We have demonstrated that peroxiredoxin 2 (Prx2) released from lytic erythrocytes and damaged neurons into the subarachnoid space could activate microglia and then result in neuronal apoptosis. In this study, we tested the possibility of using Prx2 as an objective indicator for severity of the subarachnoid hemorrhage (SAH) and the clinical status of the patient. t-test was used to analyze the differences in continuous variables across cohorts.  SAH patients were prospectively enrolled and followed up for 3 months. Cerebrospinal fluid (CSF) and blood samples were collected 0-3 and 5-7 days after SAH onset. The levels of Prx2 in the CSF and the blood were measured by an enzyme-linked immunosorbent assay (ELISA). We used Spearman's rank coefficient to assess the correlation between Prx2 and the clinical scores. Receiver operating characteristic (ROC) curves were used for Prx2 levels to predict the outcome of SAH by calculating the area under the curve (AUC). Unpaired Student's R = 0.761, P < 0.001). Patients with CVS had higher levels of Prx2 in their CSF within 5-7 days after onset. Prx2 levels in the CSF within 5-7 days can be used as a predictor of prognosis. The ratio of Prx2 in the CSF and the blood within 3 days of onset was positively correlated with the Hunt-Hess score and negatively correlated with Glasgow Outcome Scale .  Prx2 levels in the CSF increased after onset while those in the blood decreased. Existing data showed that Prx2 levels within 3 days in the CSF after SAH were positively correlated with the Hunt-Hess score ( We found that the levels of Prx2 in the CSF and the ratio of Prx2 in the CSF and the blood within 3 days of onset can be used as a biomarker to detect the severity of the disease and the clinical status of the patient. The most common cause of spontaneous subarachnoid hemorrhage (SAH) is a ruptured intracranial aneurysm, accounting for approximately 85% of all SAH cases, with vascular malformations and vasculitis making up the rest . SAH accPeroxiredoxin (Prx) family is a class of peroxidase proteins that can scavenge peroxides and inhibit apoptosis , 6. Prx2The collection and use of the clinical samples were approved by the Ethics Committee of Nanjing Drum Tower Hospital (No. 2022-294-01), and informed consent was obtained from the patients.The inclusion criteria for the experimental group were as follows: (1) older than 18 years, (2) all the subjects met the diagnostic criteria for SAH and sought medical attention within 24 h of onset, and (3) computed tomography angiography/digital subtraction angiography (CTA/DSA) excluded cerebrovascular diseases such as arteriovenous malformation, arteriovenous fistula, and moyamoya disease. Participants in the control group had no central nervous system (CNS) disease but required a lumbar puncture. All the blood and CSF samples were centrifuged  immediately after collection and placed at -80°C. Brain tissue samples from the SAH patients were stored in formaldehyde for subsequent immunofluorescence (IF) detection. Thirty-four patients were enrolled in this study, of whom 20 obtained CSF within 3 days, 28 obtained CSF and the blood within 5-7 days, and 14 obtained the blood within 3 days. The patient's characteristic is shown in The levels of Prx2 in the CSF and the blood were detected using an ELISA kit . Following the steps in the instructions briefly, we added the standard working solution or the samples to the wells, sealed them, and incubated them for 90 mins at 37°C. After removing the liquid, we added biotinylated detection Ag working solution to each well and incubated for 60 mins at 37°C. After washing three times, we added the HRP conjugate working solution to each well and incubated it for 30 mins at 37°C. Then, the substrate reagent was added to each well. After incubating for about 15 mins at 37°C, stop solution was added, and the optical density (OD) value of each well was determined at once with a microplate reader set to 450 nm. We calculated Prx2 levels from the standard curve.The admission Hunt-Hess score was used to evaluate the severity of the patient's disease, and the score was evaluated by the experienced clinicians using a single-person assessment to ensure the consistency of the score.The CVS criteria we used were as follows: (1) after treatment, the symptoms of the SAH patients were significantly relieved and then progressively worsened, accompanied by symptoms such as high fever; (2) the disorders of consciousness progressively worsened; (3) the patient had neurological deficits, such as limb dysfunction and sensory impairment; (4) the patient had symptoms of increased intracranial pressure; (5) DSA showed the presence of CVS; and (6) transcranial Doppler ultrasonography showed a change in the blood flow velocity and the arterial parameters . After eThree months after the patients were discharged from the hospital, we conducted a telephone follow-up visit and assessed the GOS, 1-3 as poor prognosis and 4-5 as good prognosis . The GOSThe IF assays were tested as mentioned in our previous article . Frozen t-test was used to analyze the differences in continuous variables across cohorts. Data were expressed as mean ± standard deviation (SD), and P < 0.05 was considered statistically significant. We used Spearman's rank coefficient to assess the correlation between Prx2 and the clinical scores. Receiver operating characteristic (ROC) curves were used for Prx2 levels to predict the outcome of SAH by calculating the area under the curve (AUC).SPSS 27  and GraphPad Prism 9.0 for Windows  were used for the data analysis. Unpaired Student's As shown in P < 0.0001) and the subacute phase . There was no statistical difference in the Prx2 levels in the CSF and the blood between acute and subacute phases.We explored the trend of Prx2 expression as shown in R = 0.761, P < 0.001, We assessed the correlation of the Prx2 levels in the blood and CSF with the Hunt-Hess score at different times. The levels of Prx2 in the CSF within 3 days of onset were positively correlated with the Hunt-Hess scores (P < 0.01), although the levels of Prx2 in the blood within 0-3 days and 5-7 days were not different between the two groups.CVS occurred in 7 out of the 34 patients during hospitalization. We compared the differences between the Prx2 levels in the CSF and the blood in patients with and without CVS, as shown in R = ‐0.487, P < 0.01). We used ROC curves to assess the extent to which Prx2 can predict a poor prognosis. The AUCs for Prx2 levels in the CSF and the blood at 0-3 days, and 5-7 days, as predictors for poor outcome, were 0.6078, 0.8870, 0.7917, and 0.5913, respectively, and only CSF at 5-7 days had statistical significance (P < 0.01, Figures As shown in Figures R = 0.586, P < 0.05, R = ‐0.605, P < 0.05, P value of 0.0679. Twenty-eight patients simultaneously had CSF and blood within 5-7 days. The ratio of Prx2 during this period was not associated with the admission Hunt-Hess score (P > 0.05, We assessed the relationship between clinical scores and the ratios of Prx2 in the CSF and the blood at different times. We found that 14 patients had both CSF and blood within 3 days of the onset, and the ratio of Prx2 during this period was positively correlated with the admission Hunt-Hess score (ss score  or the Gss score  and showThe essence of Prx2 is a peroxidase encoded by the Prdx2 gene , which hAlthough a series of animal experiments have investigated the role of Prx2 in brain injury-related diseases –20, few In clinical diagnosis and treatment, blood is easier to obtain than CSF and has greater value as a clinical marker. Unfortunately, although the levels of Prx2 in the blood continued to decrease after the onset of the disease, we did not find a correlation between it and the clinical condition of the patient. Decreased levels of Prx2 in the blood may be due to stress responses, adverse systemic conditions after the onset, or suggest a diminished capacity to regulate oxidative stress. In this study, we mainly focused on the changes in the patient's nervous system. The levels of Prx2 in the blood may be related to some complications involving other systems, such as cardiac dysfunction or pulmonary edema . If we nThe levels of Prx2 in the CSF can reflect the state of the CNS, and those in the blood can reflect the systemic state or the ability to regulate oxidative stress. Therefore, we speculated that the ratio of Prx2 in the CSF and the blood could also be used as a marker, so we observed the correlation between clinical scores and the ratio of Prx2 in different periods. To our surprise, the ratio of Prx2 within 3 days of onset was positively correlated with the admission Hunt-Hess score and negatively correlated with the GOS at 3 months after the discharge, which meant that during this period, the higher the level of Prx2 in the CSF and the lower the level of Prx2 in the blood, the more severe the patient's disease and the poorer the prognosis. High levels of Prx2 in the CSF mean severe damage to the CNS, while low levels of Prx2 in the blood may mean poor self-regulation. This was the only indicator associated with both the onset and the prognosis. However, this indicator had no predictive power for poor prognosis, which may be because only 2 of the 14 patients had a poor prognosis, resulting in nonsignificant data. The ratio of Prx2 within 5-7 days had no clinical significance, which may be because the disease had entered a compensatory phase and the patient gradually recovered, resulting in a decline in the clinical significance of this ratio.P value was greater than 0.05. In the next experiment, we need to further expand the sample size to observe the potential predictive value of these two indicators for poor prognosis.In this study, we only observed changes in the Prx2 levels in the clinical samples within 0-3 days and 5-7 days after the onset of the disease. During this period, the Prx2 levels continued to rise or fall, and we did not observe a recovery of the Prx2 levels. As the most changed protein in CSF [After the onset, the level of Prx2 in the CSF and the blood increased and decreased, respectively. Levels of Prx2 in the CSF and the ratio of Prx2 in the CSF and the blood within 3 days of onset can be used as a biomarker to detect the severity of the disease and the clinical status of the patient.


