Spirochetal uveitis: Spectrum of clinical manifestations, diagnostic and therapeutic approach, final outcome and epidemiological data.

PURPOSE: Analysis of cases with spirochetal uveitis related to spirochetes in a tertiary referral academic center. METHODS: Retrospective study of patients diagnosed with uveitis attributed to Treponema pallidum, Leptospira spp. and Borrelia burgdorferi from June 1991 until December 2019. RESULTS: A total of 57 cases of spirochetal uveitis (22 patients with T. pallidum, 26 with Leptospira spp., and 9 with B. burgdorferi) that consisted 1% of the overall number of uveitics were recorded. All these cases presented with a wide spectrum of clinical presentations (anterior uveitis, posterior uveitis, panuveitis, vasculitis, papillitis, and in some rare cases concomitant posterior scleritis). The treatment included mainly penicillin or doxycycline, while corticosteroids were administered systematically in some cases with Borrelia or Leptospira infection. The final visual outcome was favorable (> 6/10 in Snellen visual acuity) in approximately 76% of our patients. CONCLUSION: Despite being rare, spirochetal uveitis can be detrimental for the vision and must always be included in the differential diagnosis.

PERFORM: a system for monitoring, assessment and management of patients with Parkinson's disease.

In this paper, we describe the PERFORM system for the continuous remote monitoring and management of Parkinson's disease (PD) patients. The PERFORM system is an intelligent closed-loop system that seamlessly integrates a wide range of wearable sensors constantly monitoring several motor signals of the PD patients. Data acquired are pre-processed by advanced knowledge processing methods, integrated by fusion algorithms to allow health professionals to remotely monitor the overall status of the patients, adjust medication schedules and personalize treatment. The information collected by the sensors (accelerometers and gyroscopes) is processed by several classifiers. As a result, it is possible to evaluate and quantify the PD motor symptoms related to end of dose deterioration (tremor, bradykinesia, freezing of gait (FoG)) as well as those related to over-dose concentration (Levodopa-induced dyskinesia (LID)). Based on this information, together with information derived from tests performed with a virtual reality glove and information about the medication and food intake, a patient specific profile can be built. In addition, the patient specific profile with his evaluation during the last week and last month, is compared to understand whether his status is stable, improving or worsening. Based on that, the system analyses whether a medication change is needed--always under medical supervision--and in this case, information about the medication change proposal is sent to the patient. The performance of the system has been evaluated in real life conditions, the accuracy and acceptability of the system by the PD patients and healthcare professionals has been tested, and a comparison with the standard routine clinical evaluation done by the PD patients' physician has been carried out. The PERFORM system is used by the PD patients and in a simple and safe non-invasive way for long-term record of their motor status, thus offering to the clinician a precise, long-term and objective view of patient's motor status and drug/food intake. Thus, with the PERFORM system the clinician can remotely receive precise information for the PD patient's status on previous days and define the optimal therapeutical treatment.

White Matter Lesion Segmentation for Multiple Sclerosis Patients implementing deep learning.

The aim of this work is to address the problem of White Matter Lesion (WML) segmentation employing Magnetic Resonance Imaging (MRI) images from Multiple Sclerosis (MS) patients through the application of deep learning. A U-net based architecture containing a contrastive path and an expanding path prior to the final pixel-wise classification is implemented. The data are provided by the Ippokratio Radiology Center of Ioannina and include Fluid-Attenuated Inversion Recovery (FLAIR) MRI images from 30 patients in three phases, baseline and two follow ups. The prediction results are quite significant in terms of pixel-wise classification. The implemented deep learning model demonstrates Dice coefficient 0.7292, Precision 75.92% and Recall 70.16% in 2D slices of FLAIR MRI non-skull stripped images.

Advance Care Planning and Care Coordination for People With Parkinson's Disease and Their Family Caregivers-Study Protocol for a Multicentre, Randomized Controlled Trial.

