Disentangling Bradykinesia and Rigidity in Parkinson's Disease: Evidence from Short- and Long-Term Subthalamic Nucleus Deep Brain Stimulation.

OBJECTIVE: Bradykinesia and rigidity are considered closely related motor signs in Parkinson disease (PD), but recent neurophysiological findings suggest distinct pathophysiological mechanisms. This study aims to examine and compare longitudinal changes in bradykinesia and rigidity in PD patients treated with bilateral subthalamic nucleus deep brain stimulation (STN-DBS). METHODS: In this retrospective cohort study, the clinical progression of appendicular and axial bradykinesia and rigidity was assessed up to 15 years after STN-DBS in the best treatment conditions (ON medication and ON stimulation). The severity of bradykinesia and rigidity was examined using ad hoc composite scores from specific subitems of the Unified Parkinson's Disease Rating Scale motor part (UPDRS-III). Short- and long-term predictors of bradykinesia and rigidity were analyzed through linear regression analysis, considering various preoperative demographic and clinical data, including disease duration and severity, phenotype, motor and cognitive scores (eg, frontal score), and medication. RESULTS: A total of 301 patients were examined before and 1 year after surgery. Among them, 101 and 56 individuals were also evaluated at 10-year and 15-year follow-ups, respectively. Bradykinesia significantly worsened after surgery, especially in appendicular segments (p < 0.001). Conversely, rigidity showed sustained benefit, with unchanged clinical scores compared to preoperative assessment (p > 0.05). Preoperative motor disability (eg, composite scores from the UPDRS-III) predicted short- and long-term outcomes for both bradykinesia and rigidity (p < 0.01). Executive dysfunction was specifically linked to bradykinesia but not to rigidity (p < 0.05). INTERPRETATION: Bradykinesia and rigidity show long-term divergent progression in PD following STN-DBS and are associated with independent clinical factors, supporting the hypothesis of partially distinct pathophysiology. ANN NEUROL 2024;96:234-246.

Rigidity in Parkinson's disease: evidence from biomechanical and neurophysiological measures.

Although rigidity is a cardinal motor sign in patients with Parkinson's disease (PD), the instrumental measurement of this clinical phenomenon is largely lacking, and its pathophysiological underpinning remains still unclear. Further advances in the field would require innovative methodological approaches able to measure parkinsonian rigidity objectively, discriminate the different biomechanical sources of muscle tone (neural or visco-elastic components), and finally clarify the contribution to 'objective rigidity' exerted by neurophysiological responses, which have previously been associated with this clinical sign (i.e. the long-latency stretch-induced reflex). Twenty patients with PD (67.3 ± 6.9 years) and 25 age- and sex-matched controls (66.9 ± 7.4 years) were recruited. Rigidity was measured clinically and through a robotic device. Participants underwent robot-assisted wrist extensions at seven different angular velocities randomly applied, when ON therapy. For each value of angular velocity, several biomechanical (i.e. elastic, viscous and neural components) and neurophysiological measures (i.e. short and long-latency reflex and shortening reaction) were synchronously assessed and correlated with the clinical score of rigidity (i.e. Unified Parkinson's Disease Rating Scale-part III, subitems for the upper limb). The biomechanical investigation allowed us to measure 'objective rigidity' in PD and estimate the neuronal source of this phenomenon. In patients, 'objective rigidity' progressively increased along with the rise of angular velocities during robot-assisted wrist extensions. The neurophysiological examination disclosed increased long-latency reflexes, but not short-latency reflexes nor shortening reaction, in PD compared with control subjects. Long-latency reflexes progressively increased according to angular velocities only in patients with PD. Lastly, specific biomechanical and neurophysiological abnormalities correlated with the clinical score of rigidity. 'Objective rigidity' in PD correlates with velocity-dependent abnormal neuronal activity. The observations overall (i.e. the velocity-dependent feature of biomechanical and neurophysiological measures of objective rigidity) would point to a putative subcortical network responsible for 'objective rigidity' in PD, which requires further investigation.

Language-related motor facilitation in Italian Sign Language signers.

