Search Results (30)

Background/Objectives: Axonal Charcot–Marie–Tooth disease type 2 (CMT2) accounts for 24% of Hereditary Motor/Sensory Peripheral Neuropathies. CMT2 type GG, due to four distinct heterozygous mutations in the Golgi brefeldin A resistant guanine nucleotide exchange factor 1 (GBF1) gene (OMIM 606483), was described in seven cases from four unrelated families with autosomal dominant inheritance. It is characterized by slowly progressive distal muscle weakness and atrophy, primarily affecting the lower limbs. Here, we present two siblings sharing a novel GBF1 variant. Methods: Patient II.1 (male, 61 years at onset) presented lower limb hypoesthesia and walking difficulty; the examination revealed a postural tremor, a positive Romberg test, and muscle atrophy in the lower limbs and hands. Patient II.2 (his sister, 59 years at onset) had lower limb dysesthesias, hand paresthesia, and lower-limb stiffness. They underwent clinical evaluations, blood tests, and electroneurography. Their father represents a potentially affected individual, although a genetic analysis was not conducted. Results: All tests for peripheral neuropathies were unremarkable, including metabolic and autoimmune screening. Both showed a mixed demyelinating–axonal sensory–motor neuropathy. Genetic analysis revealed a new heterozygous GBF1 variant of uncertain significance. Conclusions: Based on autosomal dominant inheritance, as well as clinical and physiological features, a possible novel CMT2GG was diagnosed. Further research, including functional assays and in vitro studies, is necessary to confirm this variant’s causal link. Full article

Currently, teleoperated robots, with the operator’s input, can fully perceive unknown factors in a complex environment and have strong environmental interaction and perception abilities. However, physiological tremors in the human hand can seriously affect the accuracy of processes that require high-precision control. Therefore, this paper proposes an EEMD-IWOA-LSTM model, which can decompose the physiological tremor of the hand into several intrinsic modal components (IMF) by using the EEMD decomposition strategy and convert the complex nonlinear and non-stationary physiological tremor curve of the human hand into multiple simple sequences. An LSTM neural network is used to build a prediction model for each (IMF) component, and an IWOA is proposed to optimize the model, thereby improving the prediction accuracy of the physiological tremor and eliminating it. At the same time, the prediction results of this model are compared with those of different models, and the results of EEMD-IWOA-LSTM presented in this study show obvious superior performance. In the two examples, the MSE of the prediction model proposed are 0.1148 and 0.00623, respectively. The defibrillation model proposed in this study can effectively eliminate the physiological tremor of the human hand during teleoperation and improve the control accuracy of the robot during teleoperation. Full article

Parkinson’s disease (PD) is the second-most common neurodegenerative disease worldwide. Patients are diagnosed based upon movement disorders, including bradykinesia, tremor and stiffness of movement. However, non-motor signs, including constipation, rapid eye movement sleep behavior disorder, smell deficits and pain are well recognized. Peripheral neuropathy is also increasingly recognized, as the vast majority of patients show reduced intraepidermal nerve fibers, and sensory nerve conduction and sensory function is also impaired. Many case studies in the literature show that high-dose levodopa may induce or exacerbate neuropathy in PD, which is thought to involve levodopa’s metabolism to homocysteine. Here, we treated primary cultures of dorsal root ganglia and a sensory neuronal cell line with levodopa to examine effects on cell morphology, mitochondrial content and physiology, and lysosomal function. High-dose levodopa reduced mitochondrial membrane potential. At concentrations observed in the patient, levodopa enhanced immunoreactivity to beta III tubulin. Critically, levodopa reduced lysosomal content and also reduced the proportion of lysosomes that were acidic, thereby impairing their function, whereas homocysteine tended to increase lysosome content. Levodopa is a critically important drug for the treatment of PD. However, our data suggest that at concentrations observed in the patient, it has deleterious effects on sensory neurons that are not related to homocysteine. Full article