Background: Parkinson's disease (PD) is a progressive neurodegenerative disease with motor- and non-motor symptoms. When the disease progresses, symptom burden increases. Consequently, additional care demands develop, the complexity of treatment increases, and the patient's quality of life is progressively threatened. To address these challenges, there is growing awareness of the potential benefits of palliative care for people with PD. This includes communication about end-of-life issues, such as Advance Care Planning (ACP), which helps to elicit patient's needs and preferences on issues related to future treatment and care. In this study, we will assess the impact and feasibility of a nurse-led palliative care intervention for people with PD across diverse European care settings. Methods: The intervention will be evaluated in a multicentre, open-label randomized controlled trial, with a parallel group design in seven European countries (Austria, Estonia, Germany, Greece, Italy, Sweden and United Kingdom). The "PD_Pal intervention" comprises (1) several consultations with a trained nurse who will perform ACP conversations and support care coordination and (2) use of a patient-directed "Parkinson Support Plan-workbook". The primary endpoint is defined as the percentage of participants with documented ACP-decisions assessed at 6 months after baseline (t1). Secondary endpoints include patients' and family caregivers' quality of life, perceived care coordination, patients' symptom burden, and cost-effectiveness. In parallel, we will perform a process evaluation, to understand the feasibility of the intervention. Assessments are scheduled at baseline (t0), 6 months (t1), and 12 months (t2). Statistical analysis will be performed by means of Mantel-Haenszel methods and multilevel logistic regression models, correcting for multiple testing. Discussion: This study will contribute to the current knowledge gap on the application of palliative care interventions for people with Parkinson's disease aimed at ameliorating quality of life and managing end-of-life perspectives. Studying the impact and feasibility of the intervention in seven European countries, each with their own cultural and organisational characteristics, will allow us to create a broad perspective on palliative care interventions for people with Parkinson's disease across settings. Clinical Trial Registration:www.trialregister.nl, NL8180.

Prognostic factors of Rapid symptoms progression in patients with newly diagnosed parkinson's disease.

Tracking symptoms progression in the early stages of Parkinson's disease (PD) is a laborious endeavor as the disease can be expressed with vastly different phenotypes, forcing clinicians to follow a multi-parametric approach in patient evaluation, looking for not only motor symptomatology but also non-motor complications, including cognitive decline, sleep problems and mood disturbances. Being neurodegenerative in nature, PD is expected to inflict a continuous degradation in patients' condition over time. The rate of symptoms progression, however, is found to be even more chaotic than the vastly different phenotypes that can be expressed in the initial stages of PD. In this work, an analysis of baseline PD characteristics is performed using machine learning techniques, to identify prognostic factors for early rapid progression of PD symptoms. Using open data from the Parkinson's Progression Markers Initiative (PPMI) study, an extensive set of baseline patient evaluation outcomes is examined to isolate determinants of rapid progression within the first two and four years of PD. The rate of symptoms progression is estimated by tracking the change of the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) total score over the corresponding follow-up period. Patients are ranked according to their progression rates and those who expressed the highest rates of MDS-UPDRS total score increase per year of follow-up period are assigned into the rapid progression class, using 5- and 10-quantiles partition. Classification performance against the rapid progression class was evaluated in a per quantile partition analysis scheme and in quantile-independent approach, respectively. The results shown a more accurate patient discrimination with quantile partitioning, however, a much more compact subset of baseline factors is extracted in the latter, making a more suitable for actual interventions in practice. Classification accuracy improved in all cases when using the longer 4-year follow-up period to estimate PD progression, suggesting that a prolonged patient evaluation can provide better outcomes in identifying rapid progression phenotype. Non-motor symptoms are found to be the main determinants of rapid symptoms progression in both follow-up periods, with autonomic dysfunction, mood impairment, anxiety, REM sleep behavior disorders, cognitive decline and memory impairment being alarming signs at baseline evaluation, along with rigidity symptoms, certain laboratory blood test results and genetic mutations.

An Unsupervised Methodology for the Detection of Epileptic Seizures Using EEG Signals: A Multi-Dataset Evaluation.

Although the electroencephalogram (EEG) is the most commonly used means to monitor epileptic patients, public EEG datasets are very scarce making it difficult to develop and validate seizure detection algorithms. In this work an unsupervised seizure detection methodology is used to isolate ictal EEG segments without requiring any apriori information or human intervention. Seizures are detected using four simple seizure detection conditions that are activated when rhythmical activity from different brain areas is simultaneously concentrated in the alpha (8-13 Hz), theta (4-7 Hz) or delta (1-3 Hz) frequency range. Then, only a small proportion of the EEG segments that are most likely to contain ictal activity is selected and presented to the physician for the final evaluation. In this way, large volumes of EEG signals can be annotated in a fraction of the time and effort that would be otherwise required. Using EEG data from 33 sessions from the Temple University Hospital (TUH) EEG Corpus, our unsupervised methodology reached, on average, 84.92% seizure detection sensitivity with 3.46 false detections per hour of EEG signals.