Muscle fatigue is a crucial indicator to determine whether training is in place and to protect trainers.To make full use of morphological information of surface EMG and ECG signals in the time domain, a new idea and method for the fatigue assessment of exercise muscles based on data fusion is proposed in this paper.sEMG and ECG time series with the same length were obtained by signal preprocessing and sequence normalization, feature extraction of sequence tenses was realized by a deep learning network based on sequential convolution and signal fusion model of muscle fatigue evaluation was established by D-S evidence theory.Thirty volunteers were recruited and divided into three groups. ECG signals and sEMG signals at the biceps brachii of the right upper limb were monitored in a 20-minute exercise cycle.The prediction result of TCN based on time domain signal is better than the commonly used KNN and SVM recognition algorithm, and the recognition accuracy of relaxed, excessive and fatigue by D-S fusion was 89%, 86%, 88.5%. The accuracy was 0.9055, 0.9494 and 0.9269, respectively. The recall rates of the three conditions were 0.9303, 0.9570 and 0.9435. The F-score of the three conditions was 0.8911, 0.8764 and 0.8837, respectively.Based on time series and time series convolutional network, sEMG and ECG fusion of motor muscle recognition method can better distinguish different state information and has certain practical value in the fields of muscle evaluation, clinical diagnosis, wearable devices and so on. In recent years, the detection of surface electromyography (sEMG) and electrocardiogram (ECG) has been developed rapidly. These two techniques have the advantage of directly reflecting neuromuscular activity , and havsEMG and ECG are nondestructive, rapid and convenient monitoring methods, which have been reported in the field of body condition monitoring in sports science. Chang et al.  developeThe traditional analysis method is based on sEMG or ECG signal preprocessing to obtain the characteristic values of the signal, such as integrated electromyography (iEMG), root mean square (RMS), median frequency (MF) and MPF. These characteristic values are used to establish the prediction model . HoweverTherefore, aiming at the problem of poor robustness of the muscle fatigue evaluation model based on signal features, this paper proposed a sEMG and ECG muscle fatigue identification method based on time series and TCN. This method directly established a single cycle time series of sEMG and ECG signals and input them into the TCN model. Using the feature grasping ability of TCN to the time series, it completed the extraction of signal morphological features, and established the muscle fatigue prediction models of sEMG and ECG respectively. The classical D-S evidence theory method was used to fuse the models, and finally the overall prediction model was obtained. The statistical parameters of each prediction model were compared. This research will provide technical support for the fusion application of sEMG and ECG.This section describes the data collection and interpretation analysis methods. In order to verify the improved fusion algorithm proposed in this paper, 30 healthy volunteers  were recruited for the study, aged (22.4±1.5) years old, height (173.2±5.8) cm, weight (65.3±7.2) kg, body mass index was 22.7±1.8. All subjects were healthy without any diseases. The data were collected in the gymnasium of East China University of Technology. Before the experiment, all subjects were familiar with the experiment plan and procedure and signed the volunteer informed consent. The whole experiment had been reviewed by the Ethics Sub-Committee of Human Medical Experiment of East China University of Technology (No. 2021EC(R)017). Subjects did not exercise more than twice a week in the 3 months prior to the trial and did not exercise vigorously in the 48 hours prior to the trial. SEMG signal acquisition equipment adopted sEMG signal acquisition module . The ECG was collected by KF12 multi-parameter monitor , which has its own rechargeable lithium battery and can print data results automatically. According to the physiological structure of human body, ECG signal synchronously collected the sEMG signal of right upper limb biceps brachii and motion capture data of right upper limb of each subject. The sampling frequency of sEMG signal was 1 kHz. The positions of sEMG and ECG sensor are shown in According to the physiological structure of the human body, ECG signal and sEMG signal of right upper limb biceps brachii were collected synchronously. The laboratory temperature was 20°C±3°C. Clean the skin surface of the subject with scrub cream and 75% alcohol cotton, remove the oil on the skin surface, etc., and reduce the interference of skin impedance. According to the principle of anatomy, the sEMG sensor was attached to the humps of the three muscles. To stick more closely with the muscles and obtain more accurate signals, a small amount of water can be attached to the one-time sEMG. After the sEMG sensor was fitted, wrapped the sensor around the test site with an adhesive cloth to prevent the electrode adhesive from falling off during the experiment.The subjects were divided into three groups of 10 people in each group. According to the plan, the prescribed training was carried out. Set a 20-minute training cycle. Ten subjects completed one training cycle as a group. Each experiment lasted 60 minutes, and a total of 10 experiments were conducted. The first group completed the test, the second group continued the test, and the first group rested. During each training session, subjects sat in front of the lab table with their back straight at 90° and their left arm naturally lowered. During the experiment, sEMG signal was collected for 5s without weight, and then the experimenters placed the dumbbell in the hands of the subjects. The subjects slowly lifted the dumbbell, keeping the stability of the upper arm of the right hand. Subjects reported how they felt about their fatigue state by lowering dumbbells every 120 seconds according to the RPE scale  and markThe sEMG and ECG signals of the three states were marked and saved, and the corresponding training time was recorded. 600 groups of sEMG and ECG signals were obtained in each group. According to the corresponding fatigue value, each data was divided into three states, a total of 300 sEMG and 300 ECG data. After the experiment, 6000 sEMG and ECG data were collected respectively. All subjects underwent the same signal acquisition and analysis process.CV is a method that divides the data set into two groups as ‘training’ and ‘sample’ for supervised learning. 10-fold CV firstly obtains 10 subsets having equal number of members from the data set and members are randomly shared by the subsets. For one classification process, each of the subsets is used as a training set and the other subsets as a sample set. So, the resultant prediction accuracy rate is calculated by the average of 10 prediction accuracy rates.ECG and sEMG signals are highly contaminated by environmental interference, such as 50 Hz powerline electromagnetic interference. The ECG signal was filtered by a third-order Butterworth low-pass filter. The EMG signals were pre-amplified using an instrument amplifier with gain of 5 and then filtered using second-order stopband analog filters with cutoff frequencies of 60 Hz and 160 Hz to minimize the effects of electromagnetic interference and artifacts. Finally, the signal was amplified 318 times to make it usable. So, the total amplification gain is 1590.sEMG signals are non-stationary in nature. The most appropriate way to study these signals is to use time-frequency methods such as wavelet decomposition. This method was developed by Mallat in 1980 for data compression in image coding. Wavelet decomposition represents information extracted from the time domain and frequency domain of a signal based on the application of a unique parent wavelet transform. Finally, sEMG and ECG single—period signals are obtained by periodic segmentation and periodic averaging.Signals of different individuals are not consistent, so the single-period waveform obtained is also different in data length. However, deep learning networks generally require features of consistent input length, so it is necessary to normalize the time series length of single-period signals. There are two commonly used signal normalization processing methods: tail zeroing and multi-sampling rate resampling, but the former will lead to a large amount of redundant information in short period time series. The latter may lose some important information. Therefore, in order to maintain the original form and information integrity of time series, the normalization of single-period waveform in this paper is as follows: Firstly, a uniform length of time series is set so that it can accommodate at least one single-period signal; Secondly, the single-period signal segmented by the same sample is supplemented at the end of the sequence until the set sequence length is reached. The extraction process of time series is shown in TCN is a network that can be used to deal with timing problems. For a long time series, traditional CNN cannot capture the dependent information in the series well in a limited level due to the limitation of the convolution kernel size. TCN uses a new network structure to solve the defect of CNN in dealing with timing issues. TCN is composed of multiple residual blocks in series.. In the residual block, there are two levels of dilatational causal convolution with the same parameters, and the linear rectifier function is used as the activation function after the weight normalization. In addition, regularization is performed after each convolution layer to prevent overfitting. 1×1 convolution is used to ensure that the dimensions of the input and output tensors are consistent in the process of the identity mapping with residual connection.x0, x1, x2, ⋯, xt, ⋯, xn) from the upper layer is k×1, then the causal convolution of the current layer on the time node t is:t after the j-th convolution kernel operation on l layer, j-th convolution kernel on l layer, Causal Convolutions with one-dimensional full convolutional network structure are used in TCN to transform the conventional two-dimensional convolutional neural network into the application of one-dimensional time series data, Therefore, sEMG and ECG time series which are both one-dimensional time series can be processed. Meanwhile, in the causal convolution, the output of the current node is only related to the input of the current node in the previous layer and the input before it, so the dependence between the current characteristic point in the time series and the morphological features in the past time can be reflected. In other words, assume that the convolution kernel size of the one-dimensional time series input  in the TCN residual block is set as , and the zigzag structure is repeated in the two-stage TCN. The receptive field of the front stage for time series was 509, while the receptive field of the back stage could be increased to 1017 by using two-stage TCN. Stacked TCN could achieve a large receptive field through a few layers of networks, greatly reducing the computational cost compared with traditional networks such as CNN, and also had good feature information capture capability for a long time series.In this paper, a timing signal prediction model was established based on TCN, as shown in After the TCN time series evaluation of the two monitoring techniques, the two prediction results were fused at the decision level. D-S evidence theory method is suitable for multi-source heterogeneous information fusion and can realize the fusion and complementarity of multi-source heterogeneous data to form consistent and comprehensive maintainable data. D-S evidence theory was used with reference to . AccordiTP is the number of True positives Negatives to identify, TN is the True positives Negatives to identify, and FP is the number of False positives Negatives to identify, FN is the sample number of Flase Negative recognition.The model established by TCN combined with D-S evidence theory is referred to as sEMG-ECG-TCN, Fatigue evaluation model flow based on TCN and data fusion is shown in The whole project was written in Python with Tensorflow 2.3.1 and Sklearn 0.22.2 packages as the core, supplemented by Numpy and Pandas. Drawing using Origin 2018.mean represents the average ECG power, sEMGRMS represents the root mean square (RMS) of sEMG, sEMGiEMG represents the integrated EMG value of sEMG time domain index, sEMGZC represents the zero crossing rate (ZC) of sEMG signal. sEMGARV represents the average rectified value(ARV) of sEMG. ECGLF represents the power in the low frequency segment (0.04–0.15 Hz) of the ECG sequence, and ECGLF/HF represents the power ratio in the low and high frequency bands of the ECG sequence. Other parameters are calculated as follows:Python 3.0 was used to preprocess the collected sEMG signals. The time window was set as 1000 points, and the distance of each window movement was set as 200 points. The eigenvalues of the subjects in three states were calculated respectively. Each