Biology subjects

Atzori, M.

Publications and source records attributed to Atzori, M..

8 recordsLinked to original sources

bioRxiv2025

Understanding the mismatch between in-vivo and in-silico rhinomanometry

Numerical simulations and clinical measurements of nasal resistance are in quantitative disagreement. Bias introduced by the design of medical devices has not been considered until now as a possible explanation. The aim of present paper is to study the effect of the location of the probe on the rhinomanometer that is meant to measure the ambient pressure. Rhinomanometry is carried out on a 3D silicone model of a patient-specific anatomy; a clinical device and dedicated sensors are employed side-by-side for mutual validation. The same anatomy is also employed for numerical simulations, with approaches spanning a wide range of fidelity levels. We find that the intrinsic uncertainty of the numerical simulations is of minor importance. To the contrary, the position of the pressure tap intended to acquire the external pressure in the clinical device is crucial, and can cause a mismatch comparable to that generally observed between in-silico and in-vivo rhinomanometry data. A source of systematic bias may therefore exist in rhinomanometers, designed under the assumption that measurements of the nasal resistance are unaffected by the flow development within the instruments.

bioengineering↗

bioRxiv2025

Analysis of the current state of frailty indexes and their implementation for aging intervention studies

Animal lifespan studies are foundational to developing interventions against the biological aging process. In recent years, there has been rising interest in characterizing the effects of longevity therapeutics on health span. Frailty indexes, originally developed to assess clinical frailty in aging humans, have shown promise as measurements of biological age and have been adopted for use in rodent aging biology. This perspective looks at the current state of rodent frailty indexes and how they are implemented. The differences in frailty parameters used to calculate these indexes have led to inconsistencies between studies defining frailty. In this perspective, we have highlighted those differences and made recommendations for implementing protocols for frailty index measurement.

animal behavior and cognition↗

bioRxiv2024

Benchmarking Stroke Outcome Prediction through Comprehensive Data Analysis - NeuralCup 2023

Stroke remains a leading cause of mortality and long-term disability worldwide, with variable recovery trajectories posing substantial challenges in anticipating post-event care and rehabilitation planning. To address these challenges, we established the NeuralCup consortium to benchmark predictive models of stroke outcome through a collaborative, data-driven approach. This study presents findings from 15 international teams who used a comprehensive dataset including clinical and imaging data, to identify and compare predictors of motor, cognitive, and emotional outcomes one year post-stroke. Our analyses integrated traditional statistical approaches and novel machine learning algorithms to uncover optimal recipes for predicting each domain. The differences in these optimal recipes reflect distinct brain mechanisms in response to different tasks. Key predictors across all domains included infarct characteristics, T1-weighted MRI sequences, and demographic factors. Additionally, integrating FLAIR imaging and white matter tract analysis significantly improved the prediction of cognitive and motor outcomes, respectively. These findings support a multifaceted approach to stroke outcome prediction, underscoring the potential of collaborative data science to develop personalized care strategies that enhance recovery and quality of life for stroke survivors. To encourage further model development and validation, we provide access to the training dataset at http://neuralcup.bcblab.com

neuroscience↗

bioRxiv2023

Pleiotropic effects of trisomy and pharmacologic modulation on structural, functional, molecular, and genetic systems in a Down syndrome mouse model

Down syndrome (DS) is characterized by skeletal and brain structural malformations, cognitive impairment, altered hippocampal metabolite concentration and gene expression imbalance. These alterations were usually investigated separately, and the potential rescuing effects of green tea extracts enriched in epigallocatechin-3-gallate (GTE-EGCG) provided disparate results due to different experimental conditions. We overcame these limitations by conducting the first longitudinal controlled experiment evaluating genotype and GTE-EGCG prenatal chronic treatment effects before and after treatment discontinuation. Our findings revealed that the Ts65Dn mouse model reflected the pleiotropic nature of DS, exhibiting brachycephalic skull, ventriculomegaly, neurodevelopmental delay, hyperactivity, and impaired memory robustness with altered hippocampal metabolite concentration and gene expression. GTE-EGCG treatment modulated most systems simultaneously but did not rescue DS phenotypes. On the contrary, the treatment exacerbated trisomic phenotypes including body weight, tibia microarchitecture, neurodevelopment, adult cognition, and metabolite concentration, not supporting the therapeutic use of GTE-EGCG as a prenatal chronic treatment. Our results highlight the importance of longitudinal experiments assessing the co-modulation of multiple systems throughout development when characterizing preclinical models in complex disorders and evaluating the pleiotropic effects and general safety of pharmacological treatments.

systems biology↗

bioRxiv2022

Spatial and temporal muscle synergies provide a dual characterization of low-dimensional and intermittent control of upper-limb movements