Symptoms and medications change patterns for Parkinson's disease patients stratification.

Quality of life of patients with Parkinson's disease degrades significantly with disease progression. This paper presents a step towards personalized management of Parkinson's disease patients, based on discovering groups of similar patients. Similarity is based on patients' medical conditions and changes in the prescribed therapy when the medical conditions change. We present two novel approaches. The first algorithm discovers symptoms' impact on Parkinson's disease progression. Experiments on the Parkinson Progression Markers Initiative (PPMI) data reveal a subset of symptoms influencing disease progression which are already established in Parkinson's disease literature, as well as symptoms that are considered only recently as possible indicators of disease progression by clinicians. The second novelty is a methodology for detecting patterns of medications dosage changes based on the patient status. The methodology combines multitask learning using predictive clustering trees and short time series analysis to better understand when a change in medications is required. The experiments on PPMI data demonstrate that, using the proposed methodology, we can identify some clinically confirmed patients' symptoms suggesting medications change. In terms of predictive performance, our multitask predictive clustering tree approach is mostly comparable to the random forest multitask model, but has the advantage of model interpretability.

A Long Short-Term Memory deep learning network for the prediction of epileptic seizures using EEG signals.

The electroencephalogram (EEG) is the most prominent means to study epilepsy and capture changes in electrical brain activity that could declare an imminent seizure. In this work, Long Short-Term Memory (LSTM) networks are introduced in epileptic seizure prediction using EEG signals, expanding the use of deep learning algorithms with convolutional neural networks (CNN). A pre-analysis is initially performed to find the optimal architecture of the LSTM network by testing several modules and layers of memory units. Based on these results, a two-layer LSTM network is selected to evaluate seizure prediction performance using four different lengths of preictal windows, ranging from 15 min to 2 h. The LSTM model exploits a wide range of features extracted prior to classification, including time and frequency domain features, between EEG channels cross-correlation and graph theoretic features. The evaluation is performed using long-term EEG recordings from the open CHB-MIT Scalp EEG database, suggest that the proposed methodology is able to predict all 185 seizures, providing high rates of seizure prediction sensitivity and low false prediction rates (FPR) of 0.11-0.02 false alarms per hour, depending on the duration of the preictal window. The proposed LSTM-based methodology delivers a significant increase in seizure prediction performance compared to both traditional machine learning techniques and convolutional neural networks that have been previously evaluated in the literature.

Predicting rapid progression of Parkinson's Disease at baseline patients evaluation.

The rate of Parkinson's Disease (PD) progression in the initial post-diagnosis years can vary significantly. In this work, a methodology for the extraction of the most informative features for predicting rapid progression of the disease is proposed, using public data from the Parkinson's Progression Markers Initiative (PPMI) and machine learning techniques. The aim is to determine if a patient is at risk of expressing rapid progression of PD symptoms from the baseline evaluation and as close to diagnosis as possible. By examining the records of 409 patients from the PPMI dataset, the features with the best predictive value at baseline patient evaluation are found to be sleep problems, daytime sleepiness and fatigue, motor symptoms at legs, cognition impairment, early axial and facial symptoms and in the most rapidly advanced cases speech issues, loss of smell and affected leg muscle reflexes.

EEG transient event detection and classification using association rules.

In this paper, a methodology for the automated detection and classification of transient events in electroencephalographic (EEG) recordings is presented. It is based on association rule mining and classifies transient events into four categories: epileptic spikes, muscle activity, eye blinking activity, and sharp alpha activity. The methodology involves four stages: 1) transient event detection; 2) clustering of transient events and feature extraction; 3) feature discretization and feature subset selection; and 4) association rule mining and classification of transient events. The methodology is evaluated using 25 EEG recordings, and the best obtained accuracy was 87.38%. The proposed approach combines high accuracy with the ability to provide interpretation for the decisions made, since it is based on a set of association rules.

Complications of a trophic xenotransplant approach in parkinsonian monkeys.

