Search Results (126)

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

Patients with Parkinson’s disease (PD) can present several biomechanical alterations, such as tremors, rigidity, bradykinesia, postural instability, and gait alterations. The Movement Disorder Society–Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) has a good reputation for uniformly evaluating motor and non-motor aspects of PD. However, motor clinical assessment depends on visual observations, which are mostly qualitative, with subtle differences not recognized. Many works have examined evaluations and analyses of these biomechanical alterations. However, there are no reviews on this topic. This paper presents a scoping review of computer models based on expert knowledge and machine learning (ML). The eligibility criteria and sources of evidence are represented by papers in journals indexed in the Journal Citation Report (JCR), and this paper analyzes the data, methods, results, and application opportunities in clinical environments or as support for new research. Finally, we analyze the results’ explainability and the acceptance of such systems as tools to help physicians, both now and in future contributions. Many researchers have addressed PD biomechanics by using explainable artificial intelligence or combining several analysis models to provide explainable and transparent results, considering possible biases and precision and creating trust and security when using the models. Full article

Parkinson’s Disease (PD) is a neurological condition that worsens with time and is characterized bysymptoms such as cognitive impairment andbradykinesia, stiffness, and tremors. Parkinson’s is attributed to the interference of brain cells responsible for dopamine production, a substance regulating communication between brain cells. The brain cells involved in dopamine generation handle adaptation and control, and smooth movement. Convolutional Neural Networks are used to extract distinctive visual characteristics from numerous graphomotor sample representations generated by both PD and control participants. The proposed method presents an optimal feature selection technique based on Deep Learning (DL) and the Dynamic Bag of Features Optimization Technique (DBOFOT). Our method combines neural network-based feature extraction with a strong optimization technique to dynamically choose the most relevant characteristics from biological data. Advanced DL architectures are then used to classify the chosen features, guaranteeing excellent computational efficiency and accuracy. The framework’s adaptability to different datasets further highlights its versatility and potential for further medical applications. With a high accuracy of 0.93, the model accurately identifies 93% of the cases that are categorized as Parkinson’s. Additionally, it has a recall of 0.89, which means that 89% of real Parkinson’s patients are accurately identified. While the recall for Class 0 (Healthy) is 0.75, meaning that 75% of the real healthy cases are properly categorized, the precision decreases to 0.64 for this class, indicating a larger false positive rate. Full article

Near-infrared (NIR) spectroscopy is a powerful non-invasive technique for assessing the optical properties of human tissues, capturing spectral signatures that reflect their biochemical and structural characteristics. In this study, we investigated the use of NIR reflectance spectroscopy combined with chemometric analysis to distinguish between patients with Essential Tremor (ET) and healthy individuals. ET is a common movement disorder characterized by involuntary tremors, often making it difficult to clinically differentiate from other neurological conditions. We hypothesized that NIR spectroscopy could reveal unique optical fingerprints that differentiate ET patients from healthy controls, potentially providing an additional diagnostic tool for ET. We collected NIR reflectance spectra from both extracranial (biceps and triceps) and cranial (cerebral cortex and brainstem) sites in ET patients and healthy subjects. Using Partial Least Squares Discriminant Analysis (PLS-DA) and Partial Least Squares (PLS) regression models, we analyzed the optical properties of the tissues and identified significant wavelength peaks associated with spectral differences between the two groups. The chemometric analysis successfully classified subjects based on their spectral profiles, revealing distinct differences in optical properties between cranial and extracranial sites in ET patients compared to healthy controls. Our results suggest that NIR spectroscopy, combined with machine learning algorithms, offers a promising non-invasive method for the in vivo characterization and differentiation of tissues in ET patients. Full article

Background and Objectives: Parkinson’s disease (PD) is a pathological state characterized by a combined set of abnormal movements including slow motion, resting tremors, profound stiffness of skeletal muscles, or obvious abnormalities in posture and gait, together with significant behavioral changes. Until now, no single therapeutic modality was able to provide a complete cure for PD. This work was a trial to assess the immunomodulatory effects of canagliflozin with or without levodopa/carbidopa on rotenone-induced parkinsonism in Balb/c mice. Materials and Methods: In a mouse model of PD, the effect of canagliflozin with or without levodopa/carbidopa was assessed at the behavioral, biochemical, and histopathological levels. Results: The combination of levodopa/carbidopa and canagliflozin significantly mitigated the changes induced by rotenone administration regarding the behavioral tests, striatal dopamine, antioxidant status, Nrf2 content, SIRT–1/PPAR–gamma axis, RAGE/HMGB1/NF-κB signaling, and mitochondrial dysfunction; abrogated the neuroinflammatory responses, and alleviated the histomorphologic changes induced by rotenone administration relative to the groups that received either levodopa/carbidopa or canagliflozin alone. Conclusions: Canagliflozin may represent a new adjuvant therapeutic agent that may add value to the combatting effects of levodopa/carbidopa against the pathological effects of PD. Full article