Physiological hand tremors, twitching, and the nonlinear characteristics of the relationship between surgical forceps clamping force and operating force seriously affect the clamping accuracy of surgical instruments. To address this problem, a new type of surgical forceps with a force-holding function was developed to replace traditional forceps, which was studied in terms of structural design, statics, and dynamics. The overall structure of the surgical forceps was designed based on the lever principle, the kinematic model of the clamping part of the surgical forceps was established by the geometrical method, and the correctness of the kinematic model was verified by ADAMS. To address the clamping accuracy of the surgical forceps, a stress analysis was performed, its dynamics model was established, a finite element simulation was performed, the modal of the forceps was optimized using the Box–Behnken method, and, finally, an experimental platform was built to perform the accuracy test. The results demonstrate that the designed surgical forceps exhibit high clamping accuracy and fulfill the design specifications for surgical operations. Full article

Physiological hand tremors appear to be one of the most common types of tremors that occur during the lifespan. Activities most prominently affected by hand tremors are those involving the movement of small muscles, such as fine motor skills, which in turn could be influenced by several factors, including lateral dominance. The difference in skills due to lateral dominance is defined as inter-limb imbalance or inter-limb asymmetry. When this asymmetry is attributed to the tremor and the difference in tremor between the limbs, it could be defined as the inter-limb asymmetry of tremors. This study aimed to evaluate the acute effects of wobble-board hand exercise training on the inter-limb asymmetry of tremors. Thirty-two (eighteen males and fourteen females) participants (age: 25.2 ± 2.6 years, weight: 63.9 ± 10.5 kg, height: 1.66 ± 0.8 m, and BMI: 22.8 ± 2.3 kg/m²) were involved in the study. Before (PRE) and after (POST) the wobble-board hand exercises, postural hand tremor was evaluated using a tri-axial accelerometer fixed under the palm. Recordings were taken for 15 s. One-way Analysis of Variance (ANOVA) was used to examine the effects of hand exercises on inter-limb (dominant vs. non-dominant) asymmetry of tremor in testing time (PRE vs. POST) in relation to sex (male vs. female). The statistical significance was set at p < 0.05. Significant differences were found in physiological hand tremors between limbs (dominant vs. non-dominant) in the PRE evaluation (p = 0.03) independently from sex while no differences were found in the POST evaluation. A significant difference emerged in the PRE evaluation for males (p = 0.04) and females (p = 0.03) in relation to the testing time and preferred hand. This difference was no longer present in the POST evaluation. In conclusion, wobble-board hand exercises could represent an effective strategy to reduce inter-limb asymmetry. These results emphasize the importance of task-specific training to maximize the reduction in inter-limb asymmetry of tremors following wobble-board hand exercises. Full article

Accurate tremor classification is crucial for effective patient management and treatment. However, clinical diagnoses are often hindered by misdiagnoses, necessitating the development of robust technical methods. Here, we present a two-stage convolutional neural network (CNN)-based system for classifying physiological tremor, essential tremor (ET), and Parkinson’s disease (PD) tremor. Employing acceleration signals from the hands of 408 patients, our system utilizes both medically motivated signal features and (nearly) raw data (by means of spectrograms) as system inputs. Our model employs a hybrid approach of data-based and feature-based methods to leverage the strengths of both while mitigating their weaknesses. By incorporating various data augmentation techniques for model training, we achieved an overall accuracy of 88.12%. This promising approach demonstrates improved accuracy in discriminating between the three tremor types, paving the way for more precise tremor diagnosis and enhanced patient care. Full article

