Country-level factors dynamics and ABO/Rh blood groups contribution to COVID-19 mortality

The identification of factors associated to COVID-19 mortality is important to design effective containment measures and safeguard at-risk categories. In the last year, several investigations have tried to ascertain key features to predict the COVID-19 mortality tolls in relation to country-specific dynamics and population structure. Most studies focused on the first wave of the COVID-19 pandemic observed in the first half of 2020. Numerous studies have reported significant associations between COVID-19 mortality and relevant variables, for instance obesity, healthcare system indicators such as hospital beds density, and bacillus Calmette-Guerin immunization. In this work, we investigated the role of ABO/Rh blood groups at three different stages of the pandemic while accounting for demographic, economic, and health system related confounding factors. Using a machine learning approach, we found that the “B+” blood group frequency is an important factor at all stages of the pandemic, confirming previous findings that blood groups are linked to COVID-19 severity and fatal outcome

Multi-time-scale features for accurate respiratory sound classification

The COVID-19 pandemic has amplified the urgency of the developments in computer-assisted medicine and, in particular, the need for automated tools supporting the clinical diagnosis and assessment of respiratory symptoms. This need was already clear to the scientific community, which launched an international challenge in 2017 at the International Conference on Biomedical Health Informatics (ICBHI) for the implementation of accurate algorithms for the classification of respiratory sound. In this work, we present a framework for respiratory sound classification based on two different kinds of features: (i) short-term features which summarize sound properties on a time scale of tenths of a second and (ii) long-term features which assess sounds properties on a time scale of seconds. Using the publicly available dataset provided by ICBHI, we cross-validated the classification performance of a neural network model over 6895 respiratory cycles and 126 subjects. The proposed model reached an accuracy of 85% ± 3% and an precision of 80% ± 8%, which compare well with the body of literature. The robustness of the predictions was assessed by comparison with state-of-the-art machine learning tools, such as the support vector machine, Random Forest and deep neural networks. The model presented here is therefore suitable for large-scale applications and for adoption in clinical practice. Finally, an interesting observation is that both short-term and long-term features are necessary for accurate classification, which could be the subject of future studies related to its clinical interpretation

An equity-oriented rethink of global rankings with complex networks mapping development

Nowadays, world rankings are promoted and used by international agencies, governments and corporations to evaluate country performances in a specific domain, often providing a guideline for decision makers. Although rankings allow a direct and quantitative comparison of countries, sometimes they provide a rather oversimplified representation, in which relevant aspects related to socio-economic development are either not properly considered or still analyzed in silos. In an increasingly data-driven society, a new generation of cutting-edge technologies is breaking data silos, enabling new use of public indicators to generate value for multiple stakeholders. We propose a complex network framework based on publicly available indicators to extract important insight underlying global rankings, thus adding value and significance to knowledge provided by these rankings. This approach enables the unsupervised identification of communities of countries, establishing a more targeted, fair and meaningful criterion to detect similarities. Hence, the performance of states in global rankings can be assessed based on their development level. We believe that these evaluations can be crucial in the interpretation of global rankings, making comparison between countries more significant and useful for citizens and governments and creating ecosystems for new opportunities for development

Economic Interplay Forecasting Business Success

A startup ecosystem is a dynamic environment in which several actors, such as investors, venture capitalists, angels, and facilitators, are the protagonists of a complex interplay. Most of these interactions involve the flow of capital whose size and direction help to map the intricate system of relationships. This quantity is also considered a good proxy of economic success. Given the complexity of such systems, it would be more desirable to supplement this information with other informative features, and a natural choice is to adopt mathematical measures. In this work, we will specifically consider network centrality measures, borrowed by network theory. In particular, using the largest publicly available dataset for startups, the Crunchbase dataset, we show how centrality measures highlight the importance of particular players, such as angels and accelerators, whose role could be underestimated by focusing on collected funds only. We also provide a quantitative criterion to establish which firms should be considered strategic and rank them. Finally, as funding is a widespread measure for success in economic settings, we investigate to which extent this measure is in agreement with network metrics; the model accurately forecasts which firms will receive the highest funding in future years

Potential energy of complex networks: a quantum mechanical perspective

We propose a characterization of complex networks, based on the potential of an associated Schrödinger equation. The potential is designed so that the energy spectrum of the Schrödinger equation coincides with the graph spectrum of the normalized Laplacian. Crucial information is retained in the reconstructed potential, which provides a compact representation of the properties of the network structure. The median potential over several random network realizations, which we call ensemble potential, is fitted via a Landau-like function, and its length scale is found to diverge as the critical connection probability is approached from above. The ruggedness of the ensemble potential profile is quantified by using the Higuchi fractal dimension, which displays a maximum at the critical connection probability. This demonstrates that this technique can be successfully employed in the study of random networks, as an alternative indicator of the percolation phase transition. We apply the proposed approach to the investigation of real-world networks describing infrastructures (US power grid). Curiously, although no notion of phase transition can be given for such networks, the fractality of the ensemble potential displays signatures of criticality. We also show that standard techniques (such as the scaling features of the largest connected component) do not detect any signature or remnant of criticality

