scholarly journals Identification and Validation of N-Acetylputrescine in Combination With Non-Canonical Clinical Features As a Parkinson’s Disease Biomarker Panel

2021 ◽  
Author(s):  
Kuan-Wei Peng ◽  
Allison Klotz ◽  
Arcan Guven ◽  
Unnati Kapadnis ◽  
Shobha Ravipaty ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Predictive Value ◽  
Neurodegenerative Disorder ◽  
Area Under The Curve ◽  
Disease Diagnosis ◽  
Intelligence Analysis ◽  
Acting Out ◽  
Biomarker Panel ◽  
Diagnostic Panel

Parkinson’s disease is a progressive neurodegenerative disorder in which loss of dopaminergic neurons in the substantia nigra results in a clinically heterogeneous group with variable motor and non-motor symptoms with a degree of misdiagnosis. Only 3-5% of sporadic Parkinson’s patients present with genetic abnormalities, thus environmental, metabolic, and other unknown causes contribute to the pathogenesis of Parkinson’s disease, which highlights the critical need for biomarkers. There could be a significant clinical benefit to treating Parkinson’s disease at the earliest stage and identify at-risk populations once disease-modifying treatments are available. In the present study, we prospectively collected and analyzed plasma samples from 201 Parkinson’s disease patients and 199 age-matched non-diseased controls. Multiomic and Bayesian artificial intelligence analysis of molecular and clinical data identified the diagnostic utility of N-acetyl putrescine (NAP) in combination with smell (B-SIT), depression/anxiety (HADS), and acting out dreams (RBD1Q) clinical measurements. The clinical and biomarker panel demonstrated an area under the curve, AUC = 0.9, positive predictive value, PPV=0.91, and negative predictive value, NPV=0.66 utilizing all four variables. The assessed diagnostic panel demonstrates combinatorial utility in diagnosing Parkinson’s disease, allowing for an integrated interpretation of disease pathophysiology and highlighting the use of multi-tiered panels in neurological disease diagnosis.

Driving with Parkinson’s disease: Cut points for clinical predictors of on-road outcomes

2018 ◽  
Vol 85 (3) ◽  
pp. 232-241 ◽  
Author(s):  
Liliana Alvarez ◽  
Sherrilene Classen
Keyword(s):  
Parkinson’S Disease ◽  
At Risk ◽  
Parkinson's Disease ◽  
Contrast Sensitivity ◽  
Predictive Value ◽  
Neurodegenerative Disorder ◽  
Area Under The Curve ◽  
Clinical Predictors ◽  
Predictive Values ◽  
Cut Points

Background. Parkinson’s disease (PD) is a common neurodegenerative disorder that impacts a person’s fitness to drive. Practitioners require a sensitive and predictive battery of clinical tests to identify at-risk drivers. Purpose. This study aimed to identify clinical predictors and their optimal cut points, sensitivity, specificity, and predictive values of on-road outcomes in drivers with PD. Method. Participants ( N = 101) underwent a comprehensive driving evaluation. We identified predictors of pass/fail outcomes through logistic regression and computed optimal cut points through receiver operating characteristic curves and corresponding Youden indexes. Findings. The Trail Making Test Part B (Trails B; sensitivity = .89, specificity = .74; positive predictive value [PPV] = .71; negative predictive value [NPV] = .91) and contrast sensitivity (sensitivity = .82, specificity = .63; PPV = .61; NPV = .84) emerged as significant predictors. The optimal cut point for the Trails B was 108 s (area under the curve = .86). Implications. Occupational therapists can benefit from implementing Trails B and contrast sensitivity screening as part of in-office screening of potentially at-risk drivers with PD.

scholarly journals Parkinson’s disease and dopamine transporter neuroimaging: a critical review

2006 ◽  
Vol 124 (3) ◽  
pp. 168-175 ◽  
Author(s):  
Ming Chi Shih ◽  
Marcelo Queiroz Hoexter ◽  
Luiz Augusto Franco de Andrade ◽  
Rodrigo Affonseca Bressan
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Dopamine Transporter ◽  
Neurodegenerative Disorder ◽  
Single Photon ◽  
Disease Diagnosis ◽  
Critical Discussion ◽  
Emission Tomography ◽  
Disease Evolution

Parkinson’s disease (PD) is a common neurodegenerative disorder that is mainly caused by dopaminergic neuron loss in the substantia nigra. Several nuclear medicine radiotracers have been developed to evaluate PD diagnoses and disease evolution in vivo in PD patients. Positron emission tomography (PET) and single photon computerized emission tomography (SPECT) radiotracers for the dopamine transporter (DAT) provide good markers for the integrity of the presynaptic dopaminergic system affected in PD. Over the last decade, radiotracers suitable for imaging the DAT have been the subject of most efforts. In this review, we provide a critical discussion on the utility of DAT imaging for Parkinson’s disease diagnosis (sensitivity and specificity).