Physiologic hand tremors are a critical factor affecting the aim of air pistol shooters. However, the extent of the effect of hand tremors on shooting performance is unclear. In this study, we aim to explore the relationship between hand tremors and shooting performance scores as well as investigate potential links between muscle activation and hand tremors. In this study, 17 male air pistol shooters from China’s national team and the Air Pistol Sports Center were divided into two groups: the elite group and the sub-elite group. Each participant completed 40 shots during the experiment, with shooters’ hand tremors recorded using three-axis digital accelerometers affixed to their right hands. Muscle activation was recorded using surface electromyography on the right anterior deltoid, posterior deltoid, biceps brachii (short head), triceps brachii (long head), flexor carpi radialis, and extensor carpi radialis. Our analysis revealed weak correlations between shooting scores and hand tremor amplitude in multiple directions (middle-lateral, ML: r² = −0.22, p < 0.001; vertical, VT: r² = −0.25, p < 0.001), as well as between shooting scores and hand tremor complexity (ML: r² = −0.26, p < 0.001; VT: r² = −0.28, p < 0.001), across all participants. Notably, weak correlations between shooting scores and hand tremor amplitude (ML: r² = −0.27, p < 0.001; VT: r² = −0.33, p < 0.001) and complexity (ML: r² = −0.31, p < 0.001) were observed in the elite group but not in the sub-elite group. Moderate correlation were found between the biceps brachii (short head) RMS and hand tremor amplitude in the VT and ML directions (ML: r² = 0.49, p = 0.010; VT: r² = 0.44, p = 0.025) in all shooters, with a moderate correlation in the ML direction in elite shooters (ML: r² = 0.49, p = 0.034). Our results suggest that hand tremors in air pistol shooters are associated with the skill of the shooters, and muscle activation of the biceps brachii (long head) might be a factor affecting hand tremors. By balancing the agonist and antagonist muscles of the shoulder joint, shooters might potentially reduce hand tremors and improve their shooting scores. Full article

Background and aim: Parkinson’s disease (PD), a neuro-degenerative disorder, is often detected by the onset of its motor symptoms such as rest tremor. Unfortunately, motor symptoms appear only when approximately 40–60% of the dopaminergic neurons in the substantia nigra are lost. In most cases, by the time PD is clinically diagnosed, the disease may already have started 4 to 6 years beforehand. There is therefore a need for developing a test for detecting PD before the onset of motor symptoms. This phase of PD is referred to as Presymptomatic PD (PPD). The motor symptoms of Parkinson’s Disease are manifestations of instability in the sensorimotor system that develops gradually due to the neurodegenerative process. In this paper, based on the above insight, we propose a new method that can potentially be used to detect the degradation of motor control stability, which can be employed for the detection of PPD. Methods: The proposed method tracks the tendency of a feedback control system to transition to an unstable state and uses a machine learning algorithm for its robust detection. This method is explored using a simple simulation example consisting of a simple pendulum with a proportional-integral-derivative (PID) controller as a conceptual representation for both healthy and PPD individuals with a noise variance of 0.01 and a noise variance of 0.1. The present study adopts a longitudinal design to evaluate the effectiveness of the proposed methodology. Specifically, the performance of the proposed approach, with specific choices of features, is compared to that of the Support Vector Machine (SVM) algorithm for machine learning under conditions of incremental delay-induced instability. This comparison is made with results obtained using the Longitudinal Support Vector Machine (LSVM) algorithm for machine learning, which is better suited for longitudinal studies. Results: The results of SVM with one choice of features are comparable with the results of LSVM for a noise variance of 0.01. These results are almost unaffected by a noise variance of 0.1. All of the methods showed a high sensitivity above 96% and specificity above 98% on a training data set. In addition, they perform very well with the validation synthetic data set with sensitivity above 95% and specificity above 98%. These results are robust to further increases in noise variance representing the large variances expected in patient populations. Conclusions: The proposed method is evaluated on a synthetic data set, and the machine learning results show a promise and potential for use for detecting PPD through an early diagnostic device. In addition, an example task with physiological measurement that can potentially be used as a clinical movement control test along with representative data from both healthy individuals and PD patients is also presented, demonstrating the feasibility of performing a longitudinal study to validate and test the robustness of the proposed method. Full article

