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Twenty-nine of 54 uremic patients had low levels of protein C measured as anticoagulant activity, contrasting with normal levels measured as amidolytic activity or antigenic concentration. We demonstrate that this discrepancy is due to the presence of a soluble plasma inhibitor that interferes specifically with the anticoagulant activity of activated protein C. The inhibitor does not interfere with other coagulation assays. It is resistant to diisopropylfluorophosphate, high temperatures and repeated freezing and thawing. It can be dissociated from protein C by anti-protein C antibodies or by dialysis in vitro and in vivo. It binds to positively charged resins and can be eluted with high salt concentrations without losing its inhibitory capacity. The inhibitory effect is correlated with plasma creatinine levels and fluctuates with time.

Some reports correlate the administration of all forms of interferon with the development or exacerbation of autoimmune phenomena and diseases, including sarcoidosis, because of the strong and complex immune action of interferons. We report on a case of sarcoidosis following beta-interferon treatment for multiple myeloma. Differently from what had been observed in all the 8 previously reported cases of associated multiple myeloma and sarcoidosis, where the plasmacellular malignancy followed the onset of the respiratory disease, in the case of our patient, sarcoidosis arose after multiple myeloma was first diagnosed.

Atopic dermatitis (AD) is a chronic inflammatory skin disease that commonly presents during early childhood. In the last decades the prevalence of AD has increased, especially in western societies. This frequently relapsing inflammatory condition has a strong impact on the quality of life of patients and families. The recent advances in the understanding of this disease have paved the way for the development of new strategies for the prevention and treatment of AD. Among the new therapeutic options, there is increasing interest in the potential benefit of probiotic supplementation. It has been widely demonstrated that the human microbiota plays a fundamental role not only in the maintenance of intestinal homeostasis through the interaction between microorganisms and the innate immune system, but also in the microbiota-mediated development of adaptive immunity. In addition, several studies have demonstrated that probiotics are able to influence the composition of gut microbiota and may exert immunomodulatory effects. According to these promising results, the possible application of probiotics in the therapeutic management of allergic diseases has been investigated in many studies. In particular, a considerable body of literature has been published analyzing the effects of probiotics on patients with AD. In order to shed light on frequently conflicting results, we reviewed the data regarding the application of probiotics in AD, with the aim to provide a state-of-the-art assessment of the most important studies exploring the role of probiotics both in the prevention and treatment of AD.

Multitask learning allows to model multiple tasks simultaneously through information sharing. In the context of quantitative structure–activity relationships and computational toxicology, multitask learning is gaining more and more interest, owed to its potential to improve the predictive performance of underrepresented tasks and to predict the multi-property profile of molecules. In this chapter, after introducing the multitask problem formulation, we present a hands-on tutorial on multitask neural networks.

Background: Patients with COVID-19 can develop acute respiratory distress syndrome (ARDS), which is associated with high mortality. The aim of this study was to examine the functional and morphological features of COVID-19-associated ARDS and to compare these with the characteristics of ARDS unrelated to COVID-19. Methods: This prospective observational study was done at seven hospitals in Italy. We enrolled consecutive, mechanically ventilated patients with laboratory-confirmed COVID-19 and who met Berlin criteria for ARDS, who were admitted to the intensive care unit (ICU) between March 9 and March 22, 2020. All patients were sedated, paralysed, and ventilated in volume-control mode with standard ICU ventilators. Static respiratory system compliance, the ratio of partial pressure of arterial oxygen to fractional concentration of oxygen in inspired air, ventilatory ratio (a surrogate of dead space), and D-dimer concentrations were measured within 24 h of ICU admission. Lung CT scans and CT angiograms were done when clinically indicated. A dataset for ARDS unrelated to COVID-19 was created from previous ARDS studies. Survival to day 28 was assessed. Findings: Between March 9 and March 22, 2020, 301 patients with COVID-19 met the Berlin criteria for ARDS at participating hospitals. Median static compliance was 41 mL/cm H2O (33–52), which was 28% higher than in the cohort of patients with ARDS unrelated to COVID-19 (32 mL/cm H2O [25–43]; p

