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BackgroundAortic stenosis (AS) is the most common valve disorder characterized by fibro-calcific remodeling of leaflets. Recent evidence indicated that there is a sex-related difference in AS development and progression. Fibrotic remodeling is peculiar in women’s aortic valves, while men’s leaflets are more calcified. Our study aimed to assess aortic valve fibrosis (AVF) in a severe AS cohort using non-invasive diagnostic tools and determine whether sex-specific pathological pathways and cell types are associated with severe AS.Materials and MethodsWe have included 28 men and 28 women matched for age with severe AS who underwent echocardiography and cardiac contrast-enhanced computed tomography (CT) before intervention. The calcium and fibrosis volumes were assessed and quantified using the ImageJ thresholding method, indexed calcium and fibrosis volume were calculated by dividing the volume by the aortic annular area. For a deeper understanding of molecular mechanisms characterizing AS disorder, differentially expressed genes and functional inferences between women and men’s aortic valves were carried out on a publicly available microarray-based gene expression dataset (GSE102249). Cell types enrichment analysis in stenotic aortic valve tissues was used to reconstruct the sex-specific cellular composition of stenotic aortic valves.ResultsIn agreement with the literature, our CT quantifications showed that women had significantly lower aortic valve calcium content compared to men, while fibrotic tissue composition was significantly higher in women than men. The expression profiles of human stenotic aortic valves confirm sex-dependent processes. Pro-fibrotic processes were prevalent in women, while pro-inflammatory ones, linked to the immune response system, were enhanced in men. Cell-type enrichment analysis showed that mesenchymal cells were over-represented in AS valves of women, whereas signatures for monocytes, macrophages, T and B cells were enriched men ones.ConclusionsOur data provide the basis that the fibro-calcific process of the aortic valve is sex-specific, both at gene expression and cell type level. The quantification of aortic valve fibrosis by CT could make it possible to perform population-based studies and non-invasive assessment of novel therapies to reduce or halt sex-related calcific aortic valve stenosis (CAVS) progression, acting in an optimal window of opportunity early in the course of the disease.

Abstract Background Our aim was to compare the diagnostic performance of digital breast tomosynthesis (DBT)-galactography with that of full-field digital (FFD)-galactography for detecting intraductal breast lesions using an intra-individual design. Methods Forty-nine consecutive patients with spontaneous, unilateral, single-pore nipple discharge and inconclusive FFD mammography and ultrasonography underwent galactography with a “COMBO” technique combining FFD- and DBT-galactography acquisitions. Examinations were independently analysed by two breast radiologists with 10-year experience. Sensitivity, specificity, and accuracy for both FFD- and DBT-galactography were calculated having histological examinations of surgical specimens as a reference standard. Data were presented as percentages with their 95% confidence intervals (CI). McNemar test was used. Interobserver agreement was assessed by using Cohen κ test for both techniques. Results Sensitivity was 41/43 (95%, 95% CI 84.2–99.4) for DBT-galactography and 33/43 (77%, 95% CI 61.4–88.2) for FFD-galactography (p = 0.008), specificity 6/6 (100%, 95% CI 54.1–100.0) for both imaging tools, accuracy 47/49 (96%, 95% CI 86.0–99.5) and 39/49 (80%, 95% CI 65.7–89.8) (p = 0.038), respectively. The inter-observer agreement was 0.86 for DBT-galactography and 0.78 for FFD-galactography. The AGD resulted to 1.94 ± 0.64 for the combined technique. Conclusion DBT-galactography showed a significantly higher sensitivity and accuracy than FFD-galactography for the identification of the intraductal findings, improving the possibility of a reliable diagnosis in patients with pathologic nipple-discharge.

Abstract Genetic complexity and DNA damage repair defects are common in different cancer types and can induce tumor-specific vulnerabilities. Poly(ADP-ribose) polymerase (PARP) inhibitors exploit defects in the DNA repair pathway through synthetic lethality and have emerged as promising anticancer therapies, especially in tumors harboring deleterious germline or somatic breast cancer susceptibility gene (BRCA) mutations. However, the utility of PARP inhibitors could be expanded beyond germline BRCA1/2 mutated cancers by causing DNA damage with cytotoxic agents in the presence of a DNA repair inhibitor. US Food and Drug Administration (FDA)-approved PARP inhibitors include olaparib, rucaparib, and niraparib, while veliparib is in the late stage of clinical development. Talazoparib inhibits PARP catalytic activity, trapping PARP1/2 on damaged DNA, and it has been approved by the US FDA for the treatment of metastatic germline BRCA1/2 mutated breast cancers in October 2018. The talazoparib side effect profile more closely resembles traditional chemotherapeutics rather than other clinically approved PARP inhibitors. In this review, we discuss the scientific evidence that has emerged from both experimental and clinical studies in the development of talazoparib. Future directions will include optimizing combination therapy with chemotherapy, immunotherapies and targeted therapies, and in developing and validating biomarkers for patient selection and stratification, particularly in malignancies with ‘BRCAness’.

