Past research has demonstrated that continuous exposure to pollutants, such as PM2.5 and PM10, is associated with an increased risk of developing and worsening respiratory and neurodegenerative diseases. Calculating and reducing exposure to these pollutants is crucial to assess these risks and perform proper prevention. In this study, we estimate personal exposure to PM2.5 based on the integration of sensors measurements, meteorological data and land use parameters, which could impact on actual pollution levels, especially in areas located far from the sensors. Pollution data have been collected from a dense network of sensors located in Pavia, Italy, meteorological and geographical data have been collected from public sources. We used geographical data to create graphs that model the city road structure, and applied Land Use Regression methods to estimate air pollution on its nodes, adjusting the measurements interpolated from the sensors with the effects of weather data, land use parameters such as the distance from the closest high-traffic road, and additional temporal information such as weekends/holidays and working days. We tested several regression methods: linear regression, both simple and with regularization (Ridge, LASSO and ElasticNet), Random Forest regression, Gradient Boosting and Support Vector Regression (SVR). Results show that meteorological variables, namely temperature and humidity, and temporal factors do contribute significantly in obtaining pollution values in the graph nodes that differ from values obtained exclusively through sensors interpolation.

Inflammatory Bowel Disease (IBD) is a term that includes two chronic conditions characterized by inflammation of the gastrointestinal tract, i.e., Ulcerative Colitis and Crohn’s Disease. IBD can be associated to symptoms that significantly reduce quality of life and can lead to serious complications, therefore proper disease monitoring and treatment is necessary. To this end, Telemedicine has been identified as a useful tool, since it can ease the patients’ burden on the management of nutrition and pharmacological treatment, and can improve the connection with the physicians. In this paper, we present NutriA, a new responsive web application for the management of IBD patients in Italy, created to assist patients with diet, medications and symptoms monitoring, optimizing the follow-ups frequency and providing direct assistance by the doctors. Besides being useful to improve the patients’ well-being, NutriA is also designed to be used in a new clinical study on the effects of malnutrition on IBD treatment.

The COVID-19 pandemic has profoundly impacted global health, leading to millions of deaths and overwhelming healthcare systems worldwide. This study investigates the relationship between SARS-CoV-2 sequencing rates and critical epidemiological parameters, such as cases, deaths, and ICU admissions, across 25 European countries from January 2020 to November 2023. By analyzing these relationships, we aim to determine whether sequencing efforts were reactive—in response to epidemiological pressures—or proactive, guided by public health strategies. The analysis used publicly available data from GISAID, OxCGRT, and ECDC, and included weekly aggregation, correlation analysis, and the application of TimeGPT for predictive modeling. Results show that sequencing rates were significantly correlated with ICU admissions, hospitalizations, case numbers, and deaths, though with variability between countries and over different pandemic phases. TimeGPT analysis revealed that sequencing rates were often the most informative feature for predicting future COVID-19 cases in many countries. These findings highlight the potential of sequencing rates to serve as early indicators for severe pandemic outcomes and underscore the importance of context-specific approaches for managing future health crises.

Aging is a physiological process associated with numerous cardiovascular, degenerative and neurological conditions. The current demographic changes in the world, characterized by an increasing average age especially in Europe and North America, are causing an increment in the prevalence of such conditions, leading to a public health problem as more resources are required to manage treatments. Recent research has defined a new score named Phenotypic Age, i.e. an adjusted age that takes into account the current health status considering a series of biomarkers, that can be useful to provide a more precise estimation of the probability of developing aging-related conditions. Prevention of such conditions can be performed more efficiently by studying the mechanisms that lead to an increased phenotypic age rather than attempting to treat a patient that has already a high health risk. In this work, we combine pairwise association techniques and mediation analysis to define a strategy to investigate the inner causal mechanisms that lead from specific environmental exposures to an increasing gap between phenotypic and chronological age, considering the influence of biological variables. Four environmental exposures and 11 biological traits have been identified in the NHANES dataset, and each trait has been tested as a mediation variable for each exposure. Almost all mediations reported significant indirect effects with specific results that provide new insights into the causal mechanisms that lead to a deranged aging rate.

