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Introduction: Gastroesophageal reflux disease (GERD) as well as its treatment with acid-suppressive medications have been considered possible risk factors for the development of asthma, but few studies have disentangled the role of GERD with that of its treatment. The present study aimed at estimating the association of treated and untreated GERD in the first year of life with the risk of asthma. Methods: Retrospective cohort study including all children born between 2004 and 2015 registered in Pedianet, an Italian primary care database. We analyzed the association of children exposed to GERD (both treated and untreated) in the first year of life with the risk of developing clinically assessed asthma (clinical asthma) after 3 years. Secondary outcomes included asthma identified by anti-asthmatic medications (treated asthma) and wheezing after 3 years. Hazard ratios (HR) and 95% confidence intervals (CI) were estimated comparing children with and without GERD, stratifying by treatment with acid-suppressive medications. Results: Out of 86,381 children, 1652 (1.9%) were affected by GERD in the first year of life, of which 871 (53%) were treated with acid-suppressive medications. Compared with controls, children with GERD were at increased risk of clinical asthma (HR: 1.40, 95% CI 1.15-1.70). Risks were similar between treated and untreated GERD (p = 0.41). Comparable results were found for treated asthma, but no risk increase was seen for wheezing. Discussion: Early-life GERD was associated with subsequent childhood asthma. Similar risks among children with treated and untreated GERD suggest that acid-suppressive medications are unlikely to play a major role in the development asthma.

Antineoplastic drugs are a wide and heterogeneous group of substances that, as universally known, can cause highly severe toxic effects to whoever is exposed. From an occupational safety point of view, surface contaminations inside preparation and administration units are a growing issue and therefore require the development and implementation of sensible and fast monitoring methods. The unlikelihood of a unique all-embracing chromatography, able to correctly retain and separate each analyte led to the need to create an orthogonal normal phase analysis, which might be able to fill the gaps in the more common reversed-phase ones. An existing hydrophilic interaction method has thus been expanded to 6 other drugs and applied to real samples after an evaluation of its performances. The experimental data were then used to evaluate the possibility of estimating reliable relationships between the chromatographic retention and the chemical-structural features of the drugs under analysis.

Mass spectrometry (MS) is widely used for the identification of chemical compounds by matching the experimentally acquired mass spectrum against a database of reference spectra. However, this approach suffers from a limited coverage of the existing databases causing a failure in the identification of a compound not present in the database. Among the computational approaches for mining metabolite structures based on MS data, one option is to predict molecular fingerprints from the mass spectra by means of chemometric strategies and then use them to screen compound libraries. This can be carried out by calibrating multi-task artificial neural networks from large datasets of mass spectra, used as inputs, and molecular fingerprints as outputs. In this study, we prepared a large LC-MS/MS dataset from an on-line open repository. These data were used to train and evaluate deep-learning-based approaches to predict molecular fingerprints and retrieve the structure of unknown compounds from their LC-MS/MS spectra. Effects of data sparseness and the impact of different strategies of data curing and dimensionality reduction on the output accuracy have been evaluated. Moreover, extensive diagnostics have been carried out to evaluate modelling advantages and drawbacks as a function of the explored chemical space.

Extracorporeal carbon dioxide removal (ECCO2R) is a promising strategy to manage acute respiratory failure. We hypothesized that ECCO2R could be enhanced by ventilating the membrane lung with a sodium hydroxide (NaOH) solution with high CO2 absorbing capacity. A computed mathematical model was implemented to assess NaOH-CO2 interactions. Subsequently, we compared NaOH infusion, named "alkaline liquid ventilation", to conventional oxygen sweeping flows. We built an extracorporeal circuit with two polypropylene membrane lungs, one to remove CO2 and the other to maintain a constant PCO2 (60 ± 2 mmHg). The circuit was primed with swine blood. Blood flow was 500 mL × min-1. After testing the safety and feasibility of increasing concentrations of aqueous NaOH (up to 100 mmol × L-1), the CO2 removal capacity of sweeping oxygen was compared to that of 100 mmol × L-1 NaOH. We performed six experiments to randomly test four sweep flows (100, 250, 500, 1000 mL × min-1) for each fluid plus 10 L × min-1 oxygen. Alkaline liquid ventilation proved to be feasible and safe. No damages or hemolysis were detected. NaOH showed higher CO2 removal capacity compared to oxygen for flows up to 1 L × min-1. However, the highest CO2 extraction power exerted by NaOH was comparable to that of 10 L × min-1 oxygen. Further studies with dedicated devices are required to exploit potential clinical applications of alkaline liquid ventilation.

The classification of coralline algae commonly relies on the morphology of cells and reproductive structures, along with thallus organization, observed through Scanning Electron Microscopy (SEM). Nevertheless, species identification based on morphology often leads to uncertainty, due to their general plasticity. Evolutionary and environmental studies featured coralline algae for their ecological significance in both recent and past Oceans and need to rely on robust taxonomy. Research efforts towards new putative diagnostic tools have recently been focused on cell wall ultrastructure. In this work, we explored a new classification tool for coralline algae, using fine-tuning pretrained Convolutional Neural Networks (CNNs) on SEM images paired to morphological categories, including cell wall ultrastructure. We considered four common Mediterranean species, classified at genus and at the species level (Lithothamnion corallioides, Mesophyllum philippii, Lithophyllum racemus, Lithophyllum pseudoracemus). Our model produced promising results in terms of image classification accuracy given the constraint of a limited dataset and was tested for the identification of two ambiguous samples referred to as L. cf. racemus. Overall, explanatory image analyses suggest a high diagnostic value of calcification patterns, which significantly contributed to class predictions. Thus, CNNs proved to be a valid support to the morphological approach to taxonomy in coralline algae.

Neural networks are rapidly gaining popularity in chemical modeling and Quantitative Structure–Activity Relationship (QSAR) thanks to their ability to handle multitask problems. However, outcomes of neural networks depend on the tuning of several hyperparameters, whose small variations can often strongly affect their performance. Hence, optimization is a fundamental step in training neural networks although, in many cases, it can be very expensive from a computational point of view. In this study, we compared four of the most widely used approaches for tuning hyperparameters, namely, grid search, random search, tree-structured Parzen estimator, and genetic algorithms on three multitask QSAR datasets. We mainly focused on parsimonious optimization and thus not only on the performance of neural networks, but also the computational time that was taken into account. Furthermore, since the optimization approaches do not directly provide information about the influence of hyperparameters, we applied experimental design strategies to determine their effects on the neural network performance. We found that genetic algorithms, tree-structured Parzen estimator, and random search require on average 0.08% of the hours required by grid search; in addition, tree-structured Parzen estimator and genetic algorithms provide better results than random search.