parameter index was calculated by the following formula: ECGmean decreased gradually with the deepening of movement. ECGLF and ECGLF/HF did not differentiate the three states well. The classic RMS, iEMG, ARV and ZC characteristic values of sEMG with muscle state is shown in At present, a large number of studies have found that with the deepening of fatigue degree, the characteristics of sEMG signal in time domain, frequency domain and eigenvalue will change to varying degrees . Since tIn conclusion, sEMG has many time-domain features, some of which show good sensitivity to transition and fatigue states, while some indicators may overlap in the process of change. Most of the characteristics will increase with the degree of fatigue. This is very unfriendly to the establishment of quantitative models, because there are a large number of collinearity factors in the input, and the established model has poor robustness. Therefore, it is not accurate to build a prediction model based on a single or a small number of time domain features. Current research is focused on building more features into prediction models, which does improve accuracy, but models are not universal. Different movements or monitoring positions require changes in different types of features. This is consistent with what was reported in literature .mean and sEMGRMS was strong, reaching -0.67, while the positive correlation between sEMGARV and sEMGZC was strong, reaching 0.5. In the transitional stage, the negative correlation between ECGLF and sEMGiEMG was strong, reaching -0.65, while the positive correlation between ECGmean and sEMGzc was strong, reaching 0.84. In the fatigue stage, the positive correlation between ECGmean and sEMGzc was strong, reaching 0.66. Meanwhile, it should be noted that the negative correlation between sEMGARV and sEMGiEMG was particularly strong, reaching -0.81. From these analyses, both sEMG and ECG time domain indexes were correlated to a certain extent. The model based on a single index such as sEMG or ECG had a high data redundancy due to the correlation, so the model accuracy was not ideal. This explains that the fusion model generally had better prediction accuracy than the single monitoring technology.The correlation between sEMG and ECG signals is always used to quantify the effect of artifact removal in ECG signals. Numerous studies have shown that there are artifacts in sEMG and ECG signals. In order to obtain accurate ECG signals, sEMG signals should be used to remove artifacts in ECG signals. If the correlation between the two is reduced, the ECG signal independence is better. In data fusion analysis, the less correlation between the two inputs, the better. Therefore, the correlation between the time-domain eigenvalues of sEMG and ECG signals was analyzed here, in an attempt to analyze the factors affecting muscle fatigue classification from the signal level. The correlation between sEMG and ECG time domain eigenvalues is analyzed, and the correlation coefficient thermal diagram between each eigenvalue is listed to show the relationship between the time domain eigenvalues more intuitively . The ligIn order to further analyze the causes of the results, the prediction models SEMG-TCN, ECG-TCN, SEMG-KNN and SEMG-SVM were established by sEMG and ECG single monitoring respectively. ECG—KNN, ECG—SVM. Because the prediction effect of the algorithm for each individual is not the same, the statistical accuracy result of each tester in the model is shown in The difference between the six models was not huge. The accuracy of ECG-KNN was only 77.5% for transition, 83.5% for relaxed and 90% for fatigue. However, ECG-KNN was not good at resolving individual differences, with accuracy ranging from 95% to 67% for relaxed individuals and low outliers. This model is not suitable for any tester. The prediction accuracy of SEMG-KNN for the three states is 86.5%, 76.5% and 65.5% respectively. Compared with KNN algorithm, SVM budget model performance is more balanced, ECG-SVM prediction accuracy is 78.5%, 85.5%, 84.5%, the overall balance. However, the problem of outliers also exists. The lowest accuracy of ECG-SVM in predicting relaxed state is only 63%, while the lowest accuracy of SEMG-SVM in predicting three states is 62%, 65% and 64%, which is far lower than the highest accuracy , indicating the poor stability of the model. The prediction accuracy of ECG-TCN and SEMG-TCN is average. The maximum accuracy of ECG-TCN was 93%, 85%, and 95% for the three states. The average accuracy was 86%, 78%, 85%, and the overall performance was good. But when it came to identifying fatigue, the lowest individual accuracy was only 70 percent. The maximum prediction accuracy of SEMG-TCN for the three states was 91%, 87% and 91%, and the average prediction accuracy was 81%, 82% and 85%. The results indicate that sEMG is more stable than ECG in fatigue prediction, and the prediction accuracy of three stages is more balanced. But the lowest individuals were only 68%, 67% and 74% accurate. In general, the six models have some predictive ability for fatigue state, and the sEMG-TCN and ECG-TCN models are better in terms of statistical average accuracy. However, all models are unstable, especially for the differences between individuals, so it is impossible to accurately predict each individual. Analysis showed that ECG was more sensitive in some subjects, while sEMG was more sensitive in others. Different stages vary greatly for different ECG or sEMG. Therefore, if you want to build a set of suitable fatigue detection system, you should start with a variety of monitoring methods. This was similar to the results obtained in literature .The ECG-SEMG-TCN model was established by D-S evidence fusion method. ECG-sEMG-SVM and ECG-sEMG-KNN models were obtained by combining SVM and KNN with the same D-S evidence fusion method. The predicted results are shown in Compared with the prediction results of ECG and sEMG fusion model and single monitoring model, the stability of the fusion model is better. Of the three models, the lowest predictive accuracy was found in the ECG-SEMG-KNN model . There was no particularly low predictive accuracy. The maximum accuracy of ECG-sEMG-KNN is 87%, 86%, 87%. Note that this is considering the maximum and minimum values of individual differences, indicating that the fusion model is less affected by individual differences. The mean values of ECG-sEMG-KNN were 78%, 77%, 75%, and the overall model performance was poor among the three models. The ECG-sEMG-SVM model performed well with average accuracy of 78%, 77% and 77.5%, respectively. At the same time, the range of accuracy is further reduced, the maximum value is 85%~86%, the minimum value is 72%~74%, and the range of the three states is about 14.86% of the minimum value, which further improves the stability and robustness of the model. Of the three models, the ECG-sEMG-TCN model performed best. The average accuracy of prediction is 89%, 86% and 88.5% respectively, and the range of this model is also the smallest among the three models, which is only 11%, 7% and 8%. The model has strong generalization ability and stability.In order to intuitively display the advantages and disadvantages of each model, precision, recall and F-Score are listed in Current ECG and sEMG analysis and recognition often use time-domain and frequency-domain analysis methods to extract the local characteristics of signals, and then use classical machine learning algorithm to establish classification model, which can not fully reflect the time-domain morphology of muscles under the state of motion to a certain extent is not good for human physiological information mining. Therefore, a fatigue prediction method combining ECG and sEMG time series with TCN is proposed in this paper. By comparing the proposed method with the commonly used single monitoring technique based on time domain sequence, it is found that the fused prediction model and TCN has better performance, which has been mentioned in . The appTCN algorithm is widely used in gesture recognition, prosthesis control, rehabilitation and other fields, but the fusion of sEMG and ECG time series has not been reported. In this paper, several ECG and sEMG time series models are established, and several prediction algorithms including KNN, SVM and TCN, two common machine learning classifiers are compared. These machine learning classifiers all need to extract typical signal features, such as RMS, ZC, ARV, etc., and there are many related research reports. The results showed that ECG and sEMG fusion was improved in KNN, SVM and TCN classification, but individual differences caused by individual modeling methods still existed, and the model could not be guaranteed to be suitable for all participants. The results of ECG and sEMG model based on D-S evidence theory show that TCN has better classification performance than KNN and SVM, and the classification accuracy, recall rate and F-score are improved, and the lowest recognition accuracy of individuals is also higher than those of the two methods. The average precision of TCN (90.90%) and the average accuracy (88.0%) of TCN was also higher than that of previous studies  This pro


Difficulty in interpreting white blood cell (WBC) counts in cerebrospinal fluid (CSF) complicates the diagnosis of neonatal meningitis in traumatic lumbar punctures (LP). The aim of our study was to determine the correction factor for WBC counts in traumatic LP that offers the greatest diagnostic efficacy in meningitis.3 CSF and pleocytosis as WBCs ≥ 20 cells/mm3 CSF. The CSF RBC:WBC ratio was analyzed by linear regression to determine a new correction factor. Cell count adjustments were also studied using the 500:1, the 1,000:1 ratio method, and the peripheral blood RBC:WBC ratio, using ROC curves and studies of accuracy (sensitivity and specificity).We conducted a retrospective observational study of LP in neonates between January 2014 and December 2020. Traumatic LP was defined as a red blood cell (RBC) count ≥ 1,000 cells/mmOverall, 41.0% of the 1,053 LPs included in the study were traumatic. The best results for effective WBC correction were the method based on the peripheral blood ratio  and the 400:1 ratio  obtained from linear regression .Both the peripheral blood correction and the 400:1 correction reduce the number of neonates classified with pleocytosis who were not eventually diagnosed with meningitis. Both methods might be a useful tool to clarify the neonatal meningitis diagnosis, offering neonatologists the possibility to assess the WBC count in traumatic LP. Meningitis occurs most often in the first month of life and is associated with high rates of morbidity and mortality –3. The sTraumatic LP occurs when the needle used to perform the procedure causes accidental bleeding in the subarachnoid space. It is estimated that about 30%-50% of neonatal CSF samples are contaminated with blood. The introduction of peripheral blood into the CSF complicates the interpretation of the results by increasing the WBC count, due to the higher concentration of WBC in the plasma compared to CSF –3, 5–7. One possible way of obtaining a correct interpretation of CSF WBC values in case of traumatic LP is to apply correction factors using the CSF RBC count . CorrectWe performed a retrospective observational study of lumbar punctures performed on neonates (≤ 28 days) admitted to the neonatology unit of the Hospital Universitario Nuestra Señora de Candelaria between January 1, 2014 and December 31, 2020 who underwent LP with subsequent cell count, PCR for viruses and bacteria, and CSF culture to rule out bacterial or viral meningitis in the context of neonatal sepsis. The study was approved by the ethical committee of the Complejo Hospitalario Universitario de Canarias (CHUNSC_2020_104).CSFs for which cell counts were unavailable or that had coagulated were excluded. In cases in which several CSF samples were collected from the neonate during admission, the first was selected for the study.General patient data  and diagnosis at discharge  were collected from the DragoAE Hospital Information System.3), WBC (cells/mm3), glucose (mmol/L), and proteins (g/L)], and microbiological results in CSF (cultures with identified microorganism and PCR for bacteria and viruses) and peripheral blood (cultures with identified microorganism) were obtained from the OpenLab Laboratory Computer System (LCS). The gold standard visual method, Neubauer’s chamber, was used for cell counting. CSF biochemical parameters were analyzed using the Dimension RxL Max  automated system. Blood parameters were obtained using the Sysmex XN automatic analyzer . Samples for blood culture were received in BACT/ALERT® Culture Media . As these are pediatric blood cultures, only a single sample (1–5 mL) was required, which was incubated in the BACT/ALERT® 3D for a standardized period of 5 days. In case of tested positive, subcultures were carried out in blood, chocolate and Schaedler agar, by seeding by discharge or isolation. Samples for CSF culture were received in polypropylene sterile tubes and were sown with a calibrated loop for isolation in blood, chocolate and McConkey agar and in fluid thioglycollate medium. Those were