Machine Learning Approaches in Parkinson’s Disease

2021 ◽  
Vol 28 ◽  
Author(s):  
Annamaria Landolfi ◽  
Carlo Ricciardi ◽  
Leandro Donisi ◽  
Giuseppe Cesarelli ◽  
Jacopo Troisi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Neurodegenerative Disorder ◽  
Learning Algorithms ◽  
Disease Diagnosis ◽  
Machine Learning Algorithms ◽  
Clinical Aspects ◽  
Learning Approaches

Background:: Parkinson’s disease is the second most frequent neurodegenerative disorder. Its diagnosis is challenging and mainly relies on clinical aspects. At present, no biomarker is available to obtain a diagnosis of certainty in vivo. Objective:: The present review aims at describing machine learning algorithms as they have been variably applied to different aspects of Parkinson’s disease diagnosis and characterization. Methods:: A systematic search was conducted on PubMed in December 2019, resulting in 230 publications obtained with the following search query: “Machine Learning” “AND” “Parkinson Disease”. Results:: the obtained publications were divided into 6 categories, based on different application fields: “Gait Analysis - Motor Evaluation”, “Upper Limb Motor and Tremor Evaluation”, “Handwriting and typing evaluation”, “Speech and Phonation evaluation”, “Neuroimaging and Nuclear Medicine evaluation”, “Metabolomics application”, after excluding the papers of general topic. As a result, a total of 166 articles were analyzed, after elimination of papers written in languages other than English or not directly related to the selected topics. Conclusion:: Machine learning algorithms are computer-based statistical approaches which can be trained and are able to find common patterns from big amounts of data. The machine learning approaches can help clinicians in classifying patients according to several variables at the same time.

scholarly journals Parkinson’s disease diagnosis using convolutional neural networks and figure-copying tasks

2021 ◽  
Author(s):  
Mohamad Alissa ◽  
Michael A. Lones ◽  
Jeremy Cosgrove ◽  
Jane E. Alty ◽  
Stuart Jamieson ◽  
...  
Keyword(s):  
Neural Network ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Network Architecture ◽  
Neurodegenerative Disorder ◽  
Early Stage ◽  
Disease Diagnosis ◽  
Healthy Controls ◽  
Abnormal Movements ◽  
Drawing Tasks

AbstractParkinson’s disease (PD) is a progressive neurodegenerative disorder that causes abnormal movements and an array of other symptoms. An accurate PD diagnosis can be a challenging task as the signs and symptoms, particularly at an early stage, can be similar to other medical conditions or the physiological changes of normal ageing. This work aims to contribute to the PD diagnosis process by using a convolutional neural network, a type of deep neural network architecture, to differentiate between healthy controls and PD patients. Our approach focuses on discovering deviations in patient’s movements with the use of drawing tasks. In addition, this work explores which of two drawing tasks, wire cube or spiral pentagon, are more effective in the discrimination process. With $$93.5\%$$ 93.5 % accuracy, our convolutional classifier, trained with images of the pentagon drawing task and augmentation techniques, can be used as an objective method to discriminate PD from healthy controls. Our compact model has the potential to be developed into an offline real-time automated single-task diagnostic tool, which can be easily deployed within a clinical setting.

Rapid Diagnosis of Parkinson’s Disease from Sebum using Paper Spray Ionisation Ion Mobility Mass Spectrometry

2020 ◽  
Author(s):  
Depanjan Sarkar ◽  
Drupad Trivedi ◽  
Eleanor Sinclair ◽  
Sze Hway Lim ◽  
Caitlin Walton-Doyle ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Ion Mobility ◽  
Neurodegenerative Disorder ◽  
Treatment Options ◽  
Ion Mobility Mass Spectrometry ◽  
Paper Spray ◽  
Molecular Features ◽  
Validation Set

Parkinson’s disease (PD) is the second most common neurodegenerative disorder for which identification of robust biomarkers to complement clinical PD diagnosis would accelerate treatment options and help to stratify disease progression. Here we demonstrate the use of paper spray ionisation coupled with ion mobility mass spectrometry (PSI IM-MS) to determine diagnostic molecular features of PD in sebum. PSI IM-MS was performed directly from skin swabs, collected from 34 people with PD and 30 matched control subjects as a training set and a further 91 samples from 5 different collection sites as a validation set. PSI IM-MS elucidates ~ 4200 features from each individual and we report two classes of lipids (namely phosphatidylcholine and cardiolipin) that differ significantly in the sebum of people with PD. Putative metabolite annotations are obtained using tandem mass spectrometry experiments combined with accurate mass measurements. Sample preparation and PSI IM-MS analysis and diagnosis can be performed ~5 minutes per sample offering a new route to for rapid and inexpensive confirmatory diagnosis of this disease.

