Neuropsychological tests and machine learning: identifying predictors of MCI and dementia progression.

Background: Early prediction of progression in dementia is of major importance for providing patients with adequate clinical care, with considerable impact on the organization of the whole healthcare system.

Aims: The main task is tailoring robust and consolidated machine learning models to detect which neuropsychological tests are more effective in predicting a patient's mental status. In a translational medicine perspective, such identification tool should find its place in the clinician's toolbox as a support throughout his daily diagnostic routine.

Explainable deep neural networks for predicting sample phenotypes from single-cell transcriptomics.

Recent advances in single-cell RNA-Sequencing (scRNA-Seq) technologies have revolutionized our ability to gather molecular insights into different phenotypes at the level of individual cells. The analysis of the resulting data poses significant challenges, and proper statistical methods are required to analyze and extract information from scRNA-Seq datasets. Sample classification based on gene expression data has proven effective and valuable for precision medicine applications.

Large-scale culturing of the subpolar foraminifera reveals tolerance to a large range of environmental parameters associated to different life-strategies and an extended lifespan.

The subtropical to subpolar planktic foraminifera is a calcifying marine protist, and one of the dominant foraminiferal species of the Nordic Seas. Previously, the relative abundance and shell geochemistry of fossil have been studied for palaeoceanographic reconstructions. There is however a lack of biological observations on the species and a poor understanding of its ecological tolerances, especially for high latitude genotypes.

Artificial intelligence of imaging and clinical neurological data for predictive, preventive and personalized (P3) medicine for Parkinson Disease: The NeuroArtP3 protocol for a multi-center research study.

Background: The burden of Parkinson Disease (PD) represents a key public health issue and it is essential to develop innovative and cost-effective approaches to promote sustainable diagnostic and therapeutic interventions. In this perspective the adoption of a P3 (predictive, preventive and personalized) medicine approach seems to be pivotal. The NeuroArtP3 (NET-2018-12366666) is a four-year multi-site project co-funded by the Italian Ministry of Health, bringing together clinical and computational centers operating in the field of neurology, including PD.

Endoscopy-based IBD identification by a quantized deep learning pipeline.

Background: Discrimination between patients affected by inflammatory bowel diseases and healthy controls on the basis of endoscopic imaging is an challenging problem for machine learning models. Such task is used here as the testbed for a novel deep learning classification pipeline, powered by a set of solutions enhancing characterising elements such as reproducibility, interpretability, reduced computational workload, bias-free modeling and careful image preprocessing.

Results: First, an automatic preprocessing procedure is devised, aimed to remove artifacts from clinical data, feeding then the resulting images to an aggregated per-patient model to mimic the clinicians decision process.