168 research outputs found
Screening of MAMLD1 Mutations in 70 Children with 46,XY DSD: Identification and Functional Analysis of Two New Mutations
More than 50% of children with severe 46,XY disorders of sex development (DSD) do not have a definitive etiological diagnosis. Besides gonadal dysgenesis, defects in androgen biosynthesis, and abnormalities in androgen sensitivity, the Mastermind-like domain containing 1 (MAMLD1) gene, which was identified as critical for the development of male genitalia, may be implicated. The present study investigated whether MAMLD1 is implicated in cases of severe 46,XY DSD and whether routine sequencing of MAMLD1 should be performed in these patients
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PI3Kγ inhibition circumvents inflammation and vascular leak in SARS-CoV-2 and other infections
Virulent infectious agents such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and methicillin-resistant Staphylococcus aureus (MRSA) induce tissue damage that recruits neutrophils, monocyte, and macrophages, leading to T cell exhaustion, fibrosis, vascular leak, epithelial cell depletion, and fatal organ damage. Neutrophils, monocytes, and macrophages recruited to pathogen-infected lungs, including SARS-CoV-2-infected lungs, express phosphatidylinositol 3-kinase gamma (PI3Kγ), a signaling protein that coordinates both granulocyte and monocyte trafficking to diseased tissues and immune-suppressive, profibrotic transcription in myeloid cells. PI3Kγ deletion and inhibition with the clinical PI3Kγ inhibitor eganelisib promoted survival in models of infectious diseases, including SARS-CoV-2 and MRSA, by suppressing inflammation, vascular leak, organ damage, and cytokine storm. These results demonstrate essential roles for PI3Kγ in inflammatory lung disease and support the potential use of PI3Kγ inhibitors to suppress inflammation in severe infectious diseases
Mathematical methodology to obtain and compare different embryo scores
In Vitro Fertilization (IVF) units need to decrease multiple pregnancies without affecting their overall success rate. In this study we propose a mathematical model to evaluate an embryo’s potential ability to implant in the uterus. Embryos are graded by the embryologist based on the number of blastomeres, evenness of growth and degree of fragmentation. Therefore, the following variables were considered: number of blastomeres produced by division of the egg after fertilisation (blastomeres), symmetry and fragmentation of the embryo (grade). This model evaluates the embryos assigning them a score which represents their quality. The main result derived from this model is the estimation of the significant improvement in the implantation rate due to the increase in blastomere values and the decrease in grade factor values. But the increase from two–three to four produces more improvement in the implantation rate than two–three to five–six blastomeres.
First, statistical models were used to study embryo traceability from transfer to implantation and to evaluate the effect of the quality of the embryos (embryo score) and women’s age on implantation potential. This score was obtained by making predictions from the fitted model which was used to rank embryos in terms of implantation potential. Then we totalled the scores of embryos that had been transferred to each woman for obtaining the Embryo Quality Index (EQI). In addition, we studied the effects of EQI and women’s age on pregnancy. Finally, statistical techniques such as Receiver Operating Characteristics (ROC) and bootstrap procedures were used to assess the accuracy of this model. This embryo score is a quick, efficient and accurate tool to optimise embryo selection for transfers on the second day after fertilisation. This tool is especially useful for transfers involving non-top embryos.This work was partially supported by a grant from the Generalitat Valenciana (grant no. GVPRE/2008/103). The research of AD and SC was partially supported by a grant from Ministerio de Asuntos Exteriores (grant no. A/023444/09) too. The authors are indebted to the anonymous referee whose comments and suggestions improved the paper considerably.Debón Aucejo, AM.; Molina Botella, MI.; Cabrera García, S.; Pellicer, A. (2013). Mathematical methodology to obtain and compare different embryo scores. Mathematical and Computer Modelling. 57(5-6):1380-1394. https://doi.org/10.1016/j.mcm.2012.11.027S13801394575-
Follicular fluid content and oocyte quality: from single biochemical markers to metabolomics
The assessment of oocyte quality in human in vitro fertilization (IVF) is getting increasing attention from embryologists. Oocyte selection and the identification of the best oocytes, in fact, would help to limit embryo overproduction and to improve the results of oocyte cryostorage programs. Follicular fluid (FF) is easily available during oocyte pick-up and theorically represents an optimal source on non-invasive biochemical predictors of oocyte quality. Unfortunately, however, the studies aiming to find a good molecular predictor of oocyte quality in FF were not able to identify substances that could be used as reliable markers of oocyte competence to fertilization, embryo development and pregnancy. In the last years, a well definite trend toward passing from the research of single molecular markers to more complex techniques that study all metabolites of FF has been observed. The metabolomic approach is a powerful tool to study biochemical predictors of oocyte quality in FF, but its application in this area is still at the beginning. This review provides an overview of the current knowledge about the biochemical predictors of oocyte quality in FF, describing both the results coming from studies on single biochemical markers and those deriving from the most recent studies of metabolomic
Relativistic Brownian Motion
Stimulated by experimental progress in high energy physics and astrophysics,
the unification of relativistic and stochastic concepts has re-attracted
considerable interest during the past decade. Focusing on the framework of
special relativity, we review, here, recent progress in the phenomenological
description of relativistic diffusion processes. After a brief historical
overview, we will summarize basic concepts from the Langevin theory of
nonrelativistic Brownian motions and discuss relevant aspects of relativistic
equilibrium thermostatistics. The introductory parts are followed by a detailed
discussion of relativistic Langevin equations in phase space. We address the
choice of time parameters, discretization rules, relativistic
fluctuation-dissipation theorems, and Lorentz transformations of stochastic
differential equations. The general theory is illustrated through analytical
and numerical results for the diffusion of free relativistic Brownian
particles. Subsequently, we discuss how Langevin-type equations can be obtained
as approximations to microscopic models. The final part of the article is
dedicated to relativistic diffusion processes in Minkowski spacetime. Due to
the finiteness of velocities in relativity, nontrivial relativistic Markov
processes in spacetime do not exist; i.e., relativistic generalizations of the
nonrelativistic diffusion equation and its Gaussian solutions must necessarily
be non-Markovian. We compare different proposals that were made in the
literature and discuss their respective benefits and drawbacks. The review
concludes with a summary of open questions, which may serve as a starting point
for future investigations and extensions of the theory.Comment: review article, 159 pages, references updated, misprints corrected,
App. A.4. correcte
Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen
The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca’s large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells.Peer reviewe
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