The percentages of deviations/relative deviations falling within the mean ± 1.96 SD of deviations/relative deviations were 92.50% and 95.83%, respectively. The mean time for system assessment was 35.00% less than that for clinician assessment, p < 0.05. Therefore, wearable intelligent machine learning rehabilitation assessment has a strong and significant correlation with clinician assessment, and the time spent is significantly reduced, which provides an accurate, objective, and effective solution for clinical rehabilitation assessment and remote rehabilitation without the presence of physicians.In order to solve the shortcomings of the current clinical scale assessment for stroke patients, such as excessive time consumption, strong subjectivity, and coarse grading, this study designed an intelligent rehabilitation assessment system based on wearable devices and a machine learning algorithm and explored the effectiveness of the system in assessing patients’ rehabilitation outcomes. The accuracy and effectiveness of the intelligent rehabilitation assessment system were verified by comparing the consistency and time between the designed intelligent rehabilitation assessment system scores and the clinical Fugl–Meyer assessment (FMA) scores. A total of 120 stroke patients from two hospitals participated as volunteers in the trial study, and statistical analyses of the two assessment methods were performed. The results showed that the R Stroke is a disease in which cerebral blood circulation is impaired, and brain tissue function and structure are damaged due to the obstruction or rupture of cerebral blood vessels. It is the third leading cause of death and the second leading cause of disability worldwide, and its high morbidity, mortality, and disability rates bring economic burden and mental stress to society and families . AccordiAccording to statistics, about 70–85% of first-time stroke patients have limb movement dysfunction, which seriously affects quality of life and burdens both society and the family of the patient heavily. Timely and effective rehabilitation training can help the patient to recover certain motor functions .Rehabilitation assessment is an essential part of rehabilitation treatment, which is the judgment of patients’ functional status and potential ability and is also the process of data collection, quantification, analysis, and comparison with the usual standards for all aspects of patients. Based on the results of the rehabilitation assessment, the best rehabilitation treatment plan is formulated, and the rehabilitation assessment is conducted periodically at regular intervals to assess the effect of rehabilitation and improve the treatment plan.Currently, the clinical assessment of motor function in patients with stroke often uses scale assessment methods, such as the Fugl–Meyer assessment (FMA) scale ,4 and thTo solve those problems, more and more researchers have developed applications based on new technologies to assess rehabilitation results and combined these with machine learning or artificial intelligence algorithms to realize movement recognition ,10,11, mWang et al.  showed tThe above research is only a preliminary exploration of the quantitative assessment of the motor function of stroke, and a series of problems have not been further studied, such as how to realize the quantitative assessment of motor function, and what the mapping relationship is with the commonly used clinical rehabilitation assessment scale.According to the above problems, a wearable motion capture system based on nine-axis inertial sensor Inertial Measurement Units (IMUs) and Flex sensors was designed, which was worn on the affected limbs, and the patient’s rehabilitation movement data were collected and recorded in real-time wirelessly. Through multi-sensor information fusion, feature extraction, and a machine learning algorithm, a rehabilitation assessment model consistent with the clinical Fugl–Meyer Assessment (FMA) score was established, and the quantitative rehabilitation score was given. Finally, the accuracy and effectiveness of the intelligent rehabilitation assessment system were verified by comparing the consistency and time of the designed intelligent rehabilitation assessment system and the rehabilitation physician’s rehabilitation assessment of stroke through clinical trials.https://www.chictr.org.cn/showprojen.aspx?proj=171734 (accessed on 20 November 2022).For this clinical study, stroke patients with limb motor dysfunction 15–180 days after onset were selected as subjects for the study at Tangdu Hospital and Xi’an Gaoxin Hospital and were approved by the Ethics Committees of Tangdu Hospital and Xi’an Gaoxin Hospital. In addition, all patients participating in the study had signed an informed consent form. Clinical trial registration number: ChiCTR2200061310, website: Patient inclusion criteria included: (1) stroke patients diagnosed by CT or MRI within 90 days; (2) aged between 30–75 years, male or female; (3) limb motor dysfunction caused 15–180 days after stroke onset (recovery period); (4) recovered patients with a Brunnstrom upper or lower limb classification of stage II-VI with stable disease; (5) clear cognition and able to follow the study protocol; (6) subjects who understood the purpose of the study, showed sufficient compliance with the study protocol, and signed an informed consent form.The following patients were excluded: (1) those with obvious cognitive and conscious disorders that could not complete the Fugl–Meyer assessment; (2) those with other major limb injuries, such as fractures, severe arthritis, amputations, etc.; (3) formation of limb joint contracture; (4) patients with disabilities prescribed by law ; (5) cases complicated with severe primary diseases such as cardiovascular, liver, kidney, and hematopoietic systems, mentally ill patients, and other circumstances that the researcher considered inappropriate for participating in the experiment.The patients who met the test standards were scored by the intelligent assessment system and physicians, respectively, and the assessment scores and usage time of the system and physicians were recorded, respectively. In order to study the rehabilitation assessment without the participation of physicians, during the whole rehabilitation assessment process, a rehabilitation physician or therapist was by the patient’s side, only to ensure their safety and not to participate in the rehabilitation assessment process of the patients.The block diagram of the intelligent rehabilitation assessment system based on wearable devices and machine learning is shown in Wearable equipment mainly included a nine-axis inertial sensor module for upper- and lower-limb rehabilitation assessment, rehabilitation gloves for hand rehabilitation assessment, the Zigbee wireless receiver, etc. . The wearable devices were tested by third-party inspectors and met clinical research requirements in terms of signal quality, EMC, and safety. In terms of measurement accuracy, according to the third-party inspection report, the measurement accuracy of angle, acceleration, and angular velocity were all above 95%. Each IMU module contains nine-axis motion sensors, including a three-axis accelerometer, a three-axis angular velocity meter, and a three-axis magnetometer. In the rehabilitation assessment, four IMU modules were fixed on the upper arm, forearm, thigh, and calf. Rehabilitation gloves contained one IMU and five Flex sensors to monitor the movement of wrists and individual fingers. The glove was designed with a left hand and a right hand, in three sizes of large, medium, and small, for use by different patients.The battery of the wearable device is 400 mAh, and the power consumption is 20 mAh. The battery can be charged for 20 h continuously, which can meet the needs of rehabilitation institutions for more than two days and home patients for more than ten days. Each sensor is networked through the Zigbee2007 wireless communication protocol, which is very convenient for expansion and synchronous data acquisition. The sampling rate for each wearable device is 30 times per second, which was sufficient for data collection, analysis, and rehabilitation assessment.Considering that complete assessment using the FMA scale is a time-consuming task, in the study of intelligent rehabilitation assessment, manual selection or machine learning algorithms are often used to select the most relevant items to replace the complete assessment items . In the The 32 movements selected above were used to model the mapping relationship between the 32 movements and the clinical FMA score based on the wearable device and machine learning algorithms, thus giving assessment results consistent with the FMA score.The human movement sequence is a time series, and time series similarity comparison is a common time series analysis method. However, the Euclidean distance on the time axis cannot be used directly for the similarity measure of time series . TherefoThe DTW algorithm provided a method to measure the similarity of two unequal-length sequences by stretching and compressing the time axis to remove temporal distortions, thus finding an optimal matching path and minimalizing the cumulative matching distance between the two sequences. Using the distance of normalized paths to assess the similarity between sequences can overcome the problem of unmatched sequences due to different signal lengths . It was M and N are given:Two time series with lengths X and Y can be unequal, but the elements in each sequence must have the same dimension. For example, suppose sequence X is a reference sequence and sequence Y is a test sequence.The lengths of X and Y, X match the elements in sequence Y. The number of rows of the matching path W satisfies max  ≤ L < M + N − 1, and the set of all matching paths between sequence X and Y is defined as The matching path X and Y under the matching path W, that is, the sum of the distances of all matching elements:The matching distance In general, the square of the Euclidean distance is used to represent the distance between elements, which is X and Y under the satisfaction of the matching path constraint, that is:The DTW distance is the minimum matching distance between sequences W* corresponding to the minimum matching distance is the optimal matching path. We first calculated the cumulative matching distance matrix between two sequences in this study. Then, according to the monotonic continuity constraint in path matching, the cumulative distance of the matching path is:The matching path X and Y is:According to the definition of the DTW distance and the cumulative matching distance, the DTW distance of sequence The DTW distance is a non-negative number whose smaller value indicates greater similarity between two sequences, and vice versa.Xmax is the maximum value of the sample data, Xmin is the minimum value of the sample data, iX is the raw data, and X is the normalized data. For the rehabilitation movement assessment, Xmax is the DTW distance when there is no movement at all, and Xmin is the DTW distance of the rehabilitation physician’s standard rehabilitation movement, which is 0. Therefore, the rehabilitation movement score can be obtained by the following formula and standardized to 0–100 points:The DTW distance is converted to the range  according to the linearization method with the following normalization formula:Considering that the single movement