scholarly journals Combining Automated Organoid Workflows With Artificial Intelligence‐Based Analyses: Opportunities to Build a New Generation of Interdisciplinary High‐Throughput Screens for Parkinson's Disease and Beyond

2021 ◽  
Author(s):  
Henrik Renner ◽  
Hans R. Schöler ◽  
Jan M. Bruder
Keyword(s):  
Artificial Intelligence ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
High Throughput ◽  
High Throughput Screens ◽  
New Generation

scholarly journals Will Artificial Intelligence Replace the Movement Disorders Specialist for Diagnosing and Managing Parkinson’s Disease?

2021 ◽  
pp. 1-6
Author(s):  
Matt Landers ◽  
Suchi Saria ◽  
Alberto J. Espay
Keyword(s):  
Artificial Intelligence ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Precision Medicine ◽  
Movement Disorders ◽  
Living Environment ◽  
Patient Status ◽  
Patient Symptom ◽  
Molecular Therapies ◽  
Improve Patient

The use of artificial intelligence (AI) to help diagnose and manage disease is of increasing interest to researchers and clinicians. Volumes of health data are generated from smartphones and ubiquitous inexpensive sensors. By using these data, AI can offer otherwise unobtainable insights about disease burden and patient status in a free-living environment. Moreover, from clinical datasets AI can improve patient symptom monitoring and global epidemiologic efforts. While these applications are exciting, it is necessary to examine both the utility and limitations of these novel analytic methods. The most promising uses of AI remain aspirational. For example, defining the molecular subtypes of Parkinson’s disease will be assisted by future applications of AI to relevant datasets. This will allow clinicians to match patients to molecular therapies and will thus help launch precision medicine. Until AI proves its potential in pushing the frontier of precision medicine, its utility will primarily remain in individualized monitoring, complementing but not replacing movement disorders specialists.

scholarly journals High-throughput behavioral screen in C. elegans reveals Parkinson’s disease drug candidates

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Salman Sohrabi ◽  
Danielle E. Mor ◽  
Rachel Kaletsky ◽  
William Keyes ◽  
Coleen T. Murphy
Keyword(s):  
Neural Network ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
High Throughput ◽  
Potential Candidate ◽  
Automated Scoring ◽  
C Elegans ◽  
Drug Candidates ◽  
Branched Chain ◽  
Approved Drugs

AbstractWe recently linked branched-chain amino acid transferase 1 (BCAT1) dysfunction with the movement disorder Parkinson’s disease (PD), and found that RNAi-mediated knockdown of neuronal bcat-1 in C. elegans causes abnormal spasm-like ‘curling’ behavior with age. Here we report the development of a machine learning-based workflow and its application to the discovery of potentially new therapeutics for PD. In addition to simplifying quantification and maintaining a low data overhead, our simple segment-train-quantify platform enables fully automated scoring of image stills upon training of a convolutional neural network. We have trained a highly reliable neural network for the detection and classification of worm postures in order to carry out high-throughput curling analysis without the need for user intervention or post-inspection. In a proof-of-concept screen of 50 FDA-approved drugs, enasidenib, ethosuximide, metformin, and nitisinone were identified as candidates for potential late-in-life intervention in PD. These findings point to the utility of our high-throughput platform for automated scoring of worm postures and in particular, the discovery of potential candidate treatments for PD.

Identifying drugs with disease‐modifying potential in Parkinson's disease using artificial intelligence and pharmacoepidemiology

2020 ◽  
Vol 29 (8) ◽  
pp. 864-872
Author(s):  
Laura C. Maclagan ◽  
Naomi P. Visanji ◽  
Yi Cheng ◽  
Mina Tadrous ◽  
Alix M. B. Lacoste ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Disease Modifying

CRISPR System: A High-throughput Toolbox for Research and Treatment of Parkinson’s Disease

2019 ◽  
Vol 40 (4) ◽  
pp. 477-493 ◽  
Author(s):  
Fatemeh Safari ◽  
Gholamreza Hatam ◽  
Abbas Behzad Behbahani ◽  
Vahid Rezaei ◽  
Mazyar Barekati‑Mowahed ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
High Throughput ◽  
Crispr System ◽  
System A

scholarly journals WHAT’S OLD IS NEW: USING ARTIFICIAL INTELLIGENCE TO ACCELERATE DISCOVERY OF NEW TREATMENTS

2019 ◽  
Vol 3 (Supplement_1) ◽  
pp. S16-S17
Author(s):  
Connie Marras ◽  
Laura C Maclagan ◽  
Yi Cheng ◽  
Naomi Visanji ◽  
Mina Tadrous ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Conditional Logistic Regression ◽  
Disease Diagnosis ◽  
Success Rates ◽  
Disease Modifying Drugs ◽  
Logistic Regression Models ◽  
Intelligence System ◽  
Disease Modifying

Abstract Given the high cost of drug development and low success rates, repurposing drugs already proven safe provides a promising avenue for identifying effective therapies with additional indications. The IBM Watson artificial intelligence system was used to search 1.3 million Medline abstracts to prioritize medications that may be potentially disease-modifying in Parkinson’s disease. We assessed patterns of use of the top 50 Watson-ranked drugs among 14,866 adults with Parkinson’s disease aged 70 and older who were matched to persons without Parkinson’s disease on age, sex, and comorbidity. Sociodemographic characteristics, chronic conditions, and use of other medications were compared using standardized differences. Patterns of potentially disease-modifying drug use were examined prior to and following ascertainment of Parkinson’s disease. Preliminary findings from multivariable conditional logistic regression models on the association between previous exposure to potentially disease-modifying drugs and Parkinson’s disease diagnosis will be presented.

