A decision support system for upper limb rehabilitation robot based on hybrid reasoning with RBR and CBR.

The rehabilitation robot can assist hemiplegic patients to complete the training program effectively, but it only focuses on helping the patient's training process and requires the rehabilitation therapists to manually adjust the training parameters according to the patient's condition. Therefore, there is an urgent need for intelligent training prescription research of rehabilitation robots to promote the clinical applications. This study proposed a decision support system for the training of upper limb rehabilitation robot based on hybrid reasoning with rule-based reasoning (RBR) and case-based reasoning (CBR). The expert knowledge base of this system is established base on 10 professional rehabilitation therapists from three different rehabilitation departments in Shanghai who are enriched with experiences in using desktop-based upper limb rehabilitation robot. The rule-based reasoning is chosen to construct the cycle plan inference model, which develops a 21-day training plan for the patients. The case base consists of historical case data from 54 stroke patients who underwent rehabilitation training with a desktop-based upper limb rehabilitation robot. The case-based reasoning, combined with a Random Forest optimized algorithm, was constructed to adjust the training parameters for the patients in real-time. The system recommended a rehabilitation training program with an average accuracy of 91.5%, an average AUC value of 0.924, an average recall rate of 88.7%, and an average F1 score of 90.1%. The application of this system in rehabilitation robot would be useful for therapists.

Research on multi-dimensional intelligent quantitative assessment of upper limb function based on kinematic parameters.

BACKGROUND: Rehabilitation assessment is a critical component of rehabilitation treatment. OBJECTIVE: This study focuses on a comprehensive analysis of patients' movement performance using the upper limb rehabilitation robot. It quantitatively assessed patients' motor control ability and constructed an intelligent grading model of functional impairments. These findings contribute to a deeper understanding of patients' motor ability and provide valuable insights for personalized rehabilitation interventions. METHODS: Patients at different Brunnstrom stages underwent rehabilitation training using the upper limb rehabilitation robot, and data on the distal movement positions of the patients' upper limbs were collected. A total of 22 assessment metrics related to movement efficiency, smoothness, and accuracy were extracted. The performance of these assessment metrics was measured using the Mann-Whitney U test and Pearson correlation analysis. Due to the issue of imbalanced sample categories, data augmentation was performed using the Synthetic Minority Over-sampling Technique (SMOTE) algorithm based on weighted sampling, and an intelligent grading model of functional impairment based on the Extreme Gradient Boosting Tree (XGBoost) algorithm was constructed. RESULTS: Sixteen assessment metrics were screened. These metrics were effectively normalized to their maximum values, enabling the derivation of quantitative assessment scores for motor control ability across the three dimensions through a weighted fusion approach. Notably, when applied to the data-enhanced dataset, the intelligent grading model exhibited remarkable improvement, achieving an accuracy rate exceeding 0.98. Moreover, significant enhancements were observed in terms of precision, recall, and f1-score. CONCLUSION: The research findings demonstrate that this study enables the quantitative assessment of patients' motor control ability and intelligent grading of functional impairments, thereby contributing to the efficiency enhancement of clinical rehabilitation assessment. Moreover, this method resolves the issues associated with the subjectivity and prolonged periods of traditional rehabilitation assessment methods.

Across Sessions and Subjects Domain Adaptation for Building Robust Myoelectric Interface.

Gesture interaction via surface electromyography (sEMG) signal is a promising approach for advanced human-computer interaction systems. However, improving the performance of the myoelectric interface is challenging due to the domain shift caused by the signal's inherent variability. To enhance the interface's robustness, we propose a novel adaptive information fusion neural network (AIFNN) framework, which could effectively reduce the effects of multiple scenarios. Specifically, domain adversarial training is established to inhibit the shared network's weights from exploiting domain-specific representation, thus allowing for the extraction of domain-invariant features. Effectively, classification loss, domain diversence loss and domain discrimination loss are employed, which improve classification performance while reduce distribution mismatches between the two domains. To simulate the application of myoelectric interface, experiments were carried out involving three scenarios (intra-session, inter-session and inter-subject scenarios). Ten non-disabled subjects were recruited to perform sixteen gestures for ten consecutive days. The experimental results indicated that the performance of AIFNN was better than two other state-of-the-art transfer learning approaches, namely fine-tuning (FT) and domain adversarial network (DANN). This study demonstrates the capability of AIFNN to maintain robustness over time and generalize across users in practical myoelectric interface implementations. These findings could serve as a foundation for future deployments.

