DESIGN AND SYNTHESIS OF NOVEL ZINC-DEPENDENT METALLOENZYMES INHIBITORS AS ANTI-TUMORAL DRUG CANDIDATES.

Cancer is a wide diffuse pathology, many can be the causes such as genetic predisposition, environmental, diet and life-style. Several treatment exist in order to avoid or cure cancer, but unfortunately, no one of these can lead to total healing. Matrix metalloproteinases are a family of zinc dependent enzyme, there are plenty experimental evidence that an over expression of these enzymes are correleated in cancer. Their inhibition seems to be a good weapon for fight some kind of cancer. In this PhD thesis are reported the inhibitors which have been synthesized under the advice of computational chemist of university of Napoli. Ubiquitin-proteasome is another complex system which is involved in cancer, especially in breast. BCA2, an E3 enzyme is over-express in 57% of aggressive breast cancer and among E2 enzymes, Ubc5Hb seems to be involved in apoptosis and cancer invasiveness. Computational studies mada by Cardiff Uni (Cardiff, Wales, UK) have given as result disulfiram analogue and triazinic based scaffold as inhibitor for these enzymes. Here we report synthesis and optimization

Pay-as-they-get-in : attitudes towards migrants and pension systems

This research has received funding from the following projects: FARE “LEGISCOM Complexity, Bureaucratic Efficiency: Methodological Advances” (CUP J42F17000270001); RTI2018-097271-B-I00 (Ministerio de Educacion y Ciencia).We study whether a better knowledge of the functioning of pay-as-you-go (PAYG) pension systems and recent demographic trends affects natives’ attitudes toward immigration. In two online experiments conducted in Italy and Spain, we randomly treated participants with a video explaining how, in PAYG systems, the payment of current pensions depends on the contributions paid by current workers. The video also informs participants about population aging trends in their countries. The treatment increases knowledge of PAYG systems and future demographic trends for all participants. However, it improves attitudes toward migrants only for treated participants who do not support populist and anti-immigrant parties.Publisher PDFPeer reviewe

Cannabidiol stimulates AML-1a-dependent glial differentiation and inhibits glioma stem-like cells proliferation by inducing autophagy in a TRPV2-dependent manner

Glioma stem-like cells (GSCs) correspond to a tumor cell subpopulation, involved in glioblastoma multiforme (GBM) tumor ini- tiation and acquired chemoresistance. Currently, drug-induced differentiation is considered as a promising approach to eradi- cate this tumor-driving cell population. Recently, the effect of cannabinoids (CBs) in promoting glial differentiation and inhibiting gliomagenesis has been evidenced. Herein, we demonstrated that cannabidiol (CBD) by activating transient receptor potential vanilloid-2 (TRPV2) triggers GSCs differentiation activating the autophagic process and inhibits GSCs proliferation and clonogenic capability. Above all, CBD and carmustine (BCNU) in combination overcome the high resistance of GSCs to BCNU treatment, by inducing apoptotic cell death. Acute myeloid leukemia (Aml-1) transcription factors play a pivotal role in GBM proliferation and differentiation and it is known that Aml-1 control the expression of several nociceptive receptors. So, we evaluated the expression levels of Aml-1 spliced variants (Aml-1a, b and c) in GSCs and during their differentiation. We found that Aml-1a is upregulated during GSCs differentiation, and its downregulation restores a stem cell phenotype in differ- entiated GSCs. Since it was demonstrated that CBD induces also TRPV2 expression and that TRPV2 is involved in GSCs differ- entiation, we evaluated if Aml-1a interacted directly with TRPV2 promoters. Herein, we found that Aml-1a binds TRPV2 promoters and that Aml-1a expression is upregulated by CBD treatment, in a TRPV2 and PI3K/AKT dependent manner. Alto- gether, these results support a novel mechanism by which CBD inducing TRPV2-dependent autophagic process stimulates Aml-1a-dependent GSCs differentiation, abrogating the BCNU chemoresistance in GSCs