I recettori nucleari sono fattori di trascrizione coinvolti in processi critici per la salute umana e sono un obiettivo rilevante per la valutazione del rischio tossicologico e il processo di scoperta dei farmaci. I modelli computazionali possono essere uno strumento utile (i) per dare priorità alla sperimentazione di sostanze chimiche che possono imitare gli ormoni naturali e quindi essere interferenti endocrini e (ii) per identificare nuovi possibili candidati farmaci. Pertanto, l'obiettivo principale di questo progetto è quello di studiare le potenziali interazioni tra sostanze chimiche e recettori nucleari, con il duplice scopo di sviluppare strumenti in silico per la ricerca di nuovi modulatori e di identificare possibili sostanze chimiche che alterano il sistema endocrino. Dopo aver creato una collezione esaustiva di modulatori di recettori nucleari, abbiamo applicato metodi di apprendimento automatico per colmare il vuoto di dati e predire nuovi possibili modulatori tramite modelli predittivi. In particolare, le strategie di modellazione hanno incluso algoritmi di apprendimento automatico multi-tasking per indagare le complesse relazioni tra sostanze chimiche e diversi recettori nucleari. Nuclear receptors are transcription factors involved in processes critical to human health and are a relevant target for toxicological risk assessment and the drug discovery process. Computational models can be a useful tool (i) to prioritize chemicals that can mimic natural hormones and thus be endocrine disruptors and (ii) to identify new possible lead for drug discovery. Therefore, the main goal of this project is to study potential interactions between chemicals and nuclear receptors, with the dual purpose of developing in silico tools to search for new modulators and to identify possible endocrine disrupting chemicals. After creating an exhaustive collection of nuclear receptor modulators, we applied machine learning methods to fill the data gap and prioritize modulators by building predictive models. In particular, modeling strategies included multi-tasking machine learning algorithms to investigate the complex relationships between chemicals and multiple nuclear receptors.

Minimum Spanning Tree (MST) is a well-known clustering algorithm that provides a graphical tree representation of the objects in a data set by exploiting local information to link each pair of similar objects. The a-posteriori analysis of this tree in terms of nodes and edges provides the basis to derive simple classifiers, namely semi-supervised classification approaches based on the minimum spanning tree approach. In this work, we propose different metrics to evaluate the MST ability to group objects of the same a-priori known classes. The classification capability of the proposed approach, using 13 different distance measures, was compared with that of classical supervised classification approaches such as N-Nearest Neighbour (N3), Binned Nearest Neighbour (BNN), Partial Least SquaresDiscriminant Analysis (PLS-DA), K-Nearest Neighbour (KNN), exponentially weighted K-Nearest Neighbour (wKNN) and Support Vector Machine with radial functions (SVMRBF) on 31 data sets. The proposed approach resulted to be competitive and comparable with the considered classical supervised classification methods. Finally, we analysed the role of the 13 different measures in terms of performance and percentage of not-assigned objects.

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.

The interest in multitask and deep learning strategies has been increasing in the last few years, in application to large and complex dataset for quantitative structure-activity relationship (QSAR) analysis. Multitask approaches allow the simultaneous prediction of molecular properties that are related, through information sharing, whereas deep learning strategies increase the potential of capturing nonlinear relationships. In this work, we compare the binary classification capability of multitask deep and shallow neural networks to single-task strategies used as benchmark (i.e., as k-nearest neighbours, N-nearest neighbours, random forest and Naïve Bayes), as well as multitask supervised self-organizing maps. Comparison was carried out with an extended QSAR dataset containing annotations of molecular binding, agonism and antagonism activity on 11 nuclear receptors, for a total of 14,963 molecules, divided into training and test sets and labelled for their bioactivity on at least one of 30 binary tasks. Additional 304 chemicals were used as external evaluation set to further validate models. Although no approach systematically overperformed the others, task-specific differences were found, suggesting the benefit of multitask learning for tasks that are less represented. On average, some of the single-task approaches and multitask deep learning strategies had similar performances. However, the latter can have advantages, such as a simpler management of predictions and applicability domain assessment for future samples. On the other hand, the parameter tuning required by neural networks are generally time expensive suggesting that the modelling strategy should be evaluated case by case.

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.

During the COVID-19 pandemic, a shortage of mechanical ventilators was reported and ventilator sharing between patients was proposed as an ultimate solution. Two lung simulators were ventilated by one anesthesia machine connected through two respiratory circuits and T-pieces. Five different combinations of compliances (30–50 mL × cmH2O−1) and resistances (5–20 cmH2O × L−1 × s−1) were tested. The ventilation setting was: pressure-controlled ventilation, positive end-expiratory pressure 15 cmH2O, inspiratory pressure 10 cmH2O, respiratory rate 20 bpm. Pressures and flows from all the circuit sections have been recorded and analyzed. Simulated patients with equal compliance and resistance received similar ventilation. Compliance reduction from 50 to 30 mL × cmH2O−1 decreased the tidal volume (VT) by 32% (418 ± 49 vs. 285 ± 17 mL). The resistance increase from 5 to 20 cmH2O × L−1 × s−1 decreased VT by 22% (425 ± 69 vs. 331 ± 51 mL). The maximal alveolar pressure was lower at higher compliance and resistance values and decreased linearly with the time constant (r² = 0.80, p &lt, 0.001). The minimum alveolar pressure ranged from 15.5 ± 0.04 to 16.57 ± 0.04 cmH2O. Cross-flows between the simulated patients have been recorded in all the tested combinations, during both the inspiratory and expiratory phases. The simultaneous ventilation of two patients with one ventilator may be unable to match individual patient’s needs and has a high risk of cross-interference.