incubated at 37ºC for 24–48 h in aerobiosis except chocolate agar which was incubated in chamber at 5–10% CO2. The FilmArray meningitis panel  that analyzes the most common pathogens causing meningitis, including the following viruses, bacteria and yeasts, was used to identify the causative pathogen: Escherichia coli K1, Haemophilus influenzae, Listeria monocytogenes, Neisseria meningitidis, Streptococcus agalactiae, Streptococcus pneumoniae, Cytomegalovirus (CMV), Enterovirus, Herpes simplex virus 1 (HSV-1), Herpes simplex virus 2 (HSV-2), Human herpesvirus 6 (HHV-6), Human parechovirus, Varicella zoster virus (VZV) and Cryptococcus neoformans/gatti.Blood parameters , CSF laboratory parameters  cells/mm3 for neonates with non-traumatic LPs. The median CSF WBC count was 30.0 [12.0–82.8] cells/mm3 for neonates with traumatic LPs and 7.0 [2.0–13.0] cells/mm3 for neonates with non-traumatic LPs .The median [interquartile range (IQR)] CSF RBC count was 12,093.5  cells/mmA CSF RBC:WBC correction ratio of 400:1 was estimated by linear regression  , and the proportion of peripheral blood RBCs to WBCs in determining meningitis  are shown in Table When compared with the uncorrected WBC count, different correction factors showed lower sensitivity but higher specificity and area under the curve for the detection of meningitis. With regards to bacterial meningitis, sensitivity did not differ from the uncorrected WBC count (100%), although in regards to viral meningitis, correction factors showed lower sensitivity (75%).The peripheral blood correction and 400:1 correction showed the highest specificity values for bacterial and viral meningitis  in comparison with the uncorrected WBC count . With these two methods, the peripheral blood correction and 400:1 correction, the number of neonates classified with CSF pleocytosis is substantially reduced: zero cases of bacterial meningitis and one single case of viral meningitis were misclassified.Diagnostic efficacy was also evaluated in CSF with non-traumatic LPs. The results are summarized in Table 3  to more than 10,000 RBC/mm3. However, the most widely used cut-off point used is 1,000 RBC/mm3 [The high rate of traumatic LPs in neonates complicates clinical decisions surrounding the risk of both bacterial and viral meningitis . Further RBC/mm3 .3. These data are consistent with studies reporting rates of up to 46% [In our retrospective study of neonates undergoing LP over a period of 7 years, we observed a considerable number of traumatic LPs (41.0%), defined as ≥ 1,000 RBC/mmp to 46% , 9, althBaziomo et al.  suggest The differential diagnosis between bacterial and viral meningitis is often complex, especially in neonates where clinical examination is of limited value . Most neWhen we compare the AUC of the different correction methods for WBC counts in CSF, we obtain higher figures with the corrections 400:1, 500:1, 1000:1, and with the correction based on peripheral blood than that obtained with uncorrected WBC. Moreover, specificity values for uncorrected WBCs were 38% and 37% in bacterial and viral meningitis, respectively, while these figures increased significantly with all correction factors applied. If we focus on corrections that are not based on ratios in peripheral blood, we see that, although the factors of 500:1 and 1000:1 are the most widely used in the literature, the factor 400:1 obtained the best results in terms of specificity . However, the peripheral blood correction obtained even better results .Although the peripheral blood correction factor obtained the greatest specificity, it is more difficult to apply because recent blood data are required and more complex mathematical calculations must be employed. On the other hand, although correction by the 400:1 factor yields a slightly lower specificity, this method only requires CSF data and the mathematical calculation is easier. The specificity of the 500:1 correction is slightly lower than that of the 400:1 correction and may also be recommended because neonatologists are more familiar with this method.3, conclude that WBC correction in traumatic LP is not useful because it leads to an underestimation of the real number of WBC present in CSF. It may certainly result in missed cases of meningitis that can entail serious clinical consequences. However, they did not take into account the possible morbidity resulting from unnecessary use of antibiotics in patients, which we believe should be a consideration. Lyons et al. [The two largest studies to date evaluating WBC correction in traumatic LP reach conflicting conclusions. Since all these authors, including us, used different traumatic LP cut-off points, comparisons between studies are more difficult to interpret. For example, Greenberg et al. , who defs et al.  obtainedWhen comparing our results in corrected traumatic LPs, we observed that sensitivity and specificity results barely differed from those obtained from non-traumatic LPs that represent optimal CSF conditions and maximum reachable sensitivity and specificity. This substantial reduction in false positives, in relation with uncorrected WBC in traumatic LPs, with no increase in false negatives (missed diagnosis of meningitis), regarding the optimal reachable CSF conditions (non-traumatic LPs), demonstrates that correcting the WBC in traumatic LPs is a valuable procedure that is feasible for use in daily practice.Our study has some limitations. Firstly, it is a single-center study with a limited number of cases of bacterial and viral meningitis. Large-scale multicenter studies are required to evaluate the interpretation of WBC count in traumatic LPs. It should, however, be noted that this is the first study of these characteristics to be carried out in the Spanish population. Secondly, we were unable to determine whether the study neonates received antibiotics prior to the LP procedure, which would overestimate WBC correction. However, this is an increasingly unusual clinical practice. In most cases, empirical therapy is initiated after LP is performed, except in cases where LP is not possible in the first instance due to clinical instability of the patient. Despite this, our study also included the cohort of neonates with non-traumatic LP in order to make a more reliable comparison. Thirdly, we did not select an upper limit for CSF RBC count in our definition of traumatic LP, so we cannot ensure that samples are exclusively peripheral blood. Fourthly, clinical data for the study neonates were not considered in this study. Clinicians treating neonates in the context of bacterial or viral meningitis should take into account not only CSF WBC counts, but also other clinical and laboratory variables.Although CSF correction factors should be used with caution in neonates, we show that correcting WBC values improved diagnostic yield in both bacterial and viral meningitis. Specifically, we recommend the use of both peripheral blood data and the 400:1 correction, the latter being readily available and easier to use. With both methods, it is possible to substantially reduce the number of neonates classified with CSF pleocytosis in whom bacterial or viral meningitis are finally not diagnosed. This approach offers neonatologists the possibility to assess WBC in CSF from traumatic LPs and to avoid the systematic administration of antibiotics in all these neonates.


We aimed to examine the association of CSF tumor necrosis factor-alpha (TNFα) with conversion from mild cognitive impairment (MCI) to dementia. At baseline, there were a total of 129 participants with MCI in this study. The association of CSF TNFα levels with the incidence of dementia were evaluated using Cox proportional hazards regression analysis adjusted for potential confounders. Individuals were categorized into groups based on the CSF TNFα tertiles. Compared to the low group (the reference group), the intermediate group progressed more rapidly to dementia  after adjusting for other covariates. However, the high group did not progress faster than the low group . Our study suggested a potential non-relationship between CSF TNFα levels and the risk of development of dementia among MCI older people. Mild cognitive impairment (MCI) was conceptualized as a transitional disease stage between normal cognition and Alzheimer’s disease (AD) dementia more than two decades ago . HoweverCuljak and colleagues found that serum TNFα levels were significantly higher in AD patients than in MCI subjects [Emerging data indicates that inflammation plays a critical role in the pathogenesis of AD . Tumor nsubjects . A variasubjects . In addisubjects . Howeversubjects . Similarsubjects . To the At the present study, we aimed to investigate the association of CSF TNFα and its related receptors with the risk of developing dementia among MCI subjects.www.adni-info.org).Data used in the preparation of this work were extracted from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. The ADNI study was initiated in 2003 with the primary goal of investigating whether neurocognitive assessments, neuroimaging markers, and other biological markers can be integrated to predict cognitive decline and clinical progression. Detailed information can be found at the website   score raThe levels of CSF TNFα, TNFR1 and TNFR2 levels were measured at the Department of Neurology, Emory University. CSF TNFα, TNFR1 and TNFR2 levels were examined in duplicate. Commercially available multiplex immunoassays  were utilized to examine the levels of CSF TNFα, TNFR1 and TNFR2. The inter-plate coefficients of variation (CV) of TNFα, TNFR1 and TNFR2 were 9.38%, 2.85% and 3.09%, respectively. Values were given in pg/ml.2 test was utilized to compare the distributions of categorical variables (APOE4 genotype and gender) between non-converters and converters. We categorized baseline CSF TNFα, TNFR1 and TNFR2 levels into tertiles. Associations of CSF TNFα, TNFR1 and TNFR2 levels with the incidence of dementia were evaluated using Cox proportional hazards regression analysis adjusted for age, gender, education, APOE4 genotype and CDRSB. CSF TNFα, TNFR1 and TNFR2 levels were treated as categorical variables in Cox proportional hazards regression models. All statistical work was performed using R (v. 4.0.2).T test was performed to evaluate the differences in continuous variables , and xThe demographic and clinical characteristics of the study participants are demonstrated in We categorized CSF TNFα, TNFR1 and TNFR2 levels into tertiles respectively. For TNFα, this variable was categorized into three groups: low (0.21–1.53 pg/ml), intermediate (1.53–1.97 pg/ml) and high (1.97–3.32 pg/ml) groups. For TNFR1, this variable was categorized into three groups: low (382–744 pg/ml), intermediate (744–960 pg/ml) and high (960–1850 pg/ml) groups. For TNFR2, this variable was categorized into three groups: low (525–880 pg/ml), intermediate (880–1120 pg/ml) and high (1120–2310 pg/ml) groups.Kaplan-Meier analysis was used to display the associations of CSF TNFα, TNFR1 and TNFR2 levels with conversion to dementia among MCI individuals. As shown in survive curve , there wTo further examine whether CSF TNFα levels were associated with conversion to dementia among MCI older adults, Cox proportional hazards regression models were fitted after adjusting for age, gender, educational level, APOE4 genotype and CDRSB. As shown in To the best of our knowledge, this is the first study to investigate whether CSF TNFα levels are associated with the risk of conversion to dementia among MCI patients. Compared to the low group (the reference group), the intermediate group progressed more rapidly to dementia after adjusting for other covariates. However, the high group did not progress faster than the low group. The present study suggested a potential non-linear relationship between CSF TNFα levels and the risk of development of dementia.Our finding that CSF TNFα was associated with the risk of incident dementia is in line with previous studies. For example, a cross-sectional study found increased levels of TNFα in affected brain regions of patients with AD dementia . Levels Our study has several limitations. First, the sample size of individuals whose CSF TNFα were < 1.3 pg/ml was relatively small. Further studies are needed to have sufficient sample size to increase the statistical power. Second, participants of the ANDI study were highly educated, which may limit our ability to generalize our findings to other population. Therefore, larger population-based cohorts are needed to replicate our results.In conclusion, we found that high CSF TNFα levels were associated with the risk of conversion to dementia among MCI subjects. Our data highlight the importance of TNFα in the pathogenesis of AD and suggest that CSF TNFα could be used to predict subsequent conversion to dementia among MCI subjects.S1 FigCSF TNFR1 levels were categorized into three groups according to tertiles of its levels. CSF TNFR1 levels were not associated with conversion to dementia.(TIF)Click here for additional data file.S2 FigCSF TNFR2 levels were categorized into three groups according to tertiles of its levels. CSF TNFR2 levels were not associated with conversion to dementia.(TIF)Click here for additional data file.