scholarly journals Non-Coding RNAs in the Brain-Heart Axis: The Case of Parkinson’s Disease

2020 ◽  
Vol 21 (18) ◽  
pp. 6513 ◽  
Author(s):  
Shubhra Acharya ◽  
Antonio Salgado-Somoza ◽  
Francesca Maria Stefanizzi ◽  
Andrew I. Lumley ◽  
Lu Zhang ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
High Throughput ◽  
Regulatory Networks ◽  
Current Knowledge ◽  
Circular Rnas ◽  
Clinical Markers ◽  
Wide Range ◽  
Non Coding Rnas ◽  
The Brain

Parkinson’s disease (PD) is a complex and heterogeneous disorder involving multiple genetic and environmental influences. Although a wide range of PD risk factors and clinical markers for the symptomatic motor stage of the disease have been identified, there are still no reliable biomarkers available for the early pre-motor phase of PD and for predicting disease progression. High-throughput RNA-based biomarker profiling and modeling may provide a means to exploit the joint information content from a multitude of markers to derive diagnostic and prognostic signatures. In the field of PD biomarker research, currently, no clinically validated RNA-based biomarker models are available, but previous studies reported several significantly disease-associated changes in RNA abundances and activities in multiple human tissues and body fluids. Here, we review the current knowledge of the regulation and function of non-coding RNAs in PD, focusing on microRNAs, long non-coding RNAs, and circular RNAs. Since there is growing evidence for functional interactions between the heart and the brain, we discuss the benefits of studying the role of non-coding RNAs in organ interactions when deciphering the complex regulatory networks involved in PD progression. We finally review important concepts of harmonization and curation of high throughput datasets, and we discuss the potential of systems biomedicine to derive and evaluate RNA biomarker signatures from high-throughput expression data.

scholarly journals Artificial intelligence applications and robotic systems in Parkinson's disease (Review)

2021 ◽  
Vol 23 (2) ◽  
Author(s):  
Lacramioara Perju‑dumbrava ◽  
Maria Barsan ◽  
Daniel Leucuta ◽  
Luminita C. Popa ◽  
Cristina Pop ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Robotic Systems

scholarly journals Remote Evaluation of Parkinson's Disease Using a Conventional Webcam and Artificial Intelligence

2021 ◽  
Vol 12 ◽  
Author(s):  
Mariana H. G. Monje ◽  
Sergio Domínguez ◽  
Javier Vera-Olmos ◽  
Angelo Antonini ◽  
Tiago A. Mestre ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Upper Limb ◽  
Rating Scale ◽  
Hand Movement ◽  
Movement Speed ◽  
Clinical Scales ◽  
Video Frames ◽  
Disease Rating

Objective: This study aimed to prove the concept of a new optical video-based system to measure Parkinson's disease (PD) remotely using an accessible standard webcam.Methods: We consecutively enrolled a cohort of 42 patients with PD and healthy subjects (HSs). The participants were recorded performing MDS-UPDRS III bradykinesia upper limb tasks with a computer webcam. The video frames were processed using the artificial intelligence algorithms tracking the movements of the hands. The video extracted features were correlated with clinical rating using the Movement Disorder Society revision of the Unified Parkinson's Disease Rating Scale and inertial measurement units (IMUs). The developed classifiers were validated on an independent dataset.Results: We found significant differences in the motor performance of the patients with PD and HSs in all the bradykinesia upper limb motor tasks. The best performing classifiers were unilateral finger tapping and hand movement speed. The model correlated both with the IMUs for quantitative assessment of motor function and the clinical scales, hence demonstrating concurrent validity with the existing methods.Conclusions: We present here the proof-of-concept of a novel webcam-based technology to remotely detect the parkinsonian features using artificial intelligence. This method has preliminarily achieved a very high diagnostic accuracy and could be easily expanded to other disease manifestations to support PD management.

scholarly journals High-throughput behavioral screen in C. elegans reveals novel Parkinson’s disease drug candidates

2020 ◽  
Author(s):  
Salman Sohrabi ◽  
Danielle E. Mor ◽  
Rachel Kaletsky ◽  
William Keyes ◽  
Coleen T. Murphy
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
High Throughput ◽  
C Elegans ◽  
Drug Candidates ◽  
Branched Chain Amino Acid ◽  
Branched Chain ◽  
Approved Drugs ◽  
Fda Approved Drugs ◽  
Behavioral Screen

AbstractWe recently linked branched-chain amino acid transferase 1 (BCAT1) with the movement disorder Parkinson’s disease (PD), and found that reduction of C. elegans bcat-1 causes abnormal spasm-like ‘curling’ behavior with age. Here, we report the development of a high-throughput automated curling assay and its application to the discovery of new potential PD therapeutics. Four FDA-approved drugs were identified as candidates for late-in-life intervention, with metformin showing the greatest promise for repurposing to PD.

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