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.

In the recent years, ecotoxicological hazard potential of biocidal products has been receiving increasing attention in the industries and regulatory agencies. Biocides/pesticides are currently one of the most studied groups of compounds, and their registration cannot be done without the empirical toxicity information. In view of limited experimental data available for these compounds, we have developed Quantitative Structure-Activity Relationship (QSAR) models for the toxicity of biocides to fish and Daphnia magna following principles of QSAR modeling recommended by the OECD (Organization for Economic Cooperation and Development). The models were developed using simple and interpretable 2D descriptors and validated using stringent tests. Both models showed encouraging statistical quality in terms of determination coefficient R2 (0.800 and 0.648), cross-validated leave-one-out Q2 (0.760 and 0.602) and predictive R2pred or Q2ext (0.875 and 0.817) for fish (nTraining = 66, nTest = 22) and Daphnia magna (nTraining = 100, nTest = 33) toxicity datasets, respectively. These models should be applicable for data gap filling in case of new or untested biocidal compounds falling within the applicability domain of the models. In general, the models indicate that the toxicity increases with lipophilicity and decreases with polarity, branching and unsaturation. We have also developed interspecies toxicity models for biocides using the daphnia and fish toxicity data and used the models for data gap filling.

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.

Nuclear receptors (NRs) are involved in fundamental human health processes and are a relevant target for toxicological risk assessment. To help prioritize chemicals that can mimic natural hormones and be endocrine disruptors, computational models can be a useful tool.1,2 In this work we i) created an exhaustive collection of NR modulators and ii) applied machine learning methods to fill the data-gap and prioritize NRs modulators by building predictive models.

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.

In the analysis of complex and multivariate systems, set priorities and rank all the available objects is not a trivial task. In this framework, several multi-criteria decision-making (MCDM) techniques have been proposed, with successful applications in different research fields. However, it would be desirable to customize the ranking by weighting differently the criteria and to quantitatively assess the influence of each criterion on the final ranking. In this study, we applied a recently proposed ranking approach, called Deep Ranking Analysis by Power Eigenvectors (DRAPE) to study the sustainability of 154 countries expressed by 21 human, environmental and economic wellbeing criteria. The application of two systems for weighting the criteria and a retrospective analysis of the contribution of each criterion to the final ranking allowed the great potential of the considered approach to be expressed.

Nuclear receptors (NRs) are key regulators of human health and constitute a relevant target for medicinal chemistry applications as well as for toxicological risk assessment. Several open databases dedicated to small molecules that modulate NRs exist; however, depending on their final aim (i.e., adverse effect assessment or drug design), these databases contain a different amount and type of annotated molecules, along with a different distribution of experimental bioactivity values. Stemming from these considerations, in this work we aim to provide a unified dataset, NURA (NUclear Receptor Activity) dataset, collecting curated information on small molecules that modulate NRs, to be intended for both pharmacological and toxicological applications. NURA contains bioactivity annotations for 15,247 molecules and 11 selected NRs, and it was obtained by integrating and curating data from toxicological and pharmacological databases (i.e., Tox21, ChEMBL, NR-DBIND and BindingDB). Our results show that NURA dataset is a useful tool to bridge the gap between toxicology- and medicinal-chemistry-related databases, as it is enriched in terms of number of molecules, structural diversity and covered atomic scaffolds compared to the single sources. To the best of our knowledge, NURA dataset is the most exhaustive collection of small molecules annotated for their modulation of the chosen nuclear receptors. NURA dataset is intended to support decision-making in pharmacology and toxicology, as well as to contribute to data-driven applications, such as machine learning. The dataset and the data curation pipeline can be downloaded free of charge on Zenodo at the following DOI: https://doi.org/10.5281/zenodo.3991561.

The aim of the present study is to evaluate the effects of 60-day artichoke leaf extract (ALE) supplementation (250mg, twice daily) on cytokines levels, natural killer cell (NK) response, and lipo-metabolic profile (HDL, LDL, and total-cholesterol, triglycerides (TG), ApoB, ApoA, lipid accumulation product (LAP), glucose, insulin, and homeostasis model assessment of insulin resistance (HOMA-IR)) in twenty adults (9/11 males/females, age=49.10 ± 13.74 years, and BMI=33.12 ± 5.14 kg/m2) with low HDL-C and mild hypercholesterolemia. Hierarchical generalized linear model, adjusted for sex, BMI, and age, has been used to evaluate pre-post treatment changes. A significant increase for HDL-C (β=0.14, p=0.0008) and MCP-1 (β=144.77, p=0.004) and a significant decrease for ApoB/ApoA (β=-0.07, p=0.03), total-C/HDL-C ratio (β=-0.58, pβ=0.43, p=0.04), medium (β=0.40, pβ=0.42, p=0.001) have been found. These results support the benefits of ALE supplementation on metabolic profile.