score of the intelligent rehabilitation assessment system is 0–100, whereas the corresponding FMA score has only three values of 0, 1, and 2, direct correspondence will lead to a large error. Therefore, the scores of 32 movements were used as the input features of the machine learning algorithm to obtain the total system score, and this were compared with the rehabilitation physician’s FMA score to evaluate the designed intelligent rehabilitation assessment system.The machine learning rehabilitation assessment model building process is shown in The rehabilitation assessment system software based on wearable devices and machine learning is shown in In the two rehabilitation medical centers, 120 subjects were actually enrolled. Among these, 60 cases were enrolled in Tangdu Hospital and 60 in Xi’an Gaoxin Hospital. For all 120 stroke patients, motor function was assessed by the intelligent rehabilitation assessment system and the rehabilitation physician, respectively. The intelligent rehabilitation assessment system used the designed wearable device-based and machine learning model for scoring, and the rehabilitation physician assessed according to the FMA scale.2 value. The regression diagram was drawn with the rehabilitation physician’s score as the x-axis and the intelligent rehabilitation assessment system’s score as the y-axis; calculation of the intelligent rehabilitation assessment system score’s deviation, calculation of the mean and 95% confidence interval, drawing Bland-Altman diagram. In addition, the time required to record the score was counted, and a student’s t test was used to compare the difference between the time taken by the physician and the time taken by the intelligent rehabilitation assessment system.SAS software (version 9.4) was used for analysis in this study. After all rehabilitation assessments were completed, regression analysis was performed on the intelligent rehabilitation assessment system score and physician’s FMA score to calculate the intercept, slope, 95.0% CI of the slope, and RIn this study, 369 subjects were assessed for eligibility, and 246 subjects were excluded, of which 231 did not meet the inclusion criteria, 15 were unwilling to participate, and 3 had other reasons. Finally, 120 stroke patients were recruited as subjects, including 86 males and 34 females. All 120 patients completed the clinic trial according to their rehabilitation assessment. The comparison results of the intelligent rehabilitation assessment system score and the physician’s Fugl–Meyer score are shown in 2 was 0.9667, p < 0.001; the regression analysis of the upper-limb score of the intelligent rehabilitation assessment system and the physician’s FMA showed that the intercept of the fitting curve was 1.98, the slope was 0.94, the 95% CI of the slope was 0.90–0.98, R2 was 0.9577, p < 0.001; the regression analysis of the lower-limb score of the intelligent rehabilitation assessment system and the physician’s FMA showed that the intercept of the fitting curve was 0.22, the slope was 0.97, the 95.0% CI of the slope was 0.90–1.04, and the R2 was 0.8575, p < 0.001.The regression analysis results of the intelligent rehabilitation assessment system score and the physician’s FMA score are shown in p values of the total score, the upper-limb score, and the lower-limb score regression curve were less than 0.001, indicating that the system score was significantly correlated with the physician’s FMA score. In addition, the R2 of the total score was the highest, and the system had the best fitting effect on the assessment of the whole-body limb motor function of patients, and the R2 of the lower limbs was the lowest. The reason may be that the lower-limb items in the FMA project were relatively few (33 upper limbs and 17 lower limbs).In the regression analysis of the intelligent rehabilitation assessment system’s score and the physician’s FMA score, all the In this study, the deviation between the score of the intelligent rehabilitation assessment system and the score of the physician’s Fugl–Meyer was analyzed, as shown in In the analysis of the deviation and relative deviation of the assessment effect of the system, the absolute value of the deviation and relative deviation of the lower limb was the smallest. In other words, the dispersion degree of the lower-limb score was the smallest, and the deviation degree from the physician’s score was the smallest. The system showed good consistency with the physician’s FMA score, because the lower limb movement was close to daily life activities, and both the physician’s score and the system score could accurately assess the movement of patients. In addition, the absolute value of upper-limb movement deviation and relative deviation was the largest, because there were multiple movement degrees of freedom in upper-limb movement, and some serious patients had a certain compensation in performing tasks, although they had been required to remain upright. In summary, the relative deviation between the rehabilitation score of the system and the FMA score of the rehabilitation physician was less than or equal to 7.20%, which was within the acceptable range.In this study, the distribution of deviations and relative deviations of the intelligent rehabilitation assessment system’s score and the physician’s FMA score were analyzed, as shown in In the analysis of the distribution of score deviations and relative deviations of the intelligent rehabilitation assessment system, the number of patients with lower-limb score deviation and relative deviation outside the limits of agreement was the lowest, reaching more than 95% of the clinically acceptable range. In contrast, the number of patients with total score deviation and relative deviation outside the consistency limits was the highest, which was because the total score included both upper- and lower-limb items, and the total score deviation and relative deviation increased, resulting in a relatively high distribution of patients outside the limits. However, the overall number of patients within the consistency limits reached more than 90%, indicating that the intelligent rehabilitation assessment system can be effectively used for clinical and remote rehabilitation assessment.t-test. The statistical results showed that the time for the intelligent rehabilitation assessment system’s score was 13.82 ± 6.61 (min), and the 95% CI was 12.62–15.01, and the time for the physician’s FMA score was 21.26 ± 7.79 (min), with the 95% CI being 19.85–22.67 (min). Considering that the preparation time required for using the intelligent rehabilitation assessment system was about 1 min, including putting on the devices and opening software, 1 min preparation time was included in the results and reports. The difference between the two groups was statistically significant (p < 0.05), indicating that the intelligent rehabilitation assessment system could significantly shorten the rehabilitation assessment time of patients.The time required for the intelligent rehabilitation assessment system’s score and the physician’s FMA score were compared and analyzed by In addition, in terms of safety, no adverse events related to the intelligent rehabilitation assessment system occurred throughout the clinical study. Therefore, the designed intelligent rehabilitation assessment system based on wearable devices and machine learning had good safety when used for rehabilitation assessments of patients with limb motor dysfunction such as stroke.In this study, we designed an intelligent rehabilitation assessment system to accurately and quickly assess the rehabilitation of patients with limb motor dysfunction such as stroke. The system is based on multiple wireless wearable devices of a nine-axis IMU and a Flex sensor, and the rehabilitation movement data of patients were collected by Zigb ee wireless networking technology. The DTW algorithm and machine learning algorithm were adopted to establish an intelligent rehabilitation assessment model consistent with the clinical FMA score by completing 32 movement tasks. The results of 120 clinical trials showed that the designed intelligent rehabilitation assessment system based on wearable devices and machine learning could accurately, objectively, and rapidly realize the rehabilitation assessment and effectively solve the defects of excessive time consumption, strong subjectivity, and coarse classification of the clinical scale rehabilitation assessment.Compared with most rehabilitation assessment techniques, such as an upper-limb motor function assessment system based on a desktop rehabilitation robot , a wristThis study adopted the integrated learning model based on DTW and SVR in assessment algorithms. The DTW algorithm calculated the similarity between each rehabilitation assessment movement sequence and the corresponding standard data template sequence. Compared with the recognition algorithm based on probability statistics, the recognition algorithm based on the DTW model had a simple structure and a simple training process. It could assess the quality of rehabilitation assessment tasks from deep characteristics. The SVR machine learning algorithm was robust to outliers, with excellent generalization ability and high prediction accuracy. The experimental results showed that the designed intelligent rehabilitation assessment model based on ensemble learning had a high consistency with the FMA in limb motor function rehabilitation assessment. In this study, the subsequent signal processing was very sensitive to the initial position of wearable sensors, and incorrect wear may lead to a deviation in rehabilitation assessment results. For more serious patients, body compensation may affect the accuracy of the assessment, and future research will eliminate the deviation caused by movement compensation by adding body sensors. In addition, we will study more advanced artificial intelligence algorithms, such as deep learning algorithms, to further improve the accuracy and refinement of the intelligent rehabilitation assessment system.The driving force of this study was to establish an intelligent rehabilitation assessment model and system based on wearable devices and machine learning for patients with limb motor dysfunction such as stroke. The system can be used as a clinical rehabilitation assessment and remote home rehabilitation assessment tool, which can effectively solve the shortcomings of the current clinical assessment scale, such as excessive time consuming, strong subjectivity, and coarse classification. In the future, the system will realize more exercise scales and rehabilitation assessment projects and carry out experiments and clinical research on a wider range of people.In this study, we found that the designed intelligence rehabilitation assessment system based on wearable devices and machine learning was highly consistent with physician’s FMA scores in terms of the rehabilitation assessment. It took significantly less time than physicians and had good safety parameters. Therefore, the intelligent rehabilitation assessment system could effectively help physicians and patients conduct an accurate, objective, and rapid rehabilitation assessment to save valuable rehabilitation medical resources. In the future, it will be approved by the CFDA and used in clinical rehabilitation assessment, such in rehabilitation hospitals and other rehabilitation institutions, as well as in remote home environments.


