A 12-month prospective real-life study of opicapone efficacy and tolerability in Emirati and non-White subjects with Parkinson's disease based in United Arab Emirates.

Parkinson's disease (PD) is the second most common neurodegenerative disorder, and the condition is complicated by the emergence of wearing off/motor fluctuations with levodopa treatment after a variable period. COMT inhibitors when used as adjunct therapy to levodopa tend to smoothen out these wearing off fluctuations by enhancing delivery of levodopa and increasing its bioavailability to the brain. The study was conducted to investigate the motor and nonmotor effect, safety and tolerability of the third generation once-daily COMT inhibitor (opicapone), as add-on, adjuvant therapy to levodopa and at 6 and 12 months follow-up in a real-life cohort of consecutive Emirati and non-White PD patients. A real-life observational analysis using tolerability parameters as used previously by Rizos et al. and Shulman et al. based on clinical database of cases rat Kings College Hospital Dubai Parkinson care database. This was a prospective, single-arm follow-up clinical evaluation study that evaluated the effectiveness of opicapone 50 mg once-daily regime in 50 patients diagnosed with idiopathic neurodegenerative disorder. All patients were assessed with scales used in clinical pathway and include motor Unified Parkinson's Disease Rating Scale (UPDRS), nonmotor symptom scale (NMSS), quality of life (PDQ8) Parkinson's fatigue scale (PFS16) and King's Parkinson's Pain Scale (KIPS). Out of 50 patients treated with opicapone (72% male, mean age 66.9 years (SD 9.9, range 41-82 years) and mean duration of disease 5.7 years (SD 2.5 range (2-11), there was significant statistical improvements shown in motor function-UPDRS part 3: baseline 40.64 ± 2.7, at 6 months 32.12 ± 3.14 and after 12 months 33.72 ± 3.76. Nonmotor burden NMSS: 107.00 ± 21.86, at 6 months 100.78 ± 17.28 and 12 months 96.88 ± 16.11. Reduction in dyskinesias (UPDRS part 4): baseline 8.78 ± 1.07, at 6 months 7.4 ± 0.81 and 12 months 6.82 ± 0.75. Opicapone provides beneficial motor and nonmotor effects in Emirati and other non-White Parkinson's patients, resident in UAE, proving its efficacy across different racial groups as COMT activity may vary between races.

Hiccups, Hypersalivation, Hallucinations in Parkinson's Disease: New Insights, Mechanisms, Pathophysiology, and Management.

Parkinson's disease (PD) is a chronic, progressive neurological disorder and the second most common neurodegenerative condition. We report three common but overlooked symptoms in PD-hiccups, hypersalivation, and hallucinations-in terms of their prevalence, pathophysiology, and up-to-date evidence-based treatment strategies. Whilst all these three symptoms do occur in many other neurological and non-neurological conditions, early recognition and treatment are paramount. Whilst hiccups affect 3% of healthy people, their rate of occurrence is higher (20%) in patients with PD. Hypersalivation (Sialorrhea) is another common neurological manifestation of many neurological and other neurodegenerative conditions such as motor neuron disease (MND), with a median prevalence rate of 56% (range: 32-74%). A 42% prevalence of sialorrhea is also reported in sub-optimally treated patients with PD. Hallucinations, especially visual hallucinations, are commonly reported, with a prevalence of 32-63% in PD, and a 55-78% prevalence is noted in patients with dementia with Lewy bodies (DLB), followed by tactile hallucinations, which are indicated by a sensation of crawling bugs or imaginary creatures across the skin surface. Whilst mainstay and primary management strategies for all these three symptoms are carried out through history taking, it is also essential to identify and treat possible potential triggers such as infection, minimise or avoid causative (such as drug-induced) factors, and especially carry out patient education before considering more definitive treatment strategies, such as botulinum toxin therapies for hypersalivation, to improve the quality of life of patients. This original review paper aims to provide a comprehensive overview of the disease mechanisms, pathophysiology, and management of hiccups, hypersalivation, and hallucinations in Parkinson's disease.

First Two-Year Observational Exploratory Real Life Clinical Phenotyping, and Societal Impact Study of Parkinson's Disease in Emiratis and Expatriate Population of United Arab Emirates 2019-2021: The EmPark Study.