Linguistic tasks facilitate corticospinal excitability as revealed by increased motor evoked potential (MEP) induced by transcranial magnetic stimulation (TMS) in the dominant hand. This modulation of the primary motor cortex (M1) excitability may reflect the relationship between speech and gestures. It is conceivable that in healthy individuals who use a sign language this cortical excitability modulation could be rearranged. The aim of this study was to evaluate the effect of spoken language tasks on M1 excitability in a group of hearing signers. Ten hearing Italian Sign Language (LIS) signers and 16 non-signer healthy controls participated. Single-pulse TMS was applied to either M1 hand area at the baseline and during different tasks: (i) reading aloud, (ii) silent reading, (iii) oral movements, (iv) syllabic phonation and (v) looking at meaningless non-letter strings. Overall, M1 excitability during the linguistic and non-linguistic tasks was higher in LIS group compared to the control group. In LIS group, MEPs were significantly larger during reading aloud, silent reading and non-verbal oral movements, regardless the hemisphere. These results suggest that in hearing signers there is a different modulation of the functional connectivity between the speech-related brain network and the motor system.

Multisensor Integrated Platform Based on MEMS Charge Variation Sensing Technology for Biopotential Acquisition.

We propose a new methodology for long-term biopotential recording based on an MEMS multisensor integrated platform featuring a commercial electrostatic charge-transfer sensor. This family of sensors was originally intended for presence tracking in the automotive industry, so the existing setup was engineered for the acquisition of electrocardiograms, electroencephalograms, electrooculograms, and electromyography, designing a dedicated front-end and writing proper firmware for the specific application. Systematic tests on controls and nocturnal acquisitions from patients in a domestic environment will be discussed in detail. The excellent results indicate that this technology can provide a low-power, unexplored solution to biopotential acquisition. The technological breakthrough is in that it enables adding this type of functionality to existing MEMS boards at near-zero additional power consumption. For these reasons, it opens up additional possibilities for wearable sensors and strengthens the role of MEMS technology in medical wearables for the long-term synchronous acquisition of a wide range of signals.

Familial adult myoclonus epilepsy: Neurophysiological investigations.

Familial adult myoclonus epilepsy (FAME) also described as benign adult familial myoclonus epilepsy (BAFME) is a high-penetrant autosomal dominant condition featuring cortical myoclonus of varying frequency and occasional/rare convulsive seizures. In this update we provide a detailed overview of the main neurophysiological findings so far reported in patients with FAME/BAFME. After reviewing the diagnostic contribution of each neurophysiological technique, we discuss the possible mechanisms underlying cortical hyperexcitability and suggest the involvement of more complex circuits engaging cortical and subcortical structures, such as the cerebellum. We, thus, propose that FAME/BAFME clinical features should arise from an "abnormal neuronal network activity," where the cerebellum represents a possible common denominator. In the last part of the article, we suggest that future neurophysiological studies using more advanced transcranial magnetic stimulation (TMS) protocols could be used to evaluate the functional connectivity between the cerebellum and cortical structures. Finally, non-invasive brain stimulation techniques such as repetitive TMS or transcranial direct current stimulation could be assessed as potential therapeutic tools to ameliorate cortical excitability.

Driving motor cortex oscillations modulates bradykinesia in Parkinson's disease.

In patients with Parkinson's disease, beta (ß) and gamma (γ) oscillations are altered in the basal ganglia, and this abnormality contributes to the pathophysiology of bradykinesia. However, it is unclear whether ß and γ rhythms at the primary motor cortex (M1) level influence bradykinesia. Transcranial alternating current stimulation (tACS) can modulate cortical rhythms by entraining endogenous oscillations. We tested whether ß- and γ-tACS on M1 modulate bradykinesia in patients with Parkinson's disease by analysing the kinematic features of repetitive finger tapping, including movement amplitude, velocity and sequence effect, recorded during ß-, γ- and sham tACS. We also verified whether possible tACS-induced bradykinesia changes depended on modifications in specific M1 circuits, as assessed by short-interval intracortical inhibition and short-latency afferent inhibition. Patients were studied OFF and ON dopaminergic therapy. Results were compared to those obtained in a group of healthy subjects. In patients, movement velocity significantly worsened during ß-tACS and movement amplitude improved during γ-tACS, while the sequence effect did not change. In addition, short-latency afferent inhibition decreased (reduced inhibition) during ß-tACS and short-interval intracortical inhibition decreased during both γ- and ß-tACS in Parkinson's disease. The effects of tACS were comparable between OFF and ON sessions. In patients OFF therapy, the degree of short-interval intracortical inhibition modulation during ß- and γ-tACS correlated with movement velocity and amplitude changes. Moreover, there was a positive correlation between the effect of γ-tACS on movement amplitude and motor symptoms severity. Our results show that cortical ß and γ oscillations are relevant in the pathophysiology of bradykinesia in Parkinson's disease and that changes in inhibitory GABA-A-ergic interneuronal activity may reflect compensatory M1 mechanisms to counteract bradykinesia. In conclusion, abnormal oscillations at the M1 level of the basal ganglia-thalamo-cortical network play a relevant role in the pathophysiology of bradykinesia in Parkinson's disease.