Abstract: Background: An important neurological disorder of Parkinson’s Disease (PD) is characterized by motor and non-motor activity of the patients. Empirical condition of the patient: PD assessment uses the Movement Disorder Society Unified Parkinson’s Rating Scale part III (MDS-UPDRS-III) measures for identifying the prediction of PD. Due to the unstable value of the measurement, the PD prediction and tracking lead to a lower prediction rate. Methods: To overcome this limitation, this paper proposed the Graph Wavelet Transform (GWT) based weighted feature extraction along with the Graph Neutral Network (GNN) classification. The main contribution of this research is (i) The weighted correlation between the data is calculated by GWT for effective prediction of PD. (ii) Machine learning algorithms were trained to predict Parkinson’s disease based on these patterns. In this research, we developed a new model called Graph Neural Network (GNN) to predict PD tremors’ MDS-UPDRS-III score using input data. To strengthen PD research and enable the construction of individualized treatment plans, these linked networks work together to methodically examine the data and find significant discoveries. Results: The proposed approach for predicting PD severity (motor- and MDS_UPDRS) has a mean squared error of 0.1796 and a root mean squared error of 0.2845, according to the experimental data. The prediction accuracy is increased by 27.66%, 54.11%, and 0.71%, correspondingly, when compared with the most effective State-of-the-Art methods of DNN, ANFIS + SVR, and Mixed MLP models. Conclusion: In conclusion, this proves that the proposed strategy is more effective at making predictions. 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

Parkinson’s disease (PD) is a neurodegenerative disorder characterized by motor symptoms like tremors and bradykinesia. PD’s pathology involves the aggregation of α-synuclein and loss of dopaminergic neurons, leading to altered neural oscillations in the cortico-basal ganglia-thalamic network. Despite extensive research, the relationship between the motor symptoms of PD and transient changes in brain oscillations before and after motor tasks in different brain regions remain unclear. This study aimed to investigate neural oscillations in both healthy and PD model mice using local field potential (LFP) recordings from multiple brain regions during rest and locomotion. The histological evaluation confirmed the significant dopaminergic neuron loss in the injection side in 6-OHDA lesioned mice. Behavioral tests showed motor deficits in these mice, including impaired coordination and increased forelimb asymmetry. The LFP analysis revealed increased delta, theta, alpha, beta, and gamma band activity in 6-OHDA lesioned mice during movement, with significant increases in multiple brain regions, including the primary motor cortex (M1), caudate–putamen (CPu), subthalamic nucleus (STN), substantia nigra pars compacta (SNc), and pedunculopontine nucleus (PPN). Taken together, these results show that the motor symptoms of PD are accompanied by significant transient increases in brain oscillations, especially in the gamma band. This study provides potential biomarkers for early diagnosis and therapeutic evaluation by elucidating the relationship between specific neural oscillations and motor deficits in PD. Full article

A method is proposed for analyzing the tremor of the earth’s surface, measured by GPS, in order to highlight prognostic effects. The method is applied to the analysis of daily time series of vertical displacements in Japan. The network of 1047 stations is divided into 15 clusters. The Huang Empirical Mode Decomposition (EMD) is applied to the time series of the principal components from the clusters, with subsequent calculation of instantaneous amplitudes using the Hilbert transform. To ensure the stability of estimates of the waveforms of the EMD decomposition, 1000 independent additive realizations of white noise of limited amplitude were averaged before the Hilbert transform. Using a parametric model of the intensities of point processes, we analyze the connections between the instants of sequences of times of the largest local maxima of instantaneous amplitudes, averaged over the number of clusters and the times of earthquakes in the vicinity of Japan with minimum magnitude thresholds of 5.5 for the time interval 2012–2023. It is shown that the sequence of the largest local maxima of instantaneous amplitudes significantly more often precedes the moments of time of earthquakes (roughly speaking, has an “influence”) than the reverse “influence” of earthquakes on the maxima of amplitudes. Full article