The existence of the physiological tremor of the human hand significantly affects the application of tele-operation systems in performing high-precision tasks, such as tele-surgery, and currently, the process of effectively eliminating the physiological tremor has been an important yet challenging research topic in the tele-operation robot field. Some scholars propose using deep learning algorithms to solve this problem, but a large number of hyperparameters lead to a slow training speed. Later, the support-vector-machine-based methods have been applied to solve the problem, thereby effectively canceling tremors. However, these methods may lose the prediction accuracy, because learning energy cannot be accurately assigned. Therefore, in this paper, we propose a broad-learning-system-based tremor filter, which integrates a series of incremental learning algorithms to achieve fast remodeling and reach the desired performance. Note that the broad-learning-system-based filter has a fast learning rate while ensuring the accuracy due to its simple and novel network structure. Unlike other algorithms, it uses incremental learning algorithms to constantly update network parameters during training, and it stops learning when the error converges to zero. By focusing on the control performance of the slave robot, a sliding mode control approach has been used to improve the performance of closed-loop systems. In simulation experiments, the results demonstrated the feasibility of our proposed method. Full article

Parkinson’s Disease (PD) is the second most common neurodegenerative disorder seen, especially in the elderly. Tremor, shaking, movement problems, and difficulty with balance and coordination are among the hallmarks, and dopaminergic neuronal loss in substantia nigra pars compacta of the brain and aggregation of intracellular protein α-synuclein are the pathological characterizations. Neuroinflammation has emerged as an involving mechanism at the initiation and development of PD. It is a complex network of interactions comprising immune and non-immune cells in addition to mediators of the immune response. Microglia, the resident macrophages in the CNS, take on the leading role in regulating neuroinflammation and maintaining homeostasis. Under normal physiological conditions, they exist as “homeostatic” but upon pathological stimuli, they switch to the “reactive state”. Pro-inflammatory (M1) and anti-inflammatory (M2) phenotypes are used to classify microglial activity with each phenotype having its own markers and released mediators. When M1 microglia are persistent, they will contribute to various inflammatory diseases, including neurodegenerative diseases, such as PD. In this review, we focus on the role of microglia mediated neuroinflammation in PD and also signaling pathways, receptors, and mediators involved in the process, presenting the studies that associate microglia-mediated inflammation with PD. A better understanding of this complex network and interactions is important in seeking new therapies for PD and possibly other neurodegenerative diseases. Full article

The increase in life expectancy, according to the World Health Organization, is a fact, and with it rises the incidence of age-related neurodegenerative diseases. The most recurrent symptoms are those associated with tremors resulting from Parkinson’s disease (PD) or essential tremors (ETs). The main alternatives for the treatment of these patients are medication and surgical intervention, which sometimes have restrictions and side effects. Through computer simulations in Matlab software, this work investigates the performance of adaptive algorithms based on least mean squares (LMS) to suppress tremors in upper limbs, especially in the hands. The signals resulting from pathological hand tremors, related to PD, present components at frequencies that vary between 3 Hz and 6 Hz, with the more significant energy present in the fundamental and second harmonics, while physiological hand tremors, referred to ET, vary between 4 Hz and 12 Hz. We simulated and used these signals as reference signals in adaptive algorithms, filtered-x least mean square (Fx-LMS), filtered-x normalized least mean square (Fx-NLMS), and a hybrid Fx-LMS–NLMS purpose. Our results showed that the vibration control provided by the Fx-LMS–LMS algorithm is the most suitable for physiological tremors. For pathological tremors, we used a proposed algorithm with a filtered sinusoidal input signal, Fsinx-LMS, which presented the best results in this specific case. Full article

The aim of the study was to determine the relationship between changes in physiological tremor after exercise and changes in the traction properties of the stretch reflex indirectly assessed using the Hoffmann reflex test. The research involved 19 young men practicing canoe sprint (age 16.4 ± 0.7 years, body mass 74.4 ± 6.7 kg, body height 182.1 ± 4.3 cm, training experience 4.8 ± 1.6 years). During resting tests, Hoffmann reflex measurements were performed from the soleus muscle, physiological tremor of the lower limb, and the blood lactate concentration was determined. Then, a graded test was carried out on the kayak/canoe ergometer. Immediately after the exercise and in the 10th and 25th minute following the exercise, Hoffmann’s reflex of the soleus muscle was measured. The physiological tremor was measured at 5, 15 and 30 min after exercise. Blood lactate concentrations were determined immediately after physiological tremor. Both the parameters of Hoffmann’s reflex and physiological tremor changed significantly after exercise. There were no significant interrelationships between Hoffmann reflex measurements and physiological tremor in resting and post-exercise conditions. No significant correlation was detected between changes in physiological tremor and changes in Hoffmann reflex parameters. It is to be assumed that there is no connection between a stretch reflex and a physiological tremor. Full article

