An ECG-based machine-learning approach for mortality risk assessment in a large European population.

Aims: Through a simple machine learning approach, we aimed to assess the risk of all-cause mortality after 5 years in a European population, based on electrocardiogram (ECG) parameters, age, and sex.

Methods: The study included patients between 40 and 90 years old who underwent ECG recording between January 2008 and October 2022 in the metropolitan area of Modena, Italy. Exclusion criteria established a patient cohort without severe ECG abnormalities, namely, tachyarrhythmias, bradyarrhythmias, Wolff-Parkinson-White syndrome, second- or third- degree AV block, bundle-branch blocks, more than three premature beats, poor signal quality, and presence of pacemakers and implantable cardioverter- defibrillators.

A Review of Degradation Models and Remaining Useful Life Prediction for Testing Design and Predictive Maintenance of Lithium-Ion Batteries.

We present a novel decision-making framework for accelerated degradation tests and predictive maintenance that exploits prior knowledge and experimental data on the system's state. As a framework for sequential decision making in these areas, dynamic programming and reinforcement learning are considered, along with data-driven degradation learning when necessary. Furthermore, we illustrate both stochastic and machine learning degradation models, which are integrated in the framework, using data-driven methods.

New optimization strategies for SWMM modeling of stormwater quality applications in urban area.

Build-up/wash-off models were originally developed for small-scale laboratory facilities with uniform properties. The effective translation of these models to catchment scale necessitates the meticulous calibration of model parameters. The present study combines the Mat-SWMM tool with a genetic algorithm (GA) to improve the calibration of build-up and wash-off parameters.

The Poses for Equine Research Dataset (PFERD).

Studies of quadruped animal motion help us to identify diseases, understand behavior and unravel the mechanics behind gaits in animals. The horse is likely the best-studied animal in this aspect, but data capture is challenging and time-consuming. Computer vision techniques improve animal motion extraction, but the development relies on reference datasets, which are scarce, not open-access and often provide data from only a few anatomical landmarks.

Effects of working from home on lifestyle behaviors and mental health during the COVID-19 pandemic: A survey study.

The COVID-19 outbreak has led to relevant changes in everyday life worldwide. One of these changes has been a rapid transition to and an increasing implementation of working from home (WH) modality. This study aimed to evaluate the impact of mandatory WH during the COVID-19 pandemic on lifestyle behaviors, Mediterranean diet adherence, body weight, and depression.

Modelling contagious viral dynamics: a kinetic approach based on mutual utility.

The temporal evolution of a contagious viral disease is modelled as the dynamic progression of different classes of population with individuals interacting pairwise. This interaction follows a binary mechanism typical of kinetic theory, wherein agents aim to improve their condition with respect to a mutual utility target. To this end, we introduce kinetic equations of Boltzmann-type to describe the time evolution of the probability distributions of the multi-agent system.

Connection between Variations of the Probability Distribution of the Recurrence Time and Phases of the Seismic Activity.

The probability distribution of the interevent time between two successive earthquakes has been the subject of numerous studies for its key role in seismic hazard assessment. In recent decades, many distributions have been considered, and there has been a long debate about the possible universality of the shape of this distribution when the interevent times are properly rescaled. In this work, we aim to discover if there is a link between the different phases of a seismic cycle and the variations in the distribution that best fits the interevent times.

Corrigendum: The medium-term perceived impact of work from home on life and work domains of knowledge workers during COVID-19 pandemic: a survey at the National Research Council of Italy.

[This corrects the article DOI: 10.3389/fpubh.2023.

The medium-term perceived impact of work from home on life and work domains of knowledge workers during COVID-19 pandemic: A survey at the National Research Council of Italy.

Objective: The study aimed to investigate perceptions and determinants of the overall impact on life and work domains among a community of knowledge workers after 18 months of forced work from home due to the pandemic.