Botulinum toxin in clinical practice: state of the art of administration provisions in Italy.

Botulinum neurotoxin type A is a remarkable therapeutic approach for muscle hyperactivity syndromes, pain, and related disorders. Despite its wide application in neurology, there is a poor knowledge on delivery protocols and dispatch from the healthcare providers. In this study, we reported the result of a 2020 survey about the administration provisions of botulinum neurotoxin type A in Italy. Seven questions including information on characteristics of botulinum neurotoxin facilities, prescription, reimbursement, and execution modalities were adopted. Sixty participants answered the survey. Despite the wide availability of dedicated centers all over the national territory, there was a surprising lack of standardized and shared administration provisions. Most of the Italian medical structures delivered botulinum neurotoxin through outpatient clinics located in public hospital facilities, through the "F file" reimbursement modality. However, there was no agreement on the reimbursement request modality, creating differences in public costs relative to the botulinum toxin consumption across Italy.

Artificial Intelligence-Based Voice Assessment of Patients with Parkinson's Disease Off and On Treatment: Machine vs. Deep-Learning Comparison.

Parkinson's Disease (PD) is one of the most common non-curable neurodegenerative diseases. Diagnosis is achieved clinically on the basis of different symptoms with considerable delays from the onset of neurodegenerative processes in the central nervous system. In this study, we investigated early and full-blown PD patients based on the analysis of their voice characteristics with the aid of the most commonly employed machine learning (ML) techniques. A custom dataset was made with hi-fi quality recordings of vocal tasks gathered from Italian healthy control subjects and PD patients, divided into early diagnosed, off-medication patients on the one hand, and mid-advanced patients treated with L-Dopa on the other. Following the current state-of-the-art, several ML pipelines were compared usingdifferent feature selection and classification algorithms, and deep learning was also explored with a custom CNN architecture. Results show how feature-based ML and deep learning achieve comparable results in terms of classification, with KNN, SVM and naïve Bayes classifiers performing similarly, with a slight edge for KNN. Much more evident is the predominance of CFS as the best feature selector. The selected features act as relevant vocal biomarkers capable of differentiating healthy subjects, early untreated PD patients and mid-advanced L-Dopa treated patients.

Four Days Are Enough to Provide a Reliable Daily Step Count in Mild to Moderate Parkinson's Disease through a Commercial Smartwatch.

Daily steps could be a valuable indicator of real-world ambulation in Parkinson's disease (PD). Nonetheless, no study to date has investigated the minimum number of days required to reliably estimate the average daily steps through commercial smartwatches in people with PD. Fifty-six patients were monitored through a commercial smartwatch for 5 consecutive days. The total daily steps for each day was recorded and the average daily steps was calculated as well as the working and weekend days average steps. The intraclass correlation coefficient (ICC) (3,k), standard error of measurement (SEM), Bland-Altman statistics, and minimum detectable change (MDC) were used to evaluate the reliability of the step count for every combination of 2-5 days. The threshold for acceptability was set at an ICC ≥ 0.8 with a lower bound of CI 95% ≥ 0.75 and a SAM < 10%. ANOVA and Mann-Whitney tests were used to compare steps across the days and between the working and weekend days, respectively. Four days were needed to achieve an acceptable reliability (ICC range: 0.84-0.90; SAM range: 7.8-9.4%). In addition, daily steps did not significantly differ across the days and between the working and weekend days. These findings could support the use of step count as a walking activity index and could be relevant to developing monitoring, preventive, and rehabilitation strategies for people with PD.