Abstract Background Screening programs use mammography as primary diagnostic tool for detecting breast cancer at an early stage. The diagnosis of some lesions, such as microcalcifications, is still difficult today for radiologists. In this paper, we proposed an automatic binary model for discriminating tissue in digital mammograms, as support tool for the radiologists. In particular, we compared the contribution of different methods on the feature selection process in terms of the learning performances and selected features. Results For each ROI, we extracted textural features on Haar wavelet decompositions and also interest points and corners detected by using Speeded Up Robust Feature (SURF) and Minimum Eigenvalue Algorithm (MinEigenAlg). Then a Random Forest binary classifier is trained on a subset of a sub-set features selected by two different kinds of feature selection techniques, such as filter and embedded methods. We tested the proposed model on 260 ROIs extracted from digital mammograms of the BCDR public database. The best prediction performance for the normal/abnormal and benign/malignant problems reaches a median AUC value of 98.16% and 92.08%, and an accuracy of 97.31% and 88.46%, respectively. The experimental result was comparable with related work performance. Conclusions The best performing result obtained with embedded method is more parsimonious than the filter one. The SURF and MinEigen algorithms provide a strong informative content useful for the characterization of microcalcification clusters.

A pharmaceutical industry viewpoint on how the fundamental laws of photochemistry are used to identify the parameters required to implement photochemistry from lab to scale. Parameters such as photon stoichiometry and light intensity are highlighted within to inform future publications.

Summary: Compelling evidence support an involvement of oxidative stress and intestinal inflammation as early events in the predisposition and development of obesity and its related comorbidities. Here, we show that deficiency of the major mitochondrial antioxidant enzyme superoxide dismutase 2 (SOD2) in the gastrointestinal tract drives spontaneous obesity. Intestinal epithelium-specific Sod2 ablation in mice induced adiposity and inflammation via phospholipase A2 (PLA2) activation and increased release of omega-6 polyunsaturated fatty acid arachidonic acid. Remarkably, this obese phenotype was rescued when fed an essential fatty acid-deficient diet, which abrogates de novo biosynthesis of arachidonic acid. Data from clinical samples revealed that the negative correlation between intestinal Sod2 mRNA levels and obesity features appears to be conserved between mice and humans. Collectively, our findings suggest a role of intestinal Sod2 levels, PLA2 activity, and arachidonic acid in obesity presenting new potential targets of therapeutic interest in the context of this metabolic disorder.

Abstract Background The automatic segmentation of kidneys in medical images is not a trivial task when the subjects undergoing the medical examination are affected by Autosomal Dominant Polycystic Kidney Disease (ADPKD). Several works dealing with the segmentation of Computed Tomography images from pathological subjects were proposed, showing high invasiveness of the examination or requiring interaction by the user for performing the segmentation of the images. In this work, we propose a fully-automated approach for the segmentation of Magnetic Resonance images, both reducing the invasiveness of the acquisition device and not requiring any interaction by the users for the segmentation of the images. Methods Two different approaches are proposed based on Deep Learning architectures using Convolutional Neural Networks (CNN) for the semantic segmentation of images, without needing to extract any hand-crafted features. In details, the first approach performs the automatic segmentation of images without any procedure for pre-processing the input. Conversely, the second approach performs a two-steps classification strategy: a first CNN automatically detects Regions Of Interest (ROIs); a subsequent classifier performs the semantic segmentation on the ROIs previously extracted. Results Results show that even though the detection of ROIs shows an overall high number of false positives, the subsequent semantic segmentation on the extracted ROIs allows achieving high performance in terms of mean Accuracy. However, the segmentation of the entire images input to the network remains the most accurate and reliable approach showing better performance than the previous approach. Conclusion The obtained results show that both the investigated approaches are reliable for the semantic segmentation of polycystic kidneys since both the strategies reach an Accuracy higher than 85%. Also, both the investigated methodologies show performances comparable and consistent with other approaches found in literature working on images from different sources, reducing both the invasiveness of the analyses and the interaction needed by the users for performing the segmentation task.