Methods: A cross-sectional study with a retrospective assessment was conducted early in 2022 at the National Research Council of Italy. Five single-item questions explored the perceived impact on life domain while a 7-item scale the impact on the work domain.

COVID-19 and excess mortality in Russia: Regional estimates of life expectancy losses in 2020 and excess deaths in 2021.

Accurately counting the human cost of the COVID-19 at both the national and regional level is a policy priority. The Russian Federation currently reports one of the higher COVID-19 mortality rates in the world; but estimates of mortality differ significantly. Using a statistical method accounting for changes in the population age structure, we present the first national and regional estimates of excess mortality for 2021; calculations of excess mortality by age, gender, and urban/rural status for 2020; and mean remaining years of life expectancy lost at the regional level.

Economic Evaluations of Magnetic Resonance Image-Guided Radiotherapy (MRIgRT): A Systematic Review.

Objectives: This review systematically summarizes the evidence on the economic impact of magnetic resonance image-guided RT (MRIgRT).

Methods: We systematically searched INAHTA, MEDLINE, and Scopus up to March 2022 to retrieve health economic studies. Relevant data were extracted on study type, model inputs, modeling methods and economic results.

Optimal control of epidemic spreading in the presence of social heterogeneity.

The spread of COVID-19 has been thwarted in most countries through non-pharmaceutical interventions. In particular, the most effective measures in this direction have been the stay-at-home and closure strategies of businesses and schools. However, population-wide lockdowns are far from being optimal, carrying heavy economic consequences.

Kinetic exchange models of societies and economies.

The statistical nature of collective human behaviour in a society is a topic of broad current interest. From formation of consensus through exchange of ideas, distributing wealth through exchanges of money, traffic flows, growth of cities to spread of infectious diseases, the application range of such collective responses cuts across multiple disciplines. Kinetic models have been an elegant and powerful tool to explain such collective phenomena in a myriad of human interaction-based problems, where an energy consideration for dynamics is generally inaccessible.

A postural program to reduce dynamic knee valgus during single-limb squatting in young athletes: a preliminary study.

Background: Dynamic knee valgus (DKV) is an undesirable multi-joint movement pattern associated with anterior cruciate ligament injury and patellofemoral pain syndrome, especially in sport activities. We assessed DKV in young athletes who followed a postural program to reduce a posterior rigidity mostly attributable to the tightness of hamstring muscles.

Methods: We considered 12- to 18-year-old athletes that followed a six-week program simply based on hamstring stretching and abdominal muscle activation/strengthening.

Health Technology Assessment for In Silico Medicine: Social, Ethical and Legal Aspects.

The application of in silico medicine is constantly growing in the prevention, diagnosis, and treatment of diseases. These technologies allow us to support medical decisions and self-management and reduce, refine, and partially replace real studies of medical technologies. In silico medicine may challenge some key principles: transparency and fairness of data usage; data privacy and protection across platforms and systems; data availability and quality; data integration and interoperability; intellectual property; data sharing; equal accessibility for persons and populations.

Perspectives in machine learning for wildlife conservation.

Inexpensive and accessible sensors are accelerating data acquisition in animal ecology. These technologies hold great potential for large-scale ecological understanding, but are limited by current processing approaches which inefficiently distill data into relevant information. We argue that animal ecologists can capitalize on large datasets generated by modern sensors by combining machine learning approaches with domain knowledge.

Burnout Among Healthcare Workers in the COVID 19 Era: A Review of the Existing Literature.

In the current period of global public health crisis due to the COVID-19, healthcare workers are more exposed to physical and mental exhaustion - burnout - for the torment of difficult decisions, the pain of losing patients and colleagues, and the risk of infection, for themselves and their families. The very high number of cases and deaths, and the probable future "waves" raise awareness of these challenging working conditions and the need to address burnout by identifying possible solutions. Measures have been suggested to prevent or reduce burnout at individual level (physical activity, balanced diet, good sleep hygiene, family support, meaningful relationships, reflective practices and small group discussions), organizational level (blame-free environments for sharing experiences and advices, broad involvement in management decisions, multi-disciplinary psychosocial support teams, safe areas to withdraw quickly from stressful situations, adequate time planning, social support), and cultural level (involvement of healthcare workers in the development, implementation, testing, and evaluation of measures against burnout).

