween January 2013 and January 2017, 11 patients of old scaphoid fracture and nonunion were treated with nitinol memory alloy two foot fixator and autologous cancellous bone grafting. All patients were male with an average age of 26.1 years (range, 18-42 years). The fractures were caused by sport in 3 cases, falling in 7 cases, and a crashing object in 1 case. The interval between injury and operation was 6-18 months (mean, 8.9 months). Postoperative outcome measures included operation time, fracture healing time, grip strength, range of motion (ROM) of flexion, extension, ulnar deviation, and radial deviation, Mayo score, visual analogue scale (VAS) score, and the Disabilities of the Arm, Shoulder, and Hand (DASH) score.ResultsThe operation time was 35-63 minutes (mean, 48 minutes). All incisions had primary healing with no infection and loosening or breakage of internal fixator. All patients were followed up 12-30 months (mean, 20.7 months). X-ray films showed that fracture healing was achieved in all patients with an average time of 15 weeks (range, 12-25 weeks). All internal fixators were removed after 10-12 months of operation (mean, 11.2 months). At last follow-up, the grip strength, ROMs of flexion, ulnar deviation, and radial deviation were superior to those before operation (P<0.05), no significant difference was found in ROM of extension between pre- and post-operation (t=0.229, P=0.824). There were significant differences in above indexes between affected and normal sides (P<0.05). At last follow-up, the Mayo, VAS, DASH scores were also significantly superior to those before operation (P<0.05).ConclusionFor the old scaphoid fracture and nonunion, Ni-Ti arched shape-memory alloy fixator and autologous cancellous bone grafting can obtain good effectiveness, which is an effective treatment.
OBJECTIVE: To investigate the hemodynamic changes of the end-to-end anastomosed arteries with nitinol clips. METHODS: Fifteen New Zealand rabbits were divided into anastomosis clip group, suture group and control group randomly. The carotid arteries were resected and end-to-end anastomosis were carried out with nitinol clips in anastomosis clip group and with traditional suture in suture group. The carotid arteries remained undamaged in control group. On the days of 3, 9, 21 and 30 postoperatively, mean blood velocity (Vm), pulsatility index (PI) and resistance index (RI) of anastomosed arteries were determined by Ultrasonography Doppler. RESULTS: On the days of 8 and 9 postoperatively, there were no significant differences of VM, PI and RI between two experimental groups (P gt; 0.05). On the days of 20 and 30 postoperatively, the differences of Vm and RI were significant (Vm: P lt; 0.01, P lt; 0.05: RI: P lt; 0.01, P lt; 0.05). The hemodynamic restoration of the anastomosis clip group was better than that of the suture group. CONCLUSION: The hemodynamics of arteries anastomosed with nitinol clips is better than that with traditional suture. This technique has practical value clinically.
Fetal electrocardiogram signal extraction is of great significance for perinatal fetal monitoring. In order to improve the prediction accuracy of fetal electrocardiogram signal, this paper proposes a fetal electrocardiogram signal extraction method (GA-LSTM) based on genetic algorithm (GA) optimization with long and short term memory (LSTM) network. Firstly, according to the characteristics of the mixed electrocardiogram signal of the maternal abdominal wall, the global search ability of the GA is used to optimize the number of hidden layer neurons, learning rate and training times of the LSTM network, and the optimal combination of parameters is calculated to make the network topology and the mother body match the characteristics of the mixed signals of the abdominal wall. Then, the LSTM network model is constructed using the optimal network parameters obtained by the GA, and the nonlinear transformation of the maternal chest electrocardiogram signals to the abdominal wall is estimated by the GA-LSTM network. Finally, using the non-linear transformation obtained from the maternal chest electrocardiogram signal and the GA-LSTM network model, the maternal electrocardiogram signal contained in the abdominal wall signal is estimated, and the estimated maternal electrocardiogram signal is subtracted from the mixed abdominal wall signal to obtain a pure fetal electrocardiogram signal. This article uses clinical electrocardiogram signals from two databases for experimental analysis. The final results show that compared with the traditional normalized minimum mean square error (NLMS), genetic algorithm-support vector machine method (GA-SVM) and LSTM network methods, the method proposed in this paper can extract a clearer fetal electrocardiogram signal, and its accuracy, sensitivity, accuracy and overall probability have been better improved. Therefore, the method could extract relatively pure fetal electrocardiogram signals, which has certain application value for perinatal fetal health monitoring.