In this study, a Back Propagation (BP) neural network algorithm based on Genetic Algorithm (GA) optimization is proposed to plan and optimize the trajectory of a redundant robotic arm for the upper limb rehabilitation of patients. The feasibility of the trajectory was verified by numerical simulations. First, the collected dataset was used to train the BP neural network optimized by the GA. Subsequently, the critical points designated by the rehabilitation physician for the upper limb rehabilitation were used as interpolation points for cubic B−spline interpolation to plan the motion trajectory. The GA optimized the planned trajectory with the goal of time minimization, and the feasibility of the optimized trajectory was analyzed with MATLAB simulations. The planned trajectory was smooth and continuous. There was no abrupt change in location or speed. Finally, simulations revealed that the optimized trajectory reduced the motion time and increased the motion speed between two adjacent critical points which improved the rehabilitation effect and can be applied to patients with different needs, which has high application value. Multi−Degree−Of−Freedom (MDOF) robotic arms and automation systems have been widely used in the field of welding and the tools used for welding were often installed on the end of the robotic arm, to replace the worker to complete some complex welding process. The welding action of the robotic arm and the corresponding various parameters have a great impact on the welding effect of the robotic arm. Kang et al.  proposedThere are many factors that can cause damage to the upper limb. For example, stroke , prolongAll of the above causes can lead to loss of partial function of the upper limb in normal people. This will limit the movement of the upper limb to some extent, and in severe cases, the upper limb will not even be able to move on its own. As a result, there is a need to develop upper limb rehabilitation robotic arms to assist physicians in the rehabilitation of patients’ upper limb. Richardson et al.  developeIn the present, the application of machine learning and deep learning has become more and more widespread. For engineering applications, a hybrid gradient boosting method combining standard statistical and machine learning models was performed to predict engineering failures , and fatHowever, solving the inverse kinematic solution of a MDOF robotic arm by the conventional methods was complicated but not highly accurate. Therefore, the machine learning algorithms were used to solve the inverse kinematic solution of the robotic arm. Tejomurtula et al.  proposedWhen the robotic arm is in motion, the robotic arm must move along a predetermined trajectory and ensure smoothness and continuity of the motion at the robot’s end. Therefore, it is necessary to ensure that the location or speed of the joints did not change abruptly, which eliminated the large errors in the robotic arm and the patients were prevented from receiving additional injuries. That is why we need to perform trajectory planning . Tian etTo ensure the accuracy and smoothness of the rehabilitation process of the upper limb, the eight critical points in the rehabilitation process were collected from rehabilitation physicians to plan the motion trajectory of the end of the 7DOF redundant upper limb rehabilitation of the robotic arm. To improve the accuracy of the inverse kinematic solution of the robotic arm, as well as the trajectory planning, an algorithm of Back Propagation (BP) neural network optimized by GA was used to calculate the inverse kinematic solution of the robotic arm. The results were used for trajectory planning with the cubic B−spline interpolation method and the trajectory was optimized on the basis of GA for time minimization, which increased the speed of motion between two adjacent critical points and improved the effectiveness of rehabilitation.The eight critical points were selected by the rehabilitator for the patient’s rehabilitation process in the same two−dimensional plane. The motion trajectory consisting of these eight critical points did not guarantee the continuity of speed at the end of the rehabilitation robotic arm, so the corresponding trajectory planning was required to ensure its rehabilitation effect.In this section, the iterative algorithm of the GA optimized BP neural network was used to find the inverse kinematic solution of the robotic arm. We have fabricated and built a redundant robotic arm for upper limb rehabilitation and have performed some test works on it. The BP neural network is a multilayer feedforward neural network trained according to a back−transmission error algorithm, and The network contains two stages in the calculation, the phase of forwardly transmission of the signal and backwardly transmission of the error. Before training the neural network, the target value of the final output result should be set first. In the process of training the neural network, the size of the weights and thresholds are constantly revised, and the actual value of the output is constantly close to the preset target value, and the optimal neural network was finally obtained.j , the nodes in the hidden layer of the network are assigned numbers n , and the nodes in the output layer of the network are given numbers k . The signal passes the data through the input layer to the hidden layer. The input nN of the nth node of the input layer is given by Equation (1):nb is the threshold of the nth node in the hidden layer, njw is the weight of the jth node in the input layer to the nth node in the hidden layer, and jx is the input value of the jth node in the input layer. Substituting Equation (1) into the activation function fh in the hidden layer, the output of the nth node of the hidden layer is given as follows:The first step is the web forward transmission. Let the nodes in the input layer of the network be numbered kth node in the output layer is obtained as follows:ka is the threshold value of the kth node of the output layer, knw is the weight of the nth node in the input layer to the kth node in the output layer, and ny is the output value of the nth node of the hidden layer. Finally, the output equation for the kth node of the output layer was obtained by substituting the Equation (3) into the activation function fo of the output layer:Using the output of the hidden layer as the input of the output layer, the input of the pE, of a single sample datum p was calculated by the following equation:kT is the expected value of the kth node. Using the gradient descent method to find the weight and threshold correction amount of the output layer and the hidden layer, respectively. The specific equations are shown in Equations (6)–(9), where ∆knw and ∆njw represent the weight correction of the output layer and the hidden layer, respectively, ∆ka and ∆nb denote the threshold correction of the output layer and the hidden layer, respectively.η is the learning rate of the network, and P is the number of training samples.The second step is the network back transmission. When the forward transmission of the signal was completed, the network will calculate the output error of the neurons in each layer in reverse from the output layer. Then the error gradient descent method was used to adjust the weights and thresholds of each layer. Firstly, the Mean Square Error (MSE) GA is to generate a new population by continuously searching to select the optimal solution, the specific process is similar to the process of human genetic evolution. The specific process of GA is the selection and initialization of the population, fitness function, selection, crossover, mutation, getting new individuals, and judging whether the new individuals meet the required conditions. If they do not meet the conditions, then return to the selection operator to continue the search for the optimal solution. If they meet the conditions, then the optimal solution is the output. Individuals are often encoded by binary encoding, multiparameter cascade encoding, and floating−point encoding. The encoding methods have their characteristics and were adapted to different practical application scenarios. During the operation process, the individuals were selected and crossed between two operators. The individuals themselves are affected by the variation operators.In the present study, the process of the BP neural network algorithm based on GA optimization has four steps. I was set to 12, the number of output layer neurons O was set to 7, and the number of hidden layer neurons H was calculated according to the empirical Equation (10). The number of neurons in the hidden layer was calculated to be 14 and the Pureline function . Set the corresponding time interval according to the angle difference between adjacent points, specific time intervals were: t =  and it took a total of 25 s to complete the rehabilitation trajectory. As shown in Based on the trajectory planned with the cubic B−spline interpolation method under the Cartesian space above, the kinematic analysis was performed for each joint under the joint space to verify the feasibility of the trajectory. Take joint #1 as an example, its angles at each of the pre−given critical points during the motion were: In addition to interpolation method of the cubic B−spline curve, we also used the cubic polynomial interpolation, the five−polynomial interpolation, and the seven−polynomial interpolation method to plan the motion trajectory of the robotic arm. In