While master-slave teleoperated robotic systems have extensive applications in practice, the physiological tremors can easily affect the control accuracy and even destroy the stability of the closed-loop control systems during operation. Hence, the development of some effective approaches for counteracting physiological tremors is of both theoretical and practical importance. In this paper, a broad learning network-based filter integrating a deep learning network and modified incremental learning algorithms is proposed to reconstruct and compensate for tremor signals. To strengthen the recognition of correlations between different moments, the lateral connectivity structure is adopted to obtain multi-scale feature maps. Each feature window is obtained from multi-scale feature maps generated by the convolutional neural network, which has an advantage that makes the feature nodes fuse the feature information of long time series and short time series by the lateral connection. The broad learning network is a unique construction, which only needs to obtain the input and the output to conveniently calculate the connection weights by the pseudo-inverse without involving backpropagation. It is known that the relation between the data X and the label Y can be represented as $XW=Y$, and the solution W can be obtained by the pseudo-inverse $W=X^{+}Y$. In addition, to guarantee the ill-posed problem, a ridge regression algorithm is used for the pseudo-inverse calculation. The effectiveness of our raised network architecture is illustrated by comparative simulation and experiment results. Full article

(1) Background: The dynamics of hand tremors involve nonrandom and short-term motor patterns (STMPs). This study aimed to (i) identify STMPs in Parkinson’s disease (PD) and physiological resting tremor and (ii) characterize STMPs by amplitude, persistence, and regularity. (2) Methods: This study included healthy (N = 12, 60.1 ± 5.9 years old) and PD (N = 14, 65 ± 11.54 years old) participants. The signals were collected using a triaxial gyroscope on the dorsal side of the hand during a resting condition. Data were preprocessed and seven features were extracted from each 1 s window with 50% overlap. The STMPs were identified using the clustering technique k-means applied to the data in the two-dimensional space given by t-Distributed Stochastic Neighbor Embedding (t-SNE). The frequency, transition probability, and duration of the STMPs for each group were assessed. All STMP features were averaged across groups. (3) Results: Three STMPs were identified in tremor signals (p < 0.05). STMP 1 was prevalent in the healthy control (HC) subjects, STMP 2 in both groups, and STMP3 in PD. Only the coefficient of variation and complexity differed significantly between groups. (4) Conclusion: These results can help professionals characterize and evaluate tremor severity and treatment efficacy. Full article

Tremor is the most common movement disorder. Several drugs reduce tremor severity, but no cures are available. Propranolol, a β-adrenergic receptor blocker, is the leading treatment for tremor. However, the in vivo circuit mechanisms by which propranolol decreases tremor remain unclear. Here, we test whether propranolol modulates activity in the cerebellum, a key node in the tremor network. We investigated the effects of propranolol in healthy control mice and Car8^wdl/wdl mice, which exhibit pathophysiological tremor and ataxia due to cerebellar dysfunction. Propranolol reduced physiological tremor in control mice and reduced pathophysiological tremor in Car8^wdl/wdl mice to control levels. Open field and footprinting assays showed that propranolol did not correct ataxia in Car8^wdl/wdl mice. In vivo recordings in awake mice revealed that propranolol modulates the spiking activity of control and Car8^wdl/wdl Purkinje cells. Recordings in cerebellar nuclei neurons, the targets of Purkinje cells, also revealed altered activity in propranolol-treated control and Car8^wdl/wdl mice. Next, we tested whether propranolol reduces tremor through β₁ and β₂ adrenergic receptors. Propranolol did not change tremor amplitude or cerebellar nuclei activity in β₁ and β₂ null mice or Car8^wdl/wdl mice lacking β₁ and β₂ receptor function. These data show that propranolol can modulate cerebellar circuit activity through β-adrenergic receptors and may contribute to tremor therapeutics. Full article