Accelerating Parkinson's Disease drug development with federated learning approaches.

Parkinson's Disease is a progressive neurodegenerative disorder afflicting almost 12 million people. Increased understanding of its complex and heterogenous disease pathology, etiology and symptom manifestations has resulted in the need to design, capture and interrogate substantial clinical datasets. Herein we advocate how advances in the deployment of artificial intelligence models for Federated Data Analysis and Federated Learning can help spearhead coordinated and sustainable approaches to address this grand challenge.

Identifying and characterising sources of variability in digital outcome measures in Parkinson's disease.

Smartphones and wearables are widely recognised as the foundation for novel Digital Health Technologies (DHTs) for the clinical assessment of Parkinson's disease. Yet, only limited progress has been made towards their regulatory acceptability as effective drug development tools. A key barrier in achieving this goal relates to the influence of a wide range of sources of variability (SoVs) introduced by measurement processes incorporating DHTs, on their ability to detect relevant changes to PD.

How can natural language processing help model informed drug development?: a review.

Objective: To summarize applications of natural language processing (NLP) in model informed drug development (MIDD) and identify potential areas of improvement.

Materials And Methods: Publications found on PubMed and Google Scholar, websites and GitHub repositories for NLP libraries and models. Publications describing applications of NLP in MIDD were reviewed.

Metadata Framework to Support Deployment of Digital Health Technologies in Clinical Trials in Parkinson's Disease.

Sensor data from digital health technologies (DHTs) used in clinical trials provides a valuable source of information, because of the possibility to combine datasets from different studies, to combine it with other data types, and to reuse it multiple times for various purposes. To date, there exist no standards for capturing or storing DHT biosensor data applicable across modalities and disease areas, and which can also capture the clinical trial and environment-specific aspects, so-called metadata. In this perspectives paper, we propose a metadata framework that divides the DHT metadata into metadata that is independent of the therapeutic area or clinical trial design (concept of interest and context of use), and metadata that is dependent on these factors.

Improved Precision in Surface-Enhanced Raman Scattering Quantification of Analyte through Dual-Modality Multisite Sensing.

Accurate quantification of analyte using surface-enhanced Raman spectroscopy (SERS) is a desired, yet unfulfilled, ability that could enable a plethora of diagnostic- and defense-related applications. The major hurdles to overcome to achieve this goal are expensive manufacturing for highly ordered and reproducible substrates and low reproducibility of substrates produced through low cost methods. A technology that can set industry standards for manufacturing/processing of SERS substrates is still yet to be achieved.