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Herein is reported the first evidence of the protective role of D-aspartic acid (D-Asp) in preventing the toxic effect exerted by the alkylating agent ethane dimethane sulfonate (EDS) in the rat testis. We confirmed that EDS treatment specifically destroyed Leydig cells (LC), resulting in the drastic decrease of the serum testosterone level and producing morphological changes in the germinal tubules, i.e., altered organization of the epithelium, loss of cell contacts and the consequent presence of empty spaces between them, and a reduce number of spermatozoa. Moreover, an increase of TUNEL-positive germ cells, other than alteration in the protein level and localization of two LC &ldquo, markers&rdquo, StAR and PREP, were observed. Interestingly, results obtained from rats pre-treated with D-Asp for 15 days before EDS-injection showed that all the considered parameters were quite normal. To explore the probable mechanism(s) involved in the protection exerted by D-Asp, we considered the increased oxidative stress induced by EDS and the D-Asp antioxidant effects. Thiobarbiturc acid-reactive species (TBARS) levels increased following EDS-injection, while no change was observed in the D-Asp + EDS treated rats. Our results showed that D-Asp may be used as a strategy to mitigate the toxic effects exerted by environmental pollutants, as endocrine disrupters, in order to preserve the reproductive function.

Cardiotocography (CTG) is one of the fundamental prenatal diagnostic methods for both antepartum and intrapartum fetal surveillance. Although it has allowed a significant reduction in intrapartum and neonatal mortality and morbidity, its diagnostic accuracy is, however, still far from being fully satisfactory. In particular, the identification of uncertain and suspicious CTG traces remains a challenging task for gynecologists. The introduction of computerized analysis systems has enabled more objective evaluations, possibly leading to more accurate diagnoses. In this work, the problem of classifying suspicious CTG recordings was addressed through a machine learning approach. A machine-based labeling was proposed, and a binary classification was carried out using a support vector machine (SVM) classifier to distinguish between suspicious and normal CTG traces. The best classification metrics showed accuracy, sensitivity, and specificity values of 92%, 92%, and 90%, respectively. The main results were compared both with results obtained by considering a more unbalanced dataset and with relevant literature studies in the field. The use of the SVM proved to be promising in the field of CTG classification. However, appropriate feature selection and dataset balancing are crucial to achieve satisfactory performance of the classifier.

The availability of standardized guidelines regarding the use of electronic fetal monitoring (EFM) in clinical practice has not effectively helped to solve the main drawbacks of fetal heart rate (FHR) surveillance methodology, which still presents inter- and intra-observer variability as well as uncertainty in the classification of unreassuring or risky FHR recordings. Given the clinical relevance of the interpretation of FHR traces as well as the role of FHR as a marker of fetal wellbeing autonomous nervous system development, many different approaches for computerized processing and analysis of FHR patterns have been proposed in the literature. The objective of this review is to describe the techniques, methodologies, and algorithms proposed in this field so far, reporting their main achievements and discussing the value they brought to the scientific and clinical community. The review explores the following two main approaches to the processing and analysis of FHR signals: traditional (or linear) methodologies, namely, time and frequency domain analysis, and less conventional (or nonlinear) techniques. In this scenario, the emerging role and the opportunities offered by Artificial Intelligence tools, representing the future direction of EFM, are also discussed with a specific focus on the use of Artificial Neural Networks, whose application to the analysis of accelerations in FHR signals is also examined in a case study conducted by the authors.

In the field of electronic fetal health monitoring, computerized analysis of fetal heart rate (FHR) signals has emerged as a valid decision-support tool in the assessment of fetal wellbeing. Despite the availability of several approaches to analyze the variability of FHR signals (namely the FHRV), there are still shadows hindering a comprehensive understanding of how linear and nonlinear dynamics are involved in the control of the fetal heart rhythm. In this study, we propose a straightforward processing and modeling route for a deeper understanding of the relationships between the characteristics of the FHR signal. A multiparametric modeling and investigation of the factors influencing the FHR accelerations, chosen as major indicator of fetal wellbeing, is carried out by means of linear and nonlinear techniques, blockwise dimension reduction, and artificial neural networks. The obtained results show that linear features are more influential compared to nonlinear ones in the modeling of HRV in healthy fetuses. In addition, the results suggest that the investigation of nonlinear dynamics and the use of predictive tools in the field of FHRV should be undertaken carefully and limited to defined pregnancy periods and FHR mean values to provide interpretable and reliable information to clinicians and researchers.

Long and short-term exposure to atmospheric particulate matter (PM) has detrimental effects on human health. The effective mechanisms leading to PM toxicity are still not fully understood, even if it is known that physical-chemical properties, strongly influenced by sources and atmospheric processes, are known to play an important role. In this work, PM10 samples were collected, at an urban background site in southern Italy, to determine cytotoxicity (using MTT test on A549 cells), genotoxicity (using the comet assay), and intracellular oxidative stress on A549 cells exposed for 24 h to aqueous extracts of PM10 samples. Organic carbon (OC) and elemental carbon (EC) content of PM10 and acellular determination of oxidative potential with DTT assay were performed to compare results of acellular and cellular biological assays. Cellular (OSGCV and MTTV) and acellular (OPDTTV) outcomes, normalized in volume, are well correlated (statistically significant results) with carbon content suggesting that combustion sources play an important role in determining cellular oxidative stress and cytotoxicity of PM10. Even if the number of data is limited, genotoxicity results are well correlated (Pearson r &gt, 0.95) with OSGCV and MTTV, and a weaker, but statistically significant correlation was observed with OPDTTV. OSGCV is well correlated with the cell mortality observed with the MTTV test and a lower, but still statistically significant correlation is observed between MTTV and OPDDTV. A statistically significant correlation was found between OPDTTV and OSGCV results. When the outcomes of the cellular and acellular assay are compared normalized in mass (i.e., intrinsic values), the correlations become significantly weaker suggesting that the different sources acting on the site produces particulate matter with different toxicological potential influencing differently the biological tests studied.

Structural adhesives play an important role in aerospace manufacturing, since they provide fewer points of stress concentration compared to faster joints. The importance of adhesives in aerospace is increasing significantly because composites are being adopted to reduce weight and manufacturing costs. Furthermore, adhesive joints are also studied to determine the crashworthiness of airframe structure, where the main task for the adhesive is not to dissipate the impact energy, but to keep joint integrity so that the impact energy can be consumed by plastic work. Starting from an extensive campaign of experimental tests, a finite element model and a methodology are implemented to develop an accurate adhesive model in a single lap shear configuration. A single lap joint finite element model is built by MSC Apex, defining two specimens of composite material connected to each other by means of an adhesive; by the Digimat multi-scale modeling solution, the composite material is treated; and finally, by MSC’s Marc, the adhesive material is characterized as a cohesive applying the Cohesive Zone Modeling theory. The objective was to determine an appropriate methodology to predict interlaminar crack growth in composite laminates, defining the mixed mode traction separation law variability in function of the cohesive energy (Gc), the ratio between the shear strength τ and the tensile strength σ (β1), and the critical opening displacement υc.