Visual Effects of Deep Brain Stimulation in a Patient with Parkinson’s Disease

2019 ◽  
pp. 158-173
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Deep Brain Stimulation ◽  
Visual System ◽  
Brain Stimulation ◽  
Neurodegenerative Disorder ◽  
Initial Examination ◽  
Before And After ◽  
Deep Brain ◽  
Cover Test

Background: Parkinson’s disease (PD) is a progressive neurodegenerative disorder caused by a dopamine deficiency that presents with motor symptoms. Visual disorders can occur concomitantly but are frequently overlooked. Deep brain stimulation (DBS) has been an effective treatment to improve tremors, stiffness and overall mobility, but little is known about its effects on the visual system. Case Report: A 75-year-old Caucasian male with PD presented with longstanding binocular diplopia. On baseline examination, the best-corrected visual acuity was 20/25 in each eye. On observation, he had noticeable tremors with an unsteady gait. Distance alternating cover test showed exophoria with a right hyperphoria. Near alternating cover test revealed a significantly larger exophoria accompanied by a reduced near point of convergence. Additional testing with a 24-2 Humphrey visual field and optical coherence tomography (OCT) of the nerve and macula were unremarkable. The patient underwent DBS implantation five weeks after initial examination, and the device was activated four weeks thereafter. At follow up, the patient still complained of intermittent diplopia. There was no significant change in the manifest refraction or prism correction. On observation, the patient had remarkably improved tremors with a steady gait. All parameters measured were unchanged. The patient was evaluated again seven months after device activation. Although vergence ranges at all distances were improved, the patient was still symptomatic for intermittent diplopia. OCT scans of the optic nerve showed borderline but symmetric thinning in each eye. All other parameters measured were unchanged. Conclusion: The case found no significant changes on ophthalmic examination after DBS implantation and activation in a patient with PD. To the best of the authors’ knowledge, there are no other cases in the literature that investigated the effects of DBS on the visual system pathway in a patient with PD before and after DBS implantation and activation.

Peripheral Immunity, Immunoaging and Neuroinflammation in Parkinson’s Disease

2019 ◽  
Vol 26 (20) ◽  
pp. 3719-3753 ◽  
Author(s):  
Natasa Kustrimovic ◽  
Franca Marino ◽  
Marco Cosentino
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Immune System ◽  
Neurodegenerative Disorder ◽  
Neurodegenerative Process ◽  
Progressive Degeneration ◽  
Cytokine Levels ◽  
Inflammatory Phenotype ◽  
Immune Challenges

:Parkinson’s disease (PD) is the second most common neurodegenerative disorder among elderly population, characterized by the progressive degeneration of dopaminergic neurons in the midbrain. To date, exact cause remains unknown and the mechanism of neurons death uncertain. It is typically considered as a disease of central nervous system (CNS). Nevertheless, numerous evidence has been accumulated in several past years testifying undoubtedly about the principal role of neuroinflammation in progression of PD. Neuroinflammation is mainly associated with presence of activated microglia in brain and elevated levels of cytokine levels in CNS. Nevertheless, active participation of immune system as well has been noted, such as, elevated levels of cytokine levels in blood, the presence of auto antibodies, and the infiltration of T cell in CNS. Moreover, infiltration and reactivation of those T cells could exacerbate neuroinflammation to greater neurotoxic levels. Hence, peripheral inflammation is able to prime microglia into pro-inflammatory phenotype, which can trigger stronger response in CNS further perpetuating the on-going neurodegenerative process.:In the present review, the interplay between neuroinflammation and the peripheral immune response in the pathobiology of PD will be discussed. First of all, an overview of regulation of microglial activation and neuroinflammation is summarized and discussed. Afterwards, we try to collectively analyze changes that occurs in peripheral immune system of PD patients, suggesting that these peripheral immune challenges can exacerbate the process of neuroinflammation and hence the symptoms of the disease. In the end, we summarize some of proposed immunotherapies for treatment of PD.

Current Therapeutic Strategies and Perspectives for Neuroprotection in Parkinson’s Disease

2020 ◽  
Vol 26 (37) ◽  
pp. 4738-4746
Author(s):  
Mohan K. Ghanta ◽  
P. Elango ◽  
Bhaskar L. V. K. S.
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Early Diagnosis ◽  
Neurodegenerative Disorder ◽  
Therapeutic Strategies ◽  
Neuroprotective Agents ◽  
Striatal Neurons ◽  
The Status ◽  
Advanced Stages ◽  
Dopaminergic Pathways

Parkinson’s disease is a progressive neurodegenerative disorder of dopaminergic striatal neurons in basal ganglia. Treatment of Parkinson’s disease (PD) through dopamine replacement strategies may provide improvement in early stages and this treatment response is related to dopaminergic neuronal mass which decreases in advanced stages. This treatment failure was revealed by many studies and levodopa treatment became ineffective or toxic in chronic stages of PD. Early diagnosis and neuroprotective agents may be a suitable approach for the treatment of PD. The essentials required for early diagnosis are biomarkers. Characterising the striatal neurons, understanding the status of dopaminergic pathways in different PD stages may reveal the effects of the drugs used in the treatment. This review updates on characterisation of striatal neurons, electrophysiology of dopaminergic pathways in PD, biomarkers of PD, approaches for success of neuroprotective agents in clinical trials. The literature was collected from the articles in database of PubMed, MedLine and other available literature resources.

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