addition, we performed trajectory planning as well as MATLAB simulation for each method following the procedure of cubic B−spline interpolation.First is the cubic polynomial interpolation method, the simulation results are shown in The second is the five−polynomial interpolation method, the simulation results are shown in Finally, the simulation results of the seven−polynomial interpolation are shown in The simulation results show that the location and speed of the trajectories planned by these three methods were relatively stable and ensured the smoothness of the motion of the robotic arm.it. The time of each part of the trajectory was optimized separately to obtain the final result. The objective function was set as follows:T is the total operating time of the robotic arm. For time−targeted optimization, the motion time could not be reduced indefinitely and constraints needed to be added. Kinematic constraints include speed, acceleration, and jerk (derivative of acceleration).Since different patients have different needs for the upper limb rehabilitation, we need to expand the applicable range of the redundant robotic arm for upper limb rehabilitation. We can change certain parameters in the rehabilitation process using trajectory optimization to achieve the purpose of expanding the applicable range. For example, we could change the motion time of the end of the robotic arm between the critical points and thus change the speed of the end movement. We could also modify the location of the critical points and change the shape of the rehabilitation trajectory to meet the needs of different patients for rehabilitation. The curve after trajectory planning consists of multiple cubic B−spline curves that have been connected. Taking the total running time of the robot arm as the optimization objective, the running time of each segment of the curve was set to itto it+1, there was an expression as follows:ti, and it to it+1. It was difficult to solve the equation for the absolute maximum value, and the golden mean method was used to solve the equation for the maximum value of First is the speed constraint. The joint angular speed equation was derived from the joint equation to the first−order derivative of time, the maximum angular speed of the robotic arm in motion might be at any point on the interval when the robotic arm motion time was at it, and it to it+1. The acceleration constraint form of the robotic arm was as follows:The second is the acceleration constraint. The joint angular acceleration equation was obtained from the joint angle equation by taking the second−order derivative of time and solving for the maximum value in the same way as the speed constraint. The maximum joint angular acceleration equation was solved as follows:it, and itto it+1.The third is the jerk constraint. The joint angular jerk equation was solved for the maximum joint angular acceleration equation by taking the third−order derivative of the joint angle equation for a time and was given as follows:m joint angle values and the motion time between each adjacent two points was it, a GA was used to find the optimal solution for it in the model. In this study, the time interval matrix between two adjacent points was set as t = [8.0 s, 2.5 s, 2.5 s, 4.0 s, 4.0 s, 2.0 s, 2.0 s, and 50 groups of individuals were randomly generated at each given time. For each random individual, the fitness size f was assigned by the following equation.We assumed that the model had Vmax in the algorithm was set to 15°/s, the maximum joint angular acceleration Amax was set to 20°/s2, and the maximum joint angular jerk Imax was set to 100°/s3. Under the condition of satisfying the kinematic constraints, the total motion time of the robotic arm before the optimization was 25 s, which was reduced to 20.44 s after the time optimization. The time comparison before and after track optimization is shown in The final results were calculated by the iterating depending on the iteration rules of the GA. According to the application scenario of the upper limb rehabilitation, the maximum joint angular speed We wrote the above algorithm based on MATLAB. The kinematic analysis was performed on the curve obtained by cubic B−spline interpolation for joint #1 of the robotic arm and the trajectory optimized by the GA. As shown in We have read the literature to the extent of our ability and describe the structure and movement of the upper limb as follows .In terms of the upper limb structure, the upper limb consists of the shoulder girdle and the arm. The shoulder girdle consists of the clavicle and the scapula. The shoulder joint, which connects the arm to the scapula, is a multiaxial synovial ball and socket joint. The humeral head was shaped as a ball which coincides with the spherical surface of the glenoid cavity of the scapula, and the surfaces of the humeral head and cavity were covered with articular cartilage. In addition, the joint capsule was strengthened by muscles, tendons, and ligaments such as the glenohumeral, coracohumeral, and coracoacromial ligaments.The arm was built of the humerus, ulna, radius bone, 8 carpal bones of the wrist, 5 metacarpal bones, and 14 phalanges of the fingers. The elbow consists of the humeroulnar, humeroradial, and proximal radioulnar joints, which were surrounded by a common joint capsule. The frontal stability was mainly ensured by the ulnar collateral ligament and the radial collateral ligament. Patients with impaired upper limb function usually suffer from wear and tear of the ligaments or joints of the upper limb. Therefore, the upper limb rehabilitation robotic arm in this study treated the patient’s upper limb by helping the patient to perform upper limb movements through end−traction method of the robotic arm.In terms of the upper limb movement, the upper limb skeletal movement was produced by the 43 muscles being connected to structures mentioned above. The shoulder joint allows for abduction/adduction, flexion/extension, and internal/external rotation. At the elbow joint, there is elbow flexion/extension and forearm supination/pronation. In the wrist, there is flexion/extension and radial/ulnar deviation. However, because of the presence of the upper limb joints, there is a limited range of motion in each of these joints. Therefore, the upper limb rehabilitation of the robotic arm in this study planned the rehabilitation trajectory by pre−given critical points, thus avoiding the rehabilitation trajectory to reach the position where the patient’s upper limb cannot reach, which can ensure the safety of the patient during the rehabilitation process.This redundant robotic arm for upper limb rehabilitation is still in the stage of theoretical modeling, numerical simulation, development, and testing of the prototype. Therefore, for safety reasons, it has not been commissioned and applied to patients yet. However, to obtain a better perceptual feedback on the human body, we had conducted preliminary tests with the manufactured robotic arm on several patients with healthy upper limb. These patients, with their sensitive upper limb perception, were able to provide better feedback on the treatment effect of the upper limb rehabilitation of the robotic arm, the comfort level during treatment (no sudden changes in force) and the continuity of the rehabilitation trajectory (continuous and smooth location and speed of the robotic arm). The rehabilitation movement of the end traction was performed according to the planned trajectory in this study, as shown in The rehabilitation trajectory passed the eight critical points mentioned in this study, and the positions and postures of the robotic arm and the treated person during the rehabilitation process corresponded to a to h in We will cooperate with the TCM rehabilitators from Beijing Red Medical Star Intelligent Technology Development Company Limited in the future and use the robotic arm in the actual rehabilitation process. We will discuss the effectiveness of the treatment and the practical application in the next articles.In this study, we proposed a BP neural network algorithm optimized by GA. Compared with other algorithms, the computational results of this algorithm have better accuracy. We used cubic B−spline interpolation method for trajectory planning and performed numerical simulation with MATLAB. The results show that the location and speed profiles of the planned curve were relatively smooth and could meet the needs of patient rehabilitation. To further improve the rehabilitation effect, we optimized the planned trajectory based on GA with the goal of time minimization, and the numerical simulation results in MATLAB showed that the optimized curve reduced the motion time, thus increasing the motion speed of the end of the robotic arm and improving the rehabilitation effect. For safety reasons, we have not yet applied this upper limb rehabilitation of the robotic arm to patient rehabilitation treatment. We had tested it on several patients with normal upper limb and the results show that the force and speed applied to the upper limbs during the rehabilitation process was relatively stable and could achieve the effect of assisting in guiding the rehabilitation of the upper limbs. In the future, we will cooperate with TCM rehabilitators and use the upper limb rehabilitation of the robotic arm in the rehabilitation process of patients. We will discuss the treatment effect and practical application of the upper limb rehabilitation arm in our future work.