A Real-Time Control Method for Upper Limb Exoskeleton Based on Active Torque Prediction Model.

Exoskeleton rehabilitation robots have been widely used in the rehabilitation treatment of stroke patients. Clinical studies confirmed that rehabilitation training with active movement intentions could improve the effectiveness of rehabilitation treatment significantly. This research proposes a real-time control method for an upper limb exoskeleton based on the active torque prediction model. To fulfill the goal of individualized and precise rehabilitation, this method has an adjustable parameter assist ratio that can change the strength of the assist torque under the same conditions. In this study, upper limb muscles' EMG signals and elbow angle were chosen as the sources of control signals. The active torque prediction model was then trained using a BP neural network after appropriately extracting features. The model exhibited good accuracy on PC and embedded systems, according to the experimental results. In the embedded system, the RMSE of this model was 0.1956 N·m and 94.98%. In addition, the proposed real-time control system also had an extremely low delay of only 40 ms, which would significantly increase the adaptability of human-computer interactions.

Cognitive and motor cortex activation during robot-assisted multi-sensory interactive motor rehabilitation training: An fNIRS based pilot study.

Objective: This study aimed to evaluate the effects of multiple virtual reality (VR) interaction modalities based on force-haptic feedback combined with visual or auditory feedback in different ways on cerebral cortical activation by functional near-infrared spectroscopy (fNIRS). Methods: A modular multi-sensory VR interaction system based on a planar upper-limb rehabilitation robot was developed. Twenty healthy participants completed active elbow flexion and extension training in four VR interaction patterns, including haptic (H), haptic + auditory (HA), haptic + visual (HV), and haptic + visual + auditory (HVA). Cortical activation changes in the sensorimotor cortex (SMC), premotor cortex (PMC), and prefrontal cortex (PFC) were measured. Results: Four interaction patterns all had significant activation effects on the motor and cognitive regions of the cerebral cortex (p < 0.05). Among them, in the HVA interaction mode, the cortical activation of each ROI was the strongest, followed by HV, HA, and H. The connectivity between channels of SMC and bilateral PFC, as well as the connectivity between channels in PMC, was the strongest under HVA and HV conditions. Besides, the two-way ANOVA of visual and auditory feedback showed that it was difficult for auditory feedback to have a strong impact on activation without visual feedback. In addition, under the condition of visual feedback, the effect of fusion auditory feedback on the activation degree was significantly higher than that of no auditory feedback. Conclusions: The interaction mode of visual, auditory, and haptic multi-sensory integration is conducive to stronger cortical activation and cognitive control. Besides, there is an interaction effect between visual and auditory feedback, thus improving the cortical activation level. This research enriches the research on activation and connectivity of cognitive and motor cortex in the process of modular multi-sensory interaction training of rehabilitation robots. These conclusions provide a theoretical basis for the optimal design of the interaction mode of the rehabilitation robot and the possible scheme of clinical VR rehabilitation.

Cortical activation in robot-assisted dynamic and static resistance training combining VR interaction: An fNIRS based pilot study.

BACKGROUND: There are few isometric training systems based on upper limb rehabilitation robots. Its efficacy and neural mechanism are not well understood. OBJECTIVE: This study aims to investigate the cortex activation of dynamic resistance and static (isometric) training based on upper limb rehabilitation robot combined with virtual reality (VR) interaction by using functional near-infrared spectroscopy (fNIRS). METHODS: Twenty subjects were included in this study. The experiment adopts the block paradigm design. Experiment in dynamic and static conditions consisted of three trials, each consisting of task (60 s)-rest (40 s). The neural activities of the sensorimotor cortex (SMC), premotor cortex (PMC) and prefrontal cortex (PFC) were measured. The cortex activation and functional connectivity (FC) were analyzed. RESULTS: Both the dynamic and static training can activate SMC, PMC, and PFC. In SMC and PMC, the activation of static training was stronger than dynamic training, there were significant differences between the two modes of each region of interest (ROI) (p < 0.05) (SMC: p = 0.022, ES = 0.72, PMC: p = 0.039, ES = 0.63). Besides, the FC between all ROIs of the static training was stronger than that of the dynamic training. CONCLUSION: The static training based on upper limb rehabilitation robot may better facilitate the cortical activation associated with motor control.

Research of intent recognition in rehabilitation robots: a systematic review.

