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Antimicrobial resistance in bacteria has been associated with significant morbidity and mortality in hospitalized patients. In the era of big data and of the consequent frequent need for large study populations, manual collection of data for research studies on antimicrobial resistance and antibiotic use has become extremely time-consuming and sometimes impossible to be accomplished by overwhelmed healthcare personnel. In this review, we discuss relevant concepts pertaining to the automated extraction of antibiotic resistance and antibiotic prescription data from laboratory information systems and electronic health records to be used in clinical studies, starting from the currently available literature on the topic. Leveraging automatic extraction and standardization of antimicrobial resistance and antibiotic prescription data is an tremendous opportunity to improve the care of future patients with severe infections caused by multidrug-resistant organisms, and should not be missed.

IntroductionDespite the high level of efficacy of modern antiretroviral therapy (ART) in reducing HIV viremia and the control of viral replication, some people living with HIV (PLWH) do not recover their CD4+ T cell count.MethodsTo evaluate the frequency and predictive factors of discordant immune responses, we performed a retrospective cohort study of 324 antiretroviral-naïve PLWH who initiated first-line ART between 2008 and 2018 and maintained HIV RNA

The quantification of proviral DNA is raising interest in view of clinical management and functional HIV eradication. Measures of all unintegrated HIV DNA (uDNA) forms in infected reservoir cells provides information on recent replication events that is not found from other proviral DNA assays. To evaluate its actual relevance in a cohort of perinatally-infected adult HIV patients (PHIV), we studied how peripheral blood mononuclear cell uDNA levels correlated with total HIV DNA (tDNA) and with overall replication or innate immune control parameters including NK cell activation/exhaustion and lymphoid turnover. Twenty-two PHIV were included, with successfully controlled HIV (HIV RNA

Sepsis is a major cause of death worldwide. Over the past years, prediction of clinically relevant events through machine learning models has gained particular attention. In the present perspective, we provide a brief, clinician-oriented vision on the following relevant aspects concerning the use of machine learning predictive models for the early detection of sepsis in the daily practice: (i) the controversy of sepsis definition and its influence on the development of prediction models; (ii) the choice and availability of input features; (iii) the measure of the model performance, the output, and their usefulness in the clinical practice. The increasing involvement of artificial intelligence and machine learning in health care cannot be disregarded, despite important pitfalls that should be always carefully taken into consideration. In the long run, a rigorous multidisciplinary approach to enrich our understanding in the application of machine learning techniques for the early recognition of sepsis may show potential to augment medical decision-making when facing this heterogeneous and complex syndrome.

Purpose: Prospectively-gated Cartesian 4D-flow (referred to as Cartesian-4D-flow) imaging suffers from long TE and intensified flow-related intravoxel-dephasing especially in preclinical ultra-high field MRI. The ultra-short-echo (UTE) 4D-flow technique can resolve the signal loss in higher-order blood flows; however, the long scan time of the high resolution UTE-4D-flow is considered as a disadvantage for preclinical imaging. To compensate for prolonged acquisitions, an accelerated k0-navigated golden-angle center-out stack-of-stars 4D-flow sequence (referred to as SoS-4D-flow) was implemented at 9.4T and the results were compared to conventional Cartesian-4D-flow mapping in-vitro and in-vivo.Methods: The study was conducted in three steps (A) In-vitro evaluation in a static phantom: to quantify the background velocity bias. (B) In-vitro evaluation in a flowing water phantom: to investigate the effects of polar undersampling (US) on the measured velocities and to compare the spatial velocity profiles between both sequences. (C) In-vivo evaluations: 24 C57BL/6 mice were measured by SoS-4D-flow (n = 14) and Cartesian-4D-flow (n = 10). The peak systolic velocity in the ascending aorta and the background velocity in the anterior chest wall were analyzed for both techniques and were compared to each other.Results: According to the in-vitro analysis, the background velocity bias was significantly lower in SoS-4D-flow than in Cartesian-4D-flow (p < 0.05). Polar US in SoS-4D-flow influenced neither the measured velocity values nor the spatial velocity profiles in comparison to Cartesian-4D-flow. The in-vivo analysis showed significantly higher diastolic velocities in Cartesian-4D-flow than in SoS-4D-flow (p < 0.05). A systemic background bias was observed in the Cartesian velocity maps which influenced their streamline directions and magnitudes.Conclusion: The results of our study showed that at 9.4T SoS-4D-flow provided higher accuracy in slow flow imaging than Cartesian-4D-flow, while the same measurement time could be achieved.

The compulsive habit model proposed by Everitt and Robbins has accumulated important empirical evidence. One of their proposals is the existence of an axis, on which each a person with a particular addiction can be located depending on the evolutionary moment of his/her addictive process. The objective of the present study is to contribute in addressing the identification of such axis, as few studies related to it have been published to date. To do so, the use/abuse of Information and Communication Technologies (ICT) was quantified on an initial sample of 807 subjects. Questionnaires were also delivered to measure impulsivity, compulsivity and symptoms of prefrontal dysfunction. Evidence of the existence of the proposed axis was obtained by means of Machine Learning techniques, thus allowing the classification of each subject along the continuum. The present study provides preliminary evidence of the existence of the Impulsivity-Compulsivity axis, as well as an IT tool so that each patient that starts getting treatment for an addiction can be statistically classified as “impulsive” or “compulsive.” This would allow the matching of each person with the most appropriate treatment depending on his/her moment in the addiction/abuse process, thus facilitating the individualized design of each therapeutic process and a possible improvement of the results of the treatment.

A growing number of parents delay vaccinations or are deciding not to vaccinate their children altogether. This increases the risk of contracting vaccine-preventable diseases and disrupting herd immunity, and also impairs the trust in the capacities of health care systems to protect people. Vaccine hesitancy is related to a range of both psychological and demographic determinants, such as attitudes toward vaccinations, social norms, and trust in science. Our aim is to understand those determinants in parents, because they are a special group in this issue—they act as proxy decision makers for their children, who are unable to decide for themselves. The fact that deciding to vaccinate is a socially forced choice that concerns a child's health makes vaccine-related decisions highly important and involving for parents. This high involvement might lead to parents overemphasizing the potential side effects that they know to be vaccine-related, and by amplifying those, parents are more focused on the potential outcomes of vaccine-related decisions, which can yield specific pattern of the outcome bias. We propose two related studies to investigate factors which promote vaccine hesitancy, protective factors that determine parental vaccination decisions, and outcome bias in parental vaccination intentions. We will explore demographic and psychological factors, and test parental involvement related to vaccine hesitancy using an online battery in a correlation panel design study. The second study is an experimental study, in which we will investigate the moderating role of parents' high involvement in the specific domain of vaccination decision making. We expect that higher involvement among parents, compared to non-parents, will shape the pattern of the proneness to outcome bias. The studies will be conducted across eight countries in Europe and Asia (Finland, Germany, Hong Kong, the Netherlands, Serbia, Slovenia, Spain, and the United Kingdom), rendering findings that will aid with understanding the underlying mechanisms of vaccine hesitancy and paving the way for developing interventions custom-made for parents.