Higher rates of mortality following an in-hospital cardiac arrest (IHCA) has been shown during nights and weekends, changes in staff density and composition has been suggested as a possible explanation. Changes in hospital staffing patterns are also common during holiday periods.To investigate whether holiday periods are associated with decreased survival following an IHCA.All patients ≥18 years who experienced an IHCA at Karolinska University Hospital between 2006 and 2019 were included. Patients were identified via and data was collected from the Swedish Registry for Cardiopulmonary Resuscitation. Holiday was defined as two periods, a seven-week summer period and an approximately two-week Christmas period. The primary outcome was return of spontaneous circulation (ROSC), secondary survival to hospital discharge. Logistic regression was performed to calculate odds ratio (OR) with 95% confidence intervals (CI), adjustment was done for known confounders.Out of 1936 registered cases, 264 (14%) occurred during holiday periods. Patient and event characteristics were similar on holidays compared to non-holidays. Both ratio for ROSC  and survival (25% and 32% respectively) was poorer during holiday periods Adjusted OR for ROSC and survival was poorer during holiday periods compared non-holiday periods .Outcomes after IHCA was poorer during holiday periods compared to non-holiday periods even if patient and event characteristics was similar. Further research is needed to better understand to what degree staffing patterns and other factors contribute to the observed difference. Development of resuscitative management systems have improved survival after cardiac arrest over the last decades, however survival is still low.A potentially similar situation, with regards to changes in hospital staffing patterns, are holiday periods. Within the Swedish healthcare system, the summer holiday period is characterized by staff shortages, overcrowded hospitals, and many junior doctors with lesser experience, annually generating extensive media coverage.In Sweden, holiday periods are by tradition relatively distinct in time. Swedish employee holiday leave benefits are regulated through the “Annual Leave Act” dated back to 1938 declaring that every employee shall be entitled to twenty-five days of annual leave in every annual leave year. Furthermore, unless otherwise agreed, annual leave dates shall be scheduled so that the employees have at least four weeks’ annual leave during the period June to August. During the following decades, a period during which a large proportion of the workforce were industrial workers, it became customary for industries to close for the month of July hence this period became known as the “industrial holidays”. Although large companies and certainly hospitals no longer adhere to such practices, it is still highly customary for Swedish employees to schedule their summer holidays sometime from the end of June to early August. Moreover, Christmas time including New Year’s Eve is a major holiday season. Therefore, Sweden offers an ideal setting for studying the effects of holiday periods on survival following an IHCA.To the best of the authors knowledge, it is not currently known whether there are differences in survival following an IHCA during holiday periods compared to the rest of the year. Therefore, the aim of this study was to investigate whether inpatient survival following a cardiac arrest is different during summer and Christmas vacation periods compared to the rest of the year.This hospital-based cohort study used the Swedish Registry for Cardiopulmonary Resuscitation (SRCR)Karolinska University Hospital is located in Stockholm, which is home to approximately 2,000,000 people. Karolinska is one of five large hospitals and has two equally sized sites 30 km apart: Solna and Huddinge. The Solna site is a level one trauma unit, has neuro and thoracic surgery units and provides 24/7 angiography for ST-elevation myocardial infarctions. The Huddinge site includes a geriatric ward and relatively fewer intensive care unit (ICU) beds. Karolinska has about 1 300 beds, 108 000 admissions and 1.8 million patient visits yearly.“a hospitalised patient who is unresponsive with apnoea  where CPR and/or defibrillation have been initiated.” The inclusion criteria were all adult (18 years and older) patients who had an IHCA at Karolinska University Hospital. No patients or location of the IHCA were excluded. In the case of multiple IHCAs, only the first event per year was included.We defined IHCA according to the SRCRRegistrations into the SRCR are completed in two steps online. In step one, data concerning date and time of event, age and sex of patient, time intervals from discovery to initiation of treatments, whether the event was witnessed, whether patient was monitored via electrocardiography (ECG) at time of event, place of cardiac arrest within hospital, registered first rhythm, types of treatments given, and whether the patient had return of spontaneous circulation (ROSC), is registered. In step two, data concerning co-morbidities, whether the patient was alive at hospital discharge and cerebral functioning, is registered using information from medical files.The study exposure was the timing of an IHCA within or outside holiday periods. Two holiday periods were considered. One including week 26–32 representing a 7-week summer holiday period. And one including December 20 to January 6 representing a Christmas holiday period. The choice of week numbers (according to the ISO8601 system) to define the summer holiday period is due to the fact that this system of time is widely spread in Sweden in general and used to schedule summer holidays in particular. Defining summer holidays in terms of week numbers offered higher validity compared to specific dates as these two do not correspond between different years. Defining the Christmas holiday period in terms of specific dates was arbitrary to approximate the period during which schools are closed as a proxy for potential changes in hospital staffing patterns. Data on the exposure were retrieved from the SRCR as the registered date of the event.The main outcome was ROSC, while survival to hospital discharge was the secondary outcome. The main outcome ROSC was defined as whether the patient had an own circulation with or without support when the acute resuscitative treatment ended. The secondary outcome was survival to hospital discharge, defined as whether the patient was alive when discharged from hospital. The reasoning behind these choices were that ROSC better reflects the circumstances immediately associated with the event, whereas survival to hospital discharge introduces more unknown factors as to the exact conditions surrounding an eventual death.All patients surviving their IHCA were asked six months afterwards for informed consent and agreed to participate in the SRCR and on-going studies based on it. The Regional Ethical Review Board in Stockholm, Sweden approved the study, Dnr 2013/1959-31.Summary descriptive statistics were calculated for all patient and event variables.Logistic regression modelling was performed to test for associations between exposure and outcomes and adjust for a priori decided potential confounders based on previous studiesp-value 0.032, Of the 1936 events included, 1672 (86%) occurred during the non-holiday period and 264 (14%) occurred during the holiday period . Fewer IIn all, fewer IHCA achieved ROSC during holidays compared to non-holidays  orders between different time periods and unfortanely we lack such data. However, if staff density and/or experience is lower or individual workloads is higher during holidays there is a possibility that such differ.In contrast to previous studies on night versus daytime,There were no major observed differences in the prevalence of pre-existing comorbidities of the patients between non-holiday and holiday periods. However, we did not have access to burden of disease. It is for example possible that patients within similar age groups with similar comorbidities that are less symptomatic and in better general physical health are more likely to be travelling or residing in holiday houses outside of the Stockholm area during the holiday period, and therefore not included in the current study.Compared to the proportion of all IHCA occurring during nights and weekends, the proportion of IHCA occurring during the holiday periods is relatively smaller. Therefore, any effort to identify amenable factors associated with these periods would do best in focusing on nights and weekends. Limitations include the small number of cases during holiday periods, lack of information on post-ROSC care including intensive care and limitations in treatments, environmental factors such as temperature differences, which coincide with the defined holiday period and could affect outcomes. At least for the summer holiday period previous studies have found increased mortality rates during heatwaves.Since this was the first study on this topic, we firstly wanted to assess if there was an association, future studies need to assess the pathway better, such as change in patient mix and or staffing which might demand access to detailed clinical rotation schedules for the whole hospital.In conclusion, this study found a decreased likelihood of survival following an IHCA during holiday periods compared to the rest of the year. Further research is needed to better understand to what degree staffing patterns and other factors contribute to the observed difference. These findings, if verified, might indicate a need for an education or training of holiday staff.Canice Drea Persson: Conceptualization, Methodology, Data curation, Writing – original draft. Therese Djärv: Conceptualization, Writing – review & editing, Supervision. Maria Ygland Rödström: Conceptualization, Writing – review & editing, Supervision.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


