2,218 research outputs found
The role of peroxiredoxins in cancer
Peroxiredoxins (PRDXs) are a ubiquitously expressed family of small (22-27 kDa) non-seleno peroxidases that catalyze the peroxide reduction of H2O2, organic hydroperoxides and peroxynitrite. They are highly involved in the control of various physiological functions, including cell growth, differentiation, apoptosis, embryonic development, lipid metabolism, the immune response, as well as cellular homeostasis. Although the protective role of PRDXs in cardiovascular and neurological diseases is well established, their role in cancer remains controversial. Increasing evidence suggests the involvement of PRDXs in carcinogenesis and in the development of drug resistance. Numerous types of cancer cells, in fact, are characterized by an increase in reactive oxygen species (ROS) production, and often exhibit an altered redox environment compared with normal cells. The present review focuses on the complex association between oxidant balance and cancer, and it provides a brief account of the involvement of PRDXs in tumorigenesis and in the development of chemoresistance
Investigation of adaptive optics imaging biomarkers for detecting pathological changes of the cone mosaic in patients with type 1 diabetes mellitus
Purpose
To investigate a set of adaptive optics (AO) imaging biomarkers for the assessment of
changes of the cone mosaic spatial arrangement in patients with type 1 diabetes mellitus
(DM1).
Methods
16 patients with 20/20 visual acuity and a diagnosis of DM1 in the past 8 years to 37 years
and 20 age-matched healthy volunteers were recruited in this study. Cone density, cone
spacing and Voronoi diagrams were calculated on 160x160 μm images of the cone mosaic
acquired with an AO flood illumination retinal camera at 1.5 degrees eccentricity from the
fovea along all retinal meridians. From the cone spacing measures and Voronoi diagrams,
the linear dispersion index (LDi) and the heterogeneity packing index (HPi) were computed
respectively. Logistic regression analysis was conducted to discriminate DM1 patients without
diabetic retinopathy from controls using the cone metrics as predictors.
Results
Of the 16 DM1 patients, eight had no signs of diabetic retinopathy (noDR) and eight had
mild nonproliferative diabetic retinopathy (NPDR) on fundoscopy. On average, cone density,
LDi and HPi values were significantly different (P<0.05) between noDR or NPDR eyes
and controls, with these differences increasing with duration of diabetes. However, each
cone metric alone was not sufficiently sensitive to discriminate entirely between membership
of noDR cases and controls. The complementary use of all the three cone metrics in
the logistic regression model gained 100% accuracy to identify noDR cases with respect to
controls.
PLOS ONE | DOI:10.1371/journal.pone.0151380 March 10, 2016 1 / 14
OPEN ACCESS
Citation: Lombardo M, Parravano M, Serrao S,
Ziccardi L, Giannini D, Lombardo G (2016)
Investigation of Adaptive Optics Imaging Biomarkers
for Detecting Pathological Changes of the Cone
Mosaic in Patients with Type 1 Diabetes Mellitus.
PLoS ONE 11(3): e0151380. doi:10.1371/journal.
pone.0151380
Editor: Knut Stieger, Justus-Liebig-University
Giessen, GERMANY
Received: December 17, 2015
Accepted: February 27, 2016
Published: March 10, 2016
Copyright: © 2016 Lombardo et al. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any
medium, provided the original author and source are
credited.
Data Availability Statement: All relevant data are
within the paper and its Supporting Information files.
Funding: Research for this work was supported by
the Italian Ministry of Health (5x1000 funding), by the
National Framework Program for Research and
Innovation PON (grant n. 01_00110) and by
Fondazione Roma. The funders had no role in study
design, data collection and analysis, decision to
publish, or preparation of the manuscript. Vision
Engineering Italy srl funder provided support in the
form of salaries for author GL, but did not have any
Conclusion
The present set of AO imaging biomarkers identified reliably abnormalities in the spatial
arrangement of the parafoveal cones in DM1 patients, even when no signs of diabetic retinopathy
were seen on fundoscopy
A tumoral and invasive phenotype independent of c-Met mutation
Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal
Constraint-Based Visual Generation
In the last few years the systematic adoption of deep learning to visual
generation has produced impressive results that, amongst others, definitely
benefit from the massive exploration of convolutional architectures. In this
paper, we propose a general approach to visual generation that combines
learning capabilities with logic descriptions of the target to be generated.