BACKGROUND: Phenotypic differences in Parkinson's Disease (PD) among locals (Emiratis) and Expatriates (Expats) living in United Arab Emirates have not been described and could be important to unravel local aspects of clinical heterogenicity of PD pointing towards genetic and epigenetic variations. OBJECTIVE: To investigate the range and nature of motor and nonmotor clinical presentations of PD and its impact on time to diagnosis, local service provisions, and quality of life in Emiratis and Expats in UAE, as well as address the presence of current unmet needs on relation to care and etiopathogenesis of PD related to possible genetic and epigenetic factors. METHODS: a cross-sectional one point in time prospective, observational real-life study of 171 patients recruited from PD and Neurology clinics across United Arab Emirates from 2019-2021. Primary outcomes were sociodemographic data, motor and nonmotor symptoms (NMS), including cognition and sleep, and quality of life (QOL) assessments, Results: A total of 171 PD patients (52 Emiratis 119 Expats) were included with mean age (Emiratis 48.5 (13.1) Expats 64.15 (13.1)) and mean disease duration (Emiratis 4.8 (3.2) Expats 6.1 (2.9)). In the Emiratis, there was a significant mean delay in initiating treatment after diagnosis (Emiratis 1.2 (0.9) Expats 1.6 (1.1)), while from a clinical phenotyping aspect, there is a high percentage of akinesia 25 (48.1) or tremor dominant (22 (42.3)) phenotypes as opposed to mixed subtype 67 (56.3) in Expat cohorts; double tremor dominant, especially Emirati females (25%), had a predominant lower limb onset PD. Both Emirati (27.9 (24.0)) and Expat 29.4 (15.6) showed moderate NMS burden and the NMS profile is dominated by Sleep, Fatigue, Mood, Emotional well-being 3.0 (1.1) and Social Stigma 3.5 (0.9) aspects of PDQ8 SI measurements are predicted worse QOL in Emiratis, while lack of social support 2.3 (1.3) impaired QOL in Expat population. Awareness for advanced therapies was low and only 25% of Emiratis were aware of deep brain surgery (DBS), compared to 69% Expats. Only 2% of Emiratis, compared to 32% of Expats, heard of Apomorphine infusion (CSAI), and no (0%) Emiratis were aware of intrajejunal levodopa infusion (IJLI), compared to 13% of expats. CONCLUSION: Our pilot data suggest clinical phenotypic differences in presentation of PD in Emiratis population of UAE compared to expats. Worryingly, the data also show delayed treatment initiation, as well as widespread lack of knowledge of advanced therapies in the Emirati population.

Detection of COVID19 from X-ray images using multiscale Deep Convolutional Neural Network.

The Coronavirus disease 2019 (COVID19) pandemic has led to a dramatic loss of human life worldwide and caused a tremendous challenge to public health. Immediate detection and diagnosis of COVID19 have lifesaving importance for both patients and doctors. The availability of COVID19 tests increased significantly in many countries, thereby provisioning a limited availability of laboratory test kits Additionally, the Reverse Transcription-Polymerase Chain Reaction (RT-PCR) test for the diagnosis of COVID 19 is costly and time-consuming. X-ray imaging is widely used for the diagnosis of COVID19. The detection of COVID19 based on the manual investigation of X-ray images is a tedious process. Therefore, computer-aided diagnosis (CAD) systems are needed for the automated detection of COVID19 disease. This paper proposes a novel approach for the automated detection of COVID19 using chest X-ray images. The Fixed Boundary-based Two-Dimensional Empirical Wavelet Transform (FB2DEWT) is used to extract modes from the X-ray images. In our study, a single X-ray image is decomposed into seven modes. The evaluated modes are used as input to the multiscale deep Convolutional Neural Network (CNN) to classify X-ray images into no-finding, pneumonia, and COVID19 classes. The proposed deep learning model is evaluated using the X-ray images from two different publicly available databases, where database A consists of 1225 images and database B consists of 9000 images. The results show that the proposed approach has obtained a maximum accuracy of 96% and 100% for the multiclass and binary classification schemes using X-ray images from dataset A with 5-fold cross-validation (CV) strategy. For dataset B, the accuracy values of 97.17% and 96.06% are achieved using multiscale deep CNN for multiclass and binary classification schemes with 5-fold CV. The proposed multiscale deep learning model has demonstrated a higher classification performance than the existing approaches for detecting COVID19 using X-ray images.