However, the underlying mechanisms modifying integrin β1 protein and activity in mediating monocyte/macrophage adhesion to endothelium remain poorly understood. In this study, we demonstrated that integrin β1 protein underwent S-nitrosylation in response to nitrosative stress in macrophages. To examine the effect of elevated levels of FFA on the modulation of integrin β1 expression, we treated the macrophages with a combination of oleic acid and palmitic acid (2:1) and found that FFA activated inducible nitric oxide synthase/nitric oxide and increased the integrin β1 protein level without altering the mRNA level. FFA promoted integrin β1 S-nitrosylation via inducible nitric oxide synthase/nitric oxide and prevented its degradation by decreasing binding to E3 ubiquitin ligase c-Cbl. Furthermore, we found that increased integrin α4β1 heterodimerization resulted in monocyte/macrophage adhesion to endothelium. In conclusion, these results provided novel evidence that FFA-stimulated N--O stabilizes integrin β1via S-nitrosylation, favoring integrin α4β1 ligation to promote vascular inflammation.Hyperlipidemia characterized by high blood levels of free fatty acids (FFAs) is important for the progression of inflammatory cardiovascular diseases. Integrin β Hyperlipidemia manifested by high blood levels of free fatty acids (FFAs) is critical for the progression of multiple metabolic diseases as well as cardiovascular complications. FFA levels increase significantly in obesity, type 2 diabetes, and atherosclerosis , 2, 3. IInducible nitric oxide synthase (iNOS) is the type of NOS expressed in monocyte/macrophage and plays a critical role in physiopathological processes ranging from antimicrobial and wound healing to proinflammatory diseases. Expression of iNOS is negligible in resting cells and induced upon infection or metabolic stress, such as elevated levels of glucose and lipid . Accumul1 serves a critical function in cell adhesion and occurs on numerous cells of the immune system  and protein  4 h after treatment. Then, the effect of FFA on NO production was examined in macrophages. As shown in Fig. 1C, treatment with FFA significantly augmented NO quantity in the conditioned media of macrophages. This result was further verified using an electron paramagnetic resonance (EPR) spectroscopy . These data demonstrated that iNOS expression was upregulated by FFA in macrophages.Expression of iNOS is upregulated upon metabolic perturbation and considered as a marker of the classical and proinflammatory activation of macrophages . To inveFig. 11. Raw264.7 cells were transfected with scrambled siRNA or two pairs of iNOS-specific siRNA. Both iNOS siRNAs markedly reduced iNOS protein compared to those transfected with scrambled siRNA, indicating their specificity. iNOS silencing reduced FFA-increased integrin β1 protein level compared to that of macrophages transfected with scrambled siRNA (A). However, the integrin β1 mRNA level was not altered (B). Similar to the results of iNOS siRNA silencing, 1400W, a selective iNOS inhibitor, ablated FFA-induced integrin β1 expression (C). These data suggested that iNOS participated in FFA-increased integrin β1 protein level in macrophages.iNOS plays a critical role in inflammatory progression, promoting us to evaluate its precise regulation on integrin βed siRNA A. Howeve altered B. Similapression C. These 1 expression. Raw264.7 cells were exposed to 2-Phenyl-4,4,5,5-tetramethylimidazoline-1-oxyl 3-oxide (PTIO), a known NO scavenger. Treatment of macrophages with PTIO (60 μM and 120 μM) for 24 h abolished FFA-induced integrin β1 protein level (D) without affecting iNOS expression (1 (E). These data suggested that FFA-induced NO augmented integrin β1 protein level.Next, we investigated the effect of iNOS-derived NO on integrin βin level D withoutpression . Exposurssion  or organic salt sodium nitroprusside (SNP), two NO donors, augmented integrin β1 protein level, which was abolished by a reductant N-acetyl-L-cysteinamide (NAC). Taken together, these results suggested that NO might modulate integrin β1 protein level via a redox-sensitive reaction, promoting us to examine whether integrin β1 could be modified by S- nitrosylation. Raw264.7 cells were exposed to GSNO or SNP for 4 h and then subjected to irreversible biotinylation procedure. Immunoblotting showed marked S-nitrosylation of integrin β1, which was abrogated by NAC treatment . These data suggested that integrin β1 could be S-nitrosylated. Experiments conducted in murine bone marrow–derived macrophages also confirmed that NO might modulate integrin β1 protein level  via a S-nitrosylation-dependent mechanism .Since both iNOS silencing and NO scavenging lowered integrin β1 protein contains cysteine-rich tandem repeats which are crucial for its activation , highly conserved among species, were predicted as potential S-nitrosylated sites by using three different web-based tools . To further confirm the function residue that undergo S-nitrosylation, we replaced the three corresponding cysteines in integrin β1 with alanine  by using site-directed mutagenesis. The expression plasmids of the wildtype or mutated integrin β1 was transfected into HEK 293 cells and then treated with GSNO. We found that integrin β1 mutation at C555A, but not C536A or C568A abolished GNSO-induced S-nitrosylation (F), suggesting that S-nitrosylation modified integrin β1 predominantly at Cys555.Integrin βtivation . As showsylation F, sugges1 protein level without affecting its mRNA level (B), we studied the effect of NO on integrin β1 protein stability. HEK 293 cells were transfected with plasmid expressing integrin β1. Then the cells were exposed to GSNO after pretreated with cycloheximide (CHX), an inhibitor of de novo protein synthesis. As shown in Figure 4A, GSNO increased the half-life of integrin β1 protein.Because iNOS inhibitor attenuated the FFA-stimulated increase in integrin βNA level B, we stu1 degradation was mediated by E3 ubiquitin ligase c-Cbl (1 was largely diminished by GSNO treatment (B), which is consistent with the result of ubiquitination. As depicted in Figure 4C, GSNO decreased the detection of ubiquitinated integrin β1, suggesting that NO maintained integrin β1 stability by decreasing c-Cbl-dependent ubiquitination.It is reported that integrin βse c-Cbl . Co-immureatment B, which 1 protein level was dependent on iNOS-derived NO, we reasoned whether integrin β1 would be modified by S-nitrosylation. For this purpose, Raw264.7 and bone marrow–derived macrophages were exposed to FFA. Immunoblotting showed significant S-nitrosylation of integrin β1, which was abolished by NAC pretreatment (A). Similar with the result of GSNO, macrophages with FFA treatment after 24 h showed an increasement of integrin β1 protein level, which was ablated by NAC (B). These data suggested FFA could increase integrin β1 protein level via S-nitrosylation-dependent mechanism.As increasement of integrin βreatment A. Similad by NAC B. These 1 reduction via ubiquitin-proteasome degradation. As shown in Figure 5C, MG132 ablated integrin β1 reduction caused by iNOS silencing. Similar with the results of GSNO, FFA treatment significantly inhibited integrin β1 degradation in the presence of CHX (D). Co-immunoprecipitation indicated that the binding of E3 ubiquitin ligase c-Cbl to integrin β1 was significantly decreased by FFA treatment (E), which was consistent that FFA decreased the ubiquitination level of integrin β1 (F). To confirm the effect of c-Cbl on integrin β1 degradation, Raw264.7 cells were primarily transfected with scrambled or c-Cbl siRNA, followed with FFA treatment. As depicted in Figure 5G, c-Cbl silencing further stabilized integrin β1 protein in FFA-treated macrophages. These data indicated that FFA augmented integrin β1 stability by reducing c-Cbl mediated ubiquitination.We next examined whether iNOS silencing leads to integrin βe of CHX D. Co-immreatment E, which egrin β1 F. To con4 binds to integrin β1 in monocyte/macrophages . Protein level of integrin β1 was induced by FFA in a concentration- (C) and time-dependent manner (D). In contrast, the protein level of integrin α4 remained unchanged (C and D). Immunoprecipitation analysis was performed to examine the effect of FFA on integrin a4β1 dimerization. Their ligation was significantly enhanced in macrophages by FFA (E). Immunofluorescence staining on Raw274.7 cells confirmed that FFA upregulated integrin β1 protein level. Integrin α4 and β1 were localized on the surface of FFA-treated cells (F), suggesting that the formation of integrin α4β1 heterodimer on cellular membrane was increased by FFA in macrophages.Given that integrins are obligate heterodimers and integrin αrophages , we exampression , A and Btration- C and timt manner D. In connchanged , C and Ds by FFA E. Immunoed cells F, sugges1 in macrophages, FFA-treated Raw264.7 cells were primarily transfected with integrin β1 or iNOS siRNA. Either integrin β1 (A) or iNOS (B) siRNA markedly reduced the number of adhering macrophages to endothelial cells compared to those transfected with scrambled siRNA. Similar to the effect of iNOS siRNA, selective iNOS inhibitor, 1400W, abolished the stimulatory effect of FFA on monocyte/macrophage-endothelial interaction (C). In parallel, NAC