Abstract Radiation therapy represents an important approach in the therapeutic management of children and adolescents with malignant tumors and its application with modern techniques – including Proton Beam Therapy (PBT) – is of great interest. In particular, potential radiation-induced injuries and secondary malignancies – also associated to the prolonged life expectancy of patients – are still questions of concern that increase the debate on the usefulness of PBT in pediatric treatments. This paper presents a literary review of current applications of PBT in non-Central Nervous System pediatric tumors (such as retinoblastoma, Hodgkin Lymphoma, Wilms tumor, bone and soft tissues sarcomas). We specifically reported clinical results achieved with PBT and dosimetric comparisons between PBT and the most common photon-therapy techniques. The analysis emphasizes that PBT minimizes radiation doses to healthy growing organs, suggesting for reduced risks of late side-effects and radiation-induced secondary malignancies. Extended follow up and confirms by prospective clinical trials should support the effectiveness and long-term tolerance of PBT in the considered setting.

The use of composite biomaterials as innovative bio-friendly neuronal interfaces has been poorly developed so far. Smart strategies to target neuro-pathologies are currently exploiting the mixed and complementary characteristics of composite materials to better design future neural interfaces. Here we present a polymer-based scaffold that has been rendered suitable for primary neurons by embedding graphene nanoplatelets (GnP). In particular, the growth, network formation, and functionality of primary neurons on poly(3-hydroxybutyrate) [P(3HB)] polymer supports functionalized with various concentrations of GnP were explored. After growing primary cortical neurons onto the supports for 14 days, all specimens were found to be biocompatible, revealing physiological growth and maturation of the neuronal network. When network functionality was investigated by whole patch-clamp measurements, pure P(3HB) led to changes in the action potential waveform and reduction in firing frequency, resulting in decreased neuronal excitability. However, the addition of GnP to the polymer matrix restored the electrophysiological parameters to physiological values. Interestingly, a low concentration of graphene was able to promote firing activity at a low level of injected current. The results indicate that the P(3HB)/GnP composites show great potential for electrical interfacing with primary neurons to eventually target central nervous system disorders.

Bile acid (BA) species and the gut microbiota (GM) contribute to intestinal mucosa homeostasis. BAs shape the GM and, conversely, intestinal bacteria with bile salt hydrolase (BSH) activity modulate the BA pool composition. The mutual interaction between BAs and intestinal microorganisms also influences mucosal barrier integrity, which is important for inflammatory bowel disease (IBD) pathogenesis, prevention and therapy. High levels of secondary BAs are detrimental for the intestinal barrier and increase the intestinal inflammatory response and dysbiosis. Additionally, a lack of BSH-active bacteria plays a role in intestinal inflammation and BA dysmetabolism. Thus, BSH-competent bacteria in probiotic formulations are being actively studied in IBD. At the same time, studies exploring the modulation of the master regulator of BA homeostasis, the Farnesoid X Receptor (FXR), in intestinal inflammation and how this impacts the GM are gaining significant momentum. Overall, the choice of probiotic supplementation should be a peculiar issue of personalized medicine, considering not only the disease but also the specific BA and metabolic signatures of a given patient.

Atherosclerosis is a common cause of cardiovascular disease, which, in turn, is often fatal. Today, we know a lot about the pathogenesis of atherosclerosis. However, the main knowledge is that the disease is extremely complicated. The development of atherosclerosis is associated with more than one molecular mechanism, each making a significant contribution. These mechanisms include endothelial dysfunction, inflammation, mitochondrial dysfunction, oxidative stress, and lipid metabolism disorders. This complexity inevitably leads to difficulties in treatment and prevention. One of the possible therapeutic options for atherosclerosis and its consequences may be metformin, which has already proven itself in the treatment of diabetes. Both diabetes and atherosclerosis are complex metabolic diseases, the pathogenesis of which involves many different mechanisms, including those common to both diseases. This makes metformin a suitable candidate for investigating its efficacy in cardiovascular disease. In this review, we highlight aspects such as the mechanisms of action and targets of metformin, in addition to summarizing the available data from clinical trials on the effective reduction of cardiovascular risks.