Cerebrospinal fluid (CSF) provides a close representation of pathophysiological changes occurring in the central nervous system (CNS); therefore, it has been employed in pathogenesis research and biomarker development for CNS disorders. CSF obtained from valid mouse models relevant to CNS disorders can be an important resource for successful biomarker and drug development. However, the limited volume of CSF that can be collected from tiny intrathecal spaces and the technical difficulties involved in CSF sampling has been a bottleneck that has hindered the detailed analysis of CSF in mouse models.We developed a novel chronic dural port (CDP) method without cannulation for CSF collection of mice. This method enables easy and repeated access to the intrathecal space in a free-moving, unanesthetized mouse, thereby enabling continuous long-term CSF collection with minimal tissue damage and providing a large volume of high-quality CSF from a single mouse. When combined with chemical biosensors, the CDP method allows for real-time monitoring of the dynamic changes in neurochemicals in the CSF at a one-second temporal resolution in free-moving mice. Moreover, the CDP can serve as a direct access point for the intrathecal injection of CSF tracers and drugs.We established a CDP implantation and continuous CSF collection protocol. The CSF collected using CDP was not contaminated with blood and maintained physiological concentrations of basic electrolytes and proteins. The CDP method did not affect mouse’s physiological behavior or induce tissue damage, thereby enabling a stable CSF collection for up to four weeks. The spatio-temporal distribution of CSF tracers delivered using CDP revealed that CSF metabolism in different brain areas is dynamic. The direct intrathecal delivery of centrally acting drugs using CDP enabled real-time behavioral assessments in free-moving mice.The CDP method enables the collection of a large volume of high-quality CSF and direct intrathecal drug administration with real-time behavioral assessment in free-moving mice. Combined with animal models relevant to CNS disorders, this method provides a unique and valuable platform for biomarker and therapeutic drug research.The online version contains supplementary material available at 10.1186/s12987-022-00331-1. Cerebrospinal fluid (CSF) provides a close representation of the pathophysiological changes occurring in the central nervous system (CNS) and has therefore been used for pathogenesis research and biomarker development for CNS disorders , 2. CSF The mouse is the most commonly used model organism for human disorders and has served as a useful and adaptable tool for researchers performing basic and preclinical research . NumerouThree basic methods have been reported and used for mouse CSF collection , 10. TheThe ideal method for collecting CSF from mice has numerous specific requirements, as it must allow for (1) the collection of a large volume of CSF from a single mouse, (2) a long-term continuous collection from the same animal, (3) CSF collection under physiological conditions in an awake, free-moving mouse, and (4) repeated serial collections at different time points. This ideal CSF collection method would enable a detailed comprehensive profiling of CSF components, monitoring of longitudinal changes in biomarker levels, and determining the effects of centrally acting drugs. The choice of method is also important from an animal ethics viewpoint. Therefore, the ideal method should also reduce the number of mice needed for CSF experiments.The methods adopted for CSF collection can also be used for drug or tracer administration into the intrathecal space . The penIn this study, we describe the development of a chronic dural port (CDP) method as a novel approach for mouse CSF collection and intrathecal drug delivery. The CDP is installed on the CM along with CSF collection tubing fixed using dental cement. This system allows for easy and repeated access to the CSF, and the connection to a roller/syringe pump enables continuous long-term CSF collection from a free-moving, unanesthetized mouse. This method enables the collection of a large volume of high-quality CSF from a single animal. The CDP can also serve as a direct access point to the intrathecal space, thereby allowing direct intrathecal drug administration and real-time behavioral assessment in free-moving mice.All animal experiments were performed in compliance with the Guidelines for the Care and Use of Laboratory Animals at Osaka University School of Medicine and the National Institutes of Health Guidelines for the Care and Use of Laboratory Animals. Three–four-month-old male mice (C57BL6/J) weighing 20–25 g were used in the study (obtained from CLEA Japan). Seven-month-old male PS19 tau-transgenic mice expressing human P301S 1N4R mutated tau driven by the PrP promoter (The Jackson Laboratory)  and wiltPolyurethane CSF collection tubing for CDP  was cut to a length of 3 cm using a razor blade. The CSF collection tubing was connected to the fluorinated ethylene propylene (FEP) tube with a joint , which was connected to a peristaltic pump  or microsyringe pump . The tubing was filled with distilled water before surgery.The mice were anesthetized with an intraperitoneal injection of a combination of domitor (0.75 mg/kg), midazolam (4 mg/kg), and butorphanol (5 mg/kg) and then mounted on a stereotaxic frame , with a heating pad placed under the body. The mice were positioned so that the head formed an approximately 80° angle with the body, secured by using the head adaptor. The surgical area was disinfected with iodine and covered with a surgical drape. Using straight-tip surgical scissors, an incision (20 mm) was made in the skin at the center of the neck. Under the dissection microscope, the subcutaneous tissue and muscle layers were separated by blunt dissection with forceps and fine-tipped cotton swabs to expose the atlanto-occipital membrane (AOM) of the CM.A small hole was made in the center of the AOM by shallow centesis using a 30G needle, taking care not to damage the brain tissue and blood vessels. After confirming the CSF flowing out of the puncture due to cerebrospinal pressure, the tip of the CSF collection tubing was attached to the membrane by folding the tubing with a stereotactic arm so that it covered the small hole made on the AOM. The CSF was then withdrawn via a peristaltic pump at a flow rate of 20 µL/h, which visually confirmed that the CSF was being drained into the cannula. After confirming this, the flow rate was decreased to 10 µL/h.The CSF collection tubing, AOM, and surrounding bone structures, including the occipital crest and anterosuperior border of the atlas, were fixed with 4-META/MMA-TBB resin . After confirming that the CSF continued to be drained and that the cement was completely secured, the flow rate was further decreased to 4 µL/h.A steel wire anchor was then attached to the skull, which tethered the mice with the sensor-integrated balance arm. The periosteum on the surface of the skull was removed using a swab. The wire anchor has a small loop that is fixed to the surface of the skull using the dental cement, which should be allowed approximately 10 min to harden before finally suturing the scalp.The CSF collection tubing was disconnected from the FEP tube, and the outlet was plugged. The skin was aligned and sutured using 7 − 0 silk, and then a mixture of analgesic, sugar, and antibacterial agents was administered intraperitoneally. The mice were left on the top of the heating pad to maintain their body temperature until waking. The mice were then carefully transferred to a recovery home cage and were allowed at least four days for tissue recovery before starting the continuous CSF collection.Four days after the CDP implantation, the mice were tethered with the sensor-integrated balance arm and placed in the movement-response caging system  as previously described . The senA conventional single-collection technique for mouse CSF was performed as previously described, with minor modifications . Mice weFor the chronic CMc experiment, the implantation of a catheter into the CM was performed as previously described . Three d2 (0.25 mm × 0.25 mm) was counted. The mean number of cells for the four distinct areas was used for the analysis. The electrolyte concentrations  in the collected CSF were measured by a blood gas analyzer .A high-sensitivity spectrophotometry method was employed to measure blood contamination in the collected CSF, as previously described . HemogloThe concentrations of ApoB, ApoE, and albumin in the samples  were determined by the mouse ApoB enzyme-linked immunosorbent assay (ELISA) kit , the mouse ApoE ELISA kit , and the mouse albumin ELISA kit , respectively, according to the manufacturer’s instructions.The concentrations of total human tau in the single collection CSF and cCSF of tau-transgenic PC19 and wild-type mice were determined by Tau  Human ELISA kit , according to the manufacturer’s instructions. Samples were diluted at 1:10 using a sample diluent buffer provided by the ELISA kit.Enzyme-based biosensors were used for the real-time monitoring of glucose and lactate levels as previously described , 20. In Brains were fixed in 4% paraformaldehyde for 24 h at 4 °C, incubated for 2 days in 30% sucrose in phosphate-buffered saline (PBS) and embedded in a Tissue-Tek O.C.T. Compound ; 8-µm thick sagittal and coronal sections were then cut on a cryostat and mounted on glass slides. The brain sections were then stained with hematoxylin and eosin (H&E) to evaluate the tissue integrity and damage due to CSF collection. For immunostaining, the sections were permeabilized with 0.5% Triton X-100 in PBS, blocked in 5% bovine serum albumin in PBS, and immunostained with rabbit anti-glial fibrillary acidic protein (GFAP) antibody  and mouse anti-NeuN antibody  in 1% bovine serum albumin in PBS at room temperature overnight. Sections were washed thoroughly in PBS; the immunoreactions were then visualized by fluorescent secondary antibodies. The sections were then mounted using VECTASHIELD medium . We used the following secondary antibodies: Alexa 488-conjugated goat anti-rabbit IgG antibody  and Alexa 594-conjugated goat anti-mouse IgG antibody . Fluorescent images were captured using a fluorescence microscope  equipped with a digital camera, using the same fluorescence settings in all cases. The pixel intensities of the fluorescent signal were analyzed and quantified using National Institutes of Health Image software. For the GFAP burden analyses, data are reported as the percentage of the labeled area captured (positive pixels) divided by the full area captured .The intrathecal injection of the drug and the CSF tracer injection were performed using a 30G Hamilton microsyringe connected to the CDP via CSF collection tubing. The dead volume of the CSF collection tubing was approximately 0.6 µL. A single dose of contrast agent (5 µL in 10 s) was injected via the CDP, followed by sequential micro-CT imaging . We used an 821.14 molecular weight non-ionic, water-soluble radiographic contrast agent . The concentrations of the contrast agents in each brain region at each time point were determined based on the signal intensity, which was used to calculate metabolic parameters, such as t1/2 and Tmax. For the histological assessment, a single dose of a water-soluble nuclear-staining dye  was injected via the CDP (50 µg/5 µL in 10 s). The brain was harvested at 60 min. Coronal sections were immunostained with an anti-NeuN antibody as described above, and fluorescent images were captured using a fluorescence microscope . For the behavioral assessment of an intrathecally delivered drug, an anesthetic agent  was acutely injected via CDP in the free-moving mice (25 µg/5 µL in 1 s through a syringe connected to the CDP), whereas locomotor activities were measured by an infrared light beam crossing system as described below. After a baseline measurement for 5 min, an anesthetic agent was acutely