PURPOSE: Rehabilitation robots with intent recognition are helping people with dysfunction to enjoy better lives. Many rehabilitation robots with intent recognition have been developed by academic institutions and commercial companies. However, there is no systematic summary about the application of intent recognition in the field of rehabilitation robots. Therefore, the purpose of this paper is to summarize the application of intent recognition in rehabilitation robots, analyze the current status of their research, and provide cutting-edge research directions for colleagues. MATERIALS AND METHODS: Literature searches were conducted on Web of Science, IEEE Xplore, ScienceDirect, SpringerLink, and Medline. Search terms included "rehabilitation robot", "intent recognition", "exoskeleton", "prosthesis", "surface electromyography (sEMG)" and "electroencephalogram (EEG)". References listed in relevant literature were further screened according to inclusion and exclusion criteria. RESULTS: In this field, most studies have recognized movement intent by kinematic, sEMG, and EEG signals. However, in practical studies, the development of intent recognition in rehabilitation robots is limited by the hysteresis of kinematic signals and the weak anti-interference ability of sEMG and EEG signals. CONCLUSIONS: Intent recognition has achieved a lot in the field of rehabilitation robotics but the key factors limiting its development are still timeliness and accuracy.In the future, intent recognition strategy with multi-sensor information fusion may be a good solution.

As a technology, intent recognition can become a part of rehabilitation, assist patients to complete daily life activities, and improve their quality of life.Rehabilitation training equipment for treatment usually adopts a relatively stable prediction method, which aims to stimulate the enthusiasm of users to participate in training.Functionally enhanced rehabilitation aids have high requirements for the timeliness of movement intent recognition, and its purpose is to assist patients to complete activities of daily life.

Current status and clinical perspectives of extended reality for myoelectric prostheses: review.

Training with "Extended Reality" or X-Reality (XR) systems can undoubtedly enhance the control of the myoelectric prostheses. However, there is no consensus on which factors improve the efficiency of skill transfer from virtual training to actual prosthesis abilities. This review examines the current status and clinical applications of XR in the field of myoelectric prosthesis training and analyses possible influences on skill migration. We have conducted a thorough search on databases in the field of prostheses using keywords such as extended reality, virtual reality and serious gaming. Our scoping review encompassed relevant applications, control methods, performance evaluation and assessment metrics. Our findings indicate that the implementation of XR technology for myoelectric rehabilitative training on prostheses provides considerable benefits. Additionally, there are numerous standardised methods available for evaluating training effectiveness. Recently, there has been a surge in the number of XR-based training tools for myoelectric prostheses, with an emphasis on user engagement and virtual training evaluation. Insufficient attention has been paid to significant limitations in the behaviour, functionality, and usage patterns of XR and myoelectric prostheses, potentially obstructing the transfer of skills and prospects for clinical application. Improvements are recommended in four critical areas: activities of daily living, training strategies, feedback, and the alignment of the virtual environment with the physical devices.

[Research progress on intelligent assessment system for upper limb function of stroke patients].

At present, the upper limb function of stroke patients is often assessed clinically using a scale method, but this method has problems such as time-consuming, poor consistency of assessment results, and high participation of rehabilitation physicians. To overcome the shortcomings of the scale method, intelligent upper limb function assessment systems combining sensors and machine learning algorithms have become one of the hot research topics in recent years. Firstly, the commonly used clinical upper limb functional assessment methods are analyzed and summarized. Then the researches on intelligent assessment systems in recent years are reviewed, focusing on the technologies used in the data acquisition and data processing parts of intelligent assessment systems and their advantages and disadvantages. Lastly, the current challenges and future development directions of intelligent assessment systems are discussed. This review is hoped to provide valuable reference information for researchers in related fields.

Evaluation of Multilevel Surgeries in Children With Spastic Cerebral Palsy Based on Surface Electromyography.

