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Machine learning for analysis of gene expression data in fast- and slow-progressing amyotrophic lateral sclerosis murine models

Iadanza Ernesto, Fabbri Rachele, Goretti Francesco, Nardo Giovanni, Niccolai Elena, Bendotti Caterina, Amedei Amedeo

Biocybernetics and Biomedical Engineering

2022

Vol. 42, no. 1

273--284

Amyotrophic lateral sclerosis is a fatal motor neuron disease characterised by degenerative changes in both upper and lower motor neurons. Current treatment options in the general cohort of ALS patients have only a minimal impact on survival. Only two approved medications are available today, just addressing the management of symptoms and supporting the respiration. In this work, gene expression data from genetically modified murine motor neurons have been analysed with machine learning techniques, with the scope of distinguishing between mice developing a fast progression of the disease, and mice showing a slower progression. Results showed high accuracy (above 80%) in all tasks, with peaks of accuracy for specific ones – such as distinguishing between fast and slow progression. In the above mentioned task the best performing algorithm reached an accuracy of 100%. This research group is currently working on three more investigations on data from mice, using similar approaches and methodology, focusing on thoracic and lumbar metabolomic data as well as microbiome data. We believe that, based on the findings in the murine models, machine learning could be used to discover ALS progression markers in humans by looking at features related to the immune response. This could pave the path for the discovery of druggable targets and disease biomarkers for homogeneous ALS patient subgroups.

Pupillometry via smartphone for low-resource settings

Piaggio Davide, Namm Georgy, Melillo Paolo, Simonelli Francesca, Iadanza Ernesto, Pecchia Leandro

Biocybernetics and Biomedical Engineering

2021

Vol. 41, no. 3

891--902

The photopupillary reflex regulates the pupil reaction to changing light conditions. Being controlled by the autonomic nervous system, it is a proxy for brain trauma and for the conditions of patients in critical care. A prompt evaluation of brain traumas can save lives. With a simple penlight, skilled clinicians can do that, whereas less specialized ones have to resort to a digital pupilometer. However, many low-income countries lack both specialized clinicians and digital pupilometers. This paper presents the early results of our study aiming at designing, prototyping and validating an app for testing the photopupillary reflex via Android, following the European Medical Device Regulation and relevant standards. After a manual validation, the prototype underwent a technical validation against a commercial Infrared pupilometer. As a result, the proposed app performed as well as the manual measurements and better than the commercial solution, with lower errors, higher and significant correlations, and significantly better Bland-Altman plots for all the pupillometry-related measures. The design of this medical device was performed based on our expertise in low-resource settings. This kind of environments imposes more stringent design criteria due to contextual challenges, including the lack of specialized clinicians, funds, spare parts and consumables, poor maintenance, and harsh environmental conditions, which may hinder the safe operationalization of medical devices. This paper provides an overview of how these unique contextual characteristics are cascaded into the design of an app in order to contribute to the Sustainable Development Goal 3 of the World Health Organization: Good health and well-being.