delivered, and locomotor activity was recorded for 10 more minutes.The mice’s spontaneous locomotor activity during the continuous CSF collection or intrathecal drug delivery experiment was monitored in an open-filed cage (43 × 43 cm) with high-density arranged infrared sensors  , 22. TheAll data are expressed as the mean ± standard error of the mean. Two-group comparisons were performed by an unpaired t-test, unless otherwise stated. Comparisons among three or more groups were performed by analysis of variance and the Tukey–Kramer test, unless otherwise stated. p values < 0.05 were considered statistically significant.Figure The detailed structure of the CDP is shown in Fig. In contrast to a single-collection method , 23, theWe also investigated the quality of the CSF collected via the CDP. Blood contamination in the CSF can have a significant impact on the analytic outcomes in biomarker research . A high-We also measured tau protein concentrations in the CSF samples of tau-transgenic PS19 and wild-type mice Fig. g. Tau isWe also investigated the impact of continuous CSF collection via the CDP on mouse behavior. The locomotor activity of the animal was quantitatively measured using an infrared beam-based activity detection system . BaselinCSF collection via conventional CM cannulation (CMc) can cause tissue damage after long-term experiments lasting several days or weeks, especially when using small-sized rodents, such as mice.In contrast to the conventional CMc method, the CDP does not require the intrathecal insertion of a cannula for CSF collection Fig. a; therefWe applied the CDP method to monitor the dynamic metabolism of glucose and lactate in the CSF during peripheral glucose loading. To this end, we employed enzyme-based biosensors, which enable second-by-second recording of each molecule’s change in concentration Fig. . The bioThe mouse was administered an intraperitoneal injection of glucose during the continuous CSF collection at a flow rate of 0.05 µL/min. The CSF glucose concentration showed a rapid increase after intraperitoneal glucose loading, reaching its peak at approximately 50 min, followed by a gradual decrease down to baseline level at approximately 180 min Fig. c. The CSThe CDP can also be used as an injection port for various compounds, such as CSF tracers and drugs. We investigated the spatio-temporal distribution of CSF tracers injected into the intrathecal space via the CDP Fig. . We injeMoreover, we evaluated the dynamic metabolism of the injected tracer in each part of the intrathecal space by measuring the temporal changes in the concentrations and calculating the half-life (t1/2) and the time to peak concentration (Tmax) values Fig. d, e. In We also histologically assessed the spatial distribution and degree of penetration of a tracer injected into the brain parenchyma via the CDP Fig. f, g. We The CDP method enables the intrathecal administration of centrally acting drugs and the real-time assessment of behavioral changes in awake, free-moving mice. We injected a single acute dose of anesthetic agent via the CDP while quantitatively measuring the animal’s locomotor activity with high-temporal resolution using an infrared light beam crossing system Fig. a. The miBrain tissue is surrounded and protected by a bony structure, which makes it difficult to perform biopsies and the direct delivery of therapeutic drugs. CSF collection and intrathecal drug administration have therefore been employed as alternative methods. The small size of the intrathecal space in mice impedes the stable collection of a large volume of CSF for detailed analysis of its components, which has been the main bottleneck for CSF biomarker research using mouse models. In this study, we developed a CDP method as a novel approach for CSF collection that allows for continuous long-term CSF sampling in awake, free-moving mice. This method enables the collection of large volumes of high-quality CSF suitable for biomarker research and can also be used for the intrathecal delivery of drugs for the real-time monitoring of their pharmacological effect on the behavior of free-moving mice.The volume of CSF obtained through conventional methods, such as CM puncture (~ 10 µL per single mouse) , 10, is The potential impact of protein degradation and any biochemical alteration in the cCSF samples due to long-term exposure to room temperature during continuous collection should also be considered. In our experimental setting, it took approximately 8 h for the cCSF samples traveling through the collection tubing to reach the refrigerated fraction collector. Our preliminary experiments confirmed that the values for the CNS-related protein markers, such as tau, phosphorylated tau, α-synuclein, neuron-specific enolase, and GFAP did not significantly change after a 24-h exposure to room temperature (data not shown). In fact, the levels of CSF markers, such as tau, ApoE, and albumin, were not significantly different between the single CM puncture and the long-term collection via CDP  the potential limitation of neck movement due to CDP fixation with dental cement and 2) the requirement for surgical training. The CDP method requires fixation of the CSF collection tubing to the AOM and surrounding bone structures, including the occipital crest and anterosuperior border of the atlas, which is important for stable CSF collection in free-moving mice. We observed no apparent behavioral alterations after the CDP installation. Mice with CDP showed rapid recovery from the surgery, and surgery-related body weight loss was restored within four days after the operation (data not shown). Notably, the mice with the CDP implant survived as long as a few months after surgery, indicating the minimal physical stress exerted using this method on animals. The study mice maintained a normal circadian rhythm, even during continuous CSF collection (Fig.  the requThe CDP method provides a unique and valuable platform for CSF biomarker research and therapeutic development using animal models relevant to CNS disorders. An implanted CDP allows for the repeated and long-term access to the CSF and intrathecal space in awake, free-moving mice. A large volume of high-quality CSF collected from mice under physiological conditions can be useful for identifying novel relevant biomarkers.Additional file 1: Figure S1. Schematic of continuous CSF collection in the free-moving mice in the movement-response rotating cage. The mouse is connected to the sensor-integrated balance arm via a steel wire anchor attached on the skull.


Most chemotherapy regimens used in the setting of non-metastatic breast cancer are myelosuppressive and are associated with toxicities with significant clinical implications, including febrile neutropenia. The use of granulocyte colony-stimulating factor (G-CSF) reduces the severity and duration of febrile neutropenia, following the initiation of myelosuppressive chemotherapy. The practice of G-CSF prophylaxis is a proven form of supportive care that is shifting with the introduction of biosimilars. As published data are limited, we characterized the patterns and predictors of G-CSF use in a large real-world Canadian cohort over an 11-year period. Our results demonstrate that G-CSF use can be further optimized to align with current guidelines and to improve supportive care for patients with breast cancer.n = 3477) versus filgrastim only . G-CSF use increased linearly more than two-fold over the 11-year study period. Predictors of G-CSF use included younger age, south zone of residence, higher neighborhood education, inferior disease stage, highly neutropenic risk chemotherapy, and more recent chemotherapy initiation. Conclusions: Despite increasing G-CSF usage over time, an appreciable proportion of patients for whom G-CSF prophylaxis is recommended did not receive it. G-CSF use could be further optimized to align with supportive care clinical guidelines and reduce the impact of neutropenia and its associated complications.Background: There are limited published data in the Canadian healthcare system on the use of granulocyte colony-stimulating factor (G-CSF) among patients with breast cancer. This study characterized real-world G-CSF use during the period surrounding the introduction of filgrastim biosimilar. Methods: Electronic medical records were reviewed retrospectively for patients with breast cancer who received moderately or highly myelosuppressive (neo)adjuvant chemotherapy from 2008 to 2019 in Alberta, Canada. Trends in G-CSF usage were plotted to elucidate temporal variations and multivariable regression models were constructed to identify clinical factors associated with G-CSF use. Results: We included 6662 patients in our analyses. G-CSF was used in 57.1% of patients during their treatment trajectory. Among the 3801 patients who were treated with G-CSF, the majority received pegfilgrastim only (91.5%;  The primary curative intent treatment modality for non-metastatic breast cancer is surgery, but patients with high-risk clinical or pathological features are commonly also offered neoadjuvant or adjuvant therapy as a standard of care. Most chemotherapy regimens used in this setting are myelosuppressive and are associated with a variety of hematological toxicities, including neutropenia and febrile neutropenia (FN). In particular, FN represents the most important dose-limiting toxicity of myelosuppressive chemotherapy, posing significant issues in the management of patients and contributing to a heightened risk of significant morbidity and mortality, increased healthcare resource use, and potential dose modifications with a subsequent failure to achieve optimal relative dose intensity (RDI) ,2. ApproThe use of granulocyte colony-stimulating factor (G-CSF) reduces the severity and duration of neutropenia following myelosuppressive chemotherapy, and prophylaxis has been shown to significantly decrease the risk of FN in patients ,5. G-CSFInternational guidelines have a high degree of consensus and uniformly recommend prophylactic use of G-CSF when there is a high risk (>20%) of FN ,12. FN rSince the use of supportive care medications in real-world clinical practice varies from the guideline recommendations, and these medications are not uniformly reimbursed in Canada, there are concerns that G-CSF use may be suboptimal or overall under-utilized . In 2017This was a retrospective, population-based study conducted in Alberta, Canada, which represents the country’s fourth-largest province, with a population of over four million residents. The Alberta Cancer Registry (ACR) was the primary data source for patient demographics, tumor characteristics, and treatment patterns, which were collected prospectively for all patients diagnosed with cancer in the province. Additional data sources included ambulatory care records, physician billing claims, and hospital discharge abstracts, based on previously validated coding algorithms of the International Classification of Diseases and Related Health Problems (ICD). The study protocol was reviewed and approved by the research ethics committee prior to data collection and analysis. This study’s design, analysis, and reporting all adhere to the STROBE  guidelines .Included patients were those aged 18 years or older, newly diagnosed with stage I to III breast cancer in Alberta, from January 2008 to December 2017, who received at least one cycle of moderately or highly myelosuppressive chemotherapy in the neoadjuvant or adjuvant setting. Patients who were diagnosed with multiple cancers were excluded. Patients were also excluded if they had received G-CSF as part of a clinical study protocol or if they were treated with myelosuppressive.The main outcome of interest was the receipt of either filgrastim or pegfilgrastim during the study period, January 2008 to November 2019. The date of initiation of the first cycle of chemotherapy was considered the study index date. Primary prophylaxis was defined as G-CSF administration within seven days of the start of a