The root mean square (RMS) of the surface electromyography (sEMG) signal can respond to neuromuscular function, which displays a positive correlation with muscle force and muscle tension under positive and passive conditions, respectively. The purpose of this study was to investigate the changes in muscle force and tension after multilevel surgical treatments, functional selective posterior rhizotomy (FSPR) and tibial anterior muscle transfer surgery, and evaluate their clinical effect in children with spastic cerebral palsy (SCP) during walking. Children with diplegia (n = 13) and hemiplegia (n = 3) with ages from 4 to 18 years participated in this study. They were requested to walk barefoot at a self-selected speed on a 15-m-long lane. The patient's joints' range of motion (ROM) and sEMG signal of six major muscles were assessed before and after the multilevel surgeries. The gait cycle was divided into seven phases, and muscle activation state can be divided into positive and passive conditions during gait cycle. For each phase, the RMS of the sEMG signal amplitude was calculated and also normalized by a linear envelope (10-ms running RMS window). The muscle tension of the gastrocnemius decreased significantly during the loading response, initial swing, and terminal swing (p < 0.05), which helped the knee joint to get the maximum extension when the heel is on the ground and made the heel land smoothly. The muscle force of the gastrocnemius increased significantly (p < 0.05) during the mid-stance, terminal stance, and pre-swing, which could generate the driving force for the human body to move forward. The muscle tension of the biceps femoris and semitendinosus decreased significantly (p < 0.05) during the terminal stance, pre-swing, and initial swing. The decreased muscle tension could relieve the burden of the knee flexion when the knee joint was passively flexed. At the terminal swing, the muscle force of the tibial anterior increased significantly (p < 0.05), which could improve the ankle dorsiflexion ability and prevent foot drop and push forward. Thus, the neuromuscular function of cerebral palsy during walking can be evaluated by the muscle activation state and the RMS of the sEMG signal, which showed that multilevel surgical treatments are feasible and effective to treat SCP.

Effects of passive and active training modes of upper-limb rehabilitation robot on cortical activation: a functional near-infrared spectroscopy study.

OBJECTIVE: The purpose of this study is to investigate the cortical activation during passive and active training modes under different speeds of upper extremity rehabilitation robots. METHODS: Twelve healthy subjects completed the active and passive training modes at various speeds (0.12, 0.18, and 0.24 m/s) for the right upper limb. The functional near-infrared spectroscopy (fNIRS) was used to measure the neural activities of the sensorimotor cortex (SMC), premotor cortex (PMC), supplementary motor area (SMA), and prefrontal cortex (PFC). RESULTS: Both the active and passive training modes can activate SMC, PMC, SMA, and PFC. The activation level of active training is higher than that of passive training. At the speed of 0.12 m/s, there is no significant difference in the intensity of the two modes. However, at the speed of 0.24 m/s, there are significant differences between the two modes in activation levels of each region of interest (ROI) (P < 0.05) (SMC: F = 8.90, P = 0.003; PMC: F = 8.26, P = 0.005; SMA: F = 5.53, P = 0.023; PFC: F = 9.160, P = 0.003). CONCLUSION: This study mainly studied on the neural mechanisms of active and passive training modes at different speeds based on the end-effector upper-limb rehabilitation robot. Slow, active training better facilitated the cortical activation associated with cognition and motor control.See Video Abstract, http://links.lww.com/WNR/A621.

Voice controlled wheelchair integration rehabilitation training and posture transformation for people with lower limb motor dysfunction.

BACKGROUND: Stroke and other neurological disorders have an effect on mobility, which has a significant impact on independence and quality of life. The core rehabilitation requirements for patients with lower limb motor dysfunction are gait training, re-stand and mobility. OBJECTIVE: A wheelchair called ReChair was invented that seamlessly integrated mobility, gait training and multi-posture transformation with voice control. METHODS: The mechanical structure and control system of ReChair were designed. The overall evaluation included voice recognition and balance training test to quantitatively verify the performance of ReChair. RESULTS: The average success recognition rate of posture transformation by voice control can reach 91% to 96% in a quiet environment (20-30 DB). The balance training game is easy to operate and the score can be used as a quantified guideline for rehabilitation. CONCLUSION: The proposed rehabilitation wheelchair can realize multi-posture transformation and balance training by the voice control system.

Gamma frequency band shift of contralateral corticomuscular synchronous oscillations with force strength for hand movement tasks.

Bilateral voluntary contractions involve functional changes in both primary motor cortices. However how the unilateral voluntary contraction of hand muscles influences the contralateral corticomuscular synchronous oscillations mechanisms remains unclear. In the bimanual tasks, nine healthy subjects were instructed to generate force by abducting their left-hand index finger against a force sensor and simultaneously the right-hand precise pinch task with visual feedback. They were divided into four conditions according to the two contraction force levels of the left-hand muscles 5% and 50% maximal isometric voluntary contraction (MVC) and with/without visual feedback for the right hand. Corticomuscular synchronization of the right hand in the beta band was revealed when the subjects performed the bimanual exercise with 5% MVC of left-hand muscles, which is consistent with previous studies. As the contraction strength of the left-hand muscle increased to 50% MVC, the corticomuscular coherence (CMC) frequency of the right hand shifted to gamma band, and the CMC in beta band decreased significantly (P < 0.05) in the electroencephalography→electromyography direction. This phenomenon suggests that the corticomuscular synchronous oscillation will shift from beta band to higher frequencies (principally gamma) as the contraction force of the contralateral hand increases, which may be due to the changes in the subject's attention and more frequent synchronization of neuromuscular motor neurons oscillations. These findings will be helpful to explore the hand motion control and feedback mechanisms, and further provide a basis for the application of neuromuscular coupling in clinical rehabilitation evaluation.Video abstract: http://links.lww.com/WNR/A571.