Decipher non-canonical SPAST splicing mutations with the help of functional assays in patients affected by spastic paraplegia 4 (SPG4).

Flowchart showing the molecular approach used to decipher the non-canonical splicing mutations.

The Cerebellum in Drug-naive Children with Tourette Syndrome and Obsessive-Compulsive Disorder.

Tourette syndrome (TS) and obsessive-compulsive disorder (OCD) are two neurodevelopmental disorders characterized by repetitive behaviors. Our recent study in drug-naive children with TS and OCD provided evidence of cerebellar involvement in both disorders. In addition, cerebellar functional connectivity (FC) was similar in TS patients without comorbidities (TSpure) and TS patients with OCD comorbidity (TS + OCD), but differed in pure OCD patients. To investigate in detail the cerebellar involvement in the pathophysiology of TS and OCD, we explored cerebellar structural and functional abnormalities in drug-naive children with TSpure, TS + OCD, and OCD and assessed possible correlations with severity scores. We examined 53 drug-naive children, classified as TSpure (n = 16), TS + OCD (n = 14), OCD (n = 11), or controls (n = 12). All subjects underwent a multimodal 3T magnetic resonance imaging examination. Cerebellar lobular volumes and quantitative diffusion tensor imaging parameters of cerebellar peduncles were used as measures of structural integrity. The dentate nucleus was selected as a region of interest to examine cerebello-cerebral functional connectivity alterations. Structural analysis revealed that both TSpure and TS + OCD patients had higher fractional anisotropy in cerebellar peduncles than controls. Conversely, OCD patients were characterized by lower fractional anisotropy than both controls and TSpure and TS + OCD patients. Lastly, cerebellar functional connectivity analysis revealed significant alterations in the cerebello-thalamo-cortical circuit in TSpure, TS + OCD, and OCD patients. Early cerebellar structural and functional changes in drug-naive pediatric TSpure, TS + OCD, and OCD patients support a primary role of the cerebellum in the pathophysiology of these disorders.

Predicting Axial Impairment in Parkinson's Disease through a Single Inertial Sensor.

BACKGROUND: Current telemedicine approaches lack standardised procedures for the remote assessment of axial impairment in Parkinson's disease (PD). Unobtrusive wearable sensors may be a feasible tool to provide clinicians with practical medical indices reflecting axial dysfunction in PD. This study aims to predict the postural instability/gait difficulty (PIGD) score in PD patients by monitoring gait through a single inertial measurement unit (IMU) and machine-learning algorithms. METHODS: Thirty-one PD patients underwent a 7-m timed-up-and-go test while monitored through an IMU placed on the thigh, both under (ON) and not under (OFF) dopaminergic therapy. After pre-processing procedures and feature selection, a support vector regression model was implemented to predict PIGD scores and to investigate the impact of L-Dopa and freezing of gait (FOG) on regression models. RESULTS: Specific time- and frequency-domain features correlated with PIGD scores. After optimizing the dimensionality reduction methods and the model parameters, regression algorithms demonstrated different performance in the PIGD prediction in patients OFF and ON therapy (r = 0.79 and 0.75 and RMSE = 0.19 and 0.20, respectively). Similarly, regression models showed different performances in the PIGD prediction, in patients with FOG, ON and OFF therapy (r = 0.71 and RMSE = 0.27; r = 0.83 and RMSE = 0.22, respectively) and in those without FOG, ON and OFF therapy (r = 0.85 and RMSE = 0.19; r = 0.79 and RMSE = 0.21, respectively). CONCLUSIONS: Optimized support vector regression models have high feasibility in predicting PIGD scores in PD. L-Dopa and FOG affect regression model performances. Overall, a single inertial sensor may help to remotely assess axial motor impairment in PD patients.

Wearable Electrochemical Sensors in Parkinson's Disease.