chemotherapy cycle. Chemotherapy regimens with high neutropenic risk included AC-T (doxorubicin hydrochloride and cyclophosphamide followed by paclitaxel), DC (docetaxel and cyclophosphamide), ddAC (dose-dense doxorubicin and cyclophosphamide), FEC-D , and TCH , whereas those with moderate neutropenic risk were CMF , 3-weekly docetaxel, and FEC  [Demographic information retrieved from the ACR comprised age at treatment initiation and residential postal code. Postal codes were used to derive information on neighborhood-level socioeconomic status, including educational attainment and annual household income, based on 2011 census data, which represented the most recent year of available data. Descriptive statistics were used to analyse the baseline demographic- and treatment-related characteristics. To elucidate differences in baseline characteristics between groups, the Wilcoxon rank-sum test was used for continuous variables, whereas Pearson’s Chi-squared test or Fisher’s exact test were used for categorical variables. Additionally, standardized mean difference (SMD) was computed to reinforce comparisons of baseline characteristics, with SMD > 0.1 considered indicative of imbalance. Multivariable logistic regression analyses were performed to assess the likelihood of receiving G-CSF as a binary variable (yes/no) and that of receiving filgrastim over pegfilgrastim. All statistical tests were two-sided, and the significance level was defined a priori as <0.05. All analyses were performed using R.In total, we identified 6662 patients diagnosed with early-stage breast cancer from 2008 to 2017 who received either moderately or highly myelosuppressive (neo)adjuvant chemotherapy . Among tp < 0.001, SMD > 0.1 for each). Among the 3801 patients who received G-CSF, the vast majority received pegfilgrastim only , whereas the others received either filgrastim only  or both pegfilgrastim and filgrastim . Patients who received filgrastim only were more likely to have a younger age, Calgary zone of residence, higher neighborhood education, advanced disease stage, or more recent year of chemotherapy initiation  (n = 3323), but those who did were more likely to receive filgrastim versus pegfilgrastim .Of the entire study cohort, 3801 (57.1%) patients received G-CSF at some point during the chemotherapy treatment trajectory . Patientor each) . Among tExamining treatment patterns by calendar year, G-CSF use over the 11-year study period appeared to increase linearly from 34.2% in 2008 to 78.0% in 2018 . Among pp < 0.01 for all)   . In the for all) .This was a large retrospective study of a population-based cohort of patients with early-stage breast cancer to describe the use of G-CSF over an 11-year period in Alberta, Canada. Overall, we observed that G-CSF was not used consistently in the setting of myelosuppressive chemotherapy with a high or moderate risk of FN, where only approximately half of the study cohort had a record of at least one G-CSF prescription. In most of these cases, G-CSF was not used as primary prophylaxis for the first chemotherapy cycle. Despite guidelines that recommend primary G-CSF prophylaxis for chemotherapy regimens with an FN risk of >20%, other longitudinal real-world evidence studies have also reported an underutilization of G-CSF in patients with cancer, including patients with non-metastatic breast cancer at high risk of FN based on chemotherapy and patient-related factors . A retrospective study by Fine et al. included 395 patients who initiated G-CSF in the oncology outpatient setting between January 2008 and January 2009 in the Canadian provinces of Ontario and Quebec . OverallEven though G-CSF use was suboptimal overall, we observed sizeable and consistent growth in G-CSF use from 2008 to 2018, both graphically and through regression analysis. These temporal trends were concordant with the analysis of a US Medicare population of older women receiving adjuvant chemotherapy for early-stage breast cancer between 2002 and 2012 . HoweverThe introduction of biosimilars, such as Grastofil, may further improve G-CSF use. This biosimilar of filgrastim was introduced in Alberta in 2017, toward the end of our study period. While the change in overall G-CSF use was linear throughout our study period, we observed an increase in the proportion of patients receiving filgrastim, compared to pegfilgrastim around 2017. This may indicate a shifting treatment paradigm, attributed to the introduction of Grastofil and to the fact that some private insurance plans implemented a preferential listing of Grastofil over pegfilgrastim in fall 2016. The role of biosimilars in improving access by reducing cost-associated barriers is well-supported. The launch of biosimilar filgrastim in Europe was associated with initial cost savings of 14–27%, increased access, and decreased rates of FN-related hospitalization ,24,25. MWe found certain patient factors to be associated with the increased use of G-CSF and the preference of one G-CSF type over another. Of the demographic characteristics analysed, zone of residence, neighborhood education, and age were significant predictors of G-CSF use. Since southern-most zones of residence are also those with relatively higher population density, we hypothesize that geographic barriers to care may have contributed to the variations in the rates of G-CSF use across the province. Neighborhood education may serve as a surrogate measure of health literacy, which has well-established links with access to care and health outcomes ,29. Low Our study design has inherent limitations. We focused on patients with breast cancer due to the high incidence of FN in this population, but we inevitably excluded other patients treated with myelosuppressive chemotherapy, who may benefit from G-CSF. We extracted data for G-CSF and chemotherapy administration from pharmacy dispensing records, rather than usage records; thus, we could not assess G-CSF use after the first cycle of chemotherapy. Lastly, our retrospective approach to data collection may have introduced unknown variables with the potential to confound the relationship between patient factors and G-CSF use.There was a consistent rise in G-CSF use in the Alberta healthcare system from 2008 to 2018, yet an appreciable proportion of patients with breast cancer for whom G-CSF prophylaxis is recommended did not receive it. Opportunities exist to further optimize G-CSF use to align with current supportive care clinical guidelines and to reduce the impact of neutropenia and its associated complications. Characteristics of the patient and their cancer treatment should be carefully taken into consideration when planning strategies for supportive care. Potential directions for future research include expanding the evaluation of G-CSF use into other cancer sites and unpacking the impact of biosimilars on improving G-CSF access at a pan-Canadian level.


We hypothesized that quantitative net water uptake (NWU), a novel neuroimaging marker of early brain edema, can predict symptomatic intracranial hemorrhage (sICH) after acute ischemic stroke (AIS).We enrolled patients with AIS who completed admission multimodal computed tomography (CT) within 24 h after stroke onset. NWU within the ischemic core and penumbra was calculated based on admission CT, namely NWU-core and NWU-penumbra. sICH was defined as the presence of ICH in the infarct area within 7 days after stroke onset, accompanied by clinical deterioration. The predictive value of NWU-core and NWU-penumbra on sICH was evaluated by logistic regression analyses and the receiver operating characteristic (ROC) curve. A pure neuroimaging prediction model was built considering imaging markers, which has the potential to be automatically quantified with an artificial algorithm on image workstation.P < 0.001) and NWU-core  were independently associated with sICH with adjustments for age, sex, time from symptom onset to CT, hypertension, lesion volume, and admission National Institutes of Health Stroke Scale (NIHSS) score. ROC curve showed that NWU-penumbra had better predictive performance than NWU-core on sICH [area under the curve (AUC): 0.773 vs. 0.673]. The diagnostic efficiency of the predictive model was improved with the containing of NWU-penumbra (AUC: 0.853 vs. 0.760). A pure imaging model also presented stable predictive power (AUC = 0.812). In patients without MT, however, only admission NIHSS score  showed significance in predicting sICH in multivariate analyses.154 patients were included, of which 93 underwent mechanical thrombectomy (MT). The median time from symptom onset to admission CT was 262 min . In patients with MT, NWU-penumbra (OR =1.442; 95% CI = 1.177–1.766; NWU-penumbra may have better predictive performance than NWU-core on sICH after MT. A pure imaging model showed potential value to automatically screen patients with sICH risk by image recognition, which may optimize treatment strategy. Brain edema is an important pathophysiological process after acute ischemic stroke (AIS). The pathophysiological mechanisms are primarily cytotoxic edema, angiogenic edema, and cerebrospinal fluid influx , 2. ProgQuantitative net water uptake (NWU) is a relatively novel neuroimaging marker of early brain edema after AIS, first described in 2016 based on admission multimodal CT . ResearcThis is a single-institute retrospective study. The clinical and imaging data were obtained from an established database, which contains data from all patients with AIS admitted to the Emergency Department at Ningbo First Hospital (Advanced Stroke Center) between July 2017 and September 2019. Inclusion criteria: (1) acute anterior circulation infarction; (2) admission multimodal CT including NCCT, CT angiography (CTA), and CT perfusion (CTP) were completed within 24 h after symptom onset. In patients with wake-up stroke, we estimated the time of stroke onset by taking the midpoint between sleep time and wake time ; (3) folAccording to the European Cooperative Acute Stroke Study (ECASS) II criteria, hemorrhagic transformation (HT) of infarcted brain tissue can be divided into four types, including HI1 , HI2 , PH1 , and PH2 . sICH was defined as the presence of hemorrhagic hyperdensity in the infarction area on follow-up CT scans within 7 days, accompanied by clinical deterioration (an NIHSS score increase ≥ 4 from baseline) , 15 withDer, Denrap, and Dran) with Hounsfield units (HU) between 20 and 80 (tera) per unit volume of ischemic core (core). Similarly, NWU-penumbra was defined as Δ tera per unit volume of ischemic penumbra (penumbra), according to the following equations  with 320 axial sections and 0.5 mm thickness. All imaging data were processed by commercial software . CTP was used to identify the ischemic core and ischemic penumbra. The threshold of ischemic core was delay time (DT) > 3 s and cerebral blood flow (CBF) <30%. The threshold of ischemic penumbra was DT > 3 s and CBF > 30% . Regions0 and 80 . NWU-corquations , 7:t-test was used for group comparison. For non-normal continuous variables, data were presented in the form of the medians (interquartile ranges), and the Mann–Whitney U test was applied. Categorical variables were expressed as frequencies (%) using the χ2 test or Fisher's test as appropriate. Univariate logistic regression analyses were used to explore the associations between sICH, NWU-core, and NWU-penumbra. Backward stepwise multivariate logistic regression analyses were then performed. We used age, sex, admission NIHSS score, time from symptom onset to CT, lesion volume, and variables with P < 0.10 in the univariate analyses as covariates. The variance inflation factor (VIF) was calculated to examine multicollinearity among variables included in regression models. Predictive models containing NWU-core and NWU-penumbra were further built. The predictive power of NWU-core, NWU-penumbra, and regression models were measured by receiver operating characteristic (ROC) curve analysis.We performed the statistical analyses under the supervision of