Method for determining a disease progression and survival prognosis for patients with amyotrophic lateral sclerosis – PCT/IT2020/000057
Pugliese, R.*, Beltrami, B., Regondi, S., & Lunetta, C. (2021). Polymeric Biomaterials for 3D Printing in Medicine: An Overview. Annals of 3D Printed Medicine, 100011.
Three-dimensional (3D) printing is becoming a booming technology to fabricate scaffolds, orthoses, and prosthetic devices for tissue engineering, regenerative medicine, and rehabilitation for patients with disabling neurological diseases (such as amyotrophic lateral sclerosis, traumatic brain injuries, and spinal cord injuries). This is due to the potential of 3D printing to provide patient-specific designs, high structural complexity, and rapid on-demand fabrication at a low-cost. However, one of the major bottlenecks that limits the widespread acceptance of 3D printing for biomedical manufacturing is the lack of polymers, biomaterials, hydrogels, and bioinks functional for 3D printing, biocompatible, and more performing from the biomechanical point of view to meet the different needs. As a matter of fact the field is still struggling with processing of such materials into self-supporting devices with tunable biomechanics, optimal structures, degradation, and bioactivity. Here, will be highlighted all recent advances that have been made in the field of 3D printing in biomedicine, analyzing the polymers, hydrogels, and bioinks, according to their printability, ease of processability, cost, and properties such as mechanics, biocompatibility, and degradation rate. Finally, future considerations for 3D bio-fabrication will be discussed.
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.
Material extrusion additive manufacturing (MEAM) is an advanced manufacturing method that produces parts via layer-wise addition of material. The potential of MEAM to prototype lattice structures is remarkable, but restrictions imposed by manufacturing processes lead to practical limits on the form and dimension of structures that can be produced. For this reason, such structures are mainly manufactured by selective laser melting. Here, the capabilities of fused filament fabrication (FFF) to produce custom-made lattice structures are explored by combining the 3D printing process, including computer-aided design (CAD), with the finite element method (FEM). First, we generated four types of 3D CAD scaffold models with different geometries (reticular, triangular, hexagonal, and wavy microstructures) and tunable unit cell sizes (1–5 mm), and then, we printed them using two nozzle diameters (i.e., 0.4 and 0.8 mm) in order to assess the printability limitation. The mechanical behavior of the above-mentioned lattice scaffolds was studied using FEM, combining compressive modulus (linear and nonlinear) and shear modulus. Using this approach, it was possible to print functional 3D polymer lattice structures with some discrepancies between nozzle diameters, which allowed us to elucidate critical parameters of printing in order to obtain printed that lattices (1) fully comply with FFF guidelines, (2) are capable of bearing different compressive loads, (3) possess tunable porosity, and (3) overcome surface quality and accuracy issues. In addition, these findings allowed us to develop 3D printed wrist brace orthosis made up of lattice structures, minimally invasive (4 mm of thick), lightweight (<20 g), and breathable (porosity >80%), to be used for the rehabilitation of patients with neuromuscular disease, rheumatoid arthritis, and beyond. Altogether, our findings addressed multiple challenges associated with the development of polymeric lattice scaffolds with FFF, offering a new tool for designing specific devices with tunable mechanical behavior and porosity.
Raffaele Pugliese*, Stefano Regondi, Riccardo Marini. Machine learning-based approach: Global trends, research directions, and regulatory standpoints. (2022). Data Science and Management.
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.
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.
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.
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