Over the years, the growing “epidemic” spread of cocaine use represents a crucial public health and social problem worldwide. According to the 2023 World Drug Report, 0.4% of the world’s population aged 15 to 64 report using cocaine; this number corresponds to approximately 24.6 million cocaine users worldwide and approximately 1 million subjects with cocaine use disorder (CUD). While we specifically know the short-term side effects induced by cocaine, unfortunately, we currently do not have exhaustive information about the medium/long-term side effects of the substance on the body. The scientific literature progressively highlights that the chronic use of cocaine is related to an increase in cardio- and cerebrovascular risk and probably to a greater incidence of psychomotor symptoms and neurodegenerative processes. Several studies have highlighted an increased risk of antipsychotic-induced extrapyramidal symptoms (EPSs) in patients with psychotic spectrum disorders comorbid with psychostimulant abuse. EPSs include movement dysfunction such as dystonia, akathisia, tardive dyskinesia, and characteristic symptoms of Parkinsonism such as rigidity, bradykinesia, and tremor. In the present paper, we propose a model of interpretation of the neurobiological mechanisms underlying the hypothesized increased vulnerability in chronic cocaine abusers to neurodegenerative disorders with psychomotor symptoms. Specifically, we supposed that the chronic administration of cocaine produces significant neurobiological changes, causing a complex dysregulation of various neurotransmitter systems, mainly affecting subcortical structures and the dopaminergic pathways. We believe that a better understanding of these cellular and molecular mechanisms involved in cocaine-induced neuropsychotoxicity may have helpful clinical implications and provide targets for therapeutic intervention. Full article

Parkinson’s disease (PD) is a common neurodegenerative disorder characterized by symptoms such as bradykinesia, resting tremor, and rigidity, primarily driven by the degradation of dopaminergic (DA) neurons in the substantia nigra. A significant contributor to familial autosomal dominant PD cases is mutations in the LRRK2 gene, making it a primary therapeutic target. This study explores the role of microRNAs (miRNAs) in regulating the proteomic stress responses associated with neurodegeneration in PD using C. elegans models. Our focus is on miR-71, a miRNA known to affect stress resistance and act as a pro-longevity factor in C. elegans. We investigated miR-71’s function in C. elegans models of PD, where mutant LRRK2 expression correlates with dopaminergic neuronal death. Our findings reveal that miR-71 overexpression rescues motility defects and slows dopaminergic neurodegeneration in these models, suggesting its critical role in mitigating the proteotoxic effects of mutant LRRK2. Conversely, miR-71 knockout exacerbates neuronal death caused by mutant LRRK2. Additionally, our data indicate that miR-71’s neuroprotective effect involves downregulating the toll receptor domain protein tir-1, implicating miR-71 repression of tir-1 as vital in the response to LRRK2-induced proteotoxicity. These insights into miR-71’s role in C. elegans models of PD not only enhance our understanding of molecular mechanisms in neurodegeneration but also pave the way for potential research into human neurodegenerative diseases, leveraging the conservation of miRNAs and their targets across species. Full article

The goal of this study was to develop a model describing the relationship between the ground-displacement-caused tremors induced by underground mining, and mining and geological factors using the Random Forest Regression machine learning method. The Rudna mine (Poland) was selected as the research area, which is one of the largest deep copper ore mines in the world. The SAR Interferometry methods, Differential Interferometric Synthetic Aperture Radar (DInSAR) and Small Baseline Subset (SBAS), were used in the first case to detect line-of-sight (LOS) displacements, and in the second case to detect cumulative LOS displacements caused by mining tremors. The best-prediction LOS displacement model was characterized by R² = 0.93 and RMSE = 5 mm, which proved the high effectiveness and a high degree of explanation of the variation of the dependent variable. The identified statistically significant driving variables included duration of exploitation, the area of the exploitation field, energy, goaf area, and the average depth of field exploitation. The results of the research indicate the great potential of the proposed solutions due to the availability of data (found in the resources of each mine), and the effectiveness of the methods used. Full article

Parkinson’s disease (PD) is a complex neurodegenerative disorder characterized by numerous motor and non-motor symptoms. Recent data highlight a potential interplay between the gut microbiota and the pathophysiology of PD. The degeneration of dopaminergic neurons in PD leads to motor symptoms (tremor, rigidity, and bradykinesia), with antecedent gastrointestinal manifestations, most notably constipation. Consequently, the gut emerges as a plausible modulator in the neurodegenerative progression of PD. Key molecular changes in PD are discussed in the context of the gut–brain axis. Evidence suggests that the alterations in the gut microbiota composition may contribute to gastroenteric inflammation and influence PD symptoms. Disturbances in the levels of inflammatory markers, including tumor necrosis factor-α (TNF α), interleukin -1β (IL-1β), and interleukin-6 (IL-6), have been observed in PD patients. These implicate the involvement of systemic inflammation in disease pathology. Fecal microbiota transplantation emerges as a potential therapeutic strategy for PD. It may mitigate inflammation by restoring gut homeostasis. Preclinical studies in animal models and initial clinical trials have shown promising results. Overall, understanding the interplay between inflammation, the gut microbiota, and PD pathology provides valuable insights into potential therapeutic interventions. This review presents recent data about the bidirectional communication between the gut microbiome and the brain in PD, specifically focusing on the involvement of inflammatory biomarkers. Full article