professional statisticians. SPSS version 22.0 , MedCalc version 20.027 , and GraphPad Prism version 8.0  were used in these analyses. Baseline characteristics were compared between patients with and without sICH. For continuous variables that followed a normal distribution, data were expressed as mean ± SD, and the P = 0.008], higher median ischemic core volume , higher median NWU-core  and higher median NWU-penumbra .A total of 154 patients with acute anterior circulation ischemic stroke were included in the study. There were 58 cases of HT (37.7%), including 8 cases of HI1 (5.2%), 24 cases of HI2 (15.6%), 12 cases of PH1 (7.8%), and 14 cases of PH2 (9.1%). sICH occurred in 28 patients (18.2%). P = 0.013] and higher NWU-penumbra . Univariate analysis showed that hypertension , ischemic core volume , NWU-core , and NWU-penumbra  were associated with sICH after MT. However, in patients without MT, none of the imaging biomarkers showed significance, only admission NIHSS score  and diabetes mellitus  were significantly correlated with sICH  received successful reperfusion according to the TICI grading scale. Of the 61 patients who did not receive MT, 48 underwent intravenous thrombolysis and 13 received antiplatelet agents only. ith sICH . The assP < 0.001). Model 4 contained only imaging markers. We found that NWU-penumbra  and NWU-core  were independently associated with sICH after adjustment for age, sex, admission NIHSS score, time from symptom onset to CT, lesion volume, and hypertension. The imaging-only model contained ischemic core volume  and NWU-penumbra . In patients without MT, only admission NIHSS score  was independently associated with sICH.With multivariate regression analysis, we discovered that NWU-penumbra and NWU-core were independently associated with sICH in the MT group and built four different predictive models. ROC curve analyses were conducted to test the predictive power of individual variables for models predicting sICH. In this research, quantitative NWU was used to assess the degree of early post-stroke brain edema within the ischemic core and penumbra separately. We found that NWU-penumbra may have a better value than NWU-core for predicting sICH after MT, and a pure imaging model containing NWU-penumbra and ischemic core volume showed a potential value for predicting sICH after MT independent of clinical variables.Quantitative NWU, based on admission multimodal CT, has been described as a novel neuroimaging marker of early brain edema , 7. The In this study, we examined several different models for predicting sICH after MT and found that the inclusion of NWU-penumbra significantly improved the predictive power of such models. Importantly, the pure imaging model containing NWU-penumbra and ischemic core volume also showed a stable predictive value for sICH after MT. In most hospitals, CT at admission is the standard care for patients with AIS, with the advantages of being widely and rapidly available and intrinsically quantitative . TherefoOur study has some limitations. Foremost, is the fact that it was a retrospective and single-center study. Further external validation trials in larger cohorts are required before clinical implementation. Secondly, the incidence of sICH was relatively low in the non-MT group, which may result in insufficient statistical power.In conclusion, we found that NWU-penumbra had better predictive performance than NWU-core on sICH after MT. The pure imaging model based on admission CT may automatically flag patients with elevated sICH risk. It helps further hypothesis generation to better identify patients at risk of reperfusion injury or parenchymal hemorrhagic transformation in patients undergoing endovascular thrombectomy.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by Ningbo First Hospital, Ningbo Hospital of Zhejiang University. The patients/participants provided their written informed consent to participate in this study.TX: conceptualization, methodology, formal analysis, and writing-original draft. JY: project administration. QH: data curation. YW, YX, and AW: resources. XG, YH, and YX: funding acquisition. MP and LL: validation and writing-review and editing. All authors contributed to the article and approved the submitted version.This study was supported by Ningbo Health Branding Subject Fund (PPXK2018-04) and Medicine and Health Science and Technology Projects of Zhejiang Province .The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


To improve the muscle co‐contraction by sEMG‐BF, it may be necessary to convert muscle activation into a muscle co‐contraction. We concluded that individual sEMG‐BF does not immediately improve muscle co‐contraction during pedaling.Muscle co‐contraction between the agonist and antagonist muscles often causes low energy efficiency or movement disturbances. Surface electromyography biofeedback (sEMG‐BF) has been used to train muscle activation or relaxation but it is unknown whether sEMG‐BF reduces muscle co‐contraction. We hypothesized that auditory sEMG‐BF improves muscle co‐contraction. Our purpose was to investigate whether auditory sEMG‐BF is effective in improving muscle co‐contraction. Thirteen participants pedaled on a road bike using four different auditory sEMG‐BF conditions. We measured the surface electromyography at the lower limb muscles. The vastus lateralis (VL) and the semitendinosus (ST) activities were individually transformed into different beep sounds. Four feedback conditions were no‐feedback, VL feedback, ST feedback, and both VL and ST feedback. We compared the co‐contraction index (COI) of the knee extensor‐flexor muscles and the hip flexor‐extensor muscles among the conditions. There were no significant differences in COIs among the conditions ( We constructed EMG biofeedback system for reducing muscle co‐contraction during pedaling. EMG‐BF did not improve the muscle co‐contraction during pedaling. Individual EMG‐BF is difficult to immediately improve muscle co‐contraction during pedaling. High muSurface electromyography biofeedback (sEMG‐BF) alters muscle activity patterns and improves biomechanical outputs during movement . Participants had no muscular or neurological disorders. All participants self‐reported normal hearing. The participants provided written informed consent to participate after receiving a detailed explanation of the purpose, potential benefits, and risks associated with participation. The experimental procedures were conducted in accordance with the Declaration of Helsinki and were approved by the Local Ethics Committee of the Faculty of Sport Sciences at Waseda University (2020–034).2.2Pedaling was selected for the experiment because pedaling consists of relatively simple movements and includes a co‐contraction phase between agonist‐antagonist muscles. Moreover, muscle co‐contraction during pedaling is associated with the skill level. It has been reported that the muscle co‐contraction during pedaling is less in cyclists than triathletes  attached to a direct‐drive electromagnetically braked bike‐trainer . The bike‐trainer was controlled by a personal computer running My E‐Training software , which was tethered to the bike‐trainer via a USB ANT + dongle . The road bike was equipped with clipless pedals. We adjusted the saddle height while the participant sat on the seat. During adjustment, participants placed the foot on the pedal and locked the knee with the pedal at the bottom of the stroke . During the adaptation, participants selected the cadence and workload that kept their heart rate around 100–120 bpm. Participants’ agonist and antagonist muscle activities were feedbacked by the beep sound during the adaptation. We asked participants to reduce the overlapping of beep sounds as much as possible. A 10‐minute rest period was provided after the adaptation. A workload during the measurement was defined as that participants could keep their heart rate around 150 bpm and 80‐cadence. During determination of the workload, the workload increased by 20 W per minute until participants could no longer maintain 150 bpm. We considered that we should select a relative workload based on the heart rate because both men and women participated. Mean workloads in the measurement task in women and men were 123 ± 26 W and 176 ± 26 W, respectively. A 10‐min rest period was provided after determining the workload during the measurement tasks.2.3The sEMG electrodes  were attached to participants to measure muscle activity. Test muscles for feedback were the vastus lateralis (VL) and the semitendinosus (ST) on the right side. The sEMG data was imported into a personal computer using an analog data acquisition system , and the sEMG data was monitored by a custom‐made LabVIEW program . The sEMG data was measured at 1000 Hz and further processed in real‐time by full‐wave rectification and smoothing with the weighted moving average.The task for threshold adjustment of the sEMG‐BF system was performed prior to the adaptation to the system and measurement tasks because of different workloads between the adaptation and measurement. In determining a threshold, participants pedaled at the workload used for adaptation or measurement tasks for about one minute. VL and ST were rectified and smoothed to determine each maximum VL and ST value. Thresholds were defined as the normalized sEMG amplitude exceeding 5% of the maximum amplitude during pedaling from the amplitude at the rest. This threshold was determined in preliminary experiments. The threshold was gradually changed, and the participants were asked whether the beep sounds corresponded to the muscle activation timing. We determined the minimum threshold at which an appropriate sound could be continuously produced. Beep frequencies for VL and ST were set at 400 and 800 Hz, respectively.2.4During measurement tasks, participants pedaled for 90 s at 80‐cadence with a determined workload. Four feedback conditions during measurement tasks were: no‐feedback (NFB), VL feedback (VLFB), ST feedback (STFB), and both VL and ST feedback (VL‐STFB). In addition to NFB, the need to compare individual sEMG‐BF as in the previous study  operating at 100 Hz. We defined the upper dead center as a crank angle of zero. One cycle was defined as the upper dead center to the next upper dead center.areaEMG1 and areaEMG2 represent the integral of the sum of agonist and antagonist sEMG data. Common Area represents the common area between agonist and antagonist sEMG data.In addition to VL and ST, we measured sEMG signals from the rectus femoris (RF) and biceps femoris (BF) long head muscles to investigate the muscle co‐contraction of hip flexor‐extensor. The hip and knee joints interact with each other during pedaling. We speculated that the biofeedback of the knee flexor–extensor would affect the co‐contraction of the hip flexor–extensor. Since RF and BF activate across multiple phases, their feedback sounds become complicated. As a result, RF and BF were excluded from the feedback system. The RF and BF signals were measured at 1000 Hz . The sEMG signals were high‐pass filtered (20 Hz) with a zero‐lag fourth‐order Butterworth filter to remove motion artifacts. Thereafter, the sEMG signals were demeaned, digitally rectified, and low‐pass filtered at 15 Hz with a zero‐lag fourth‐order Butterworth filter  in the ensemble‐averaged sEMG among the conditions Figure . VL main4Contrary to our hypothesis, auditory sEMG‐BF does not improve the muscle co‐contractions. This might be because our auditory sEMG‐BF system might have some problems. We suggest an idea for improving muscle co‐contraction by auditory sEMG‐BF.4.1We considered that controlling muscle relaxation during pedaling is more difficult because modulating force during relaxation is more difficult than force generation .Kibushi B: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Validation, Roles/Writing ‐ original draft, Writing ‐ review & editing. Okada J: Conceptualization, Project administration, Supervision, Validation, Writing ‐ review & editing.