Aiming at the problem that the small samples of critical disease in clinic may lead to prognostic models with poor performance of overfitting, large prediction error and instability, the long short-term memory transferring algorithm (transLSTM) was proposed. Based on the idea of transfer learning, the algorithm leverages the correlation between diseases to transfer information of different disease prognostic models, constructs the effictive model of target disease of small samples with the aid of large data of related diseases, hence improves the prediction performance and reduces the requirement for target training sample quantity. The transLSTM algorithm firstly uses the related disease samples to pretrain partial model parameters, and then further adjusts the whole network with the target training samples. The testing results on MIMIC-Ⅲ database showed that compared with traditional LSTM classification algorithm, the transLSTM algorithm had 0.02-0.07 higher AUROC and 0.05-0.14 larger AUPRC, while its number of training iterations was only 39%-64% of the traditional algorithm. The results of application on sepsis revealed that the transLSTM model of only 100 training samples had comparable mortality prediction performance to the traditional model of 250 training samples. In small sample situations, the transLSTM algorithm has significant advantages with higher prediciton accuracy and faster training speed. It realizes the application of transfer learning in the prognostic model of critical disease with small samples.
Temporal lobe epilepsy is the most common type of epilepsy in clinic. In recent years, many studies have found that patients with temporal lobe epilepsy have different degrees of influence in executive function related fields. This influence may not only exist in a certain field of executive function, but may be affected in several fields, and may be related to the origin site of seizures. However, up to now, there is no unified standard for the composition of executive function, and it is widely accepted that the three core components of executive function are working memory, inhibitory control and cognitive flexibility/switching. In addition, the International League Against Epilepsy proposed a new definition in 2010, and epilepsy is a brain network disease. There is a close relationship between brain neural network and cognitive impairment. According to the cognitive field, the brain neural network can be divided into six types: default mode network, salience network, executive control network, dorsal attention network, somatic motor network and visual network. In recent years, there has been increasing evidence that four related internal brain networks are series in a range of cognitive processes. The executive dysfunction of temporal lobe epilepsy may be related to the changes of functional connectivity of neural network, and may be related to the left uncinate fasciculus. This article reviews the research progress related to executive function in temporal lobe epilepsy from working memory, inhibitory control and cognitive flexibility, and discusses the correlation between the changes of temporal lobe epilepsy neural network and executive function research.
Emotion plays an important role in people's cognition and communication. By analyzing electroencephalogram (EEG) signals to identify internal emotions and feedback emotional information in an active or passive way, affective brain-computer interactions can effectively promote human-computer interaction. This paper focuses on emotion recognition using EEG. We systematically evaluate the performance of state-of-the-art feature extraction and classification methods with a public-available dataset for emotion analysis using physiological signals (DEAP). The common random split method will lead to high correlation between training and testing samples. Thus, we use block-wise K fold cross validation. Moreover, we compare the accuracy of emotion recognition with different time window length. The experimental results indicate that 4 s time window is appropriate for sampling. Filter-bank long short-term memory networks (FBLSTM) using differential entropy features as input was proposed. The average accuracy of low and high in valance dimension, arousal dimension and combination of the four in valance-arousal plane is 78.8%, 78.4% and 70.3%, respectively. These results demonstrate the advantage of our emotion recognition model over the current studies in terms of classification accuracy. Our model might provide a novel method for emotion recognition in affective brain-computer interactions.
ObjectiveTo systematically review the effect of media multitasking on working memory and attention among adolescents. MethodsCNKI, CBM, WanFang Data, VIP, PubMed, Web of Science, and EMbase databases were electronically searched to collect cross-sectional studies on the effect of media multitasking on working memory and attention among adolescents from inception to January 1st, 2021. Two reviewers independently screened literature, extracted data, and assessed the risk of bias of included studies; then, meta-analysis was performed using Stata 15.1 software. ResultsA total of 16 cross-sectional studies were included. The results of meta-analysis showed that there were negative correlations between media multitasking and working memory (Cohen's d=0.40, 95%CI 0.14 to 0.66, P＝0.003), as well as in attention (Cohen's d=1.02, 95%CI 0.58 to 1.47, P<0.001). ConclusionCurrent evidence shows that media multitasking has negative impact on working memory and attention. Due to limited quality and quantity of the included studies, more high-quality studies are required to verify the above conclusion.