Parkinson's disease (PD) is a neurodegenerative disorder associated with widespread aggregation of α-synuclein and dopaminergic neuronal loss in the substantia nigra pars compacta. As a result, striatal dopaminergic denervation leads to functional changes in the cortico-basal-ganglia-thalamo-cortical loop, which in turn cause most of the parkinsonian signs and symptoms. Despite tremendous advances in the field in the last two decades, the overall management (i.e., diagnosis and follow-up) of patients with PD remains largely based on clinical procedures. Accordingly, a relevant advance in the field would require the development of innovative biomarkers for PD. Recently, the development of miniaturized electrochemical sensors has opened new opportunities in the clinical management of PD thanks to wearable devices able to detect specific biological molecules from various body fluids. We here first summarize the main wearable electrochemical technologies currently available and their possible use as medical devices. Then, we critically discuss the possible strengths and weaknesses of wearable electrochemical devices in the management of chronic diseases including PD. Finally, we speculate about possible future applications of wearable electrochemical sensors in PD, such as the attractive opportunity for personalized closed-loop therapeutic approaches.

Long-Term Polygraphic Monitoring through MEMS and Charge Transfer for Low-Power Wearable Applications.

In this work, we propose a wireless wearable system for the acquisition of multiple biopotentials through charge transfer electrostatic sensors realized in MEMS technology. The system is designed for low power consumption and low invasiveness, and thus candidates for long-time monitoring in free-living conditions, with data recording on an SD or wireless transmission to an external elaborator. Thanks to the wide horizon of applications, research is very active in this field, and in the last few years, some devices have been introduced on the market. The main problem with those devices is that their operation is time-limited, so they do not match the growing demand for long monitoring, which is a must-have feature in diagnosing specific diseases. Furthermore, their versatility is hampered by the fact that they have been designed to record just one type of signal. Using ST-Qvar sensors, we acquired an electrocardiogram trace and single-channel scalp electroencephalogram from the frontal lobes, together with an electrooculogram. Excellent results from all three types of acquisition tests were obtained. The power consumption is very low, demonstrating that, thanks to the MEMS technology, a continuous acquisition is feasible for several days.

Machine Learning and Wearable Sensors for the Early Detection of Balance Disorders in Parkinson's Disease.

Dynamic posturography combined with wearable sensors has high sensitivity in recognizing subclinical balance abnormalities in patients with Parkinson's disease (PD). However, this approach is burdened by a high analytical load for motion analysis, potentially limiting a routine application in clinical practice. In this study, we used machine learning to distinguish PD patients from controls, as well as patients under and not under dopaminergic therapy (i.e., ON and OFF states), based on kinematic measures recorded during dynamic posturography through portable sensors. We compared 52 different classifiers derived from Decision Tree, K-Nearest Neighbor, Support Vector Machine and Artificial Neural Network with different kernel functions to automatically analyze reactive postural responses to yaw perturbations recorded through IMUs in 20 PD patients and 15 healthy subjects. To identify the most efficient machine learning algorithm, we applied three threshold-based selection criteria (i.e., accuracy, recall and precision) and one evaluation criterion (i.e., goodness index). Twenty-one out of 52 classifiers passed the three selection criteria based on a threshold of 80%. Among these, only nine classifiers were considered "optimum" in distinguishing PD patients from healthy subjects according to a goodness index ≤ 0.25. The Fine K-Nearest Neighbor was the best-performing algorithm in the automatic classification of PD patients and healthy subjects, irrespective of therapeutic condition. By contrast, none of the classifiers passed the three threshold-based selection criteria in the comparison of patients in ON and OFF states. Overall, machine learning is a suitable solution for the early identification of balance disorders in PD through the automatic analysis of kinematic data from dynamic posturography.

An Efficient Solid-Phase Microextraction-Gas Chromatography-Mass Spectrometry Method for the Analysis of Methyl Farnesoate Released in Growth Medium by Daphnia pulex.

