Modifications induced in soil porosity and in stability of soil aggregates were studied for 2 years on an Italian sandy loam soil. Aerobic and anaerobic sludges and their composted mixtures with the organic fraction of urban refuse were used and compared with manure. Addition rates were equivalent to 50 and 150 metric tons/ha of manure on the organic carbon basis. A control plot was also present. Porosity and pore size distribution were measured on thin sections prepared from undisturbed soil samples by using electro‐optical image‐analysis equipment. The stability of soil aggregates was determined by a wet‐sieving method.All organic materials increased the total porosity significantly at all sampling times. Differences between the two application rates were generally not significant. The improvement of total porosity caused by sludges and composts was comparable to that of manure. Modifications of pore size distribution were also observed. Stability of soil aggregates increased slightly in treated samples. The best stabilizing effect was shown by the anaerobic sludge.

In this paper, we present a clinical decision support system (CDSS) for the analysis of heart failure (HF) patients, providing various outputs such as an HF severity evaluation, HF-type prediction, as well as a management interface that compares the different patients' follow-ups. The whole system is composed of a part of intelligent core and of an HF special-purpose management tool also providing the function to act as interface for the artificial intelligence training and use. To implement the smart intelligent functions, we adopted a machine learning approach. In this paper, we compare the performance of a neural network (NN), a support vector machine, a system with fuzzy rules genetically produced, and a classification and regression tree and its direct evolution, which is the random forest, in analyzing our database. Best performances in both HF severity evaluation and HF-type prediction functions are obtained by using the random forest algorithm. The management tool allows the cardiologist to populate a "supervised database" suitable for machine learning during his or her regular outpatient consultations. The idea comes from the fact that in literature there are a few databases of this type, and they are not scalable to our case.

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BackgroundCongestive Heart Failure (CHF) is a serious cardiac condition that brings high risks of urgent hospitalization and death. Remote monitoring systems are well-suited to managing patients suffering from CHF, and can reduce deaths and re-hospitalizations, as shown by the literature, including multiple systematic reviews.MethodsThe monitoring system proposed in this paper aims at helping CHF stakeholders make appropriate decisions in managing the disease and preventing cardiac events, such as decompensation, which can lead to hospitalization or death. Monitoring activities are stratified into three layers: scheduled visits to a hospital following up on a cardiac event, home monitoring visits by nurses, and patient's self-monitoring performed at home using specialized equipment. Appropriate hardware, desktop and mobile software applications were developed to enable a patient's monitoring by all stakeholders. For the first two layers, we designed and implemented a Decision Support System (DSS) using machine learning (Random Forest algorithm) to predict the number of decompensations per year and to assess the heart failure severity based on a variety of clinical data. For the third layer, custom-designed sensors (the Blue Scale system) for electrocardiogram (EKG), pulse transit times, bio-impedance and weight allowed frequent collection of CHF-related data in the comfort of the patient's home.We also performed a short-term Heart Rate Variability (HRV) analysis on electrocardiograms self-acquired by 15 healthy volunteers and compared the obtained parameters with those of 15 CHF patients from PhysioNet's PhysioBank archives.ResultsWe report numerical performances of the DSS, calculated as multiclass accuracy, sensitivity and specificity in a 10-fold cross-validation. The obtained average accuracies are: 71.9% in predicting the number of decompensations and 81.3% in severity assessment. The most serious class in severity assessment is detected with good sensitivity and specificity (0.87 / 0.95), while, in predicting decompensation, high specificity combined with good sensitivity prevents false alarms. The HRV parameters extracted from the self-measured EKG using the Blue Scale system of sensors are comparable with those reported in the literature about healthy people.ConclusionsThe performance of DSSs trained with new patients confirmed the results of previous work, and emphasizes the strong correlation between some CHF markers, such as brain natriuretic peptide (BNP) and ejection fraction (EF), with the outputs of interest. Comparing HRV parameters from healthy volunteers with HRV parameters obtained from PhysioBank archives, we confirm the literature that considers the HRV a promising method for distinguishing healthy from CHF patients.

SummaryIn human platelets the selenoenzyme glutathione peroxidase (GSH-Px) acts as a scavenger of the peroxides generated during the burst of arachidonic acid (AA) metabolism. Such a mechanism inhibits the biosynthesis of both thromboxane A2 (TXA2) and lipoxygenase products. The same mechanism is not effective on the prostacyclin (PGI2) biosynthesis from cultured endothelial cells. In order to evaluate this effect in vivo, besides in vitro, we activated the enzyme in eight normal volunteers by increasing their daily Se intake for 8 weeks, monitoring: platelet GSH-Px activity, platelet aggregation induced by A A and U 44069, and concurrent malondialdehyde (MDA) and thromboxane B2 (TXB2) production, urinary excretion of renal and systemic TXA2 and PGI2 metabolites, platelet enzyme activities of the hexose monophosphate pathway and glutathione content, platelet adenine nucleotides, bleeding time, plasma Se concentration. We found: a) progressive platelet GSH-Px activation by Se paralleling an enhancement of platelet aggregation threshold values for AA, but not for U 44069; b) concurrent inhibition of platelet biosynthesis of TXA2 both in vitro and in vivo while the biosynthesis of systemic prostacyclin was unaffected; c) a progressive increase in the bleeding time, unmodified by aspirin. In conclusion, we believe that Se-dependent GSH-Px represents a physiological mechanism regulating the biosynthesis of prostanoids with implications in platelet function and that a Se dietary supplement might be considered in the prevention of arterial thrombosis.