Rockbursts represent one of the most serious and severe natural hazards emerging in underground copper mines within the Legnica–Glogow Copper District (LGCD) in Poland. The contributing factor determining the scale of this event is mining-induced seismicity of the rock strata. Extensive expertise of the copper mining practitioners clearly indicates that high-energy tremors are the consequence of tectonic disturbances or can be attributed to stress/strain behaviour within the burst-prone roof strata. Apparently, seismic activity is a triggering factor; hence, attempts are made by mine operators to mitigate and control that risk. Underlying the effective rockburst control strategy is a reliable seismicity forecast, taking into account the causes of the registered phenomena. The paper summarises the geomechanics analyses aimed to verify the actual seismic and rockburst hazard levels in one of the panels within the copper mine Rudna (LGCD). Two traverses were designated at the face range and comparative analyses were conducted to establish correlations between the locations of epicentres of registered tremors and anomaly zones obtained via analytical modelling of changes in stress/strain behaviours within the rock strata. The main objective of this study was to evaluate the likelihood of activating carbonate/anhydrite layers within the main roof over the excavation being mined, with an aim to verify the potential causes and conditions which might have triggered the registered high-energy events. Special attention is given to two seismic events giving rise to rockbursts in mine workings. Results seem to confirm the adequacy and effectiveness of solutions provided by mechanics of deformable bodies in the context of forecasting the scale and risk of dynamic phenomena and selecting the appropriate mitigation and control measures in copper mines employing the room-and-pillar mining system. Full article

Parkinson’s disease (PD) is a progressive neurological disorder that is typically characterized by a range of motor dysfunctions, and its impact extends beyond physical abnormalities into emotional well-being and cognitive symptoms. The loss of dopaminergic neurons in the substantia nigra pars compacta (SNc) leads to an array of dysfunctions in the functioning of the basal ganglia (BG) circuitry that manifests into PD. While active research is being carried out to find the root cause of SNc cell death, various therapeutic techniques are used to manage the symptoms of PD. The most common approach in managing the symptoms is replenishing the lost dopamine in the form of taking dopaminergic medications such as levodopa, despite its long-term complications. Another commonly used intervention for PD is deep brain stimulation (DBS). DBS is most commonly used when levodopa medication efficacy is reduced, and, in combination with levodopa medication, it helps reduce the required dosage of medication, prolonging the therapeutic effect. DBS is also a first choice option when motor complications such as dyskinesia emerge as a side effect of medication. Several studies have also reported that though DBS is found to be effective in suppressing severe motor symptoms such as tremors and rigidity, it has an adverse effect on cognitive capabilities. Henceforth, it is important to understand the exact mechanism of DBS in alleviating motor symptoms. A computational model of DBS stimulation for motor symptoms will offer great insights into understanding the mechanisms underlying DBS, and, along this line, in our current study, we modeled a cortico-basal ganglia circuitry of arm reaching, where we simulated healthy control (HC) and PD symptoms as well as the DBS effect on PD tremor and bradykinesia. Our modeling results reveal that PD tremors are more correlated with the theta band, while bradykinesia is more correlated with the beta band of the frequency spectrum of the local field potential (LFP) of the subthalamic nucleus (STN) neurons. With a DBS current of 220 pA, 130 Hz, and a 100 microsecond pulse-width, we could found the maximum therapeutic effect for the pathological dynamics simulated using our model using a set of parameter values. However, the exact DBS characteristics vary from patient to patient, and this can be further studied by exploring the model parameter space. This model can be extended to study different DBS targets and accommodate cognitive dynamics in the future to study the impact of DBS on cognitive symptoms and thereby optimize the parameters to produce optimal performance effects across modalities. Combining DBS with rehabilitation is another frontier where DBS can reduce symptoms such as tremors and rigidity, enabling patients to participate in their therapy. With DBS providing instant relief to patients, a combination of DBS and rehabilitation can enhance neural plasticity. One of the key motivations behind combining DBS with rehabilitation is to expect comparable results in motor performance even with milder DBS currents. Full article