Physiological parameters analysis of transfemoral amputees with different prosthetic knees.

Purposeː Physiological parameters analysis allows for a precise quantification of energy expenditure of transfemoral amputees with different prosthetic knees. Comparative physiological parameters analysis that indicate the functional characteristics of knee joints is essential to the choice of transfemoral amputee. The aim of this study was to propose a microprocessor-controlled prosthetic knee (i-KNEE) and conducted physiological parameters (energy cost, gait efficiency and relative exercise intensity) comparison of transfemoral amputees with C-leg, Rheo Knee and Mauch under different walking speeds. Methodsː A microprocessor-controlled prosthetic knee with hydraulic damper (i-KNEE) was developed. A two-factor repeated measurement experiment design was used. Each subject was instructed to accept the same treatments. The two factors were type of prosthetic knees (the i-KNEE, the C-Leg, the Rheo Knee and the Mauch) and speed (0.5, 0.7, 0.9, 1.1, 1.3 m/s). The energy cost, gait efficiency and relative exercise intensity of ten transfemoral amputees were measured. Resultsː For all the prosthetic knees, the energy cost increased along with walking speed. There was no significant difference between three microprocessor-controlled prosthetic knees in energy cost. The gait efficiency of Mauch was always less than or equal to other three microprocessor-controlled prosthetic knees in specific walking speed. The relative exercise intensity increased with speed for all the prosthetic knees. More effort was needed for the transfemoral amputees with Mauch than other three microprocessorcontrolled prosthetic knees in the same walking speed. Conclusionsː The use of the microprocessor-controlled knee joints resulted in reduced energy cost, improved gait efficiency and smaller relative exercise intensity.

[Research progress on analysis methods in electroencephalography-electromyography synchronous coupling].

The motor nervous system transmits motion control information through nervous oscillations, which causes the synchronous oscillatory activity of the corresponding muscle to reflect the motion response information and give the cerebral cortex feedback, so that it can sense the state of the limbs. This synchronous oscillatory activity can reflect connectivity information of electroencephalography-electromyography (EEG-EMG) functional coupling. The strength of the coupling is determined by various factors including the strength of muscle contraction, attention, motion intention etc. It is very significant to study motor functional evaluation and control methods to analyze the changes of EEG-EMG synchronous coupling caused by different factors. This article mainly introduces and compares coherence and Granger causality of linear methods, the mutual information and transfer entropy of nonlinear methods in EEG-EMG synchronous coupling, and summarizes the application of each method, so that researchers in related fields can understand the current research progress on analysis methods of EEG-EMG synchronous systematically.

Pilot Study of a Powered Exoskeleton for Upper Limb Rehabilitation Based on the Wheelchair.

To help hemiplegic patients with stroke to restore impaired or lost upper extremity functionalities efficiently, the design of upper limb rehabilitation robotics which can substitute human practice becomes more important. The aim of this work is to propose a powered exoskeleton for upper limb rehabilitation based on a wheelchair in order to increase the frequency of training and reduce the preparing time per training. This paper firstly analyzes the range of motion (ROM) of the flexion/extension, adduction/abduction, and internal/external of the shoulder joint, the flexion/extension of the elbow joint, the pronation/supination of the forearm, the flexion/extension and ulnar/radial of the wrist joint by measuring the normal people who are sitting on a wheelchair. Then, a six-degree-of-freedom exoskeleton based on a wheelchair is designed according to the defined range of motion. The kinematics model and workspace are analyzed to understand the position of the exoskeleton. In the end, the test of ROM of each joint has been done. The maximum error of measured and desired shoulder flexion and extension joint angle is 14.98%. The maximum error of measured and desired elbow flexion and extension joint angle is 14.56%. It is acceptable for rehabilitation training. Meanwhile, the movement of drinking water can be realized in accordance with the range of motion. It demonstrates that the proposed upper limb exoskeleton can also assist people with upper limb disorder to deal with activities of daily living. The feasibility of the proposed powered exoskeleton for upper limb rehabilitation training and function compensating based on a wheelchair is proved.

