Machine Learning-Based Detection of Parkinson's Disease From Resting-State EEG: A Multi-Center Study.

Resting-state EEG (rs-EEG) has been demonstrated to aid in Parkinson's disease (PD) diagnosis. In particular, the power spectral density (PSD) of low-frequency bands (δ and θ) and high-frequency bands (α and ß) has been shown to be significantly different in patients with PD as compared to subjects without PD (non-PD). However, rs-EEG feature extraction and the interpretation thereof can be time-intensive and prone to examiner variability. Machine learning (ML) has the potential to automatize the analysis of rs-EEG recordings and provides a supportive tool for clinicians to ease their workload. In this work, we use rs-EEG recordings of 84 PD and 85 non-PD subjects pooled from four datasets obtained at different centers. We propose an end-to-end pipeline consisting of preprocessing, extraction of PSD features from clinically-validated frequency bands, and feature selection. Following, we assess the classification ability of the features via ML algorithms to stratify between PD and non-PD subjects. Further, we evaluate the effect of feature harmonization, given the multi-center nature of the datasets. Our validation results show, on average, an improvement in PD detection ability (69.6% vs. 75.5% accuracy) by logistic regression when harmonizing the features and performing univariate feature selection (k = 202 features). Our final results show an average global accuracy of 72.2% with balanced accuracy results for all the centers included in the study: 60.6%, 68.7%, 77.7%, and 82.2%, respectively.Clinical relevance- We present an end-to-end pipeline to extract clinically relevant features from rs-EEG recordings that can facilitate the analysis and detection of PD. Further, we provide an ML system that shows a good performance in detecting PD, even in the presence of centers with different acquisition protocols. Our results show the relevance of harmonizing features and provide a good starting point for future studies focusing on rs-EEG analysis and multi-center data.

Ultrasound and microbubbles to beat barriers in tumors: Improving delivery of nanomedicine.

Successful delivery of drugs and nanomedicine to tumors requires a functional vascular network, extravasation across the capillary wall, penetration through the extracellular matrix, and cellular uptake. Nanomedicine has many merits, but penetration deep into the tumor interstitium remains a challenge. Failure of cancer treatment can be caused by insufficient delivery of the therapeutic agents. After intravenous administration, nanomedicines are often found in off-target organs and the tumor extracellular matrix close to the capillary wall. With circulating microbubbles, ultrasound exposure focused toward the tumor shows great promise in improving the delivery of therapeutic agents. In this review, we address the impact of focused ultrasound and microbubbles to overcome barriers for drug delivery such as perfusion, extravasation, and transport through the extracellular matrix. Furthermore, we discuss the induction of an immune response with ultrasound and delivery of immunotherapeutics. The review discusses mainly preclinical results and ends with a summary of ongoing clinical trials.