Methyl farnesoate (MF), a juvenile hormone, can influence phenotypic traits and stimulates male production in daphnids. MF is produced endogenously in response to stressful conditions, but it is not known whether this hormone can also be released into the environment to mediate stress signaling. In the present study, for the first time, a reliable solid-phase microextraction-gas chromatography-mass spectrometry (SPME-GC-MS) method was developed and validated for the ultra-trace analysis of MF released in growth medium by Daphnia pulex maintained in presence of crowding w/o MK801, a putative upstream inhibitor of MF endogenous production. Two different clonal lineages, I and S clones, which differ in the sensitivity to the stimuli leading to male production, were also compared. A detection limit of 1.3 ng/L was achieved, along with good precision and trueness, thus enabling the quantitation of MF at ultra-trace level. The achieved results demonstrated the release of MF by both clones at the 20 ng/L level in control conditions, whereas a significant decrease in the presence of crowding was assessed. As expected, a further reduction was obtained in the presence of MK801. These findings strengthen the link between environmental stimuli and the MF signaling pathway. Daphnia pulex, by releasing the juvenile hormone MF in the medium, could regulate population dynamics by means of an autoregulatory feedback loop that controls the intra- and extra-individual-level release of MF produced by endogenous biosynthesis.

Enhancing Gamma Oscillations Restores Primary Motor Cortex Plasticity in Parkinson's Disease.

In humans, γ oscillations in cortical motor areas reflect asynchronous synaptic activity and contribute to plasticity processes. In Parkinson's disease (PD), γ oscillatory activity in the basal ganglia-thalamo-cortical network is altered and the LTP-like plasticity elicited by intermittent theta burst stimulation (iTBS) is reduced in the primary motor cortex (M1). In this study, we tested whether transcranial alternating current stimulation (tACS) delivered at γ frequency promotes iTBS-induced LTP-like plasticity in M1 in PD patients. Sixteen patients (OFF condition) and 16 healthy subjects (HSs) underwent iTBS during γ-tACS (iTBS-γ tACS) and during sham-tACS (iTBS-sham tACS) in two sessions. Motor-evoked potentials (MEPs) evoked by single-pulse transcranial magnetic stimulation and short-interval intracortical inhibition (SICI) were recorded before and after the costimulation. A subgroup of patients also underwent iTBS during ß tACS. iTBS-sham tACS facilitated single-pulse MEPs in HSs, but not in patients. iTBS-γ tACS induced a larger MEP facilitation than iTBS-sham tACS in both groups, with similar values in patients and HSs. In patients, SICI improved after iTBS-γ tACS. The effect produced by iTBS-γ tACS on single-pulse MEPs correlated with disease duration, while changes in SICI correlated with Unified Parkinson's Disease Rating Scale Part III scores. The effect of iTBS-ß tACS on both single-pulse MEPs and SICI was similar to that obtained in the iTBS-sham tACS session. Our data suggest that γ oscillations have a role in the pathophysiology of the abnormal LTP-like plasticity in PD. Entraining M1 neurons at the γ rhythm through tACS may be an effective method to restore impaired plasticity.SIGNIFICANCE STATEMENT In Parkinson's disease, the LTP-like plasticity of the primary motor cortex is impaired, and γ oscillations are altered in the basal ganglia-thalamo-cortical network. Using a combined transcranial magnetic stimulation-transcranial alternating current stimulation approach (iTBS-γ tACS costimulation), we demonstrate that driving γ oscillations restores the LTP-like plasticity in patients with Parkinson's disease. The effects correlate with clinical characteristics of patients, being more evident in less affected patients and weaker in patients with longer disease duration. These findings suggest that cortical γ oscillations play a beneficial role in modulating the LTP-like plasticity of M1 in Parkinson's disease. The iTBS-γ tACS approach may be potentially useful in rehabilitative settings in patients.

Voice Analysis with Machine Learning: One Step Closer to an Objective Diagnosis of Essential Tremor.

BACKGROUND: Patients with essential tremor have upper limb postural and action tremor often associated with voice tremor. The objective of this study was to objectively examine voice tremor and its response to symptomatic pharmacological treatment in patients with essential tremor using voice analysis consisting of power spectral analysis and machine learning. METHODS: We investigated 58 patients (24 men; mean age ± SD, 71.7 ± 9.2 years; range, 38-85 years) and 74 age- and sex-matched healthy subjects (20 men; mean age ± SD, 71.0 ± 12.4 years; range, 43-95 years). We recorded voice samples during sustained vowel emission using a high-definition audio recorder. Voice samples underwent sound signal analysis, including power spectral analysis and support vector machine classification. We compared voice recordings in patients with essential tremor who did and did not manifest clinically overt voice tremor and in patients who were and were not under the symptomatic effect of the best medical treatment. RESULTS: Power spectral analysis demonstrated a prominent oscillatory activity peak at 2-6 Hz in patients who manifested a clinically overt voice tremor. Voice analysis with support vector machine classifier objectively discriminated with high accuracy between controls and patients who did and did not manifest clinically overt voice tremor and between patients who were and were not under the symptomatic effect of the best medical treatment. CONCLUSIONS: In patients with essential tremor, voice tremor is characterized by abnormal oscillatory activity at 2-6 Hz. Voice analysis, including power spectral analysis and support vector machine classification, objectively detected voice tremor and its response to symptomatic pharmacological treatment in patients with essential tremor. © 2021 International Parkinson and Movement Disorder Society.