Muscle synergy analysis is commonly used for investigating the neurophysiological mechanisms that the central nervous system employs to control muscle activations. In the last two decades, several models have been developed to decompose EMG signals into spatial, temporal or spatiotemporal synergies. However, the presence of different approaches complicates the comparison and interpretation of results. Spatial synergies represent invariant activation weights in muscle groups modulated with variant temporal coefficients, while temporal synergies are based on invariant temporal profiles that coordinate variant muscle weights. While non-negative matrix factorization (NMF) allows to extract both spatial and temporal synergies, temporal synergies and the comparison between the two approaches have been barely investigated and so far no study targeted a large set of multi-joint upper limb movements. Here we present several analyses that highlight the duality of spatial and temporal synergies as a characterization of low-dimensional and intermittent motor coordination in the upper limb, allowing high flexibility and dexterity. First, spatial and temporal synergies were extracted from two datasets representing a comprehensive mapping of proximal (REACH PLUS) and distal (NINAPRO) upper limb movements, focusing on their differences in reconstruction accuracy and inter-individual variability. For both models, we extracted synergies achieving a given level of the goodness of reconstruction (R2), and we compared the similarity of the invariant components across participants. The two models provide a compact characterization of motor coordination at spatial or temporal level, respectively. However, a lower number of temporal synergies are needed to achieve the same R2 with a higher inter-subject similarity. Spatial and temporal synergies may thus capture different levels of motor control. Second, we showed the existence of both spatial and temporal structure in the EMG data, extracting spatial and temporal synergies from a surrogate dataset in which the phases were shuffled preserving the same frequency content of the original data. Last, a detailed characterization of the structure of the temporal synergies suggested that they can be related to an intermittent control of the movement. These results may be useful to improve muscle synergy analysis in several fields such as rehabilitation, prosthesis control and motor control studies.

neuroscience↗

bioRxiv2022

stainlib: a python library for augmentation and normalization of histopathology H&E images

Computational pathology is a domain of increasing scientific and social interest. The automatic analysis of histopathology images stained with Hematoxylin and Eosin (H&E) can help clinicians diagnose and quantify diseases. Computer vision methods based on deep learning can perform on par or better than pathologists in specific tasks [1, 2, 15]. Nevertheless, the visual heterogeneity in histopathology images due to batch effects, differences in preparation in different pathology laboratories, and the scanner can produce tissue appearance changes in the digitized whole-slide images. Such changes impede the application of the trained models in clinical scenarios where there is high variability in the images. We introduce stainlib, an easy-to-use and expandable python3 library that collects and unifies state-of-the-art methods for color augmentation and normalization of histopathology H&E images. stainlib also contains recent deep learning-based approaches that perform a robust stain-invariant training of CNN models. stainlib can help researchers build models robust to color domain shift by augmenting and harmonizing the training data, allowing the deployment of better models in the digital pathology practice.

bioinformatics↗

bioRxiv2018

Multifunction control and evaluation of a 3D printed hand prosthesis with the Myo armband by hand amputees

Upper limb amputations are highly impairing injuries that can substantially limit the quality of life of a person. The most advanced dexterous prosthetic hands have remarkable mechanical features. However, in most cases, the control systems are a simple extension of basic control protocols, making the use of the prosthesis not intuitive and sometimes complex. Furthermore, the cost of dexterous prosthetic hands is often prohibitive, especially for the pediatric population and developing countries. 3D printed hand prostheses can represent an opportunity for the future. Open 3D models are increasingly being released, even for dexterous prostheses that are capable of moving each finger individually and actively rotating the thumb. However, the usage and test of such devices by hand amputees (using electromyography and classification methods) is not well explored. The aim of this article is to investigate the usage of a cost-effective system composed of a 3D printed hand prosthesis and a low-cost myoelectric armband. Two subjects with transradial amputation were asked to wear a custom-made socket supporting the HANDi Hand and the Thalmic Labs Myo armband. Afterwards, the subjects were asked to control and use the prosthetic hand to grasp several objects by attempting to perform a set of different hand gestures. Both the HANDi Hand and the Myo armband performed well during the test, which is encouraging considering that the HANDi Hand was developed as a research platform. The results are promising and show the feasibility of the multifunction control of dexterous 3D printed hand prostheses based on low-cost setups. Factors as the level of the amputation, neuromuscular fatigue and mechanical limitations of the 3D printed hand prosthesis can influence the performance of the setup. Practical aspects such as usability and robustness will need to be addressed for successful application in daily life. A video of the tests can be found at the following link: https://youtu.be/iPSCAbd17Qw

bioengineering↗

bioRxiv2018

Deep learning based retrieval system for gigapixel histopathology cases and open access literature

Clinical practice is getting increasingly stressful for pathologists due to increasing complexity and time constraints. Histopathology is slowly shifting to digital pathology, thus creating opportunities to allow pathologists to improve reading quality or save time using Artificial Intelligence (AI)-based applications. We aim to enhance the practice of pathologists through a retrieval system that allows them to simplify their workflow, limit the need for second opinions, while also learning in the process. In this work, an innovative retrieval system for digital pathology is integrated within a Whole Slide Image (WSI) viewer, allowing to define regions of interest in images as queries for finding visually similar areas using deep representations. The back-end similarity computation algorithms are based on a multimodal approach, allowing to exploit both text information and content-based image features. Shallow and deep representations of the images were evaluated, the later showed a better overall retrieval performance in a set of 112 whole slide images from biopsies. The system was also tested by pathologists, highlighting its capabilities and suggesting possible ways to improve it and make it more usable in clinical practice. The retrieval system developed can enhance the practice of pathologists by enabling them to use their experience and knowledge to properly control artificial intelligence tools for navigating repositories of images for decision support purposes.

pathology↗