Various restorative cell transplantation strategies have been investigated to substitute for lost dopamine (DA) neurons or to enhance DA synthesis in Parkinson's disease. Intracerebral implantation of engineered cells encapsulated in a semipermeable polymer membrane constitutes one way to deliver bioactive substances unable to cross the blood-brain barrier while avoiding the need for long-term immunosuppression. Glial cell line-derived neurotrophic factor (GDNF) has shown trophic effects on DA neurons but effective and sustained delivery within the brain parenchyma remains problematic. The long-term efficacy and late complications of a xenotransplant approach utilizing GDNF-expressing encapsulated baby hamster kidney (BHK) cells were examined. Each of five MPTP-lesioned parkinsonian cynomolgus monkeys received five devices containing active or inert cells grafted bilaterally in the striatum in a two-stage procedure 9 months apart and animals were sacrificed 4 months later for analyses. No definite motor benefit was observed, DA levels were comparable between GDNF- and control cell-implanted striata, and tyrosine hydroxylase (TH) immunoreactivity in the substantia nigra showed no consistent recovery. Cell viability and GDNF synthesis in the explanted devices were negligible. The brain tissue surrounding all implants showed an intense immune reaction with prominent "foreign body" inflammatory infiltrates. Membrane biophysics, the cell type used, and the extended period of time the devices remained in situ may have contributed to the negative outcome and should be addressed in future investigations using this approach.

Automatic detection of freezing of gait events in patients with Parkinson's disease.

The aim of this study is to detect freezing of gait (FoG) events in patients suffering from Parkinson's disease (PD) using signals received from wearable sensors (six accelerometers and two gyroscopes) placed on the patients' body. For this purpose, an automated methodology has been developed which consists of four stages. In the first stage, missing values due to signal loss or degradation are replaced and then (second stage) low frequency components of the raw signal are removed. In the third stage, the entropy of the raw signal is calculated. Finally (fourth stage), four classification algorithms have been tested (Naïve Bayes, Random Forests, Decision Trees and Random Tree) in order to detect the FoG events. The methodology has been evaluated using several different configurations of sensors in order to conclude to the set of sensors which can produce optimal FoG episode detection. Signals recorded from five healthy subjects, five patients with PD who presented the symptom of FoG and six patients who suffered from PD but they do not present FoG events. The signals included 93 FoG events with 405.6s total duration. The results indicate that the proposed methodology is able to detect FoG events with 81.94% sensitivity, 98.74% specificity, 96.11% accuracy and 98.6% area under curve (AUC) using the signals from all sensors and the Random Forests classification algorithm.

An automated methodology for levodopa-induced dyskinesia: assessment based on gyroscope and accelerometer signals.

OBJECTIVE: In this study, a methodology is presented for an automated levodopa-induced dyskinesia (LID) assessment in patients suffering from Parkinson's disease (PD) under real-life conditions. METHODS AND MATERIAL: The methodology is based on the analysis of signals recorded from several accelerometers and gyroscopes, which are placed on the subjects' body while they were performing a series of standardised motor tasks as well as voluntary movements. Sixteen subjects were enrolled in the study. The recordings were analysed in order to extract several features and, based on these features, a classification technique was used for LID assessment, i.e. detection of LID symptoms and classification of their severity. RESULTS: The results were compared with the clinical annotation of the signals, provided by two expert neurologists. The analysis was performed related to the number and topology of sensors used; several different experimental settings were evaluated while a 10-fold stratified cross validation technique was employed in all cases. Moreover, several different classification techniques were examined. The ability of the methodology to be generalised was also evaluated using leave-one-patient-out cross validation. The sensitivity and positive predictive values (average for all LID severities) were 80.35% and 76.84%, respectively. CONCLUSIONS: The proposed methodology can be applied in real-life conditions since it can perform LID assessment in recordings which include various PD symptoms (such as tremor, dyskinesia and freezing of gait) of several motor tasks and random voluntary movements.

Randomized trials of dopamine agonists in restless legs syndrome: a systematic review, quality assessment, and meta-analysis.