A Multi-Variate framework to assess reliability and discrimination power of Bayesian estimation of Intravoxel Incoherent Motion parameters.

Purpose: To propose a multivariate multi-step framework for a systematic assessment of the estimation reliability and discriminability of Intravoxel Incoherent Motion (IVIM) model parameters.

Methods: Monte-Carlo simulations were generated on a range of SNRs and in different IVIM combinations considering: i) a dense discretization with 24 b-values; ii) a discretization with 9 b-values. A state-of-the-art Bayesian fitting method was adopted.

Predictive Added Value of Selected Plasma Lipids to a Re-estimated Minimal Risk Tool.

Lipidomics is emerging for biomarker discovery in cardiovascular disease, and circulating lipids are increasingly incorporated in risk models to predict cardiovascular events. Moreover, specific classes of lipids, such as sphingomyelins, ceramides, and triglycerides, have been related to coronary artery disease (CAD) severity and plaque characteristics. To avoid unnecessary testing, it is important to identify individuals at low CAD risk.

Discrimination capability of pretest probability of stable coronary artery disease: a systematic review and meta-analysis suggesting how to improve validation procedures.

Objective: Externally validated pretest probability models for risk stratification of subjects with chest pain and suspected stable coronary artery disease (CAD), determined through invasive coronary angiography or coronary CT angiography, are analysed to characterise the best validation procedures in terms of discriminatory ability, predictive variables and method completeness.

Design: Systematic review and meta-analysis.

Data Sources: Global Health (Ovid), Healthstar (Ovid) and MEDLINE (Ovid) searched on 22 April 2020.

Social contacts, epidemic spreading and health system. Mathematical modeling and applications to COVID-19 infection.

Lockdown and social distancing, as well as testing and contact tracing, are the main measures assumed by the governments to control and limit the spread of COVID-19 infection. In reason of that, special attention was recently paid by the scientific community to the mathematical modeling of infection spreading by including in classical models the effects of the distribution of contacts between individuals. Among other approaches, the coupling of the classical SIR model with a statistical study of the distribution of social contacts among the population, led some of the present authors to build a Social SIR model, able to accurately follow the effect of the decrease in contacts resulting from the lockdown measures adopted in various European countries in the first phase of the epidemic.

Simulated versus physical bench tests: The economic evaluation of the InSilc platform for designing, developing, and assessing vascular scaffolds.

Background: In silico medicine allows for pre-clinical and clinical simulated assessment of medical technologies and the building of patient-specific models to support medical decisions and forecast personal health status. While there is increasing trust in the potential central role of in silico medicine, there is a need to recognize its degree of reliability and evaluate its economic impact. An in silico platform has been developed within a Horizon 2020-funded project (In-Silc) for simulations functional to designing, developing, and assessing drug-eluting bioresorbable vascular scaffolds.

Control of tumor growth distributions through kinetic methods.

The mathematical modeling of tumor growth has a long history, and has been mathematically formulated in several different ways. Here we tackle the problem in the case of a continuous distribution using mathematical tools from statistical physics. To this extent, we introduce a novel kinetic model of growth which highlights the role of microscopic transitions in determining a variety of equilibrium distributions.

The effect of a postural exercise program on muscle power in Italian high school students.

[Purpose] The present study aimed to investigate the effect of an innovative postural program (the Canali Postural Method, CPM) on muscle power in Italian high school students. It is note that deficits in posture control may, in long term, generate posture weakness as early as childhood and adolescence. Postural programs based on stretching and strengthening exercises can remove these deficits and can be framed in general physical or sport activities.