Pay-as-they-get-in: attitudes towards migrants and pension systems

We study whether a better knowledge of the functioning of pay-as-you-go pension systems and recent demographic trends in the hosting country affects natives' attitudes towards immigration. In two online experiments in Italy and Spain, we randomly treated participants with a video explaining how, in pay-as-you-go pension systems, the payment of current pensions depends on the contributions paid by current workers. The video also explains that the ratio between the number of pensioners and the number of workers in their countries will grow substantially in the future. We find that the treatment improves participants' knowledge about how a pay-as-you-go system works and the future demographic trends in their country. However, we find that only treated participants who support non-populist parties display more positive attitudes towards migrants, even though the treatment increases knowledge of pension systems and demographic trends for all participants

Pay-as-they-get-in: attitudes towards migrants and pension systems

We study whether a better knowledge of the functioning of pay-as-you-go pension systems and recent demographic trends affects natives’ attitudes towards immigration. In two online experiments conducted in Italy and Spain, we randomly treated participants with a video explaining how, in pay-as-you-go systems, the payment of current pensions depends on the contributions paid by current workers. The video also informs participants about population aging trends in their countries. The treatment increases knowledge of pay-as-you-go systems and future demographic trends for all participants. However, it improves attitudes towards migrants only for treated participants who do not support populist and anti-immigrant parties

Accurate and highly interpretable prediction of gene expression from histone modifications

Histone Mark Modifications (HMs) are crucial actors in gene regulation, as they actively remodel chromatin to modulate transcriptional activity: aberrant combinatorial patterns of HMs have been connected with several diseases, including cancer. HMs are, however, reversible modifications: understanding their role in disease would allow the design of 'epigenetic drugs' for specific, non-invasive treatments. Standard statistical techniques were not entirely successful in extracting representative features from raw HM signals over gene locations. On the other hand, deep learning approaches allow for effective automatic feature extraction, but at the expense of model interpretation

Dress Code: High-Resolution Multi-Category Virtual Try-On

Image-based virtual try-on strives to transfer the appearance of a clothing item onto the image of a target person. Prior work focuses mainly on upper-body clothes (e.g. t-shirts, shirts, and tops) and neglects full-body or lower-body items. This shortcoming arises from a main factor: current publicly available datasets for image-based virtual try-on do not account for this variety, thus limiting progress in the field. To address this deficiency, we introduce Dress Code, which contains images of multi-category clothes. Dress Code is more than 3x larger than publicly available datasets for image-based virtual try-on and features high-resolution paired images (1024 x 768) with front-view, full-body reference models. To generate HD try-on images with high visual quality and rich in details, we propose to learn fine-grained discriminating features. Specifically, we leverage a semantic-aware discriminator that makes predictions at pixel-level instead of image- or patch-level. Extensive experimental evaluation demonstrates that the proposed approach surpasses the baselines and state-of-the-art competitors in terms of visual quality and quantitative results. The Dress Code dataset is publicly available at https://github.com/aimagelab/dress-code.Comment: Dress Code - Video Demo: https://www.youtube.com/watch?v=qr6TW3uTHG

Dress Code: High-Resolution Multi-Category Virtual Try-On

Image-based virtual try-on strives to transfer the appearance of a clothing item onto the image of a target person. Existing literature focuses mainly on upper-body clothes (e.g. t-shirts, shirts, and tops) and neglects full-body or lower-body items. This shortcoming arises from a main factor: current publicly available datasets for image-based virtual try-on do not account for this variety, thus limiting progress in the field. In this research activity, we introduce Dress Code, a novel dataset which contains images of multi-category clothes. Dress Code is more than 3x larger than publicly available datasets for image-based virtual try-on and features high-resolution paired images (1024 x 768) with front-view, full-body reference models. To generate HD try-on images with high visual quality and rich in details, we propose to learn fine-grained discriminating features. Specifically, we leverage a semantic-aware discriminator that makes predictions at pixel-level instead of image- or patch-level. The Dress Code dataset is publicly available at https://github.com/aimagelab/dress-code

The impact of preservation fluid culture on graft site arteritis: a systematic review and metanalysis

The role of culturing the graft preservation fluid (PF) is controversial and its impact on graft arteritis development remains unclear