pretreatment decreased FFA-induced monocyte/macrophage adhesion (D). These results indicated that integrin β1 participated in FFA-induced monocyte/macrophage to endothelium via iNOS/NO-dependent S-nitrosylation.To investigate the function and related mechanism of integrin βegrin β1 A or iNOS or iNOS B siRNA meraction C. In paradhesion D. These 1, which is a key integrin subunit with essential roles in leukocyte extravascular migration and inflammatory responses. Here we demonstrated for the first time that elevated FFA triggered S-nitrosylation of integrin β1 in monocytes/macrophages. The decreased ubiquitination and proteasome-dependent degradation of integrin β1 enhanced monocyte–endothelial adhesion. Furthermore, these results provide mechanistic evidence that S-nitrosylation regulated the formation of a specific integrin heterodimer to mediate monocyte/macrophage adhesion to vascular endothelium.In this study, we revealed a novel modification of integrin β1 upregulation is a major mechanism for monocyte/macrophage adhesion, whereas its downregulation is crucial for cell migration and metastasis  were emulsified in PBS containing 1% bovine serum albumin.Primary antibodies against integrin βMurine monocytic cell line Raw264.7 and HEK 293 cells were obtained from ATCC and maintained in Dulbecco’s modified Eagle’s medium supplemented with 10% fetal bovine serum (FBS). Human umbilical vein endothelial cells were cultured in M199 medium with basic fibroblast growth factor and 20% FBS.Bone marrow precursor cells were harvested from femurs of C57BL/6 mice, washed, and differentiated into macrophages in RPMI 1640 medium supplemented with 10% FBS and 20 ng/ml granulocyte-macrophage colony-stimulating factor. Cells were used 7 days after differentiation. The experiments were approved by the institutional ethics review board of Xi’an Jiaotong University  and performed in accordance with the National Institutes of Health guidelines.To detect macrophage-endothelial adhesion, Raw264.7 cells were seeded on confluent human umbilical vein endothelial cells and incubated for 1 h. After washing with PBS for 3 times, the attached Raw264.7 cells were pictured.1 was subcloned into pcDNA3.1 plasmid. Mutagenesis of Cys536, Cys555, or Cys568 to alanine were performed from by using QuickChange@ Lightning Site-Directed Mutagenesis Kit (Agilent Technologies) and confirmed by sequencing. Plasmids were transfected using Lipofectamine 2000. Experiments using these cells were executed at 48 h after transfection.Integrin β1, c-Cbl, or scrambled siRNA. Sequences were described in Raw264.7 cells were transfected with iNOS, integrin βTotal RNA was extracted from Raw264.7 cells by using TRIzol (Invitrogen). Quantitative reverse transcriptase-PCR was performed using the SYBR Green technique (Promega). Primer sequences were described in 1 and α4 antibodies, then revealed with fluorescent dye –coupled secondary antibodies. Slides were mounted with DAPI-contained imaging medium. Images were captured with Nikon confocal microscope.Cells were fixed in 4% paraformaldehyde. Detection of integrins was performed with anti-integrin βProteins were extracted in RIPA buffer supplemented with protease inhibitors. Protein concentrations were measured using the BCA protein assay. Immunoblotting was performed with appropriate primary antibodies and horseradish peroxidase-conjugated secondary antibodies followed by ECL detection.For immunoprecipitation, cell lysates were incubated with the appropriate antibodies or control IgG at 4 °C overnight followed by incubation with protein A/G-Sepharose beads. Immunoprecipitates were washed with NETN buffer . Immunoblotting was performed to detect by using corresponding antibodies.2− concentration, which was equal to the NO concentration.The quantity of NO in the conditioned cell culture medium was evaluated by using Nitrate/Nitrie Colorimetric Assay Kit . Briefly, the nitrate was conversed to nitrite utilizing nitrate reductase. Addition of the Griess Reagents converts nitrite into a deep purple azo compound. Photometric measurement of absorbance due to this azo chromophore determined NO2+ diethyldithiocarbamate [Fe(DETC)2] (200 μM) as previously described (2O was mixed with sodium DETC to generate Fe(DETC)2 colloid solution. Raw264.7 cells were incubated with [Fe(DETC)2] at 37 °C for 30 min. The NO- Fe(DETC)2 complex was detected with an EMX X-band spectrometer (Bruker Corporation) using the following parameters: microwave power, 10 mW; amplitude modulation, 7G; modulation frequency, 100 kHz; and three numbers of scans. The EPR signal was indicated as the amplitude of the triplet signal . Immunoblots shown were representative of at least three independent experiments.The detection of integrin βescribed . Brieflyt test was used to determine the significant differences between two groups. Two-way analysis of variance (ANOVA), followed by Tukey post-hoc correction was used for the comparisons more than two groups. The protein stability test with CHX was analyzed by multiple-comparison two-way ANOVA followed by Bonferroni post-hoc correction.The results are reported as means ± SD and analyzed using GraphPad Prism 8.0 software. The Student’s All raw and processed data contained in the manuscript are available from the corresponding author upon request.This article contains The authors declare no conflict of interest with the contents of the article.


Sialorrhea, a common symptom of Parkinson's disease (PD), is related to reduced oromotor control and autonomic dysfunction. However, neuromuscular electrical stimulation (NMES) helps improve overall swallowing function. We performed NMES for eight weeks in an 84-year-old woman with stage 5 PD and severe sialorrhea. The severity and frequency of drooling improved within one to three weeks of NMES. NMES can be used for patients who have lost the will to be rehabilitated or are unable to control an appropriate rehabilitation load themselves. It may also be useful for patients with multiple complications who are unable to commence new medications or injections. Sialorrhea  is a common symptom of Parkinson's disease (PD) and is related to reduced oromotor control and autonomic dysfunction. It is observed in 70-78% of patients with PD and greatly affects the patient’s quality of life -4. This Neuromuscular electrical stimulation (NMES) involves direct stimulation of the muscles to recruit motor units and increase muscle strength. The findings of a meta-analysis of seven trials provided evidence in favour of using NMES for swallowing , therebyNMES is widely used in stroke rehabilitation. The electrical stimulation of muscle contraction is synchronised with the intended motion to restore movement after paralysis. Here, we evaluate the potential of using NMES for effective rehabilitation in a patient with sialorrhea.The NMES unit  was set to stimulate the orbicularis oris and masseter muscles Figure . We perfCase presentationThe patient was an 84-year-old woman with a history of PD and hypertension. According to the Hoehn and Yahr scale, she had stage 5 PD. She used a wheelchair and was unable to live an independent life. Oral rehabilitation was performed five years prior, but its efficacy diminished as the symptoms progressed, and further motivation for rehabilitation declined. About two years prior, the sialorrhea became so severe that it interfered with daily life. The patient had been receiving oral rehabilitation in the form of swallowing training. However, she was unable to undergo rehabilitation for long periods because of fatigue; therefore, continuous oral rehabilitation could not be achieved. The Drooling Severity and Frequency Scale (DSFS) was used to evaluate the patient. According to the baseline DSFS score, which is the sum of the severity and frequency subscores Table , the patThe NMES unit was set to stimulate the orbicularis and masseter muscles Figure . We perfThe severity and frequency of drooling improved after one and three weeks, respectively, of commencing rehabilitation with NMES Table . The sevThere may be restrictions on the rehabilitation that can be performed due to various factors. Patients with movement disorders such as severe PD make it difficult to perform rehabilitation using their own weight. Patients who are unable to self-manage have difficulty performing home rehabilitation in the absence of caregivers. Mental fatigue makes it impossible to maintain motivation for physical performance . TherefoWhen performing rehabilitation with NMES, it is important to properly position the pad to the target muscle to be rehabilitated to obtain the desired effect. NMES is unlikely to be suitable for patients with electronic devices embedded in their bodies. Therefore, caution is required when using it for patients who have an indwelling pacemaker. During NMES, the facial muscles move involuntarily, which can feel strange until the patient gets accustomed to the treatment. Additionally, if the NMES output is too high, it can affect the teeth and cause pain. For patients with decreased activities of daily living, nutritional management is also very important, along with rehabilitation . In concThe use of NMES may reduce sialorrhea in patients with PD. The rehabilitation effect may give the patient a chance to improve their motivation for rehabilitation. However, further studies are required to evaluate the rehabilitation of other facial muscles, especially to determine the optimal load and treatment duration for positive outcomes. In addition, studies will be needed to determine the long-term effects of NMES to determine the optimal duration of therapy.


