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

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

A long short-term memory deep learning network for the prediction of epileptic seizures using EEG signals

Summarization: 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.Presented on: Computers in Biology and Medicin