This study investigated the associations between the levels of 27 plasma metabolites, 114 lipoprotein parameters, determined using nuclear magnetic resonance spectroscopy, and the ABO blood groups and the Rhesus (Rh) blood system in a cohort of n = 840 Italian healthy blood donors of both sexes. We observed good multivariate discrimination between the metabolomic and lipoproteomic profiles of subjects with positive and negative Rh. In contrast, we did not observe significant discrimination for the ABO blood group pairwise comparisons, suggesting only slight metabolic differences between these group-specific metabolic profiles. We report univariate associations (P-value < 0.05) between the subfraction HDL1 related to Apo A1, the subfraction HDL2 related to cholesterol and phospholipids, and the particle number of LDL2 related to free cholesterol, cholesterol, phospholipids, and Apo B and the ABO blood groups; we observed association of the lipid main fraction LDL4 related to free cholesterol, triglycerides, and Apo B; creatine; the particle number of LDL5; the subfraction LDL5 related to Apo B; the particle number of LDL4; and the subfraction LDL4 related to Apo B with Rh blood factors. These results suggest blood group-dependent (re)shaping of lipoprotein metabolism in healthy subjects, which may provide relevant information to explain the differential susceptibility to certain diseases observed in different blood groups.

Association of Plasma Metabolites and Lipoproteins with Rh and ABO Blood Systems in Healthy Subjects

Artificial intelligence (AI) is becoming increasingly important, especially in the medical field. While AI has been used in medicine for some time, its growth in the last decade is remarkable. Specifically, machine learning (ML) and deep learning (DL) techniques in medicine have been increasingly adopted due to the growing abundance of health-related data, the improved suitability of such techniques for managing large datasets, and more computational power. ML and DL methodologies are fostering the development of new “intelligent” tools and expert systems to process data, to automatize human–machine interactions, and to deliver advanced predictive systems that are changing every aspect of the scientific research, industry, and society. The Italian scientific community was instrumental in advancing this research area. This article aims to conduct a comprehensive investigation of the ML and DL methodologies and applications used in medicine by the Italian research community in the last five years. To this end, we selected all the papers published in the last five years with at least one of the authors affiliated to an Italian institution that in the title, in the abstract, or in the keywords present the terms “machine learning” or “deep learning” and reference a medical area. We focused our research on journal papers under the hypothesis that Italian researchers prefer to present novel but well-established research in scientific journals. We then analyzed the selected papers considering different dimensions, including the medical topic, the type of data, the pre-processing methods, the learning methods, and the evaluation methods. As a final outcome, a comprehensive overview of the Italian research landscape is given, highlighting how the community has increasingly worked on a very heterogeneous range of medical problems.

Exploring the State of Machine Learning and Deep Learning in Medicine: A Survey of the Italian Research Community

Abstract The COVID-19 pandemic has highlighted a debate about whether marginalized communities suffered the disproportionate brunt of the pandemic’s mortality. Empirical studies addressing this question typically suffer from statistical uncertainties and potential biases associated with uneven and incomplete reporting. We use geo-coded micro-level data for the entire population of Sweden to analyze how local neighborhood characteristics affect the likelihood of dying with COVID-19 at individual level, given the individual’s overall risk of death. We control for several individual and regional characteristics to compare the results in specific communities to overall death patterns in Sweden during 2020. When accounting for the probability to die of any cause, we find that individuals residing in socioeconomically disadvantaged neighborhoods were not more likely to die with COVID-19 than individuals residing elsewhere. Importantly, we do find that individuals show a generally higher probability of death in these neighborhoods. Nevertheless, ethnicity is an important explanatory factor for COVID-19 deaths for foreign-born individuals, especially from East Africa, who are more likely to pass away regardless of residential neighborhood. 

Evidence of COVID-19 fatalities in Swedish neighborhoods from a full population study


 Identifying factors related to COVID-19 mortality is important to deploy effective containment measures and to safeguard categories at risk. In the last months, several investigations have tried to ascertain essential features for predicting the COVID-19 mortality tolls depending on country-specific dynamics and population structure. Most studies focused on the initial outbreak of COVID-19 spanning the first half of 2020. Several variables, including obesity, health system indicators such as hospital bed density, and bacillus Calmette-Guerin vaccination have been reported as significantly associated to COVID-19 mortality. Here, we examined in different pandemic stages some of the mentioned associations as well as ABO and Rh blood group indicators, which have also been previously linked to COVID-19 severity and fatal outcome. Using a machine learning approach, we found that the “B+” blood group frequency is an important factor at all stages of the pandemic.

/pdf/country-level-factors-dynamics-and-abo-rh-blood-groups-40x0esxayr.pdf

Country-Level Factors Dynamics and ABO/Rh Blood Groups Contribution To COVID-19 Mortality

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Country-Level Factors Dynamics and ABO/Rh Blood Groups Contribution To COVID-19 Mortality