BACKGROUND: The use of dopamine agonists (DAs) for the treatment of restless legs syndrome (RLS) has been assessed in numerous randomized clinical trials (RCTs). OBJECTIVES: The aims of this study were to assess the reporting quality of published RCTs according to the Consolidated Standards of Reporting Trials (CONSORT) statement and to synthesize the study results in terms of efficacy and tolerability to inform the clinical management of RLS. METHODS: PubMed and Cochrane Controlled Trials Register were searched for English-language RCTs that assessed the effects of DAs in RLS. Quality of reporting was measured using the proportion of 17 CONSORT checklist items included in each study. The 2 primary outcomes were pooled mean change from baseline in International RLS (IRLS) Study Group rating scale score (Deltamu) (95% CI) and relative risk (RR) (95% CI) of response based on the Clinical Global Impression-Improvement (CGI-I) scale score. The pooled proportions of adverse events (PAEs) (95% CI) were also estimated. RESULTS: Eighteen RCTs (N = 2848 patients) were included. Two of the 17 CONSORT checklist items were reported in 7 studies (39%) and 9 of the 17 items were reported in all 18 studies (100%). The differences in the IRLS scores and RR for CGI-I were significantly greater with pramipexole, ropinirole, rotigotine, and cabergoline compared with placebo. Results for heterogeneity were nonsignificant. The difference in Deltamu (95% CI) was significant with pramipexole (-6.63 [-9.15 to -4.10]) versus ropinirole (-3.64 [-4.76 to 2.51]) (P = 0.04). The difference between pramipexole and rotigotine was nonsignificant. The pooled PAEs (95% CI) for pramipexole, ropinirole, and rotigotine were 4.8% (2.0% to 8.7%), 10.2% (2.6% to 22.1%), and 7.6% (1.3% to 18.5%), respectively. In the trial of sumanirole, the PAE value was 2% (0% to 5.4%). CONCLUSION: Based on the findings from the meta-analysis, DAs were significantly more efficacious in the treatment of RLS compared with placebo.

Real-time quantification of resting tremor in the Parkinson's disease.

Resting tremor (RT) is one of the most frequent signs of the Parkinson's disease (PD), occurring with various severities in about 75% of the patients. Current diagnosis is based on subjective clinical assessment, which is not always easy to capture subtle, mild and intermittent tremors. The aim of the present study is to assess the suitability and clinical value of a computer based real-time system as an aid to diagnosis of PD, in particular the presence of RT. Five healthy subjects were asked to simulate several severities of RT in hands and feet in three static activities. The behaviour of the subjects is measured using tri-axial accelerometers, which are placed at four different positions on the body. Frequency-domain features, strongly correlated with the RT activity, are extracted from the accelerometer data. The classification of RT severity based on those features, provided accuracy 76%. The real-time system designed for efficient extraction of those features and the provision of a continuous RT severity measure is described.

Levetiracetam in tardive dyskinesia: an open label study.

Levetiracetam (LEV), a novel antiepileptic drug, has demonstrated antidyskinetic effect in preclinical animal models of Parkinson's disease (PD) and in one open label study in PD patients with levodopa-induced dyskinesia. The acute antidyskinetic effects of LEV in patients with tardive dyskinesia were evaluated in an open label study. Eight patients received oral LEV (1,000 mg/day) for 1 month and blinded evaluations were performed at baseline and at the end of the treatment period. A significant reduction of the abnormal movements was recorded while psychiatric symptoms did not worsen and the adverse event profile was benign. LEV may be efficacious for the treatment of tardive dyskinesia and deserves further clinical testing.

Novel pharmacological strategies for motor complications in Parkinson's disease.

In advanced Parkinson's disease, the combination of disease progression and levodopa therapy leads to the development of motor problems complicating the therapeutic response, known as motor response complications. The nonphysiological, pulsatile stimulation produced by most currently available dopaminergic therapies triggers a complicated series of responses resulting in the dysregulation of glutamate receptors and many other neurotransmitter systems on striatal neurons. Although a number of novel compounds that provide a more continuous dopaminergic stimulation are becoming available, no practical way to accomplish this in a truly physiological manner currently exists. Novel strategies for pharmacological intervention with the use of nondopaminergic treatments, with drugs targeting selected transmitter receptors expressed on striatal neurons appear more promising. These include NMDA or AMPA antagonists, or drugs acting on 5-hydroxytryptamine subtype 2A, alpha2-adrenergic, adenosine A2A and cannabinoid CB1 receptors. Future strategies may also target pre- and postsynaptic components that regulate firing pattern, like synaptic vesicle proteins, or nonsynaptic gap junction communication mechanisms, or drugs with actions at the signal transduction systems that modulate the phosphorylation state of NMDA receptors. These new therapeutic strategies, alone or in combination, hold the promise of providing effective control or reversal of motor response complications.

Spontaneous intracranial hypotension: morphological findings and CSF flow dynamics studied by MRI.

We report on a case of spontaneous intracranial hypotension (SIH) presenting with classic MR findings, such as diffuse smooth thickening and intense contrast enhancement of the dura matter, increased size of the pituitary gland and downward displacement of the brain. In this case an engorgement of the cavernous sinuses is reported as an additional imaging finding of SIH. Moreover, phase-contrast MR study of the CSF flow dynamics revealed at the level of the aqueduct a decrease of the systolic and diastolic flow volume of CSF. A normalization of the flow volume was observed when SIH subsided.