BACKGROUND: Amyotrophic Lateral Sclerosis (ALS) is a fatal neurodegenerative disorder characterised by the progressive loss of motor neurons in the brain and spinal cord. The fact that ALS's disease course is highly heterogeneous, and its determinants not fully known, combined with ALS's relatively low prevalence, renders the successful application of artificial intelligence (AI) techniques particularly arduous. OBJECTIVE: This systematic review aims at identifying areas of agreement and unanswered questions regarding two notable applications of AI in ALS, namely the automatic, data-driven stratification of patients according to their phenotype, and the prediction of ALS progression. Differently from previous works, this review is focused on the methodological landscape of AI in ALS. METHODS: We conducted a systematic search of the Scopus and PubMed databases, looking for studies on data-driven stratification methods based on unsupervised techniques resulting in (A) automatic group discovery or (B) a transformation of the feature space allowing patient subgroups to be identified; and for studies on internally or externally validated methods for the prediction of ALS progression. We described the selected studies according to the following characteristics, when applicable: variables used, methodology, splitting criteria and number of groups, prediction outcomes, validation schemes, and metrics. RESULTS: Of the starting 1604 unique reports (2837 combined hits between Scopus and PubMed), 239 were selected for thorough screening, leading to the inclusion of 15 studies on patient stratification, 28 on prediction of ALS progression, and 6 on both stratification and prediction. In terms of variables used, most stratification and prediction studies included demographics and features derived from the ALSFRS or ALSFRS-R scores, which were also the main prediction targets. The most represented stratification methods were K-means, and hierarchical and expectation-maximisation clustering; while random forests, logistic regression, the Cox proportional hazard model, and various flavours of deep learning were the most widely used prediction methods. Predictive model validation was, albeit unexpectedly, quite rarely performed in absolute terms (leading to the exclusion of 78 eligible studies), with the overwhelming majority of included studies resorting to internal validation only. CONCLUSION: This systematic review highlighted a general agreement in terms of input variable selection for both stratification and prediction of ALS progression, and in terms of prediction targets. A striking lack of validated models emerged, as well as a general difficulty in reproducing many published studies, mainly due to the absence of the corresponding parameter lists. While deep learning seems promising for prediction applications, its superiority with respect to traditional methods has not been established; there is, instead, ample room for its application in the subfield of patient stratification. Finally, an open question remains on the role of new environmental and behavioural variables collected via novel, real-time sensors.

Alzheimer's disease (AD) is one of the most common and severe forms of Senile Dementia. Genome-wide association studies (GWAS) have identified dozens of AD susceptible loci. To better understand potential mechanism-of-action for AD, quantitative brain imaging features have been studied as mediators linking genetic variants to AD outcomes. In this study, Mediation analysis, Chow test and Mixed-effects Models are used to investigate the biological pathways by which genetic variants affect both brain structures/functions and disease diagnosis. We analyzed the imaging and genetics data collected from the Alzheimer's Disease Neuroimaging Initiative (ADNI) project, including a Polygenic Hazard Score (PHS) and 13 imaging quantitative traits (QTs) extracted from the AV45 PET scans quantifying the amyloid deposition in different brain regions of subjects from four separate diagnostic groups. Mediation analysis assessed the mediating effects of image QTs between PHS and diagnosis, whereas Chow test and Linear Mixed-Effects models were used to characterize intra-group differences in the associations between genetic scores and imaging QTs for different disease stages. Results show that promising stage-specific imaging QTs that mediate the genetic effect of the studied PHS on disease status have been identified, providing novel insights into the predictive power of the PHS and the mediating power of amyloid imaging QTs with respect to multiple stages over the AD progression.

Despite the silent effects sometimes hidden to the major audience, air pollution is becoming one of the most impactful threat to global health. Cities are the places where deaths due to air pollution are concentrated most. In order to correctly address intervention and prevention thus is essential to assest the risk and the impacts of air pollution spatially and temporally inside the urban spaces. PULSE aims to design and build a large-scale data management system enabling real time analytics of health, behaviour and environmental data on air quality. The objective is to reduce the environmental and behavioral risk of chronic disease incidence to allow timely and evidence-driven management of epidemiological episodes linked in particular to two pathologies; asthma and type 2 diabetes in adult populations. developing a policy-making across the domains of health, environment, transport, planning in the PULSE test bed cities.

Within the EU-funded Pulse project, we are implementing a data analytic platform designed to provide public health decision makers with advanced approaches to jointly analyze maps and geospatial information with health care data and air pollution measurements. In this paper we describe a component of such platform, designed to couple deep learning analysis of geospatial images of cities and some healthcare and behavioral indexes collected by the 500 cities US project, showing that, in New York City, urban landscape significantly correlates with the access to healthcare services.