Prediction of Freezing of Gait in Parkinson's Disease Using Wearables and Machine Learning.

Freezing of gait (FOG) is one of the most troublesome symptoms of Parkinson's disease, affecting more than 50% of patients in advanced stages of the disease. Wearable technology has been widely used for its automatic detection, and some papers have been recently published in the direction of its prediction. Such predictions may be used for the administration of cues, in order to prevent the occurrence of gait freezing. The aim of the present study was to propose a wearable system able to catch the typical degradation of the walking pattern preceding FOG episodes, to achieve reliable FOG prediction using machine learning algorithms and verify whether dopaminergic therapy affects the ability of our system to detect and predict FOG. METHODS: A cohort of 11 Parkinson's disease patients receiving (on) and not receiving (off) dopaminergic therapy was equipped with two inertial sensors placed on each shin, and asked to perform a timed up and go test. We performed a step-to-step segmentation of the angular velocity signals and subsequent feature extraction from both time and frequency domains. We employed a wrapper approach for feature selection and optimized different machine learning classifiers in order to catch FOG and pre-FOG episodes. RESULTS: The implemented FOG detection algorithm achieved excellent performance in a leave-one-subject-out validation, in patients both on and off therapy. As for pre-FOG detection, the implemented classification algorithm achieved 84.1% (85.5%) sensitivity, 85.9% (86.3%) specificity and 85.5% (86.1%) accuracy in leave-one-subject-out validation, in patients on (off) therapy. When the classification model was trained with data from patients on (off) and tested on patients off (on), we found 84.0% (56.6%) sensitivity, 88.3% (92.5%) specificity and 87.4% (86.3%) accuracy. CONCLUSIONS: Machine learning models are capable of predicting FOG before its actual occurrence with adequate accuracy. The dopaminergic therapy affects pre-FOG gait patterns, thereby influencing the algorithm's effectiveness.

An integrated approach for chemical water quality assessment of an urban river stretch through Effect-Based Methods and emerging pollutants analysis with a focus on genotoxicity.

The impact of emerging chemical pollutants, on both status and functionality of aquatic ecosystems is worldwide recognized as a relevant issue of concern that should be assessed and managed by researchers, policymakers, and all relevant stakeholders. In Europe, the Reach Regulation has registered more than 100.000 chemical substances daily released in the environment. Furthermore, the effects related to the mixture of substances present in aquatic ecosystems may not be predictable on the basis of chemical analyses alone. This evidence, coupled with the dramatic effects of climate changes on water resources through water scarcity and flooding, makes urgent the application of innovative, fast and reliable monitoring methods. In this context, Effect-Based Methods (EBMs) have been applied in the urban stretch of the Tiber River (Central Italy) with the aim of understanding if detrimental pressures affect aquatic environmental health. In particular, different eco-genotoxicological assays have been used in order to detect genotoxic activity of chemicals present in the river, concurrently characterized by chemical analysis. Teratogenicity and embryo-toxicity have been studied in order to cover additional endpoints. The EBMs have highlighted the presence of diffuse chemical pollution and ecotoxicological effects in the three sampling stations, genotoxicological effects have been also detected through the use of different tests and organisms. The chemical analyses confirmed that in the aquatic ecosystems there is a diffuse presence, even at low concentrations, of emerging contaminants such as pharmaceuticals, not routinely monitored pesticides, personal care products, PFAS. The results of this study can help to identify an appropriate battery of EBMs for future studies and the application of more appropriate measures in order to monitor, mitigate or eliminate chemical contamination and remediate its adverse/detrimental effects on the ecosystem health.