Riccardo Sala, Stefano Regondi, and Raffaele Pugliese*. Design Data and Finite Element Analysis of 3D Printed Poly(ε-Caprolactone)-Based Lattice Scaffolds: Influence of Type of Unit Cell, Porosity, and Nozzle Diameter on the Mechanical Behavior. (2022). Eng. 

Abstract

Raffaele Pugliese*, Stefano Regondi, Riccardo Marini. Machine learning-based approach: Global trends, research directions, and regulatory standpoints. (2022). Data Science and Management. 

Abstract

The field of machine learning (ML) is sufficiently young that it is still expanding at an accelerated pace, lying at the crossroads of computer science and statistics, and at the core of artificial intelligence (AI) and data science. Recent progress in ML has been driven both by the development of new learning algorithms theory, and by the ongoing explosion in the availability of vast amount of data (often referred to as “Big data”) and low-cost computation. The adoption of ML-based approaches can be found throughout science, technology and industry, leading to more evidence-based decision-making across many walks of life, including
healthcare, biomedicine, manufacturing, education, financial modeling, data governance, policing, and marketing. Although the past decade has seen increased interest with these fields, we are just beginning to tap the potential of these ML algorithms for studying systems that improve with experience. In this manuscript, we present a comprehensive view on geo worldwide trends (taking into account China, USA, Israel, Italy, UK, and Middle East) of ML-based approaches highlighting rapid growth in the last 5 years
attributable to the introduction of related national policies. Furthermore, based on the literature review, we also discuss the potential research directions in this field, summarizing some popular application areas of machine learning technology, such as healthcare, cyber-security systems, sustainable agriculture, data governance, and nanotechnology, suggesting that the “dissemination of research” in the ML scientific community have undergone exceptional growth in the time range of 2018–2020, reaching a value of 16,339 publications. Finally we report the challenges and the regulatory standpoints for managing ML technology. Overall, we hope that this work will help to explain the geo trends of ML approaches and their applicability in various real-world domains, as well as serve as a reference point for both academia and industry professionals, particularly from a technical, ethical and regulatory point of view.

Raffaele Pugliese*, Riccardo Sala, Stefano Regondi, Benedetta Beltrami, Christian Lunetta. Emerging technologies for management of patients with amyotrophic lateral sclerosis: from telehealth to assistive robotics and neural interfaces. (2022). Journal of Neurology. 

Abstract

Amyotrophic lateral sclerosis (ALS), also known as motor neuron disease, is characterized by the degeneration of both upper and lower motor neurons, which leads to muscle weakness and subsequently paralysis. It begins subtly with focal weakness but spreads relentlessly to involve most muscles, thus proving to be effectively incurable. Typically, death due to respiratory paralysis occurs in 3 to 5 years. To date, it has been shown that the management of ALS patients is best achieved with a multidisciplinary approach, and with the help of emerging technologies ranging from multidisciplinary teleconsults (for monitoring the dysphagia, respiratory function, and nutritional status) to brain-computer interfaces and eye tracking for alternative augmentative communication, until robotics, it may increase effectiveness. The COVID-19 pandemic created a spasmodic need to accelerate the development and implementation of such technologies in clinical practice, to improve the daily lives of both ALS patients and caregivers. However, despite the remarkable strides that have been made in the field, there are still issues to be addressed. In this review will be discussed the eureka moment of emerging technologies for ALS, used as a blueprint not only for neurodegenerative diseases, examining the current technologies already in place or being evaluated, highlighting the pros and cons for future clinical applications.

Diletta D., Duse A., Mereghetti P., Cozza F., Ambrosio F., Ponzini E., Grandori R., Lunetta C., Tavazzi S., Pezzoli F., Natalello A. Tear-Based Vibrational Spectroscopy Applied to Amyotrophic Lateral Sclerosis (Anal Chem). December 2021.

Abstract

Biofluid analysis by optical spectroscopy techniques is attracting considerable interest due to its potential to revolutionize diagnostics and precision medicine, particularly for neurodegenerative diseases. However, the lack of effective biomarkers combined with the unaccomplished identification of convenient biofluids has drastically hampered optical advancements in clinical diagnosis and monitoring of neurodegenerative disorders. Here, we show that vibrational spectroscopy applied to human tears opens a new route, offering a non-invasive, label-free identification of a devastating disease such as amyotrophic lateral sclerosis (ALS). Our proposed approach has been validated using two widespread techniques, namely, Fourier transform infrared (FTIR) and Raman microspectroscopies. In conjunction with multivariate analysis, this vibrational approach made it possible to discriminate between tears from ALS patients and healthy controls (HCs) with high specificity (∼97% and ∼100% for FTIR and Raman spectroscopy, respectively) and sensitivity (∼88% and ∼100% for FTIR and Raman spectroscopy, respectively). Additionally, the investigation of tears allowed us to disclose ALS spectroscopic markers related to protein and lipid alterations, as well as to a reduction of the phenylalanine level, in comparison with HCs. Our findings show that vibrational spectroscopy is a new potential ALS diagnostic approach and indicate that tears are a reliable and non-invasive source of ALS biomarkers.

Under revision