Evidence of the possible interaction between ultrasound and thiol precursors

The effect of ultrasound (20 kHz, 153 \ub5m) on the prefermentation extraction mechanisms in Sauvignon Blanc grapes was studied, focusing on 3-mercaptohexan-1-ol (3MH) and 4-mercapto-4-methyl-pentan-2-one (4MMP) precursors linked to glutathione (GSH) and cysteine (Cys). The treatment determined a positive extraction trend between the duration (untreated, 3 and 5 min) and the conductivity or the concentration of catechins and total phenols, significantly differentiated after 5 min. Nevertheless, the concentration of the thiol precursors in grape juice not only remained undifferentiated, but that of 3-S-glutathionyl mercaptohexan-1-ol showed a negative trend with the treatment time applied (168 \ub1 43, 156 \ub1 36, and 149 \ub1 32 \ub5g/L, respectively, for control, 3 and 5 min). The divergence on the effect between families of compounds suggests an interaction between the sonication treatment and thiol precursor molecules. In order to evaluate the possible degradation properly, ultrasound was applied in a model solution spiked with 3MH and 4MMP precursors, reproducing the conditions of grapes. Except for Cys-3MH, the mean concentration (n = 5) for the rest of the precursors was significantly lower in treated samples, predominantly in those linked to glutathione (~ 1222% and ~18% for GSH-3MH and GSH-4MMP) rather than to cysteine (~ 126%~ 128% for Cys-3MH and Cys-4MMP). The degradation of precursors was associated with a significant increase of 3MH and 4MMP. The formation of volatile thiols following sonication is interesting from a technological point of view, as they are key aroma compounds of wine and potentially exploitable in the wine industry through specific vinification protocols

Shannon entropy approach reveals relevant genes in Alzheimer's disease

Alzheimer's disease (AD) is the most common type of dementia and affects millions of people worldwide. Since complex diseases are often the result of combinations of gene interactions, microarray data and gene co-expression analysis can provide tools for addressing complexity. Our study aimed to find groups of interacting genes that are relevant in the development of AD. In this perspective, we implemented a method proposed in a previous work to detect gene communities linked to AD. Our strategy combined co-expression network analysis with the study of Shannon entropy of the betweenness. We analyzed the publicly available GSE1297 dataset, achieved from the GEO database in NCBI, containing hippocampal gene expression of 9 control and 22 AD human subjects. Co-expressed genes were clustered into different communities. Two communities of interest (composed by 72 and 39 genes) were found by calculating the correlation coefficient between communities and clinical features. The detected communities resulted stable, replicated on two independent datasets and mostly enriched in pathways closely associated with neuro-degenative diseases. A comparison between our findings and other module detection techniques showed that the detected communities were more related to AD phenotype. Lastly, the hub genes within the two communities of interest were identified by means of a centrality analysis and a bootstrap procedure. The communities of the hub genes presented even stronger correlation with clinical features. These findings and further explorations on the detected genes could shed light on the genetic aspects related with physiological aspects of Alzheimer's disease

Optimization of the failure criterion in micro-Finite Element models of the mouse tibia for the non-invasive prediction of its failure load in preclinical applications

New treatments against osteoporosis require testing in animal models and the mouse tibia is among the most common studied anatomical sites. In vivo micro-Computed Tomography (microCT) based micro-Finite Element (microFE) models can be used for predicting the bone strength non-invasively, after proper validation against experiments. The aim of this study was to evaluate the ability of different microCT-based bone parameters and microFE models to predict tibial structural mechanical properties in compression. Twenty tibiae were scanned at 10.4 μm voxel size and subsequently tested in uniaxial compression at 0.03 mm/s until failure. Stiffness and failure load were measured from the load-displacement curves. Standard morphometric parameters were measured from the microCT images. The spatial distribution of bone mineral content (BMC) was evaluated by dividing the tibia into 40 regions. MicroFE models were generated by converting each microCT image into a voxel-based mesh with homogeneous isotropic material properties. Failure load was estimated by using different failure criteria, and the optimized parameters were selected by minimising the errors with respect to experimental measurements. Experimental and predicted stiffness were moderately correlated (R2 = 0.65, error = 14% ± 8%). Normalized failure load was best predicted by microFE models (R2 = 0.81, error = 9% ± 6%). Failure load was not correlated to the morphometric parameters and weakly correlated with some geometrical parameters (R2 < 0.37). In conclusion, microFE models can improve the current estimation of the mouse tibia structural properties and in this study an optimal failure criterion has been defined. Since it is a non-invasive method, this approach can be applied longitudinally for evaluating temporal changes in the bone strength

Geroprotectors: A role in the treatment of frailty

The proportion of the population over the age of 65 is growing the most rapidly due to the longevity revolution. Frailty is prevalent in this age group and strongly associated with disability and hospitalization, having a significant impact on the costs of health and social care. New effective interventions to delay or reverse frailty are urgently required. Geroprotectors are a new class of drugs, which target fundamental mechanisms of ageing and show promise in delaying the onset of or boosting resilience in frail older people. However, there are challenges to their clinical translation. Here we review the literature for evidence that frailty can be delayed or reversed and geroprotectors can improve frailty in murine models and in patients. We will then discuss the challenges, which make their clinical testing complex and propose potential options for moving forward