Objective To investigate the effectiveness of nickel-titanium shape memory staples in treating multiple metatarsal fractures. MethodsThe clinical data of 27 patients with multiple metatarsal fractures who were treated between January 2022 and June 2023 and met the selection criteria were retrospectively analysed. The cohort consisted of 16 males and 11 females, aged 33-65 years (mean, 47.44 years). The causes of injury included heavy object impact in 11 cases, traffic accidents in 9 cases, and crush in 7 cases. Simultaneous fractures of 2, 3, 4, and 5 bones occurred in 6, 6, 4, and 8 cases, respectively, with tarsometatarsal joint injury in 3 cases. Fixation was performed using staples for 16, 22, and 9 fractures in the metatarsal neck, shaft, and the base, respectively, and 5 tarsometatarsal joint injuries. Preoperative soft tissue injuries were identified in 8 cases and classified according to the Tscherne-Oestern closed soft tissue injury classification as type Ⅰ in 5 cases and type Ⅱ in 3 cases. One case of type Ⅱexhibited preoperative skin necrosis. The patients were treated with fixation using nickel-titanium shape memory staples. Complications and fracture healing were documented. At last follow-up, the American Orthopaedic Foot and Ankle Society (AOFAS) forefoot score was used to evaluate the function, and the visual analogue scale (VAS) score was used to evaluate the pain. Results The 27 patients were followed up 9-19 months (mean, 12.4 months). Postoperative X-ray films revealed no loss of fracture reduction, and all fractures achieved bony union. No internal fixator loosening, breakage, or other mechanical failures was observed. The mean fracture healing time was 3.13 months (range, 3-4 months). Postoperatively, 4 cases (2 of Tscherne-Oestern type Ⅰ, 2 of type Ⅱ) developed superficial skin necrosis, which resolved with dressing changes. No infection was observed in the remaining patients, and all wounds healed. At last follow-up, the AOFAS forefoot score ranged from 70 to 95, with an average of 86.6, of which 19 cases were excellent, 6 cases were good, and 2 cases were fair, with an excellent and good rate of 92.6%; the VAS score ranged from 0 to 3, with an average of 0.9, of which 24 cases were excellent, and 3 cases were good, with an excellent and good rate of 100%. Conclusion The use of nickel-titanium shape memory staples in the treatment of multiple metatarsal fractures can effectively protect local skin and soft tissues and minimize secondary damage associated with internal fixator insertion. It is a viable surgical option for management of multiple metatarsal fractures.
The transmission and interaction of neural information between the hippocampus and the prefrontal cortex play an important role in learning and memory. However, the specific effects of learning memory-related tasks on the connectivity characteristics between these two brain regions remain inadequately understood. This study employed in vivo microelectrode recording to obtain local field potentials (LFPs) from the ventral hippocampus (vHPC) and medial prefrontal cortex (mPFC) in eight rats during the performance of a T-maze task, assessed both before and after task learning. Additionally, dynamic causal modeling (DCM) was utilized to analyze alterations in causal connectivity between the vHPC and the mPFC during memory task execution pre- and post-learning. Results indicated the presence of forward connections from vHPC to mPFC and backward connections from mPFC to vHPC during the T-maze task. Moreover, the forward connection between these brain regions was slightly enhanced after task learning, whereas the backward connection was diminished. These changes in connectivity corresponded with the observed trends when the rats correctly performed the T-maze task. In conclusion, this study may facilitate future investigations into the underlying mechanisms of learning and memory from the perspective of connectivity characteristics between distinct brain regions.
Objective To recognize the different phases of Korotkoff sounds through deep learning technology, so as to improve the accuracy of blood pressure measurement in different populations. Methods A classification model of the Korotkoff sounds phases was designed, which fused attention mechanism (Attention), residual network (ResNet) and bidirectional long short-term memory (BiLSTM). First, a single Korotkoff sound signal was extracted from the whole Korotkoff sounds signals beat by beat, and each Korotkoff sound signal was converted into a Mel spectrogram. Then, the local feature extraction of Mel spectrogram was processed by using the Attention mechanism and ResNet network, and BiLSTM network was used to deal with the temporal relations between features, and full-connection layer network was applied in reducing the dimension of features. Finally, the classification was completed by SoftMax function. The dataset used in this study was collected from 44 volunteers (24 females, 20 males with an average age of 36 years), and the model performance was verified using 10-fold cross-validation. Results The classification accuracy of the established model for the 5 types of Korotkoff sounds phases was 93.4%, which was higher than that of other models. Conclusion This study proves that the deep learning method can accurately classify Korotkoff sounds phases, which lays a strong technical foundation for the subsequent design of automatic blood pressure measurement methods based on the classification of the Korotkoff sounds phases.