Over recent years, significant improvements have been made in the understanding of (epi)genetics and neuropathophysiological mechanisms driving the different forms of multiple sclerosis (MS). For example, the role and importance of the bidirectional communications between the brain and the gut—also referred to as the gut-brain axis—in the pathogenesis of MS is receiving increasing interest in recent years and is probably one of the most promising areas of research for the management of people with MS. However, despite these important advances, it must be noted that these data are not—yet—used in rehabilitation. Neurorehabilitation is a cornerstone of MS patient management, and there are many techniques available to clinicians and patients, including technology-supported rehabilitation. In this paper, we will discuss how new findings on the gut microbiome could help us to better understand how rehabilitation can improve motor and cognitive functions. We will also see how the data gathered during the rehabilitation can help to get a better diagnosis of the patients. Finally, we will discuss how these new techniques can better guide rehabilitation to lead to precision rehabilitation and ultimately increase the quality of patient care. Multiple Sclerosis (MS) is one of the most prevalent neurological disorders, affecting over 2.5 million people worldwide. It is an autoimmune and demyelinating central nervous system (CNS) condition . MS is aOne of the most probable mechanisms for MS pathology has been proposed to be cytokine and adipokine imbalance in the CNS and peripheral blood circulation in persons with MS . InteresMajor advances have been made in the prognosis (thanks to the multi-omics approaches) and in the management of pwMS, largely due to the development of technology-supported rehabilitation . Omics tRehabilitation, which involves counseling and symptomatic therapy, is now thought to be one of the most effective treatments for maintaining optimal functioning and, therefore, quality of life in people with MS (pwMS) .The disease’s intricacy, the difficulties in establishing the best treatment, and the wide variety of symptoms necessitate a holistic approach to the patient that includes both pharmacology and neurorehabilitation . The avaHowever, there is currently a lack of integration of the data and information from the medical world  and rehabilitation specialists, although this information could be useful in selecting the most appropriate rehabilitation techniques. In looking at the problem from the opposite direction, there is also a lack of communication and a lack of use of the data  obtained during the rehabilitation towards the medical world to obtain a more precise diagnosis and follow-up of patients since have to undergo many rehabilitation sessions. It is, therefore, possible to perform longitudinal follow-up.In the paper, we will first discuss the latest finding of genetics analysis as well as the modification of gut microbiota observed in pwMS. Then we will discuss the different rehabilitation strategies and then see how the information from the gut microbiota can be used as an indicator of the progress of the patients during the rehabilitation process. Finally, we will discuss how the information collected during the rehabilitation process can be used to integrate more functional information in the follow-up and management of pwMS.Multi-omics is a novel approach in which data sets from various omics groups are combined during analysis. The various omics strategies analyzed include individual components: genomics, epigenomics, transcriptomics, proteomics, metabolomics and phenomics , as wellFirst, we will provide a brief summary of the current findings in genetics.A total of 233 statistically independent and genome-wide significant associations with MS susceptibility have been discovered in the most recent GWAS study . The tea13–1014 different microorganisms in the gut , exoskeleton [EksoNR® from eksoBIONICS]), (ii) devices that can be used by the clinicians in their daily practice , and iii) systems and solutions that can be used by the patients alone at home   high-tech devices whose price and complexity of use limit their use in specialized centers  .Rehabilitation can be facilitated by existing technology and help both the patients and the clinicians. Another great potential of using technology to support rehabilitation is that different measurements can be taken while patients are performing the rehabilitation, either in the clinic or at home. These set of measurements can be used as continuous outcomes  to monitor the evolution of the patients during the rehabilitation process  and adapt the plan according to the real needs  and specificities  of the patients. The current development of inexpensive and portable systems using wearable sensors is gaining popularity in the rehabilitation area, and such systems can be used in daily clinics with patients to measure, for example, upper limb motion , hand fuThe majority of devices allow the continuous recording of the motions performed by patients during rehabilitation exercises ,50. ThesThis physiologically based conceptual framework essentially adds an “-omic” layer to the scientific study of rehabilitation processes and results, personalizing a biomolecular approach to rehabilitation therapy aimed at improving individual recovery . These aMobile health technologies  that evaluate mobility in an unsupervised way on a daily basis during activities of daily living are gaining popularity as supplemental clinical assessments . Due to We have seen that the technology can be used not only to increase the quality of the rehabilitation but also provides a unique opportunity to collect continuous data in a more ecological way . This can be particularly relevant for rehabilitation as one of the biggest current limitations of the translation in rehabilitation is that the outcomes used in the research study, mainly subjective evaluated by the clinicians, may not be sensitive enough to detect subtle modifications of the patients because of low resolution and ceiling effect ,69.In the next part of the paper, we will see how we can integrate and use these data from the rehabilomics within the well-established multi-omics approach.There is, currently, to the author’s best knowledge, only a very limited number of studies trying to integrate omics information into the rehabilitation process.Two studies about the use of gut microbiota as an outcome of rehabilitation intervention have been recently published in the field of MS.In a first pilot study, authors highlighted the positive effect of a high-impact multidimensional rehabilitation program of gut microbiota on a small sample of 14 pwMS. This pilot investigation reveals the possibility of a short, high-impact multimodal rehabilitation program, including neuromotor rehabilitation, sailing course, mindfulness program and Mediterranean diet, to modify MS-typical dysbiosis by lowering pathobionts levels while restocking SCFA-producing beneficial bacteria, restoring a partial eubiotics profile. As shown by lower circulating levels of lipopolysaccharide and a drop in pro-inflammatory lymphocyte populations, such alterations might offset the normal inflammatory tone seen in MS .Prevotella counts and decreased Akkermansia counts but had no effect on circulating cytokine levels. However, Akkermansia muciniphila, prevotella and Bacteroides count changes in response to the intervention were correlated with changes in IL10  [In the second study 42 pwMS were included and participated in a six month home-based rehabilitation program . After tctively) .These two studies show promising results and indicate that partial return of a eubiotic profile may assist in counterbalancing the inflammatory tone seen in MS, as seen by lower circulating lipopolysaccharide levels and pro-inflammatory cell populations.It has been previously highlighted that multidisciplinary measurements, including clinical, functional and patient-reported outcome measures in combination with extensive patient profiling, can enhance personalized treatment and rehabilitation strategies ,73. TherConcerning rehabilitation, both motor and cognitive functions will show very important variations over time  . Of courIn this particular context, the rehabilitation process, and more specifically the use of technology-supported rehabilitation, provided a unique opportunity to switch the way we are gathering functional data of pwMS from a cross-sectional to a longitudinal way. Technology-supported rehabilitation has therefore gained popularity due to its ability to provide an objective and, if necessary, blind assessment, which can be automated (time saver) and allows for a measurable evaluation of motor function by taking into account the patient’s characteristics  and theIf the potential added values of such kinds of continuous evaluation are obvious, such as adapting treatments  to the actual need and specificity of the patients  to maximize the benefits of the intervention, building such a platform is highly challenging.The challenges can be seen at two distinct levels: technological and human.Let us first focus on the current obstacles and limitations related to the technology and its implementation.First, an important investment must be carried out to acquire the rehabilitation equipment and the sensors (remote monitoring) in order to acquire the data. The price of these systems  varies eThis takes us to the second most significant existing limitation: the lack of reimbursement from the social security system. The organization and involvement of healthcare systems in the rehabilitation process varies by nation, and we will not go into detail about reimbursement here. However, it is well known that financial concerns and a lack of expertise and familiarity with the use of (new) technology are two of the most significant barriers to the deployment of technology for patients, regardless of their diseases ,89. AnotInterestingly, it is of note that the COVID-19 epidemic has not only disturbed healthcare systems but has also accelerated the development, deployment, and acknowledgment of new technology, in particular, mHealth, in rehabilitation . It is cThird, with respect to data, it is important to ensure the quality of these. A distinction must be made here between data acquired in a supervised manner (with the physiotherapist during the rehabilitation) and data acquired in an unsupervised manner (remote monitoring). For the first one, since they are acquired under the control of a clinician, the quality can be checked directly . For homThe next challenge is then the merging and synchronization of different data sources (i.e., the different rehabilitation technologies presented in However, we do not have to reinvent the wheel. Tremendous efforts have already been made in the omics world , so all The second group of limitations concerns the acceptance of the technology by both the patients and the clinicians.Because most pwMS are young and are familiar with smartphones, apps, and mobile technology, familiarity with technology should not be a concern for the majority of them , but it In order to ease the implementation and use of new technology in the healthcare sector in general, and more specifically in rehabilitation, the next step would be to incorporate technological solutions into the healthcare system, including compensation for patients, education of healthcare professionals on these solutions, and integration of data obtained with the technologies in patients’ medical records. This might hasten and simplify the incorporation of these technologies into the everyday care and rehabilitation of pwMS. If successfully implemented, such kinds of highly multidimensional and longitudinal databases could be used to unravel the complex relationship between genetics, inflammations, imaging and functional data and allow us to further understand the implications of the genes in the functional pathophysiology of pwMS.The multi-omics approach has drastically helped the understanding of the complex pathophysiological underlying neurodegenerative disorders such as MS. While these disciplines are still currently mainly used to develop new drugs therapy, this information should also be used to advance rehabilitation.On the other hand, it is essential to incorporate more functional data both in basic research and in the clinical follow-up of patients. To do this, physiotherapists are on the front line, and thanks to the use of new technologies, a lot of data can be collected automatically during revalidation sessions but also during activities of daily life. It is, therefore, of the utmost importance to develop highly multi and interdisciplinary collaborations between researchers, clinicians, and data specialists to integrate these data into one single pipeline to move forward in this field.