The process of generation is regarded as a constrained satisfaction problem,
where the constraints describe a set of properties that characterize the
target. Interestingly, the constraints can also involve logic variables, while
all of them are converted into real-valued functions by means of the t-norm
theory. We use deep architectures to model the involved variables, and propose
a computational scheme where the learning process carries out a satisfaction of
the constraints. We propose some examples in which the theory can naturally be
used, including the modeling of GAN and auto-encoders, and report promising
results in problems with the generation of handwritten characters and face
transformations
Coexistence of three EGFR mutations in an NSCLC patient: A brief report
The epidermal growth factor receptor (EGFR) represents a molecular target for tyrosine kinase inhibitors for non-small cell lung cancer (NSCLC) patients with a mutation in the EGFR gene. Mutations of the EGFR gene that occur at a single position in NSCLC tissue are found as single, whereas two or more mutations on the same allele are poorly detected and investigated
MET Gene Amplification and MET Receptor Activation Are Not Sufficient to Predict Efficacy of Combined MET and EGFR Inhibitors in EGFR TKI-Resistant NSCLC Cells
Epidermal growth factor receptor (EGFR), member of the human epidermal growth factor receptor (HER) family, plays a critical role in regulating multiple cellular processes including proliferation, differentiation, cell migration and cell survival. Deregulation of the EGFR signaling has been found to be associated with the development of a variety of human malignancies including lung, breast, and ovarian cancers, making inhibition of EGFR the most promising molecular targeted therapy developed in the past decade against cancer. Human non small cell lung cancers (NSCLC) with activating mutations in the EGFR gene frequently experience significant tumor regression when treated with EGFR tyrosine kinase inhibitors (TKIs), although acquired resistance invariably develops. Resistance to TKI treatments has been associated to secondary mutations in the EGFR gene or to activation of additional bypass signaling pathways including the ones mediated by receptor tyrosine kinases, Fas receptor and NF-kB. In more than 30-40% of cases, however, the mechanisms underpinning drug-resistance are still unknown. The establishment of cellular and mouse models can facilitate the unveiling of mechanisms leading to drug-resistance and the development or validation of novel therapeutic strategies aimed at overcoming resistance and enhancing outcomes in NSCLC patients. Here we describe the establishment and characterization of EGFR TKI-resistant NSCLC cell lines and a pilot study on the effects of a combined MET and EGFR inhibitors treatment. The characterization of the erlotinib-resistant cell lines confirmed the association of EGFR TKI resistance with loss of EGFR gene amplification and/or AXL overexpression and/or MET gene amplification and MET receptor activation. These cellular models can be instrumental to further investigate the signaling pathways associated to EGFR TKI-resistance. Finally the drugs combination pilot study shows that MET gene amplification and MET receptor activation are not sufficient to predict a positive response of NSCLC cells to a cocktail of MET and EGFR inhibitors and highlights the importance of identifying more reliable biomarkers to predict the efficacy of treatments in NSCLC patients resistant to EGFR TKI
Obinutuzumab-mediated high-affinity ligation of FcγRIIIA/CD16 primes NK cells for IFNγ production
Natural killer (NK) cell-mediated antibody-dependent cellular cytotoxicity (ADCC), based on the recognition of IgG-opsonized targets by the low-affinity receptor for IgG FcγRIIIA/CD16, represents one of the main mechanisms by which therapeutic antibodies (mAbs) mediate their antitumor effects. Besides ADCC, CD16 ligation also results in cytokine production, in particular, NK-derived IFNγ is endowed with a well-recognized role in the shaping of adaptive immune responses. Obinutuzumab is a glycoengineered anti-CD20 mAb with a modified crystallizable fragment (Fc) domain designed to increase the affinity for CD16 and consequently the killing of mAb-opsonized targets. However, the impact of CD16 ligation in optimized affinity conditions on NK functional program is not completely understood. Herein, we demonstrate that the interaction of NK cells with obinutuzumab-opsonized cells results in enhanced IFNγ production as compared with parental non-glycoengineered mAb or the reference molecule rituximab. We observed that affinity ligation conditions strictly correlate with the ability to induce CD16 down-modulation and lysosomal targeting of receptor-associated signaling elements. Indeed, a preferential degradation of FcεRIγ chain and Syk kinase was observed upon obinutuzumab stimulation independently from CD16-V158F polymorphism. Although the downregulation of FcεRIγ/Syk module leads to the impairment of cytotoxic function induced by NKp46 and NKp30 receptors, obinutuzumab-experienced cells exhibit an increased ability to produce IFNγ in response to different stimuli. These data highlight a relationship between CD16 aggregation conditions and the ability to promote a degradative pathway of CD16-coupled signaling elements associated to the shift of NK functional progra
T-Norms Driven Loss Functions for Machine Learning
Neural-symbolic approaches have recently gained popularity to inject prior
knowledge into a learner without requiring it to induce this knowledge from
data. These approaches can potentially learn competitive solutions with a
significant reduction of the amount of supervised data. A large class of
neural-symbolic approaches is based on First-Order Logic to represent prior
knowledge, relaxed to a differentiable form using fuzzy logic. This paper shows
that the loss function expressing these neural-symbolic learning tasks can be
unambiguously determined given the selection of a t-norm generator. When
restricted to supervised learning, the presented theoretical apparatus provides
a clean justification to the popular cross-entropy loss, which has been shown
to provide faster convergence and to reduce the vanishing gradient problem in
very deep structures. However, the proposed learning formulation extends the
advantages of the cross-entropy loss to the general knowledge that can be
represented by a neural-symbolic method. Therefore, the methodology allows the
development of a novel class of loss functions, which are shown in the
experimental results to lead to faster convergence rates than the approaches
previously proposed in the literature
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