[Study on the center-driven multiple degrees of freedom upper limb rehabilitation training robot].

With the aging of the society, the number of stroke patients has been increasing year by year. Compared with the traditional rehabilitation therapy, the application of upper limb rehabilitation robot has higher efficiency and better rehabilitation effect, and has become an important development direction in the field of rehabilitation. In view of the current development status and the deficiency of upper limb rehabilitation robot system, combined with the development trend of all kinds of products of the upper limb rehabilitation robot, this paper designed a center-driven upper limb rehabilitation training robot for cable transmission which can help the patients complete 6 degrees of freedom (3 are driven, 3 are underactuated) training. Combined the structure of robot with more joints rehabilitation training, the paper choosed a cubic polynomial trajectory planning method in the joint space planning to design two trajectories of eating and lifting arm. According to the trajectory equation, the movement trajectory of each joint of the robot was drawn in MATLAB. It laid a foundation for scientific and effective rehabilitation training. Finally, the experimental prototype is built, and the mechanical structure and design trajectories are verified.

Plantar pressure analysis of above-knee amputee with a developed microprocessor-controlled prosthetic knee.

PURPOSE: Human gait motion analysis was one useful method for lower limb prosthesis study. The most often measured parameters were plantar pressure, kinetic and kinematic parameters. It was indispensable for prosthetic knee design and performance assessment. The aim of this study was to analysis the plantar pressure in traumatic above-knee amputee equipped with a developed microprocessor-controlled prosthetic knee. METHODS: The maximum force of forefoot and rearfoot, the average vertical reaction force and pressure and the centre of pressure (COP) offset trajectories of ten above-knee amputees under different walking speeds were obtained. RESULTS: Both forefoot and rearfoot force were bigger in intact leg than prosthetic leg. As the speed increased, the pressure increased in both sides. Forefoot bore more pressure than rearfoot in both legs. The average vertical pressure and force both increased along with the increase of speed. The force and pressure of intact side were always bigger than the prosthetic side. The trend of COP and gait line of the prosthetic and intact side had no significant difference. The length of the gait line of prosthetic side was greater than the intact side. CONCLUSIONS: The results of this study exhibited reduced plantar pressure in the prosthetic side. The typical butterfly diagrams were produced during different walking speeds. It indicated that the stability of the microprocessor-controlled prosthetic knee could be guaranteed.

Development and evaluation of a new contoured cushion system with an optimized normalization algorithm.

Prevention of pressure sores remains a significant problem confronting spinal cord injury patients and the elderly with limited mobility. One vital aspect of this subject concerns the development of cushions to decrease pressure ulcers for seated patients, particularly those bound by wheelchairs. Here, we present a novel cushion system that employs interface pressure distribution between the cushion and the buttocks to design custom contoured foam cushion. An optimized normalization algorithm was proposed, with which interface pressure distribution was transformed into the carving depth of foam cushions according to the biomechanical characteristics of the foam. The shape and pressure-relief performance of the custom contoured foam cushions was investigated. The outcomes showed that the contoured shape of personalized cushion matched the buttock contour very well. Moreover, the custom contoured cushion could alleviate pressure under buttocks and increase subjective comfort and stability significantly. Furthermore, the fabricating method not only decreased the unit production cost but also simplified the procedure for manufacturing. All in all, this prototype seat cushion would be an effective and economical way to prevent pressure ulcers.

Effect of thimerosal on the neurodevelopment of premature rats.

BACKGROUND: This study was undertaken to determine the effect of thimerosal on the neurodevelopment of premature rats. METHODS: Thimerosal was injected into premature SD rats at a dose of 32.8, 65.6, 98.4 or 131.2 µg/kg on postnatal day 1. Expression of dopamine D4 receptor (DRD4) and serotonin 2A receptor (5-HT2AR), apoptosis in the prefrontal cortex on post-injection day 49, and learning and memory function were studied and compared with those in a control group injected with saline. RESULTS: Expression of DRD4 and 5-HT2AR and learning function decreased, and apoptosis increased significantly in the 131.2 µg/kg group (P<0.001). Memory function was significantly impaired by 65.6 (P<0.05), 98.4 and 131.2 µg/kg (P<0.001). CONCLUSIONS: The negative adverse consequences on neurodevelopment observed in the present study are consistent with previous studies; this study raised serious concerns about adverse neurodevelopmental disorder such as autism in humans following the ongoing worldwide routine administration of thimerosalcontaining vaccines to infants.