Discarding IVF embryos: reporting on global practices

Purpose: To provide a global scale report on a representative sample of the clinical embryology community depicting the practice of discarding supernumerary IVF embryos. Methods: A web-based questionnaire titled “Anonymous questionnaire on embryo disposal practices” was designed in order to ensure anonymous participation of practicing clinical embryologists around the world. Results: During a data collection period of 8 months, 703 filled-in questionnaires from 65 countries were acquired. According to the data acquired, the majority of practitioners, dispose of embryos by placing them directly in a trash can strictly dedicated for embryo disposal for both fresh and frozen cycles (39% and 36.7% respectively). Moreover, 66.4% of practitioners discard the embryos separately—case by case—at different time points during the day. Over half of embryologists (54%) wait until day 6 to discard the surplus embryos, while 65.5% do not implement a specially allocated incubator space as a designated waiting area prior to disposal. The majority of 63.1% reported that this is a witnessed procedure. The vast majority of embryologists (93%) do not employ different protocols for different groups of patients. Nonetheless, 17.8% reported the request to perform a ceremony for these embryos. Assessing the embryologists’ perspective, 59.5% of participants stated that the embryology practice would benefit from a universally accepted and practiced protocol. Conclusion(s): This study uniquely provides insight into global embryo disposal practices and trends. Results highlight the divergence between reported practices, while indicating the significance on standardization of practice, with embryologists acknowledging the need for a universally accepted protocol implementation

Errors in chromosome segregation during oogenesis and early embryogenesis

Errors in chromosome segregation occurring during human oogenesis and early embryogenesis are very common. Meiotic chromosome development during oogenesis is subdivided into three distinct phases. The crucial events, including meiotic chromosome pairing and recombination, take place from around 11 weeks until birth. Oogenesis is then arrested until ovulation, when the first meiotic division takes place, with the second meiotic division not completed until after fertilization. It is generally accepted that most aneuploid fetal conditions, such as trisomy 21 Down syndrome, are due to maternal chromosome segregation errors. The underlying reasons are not yet fully understood. It is also clear that superimposed on the maternal meiotic chromosome segregation errors, there are a large number of mitotic errors taking place post-zygotically during the first few cell divisions in the embryo. In this chapter, we summarise current knowledge of errors in chromosome segregation during oogenesis and early embryogenesis, with special reference to the clinical implications for successful assisted reproduction

Search for the glueball candidates f0(1500) and fJ(1710) in gamma gamma collisions

Data taken with the ALEPH detector at LEP1 have been used to search for gamma gamma production of the glueball candidates f0(1500) and fJ(1710) via their decay to pi+pi-. No signal is observed and upper limits to the product of gamma gamma width and pi+pi- branching ratio of the f0(1500) and the fJ(1710) have been measured to be Gamma_(gamma gamma -> f0(1500)). BR(f0(1500)->pi+pi-) < 0.31 keV and Gamma_(gamma gamma -> fJ(1710)). BR(fJ(1710)->pi+pi-) < 0.55 keV at 95% confidence level.Comment: 10 pages, 3 figure

Search for supersymmetry with a dominant R-parity violating LQDbar couplings in e+e- collisions at centre-of-mass energies of 130GeV to 172 GeV

A search for pair-production of supersymmetric particles under the assumption that R-parity is violated via a dominant LQDbar coupling has been performed using the data collected by ALEPH at centre-of-mass energies of 130-172 GeV. The observed candidate events in the data are in agreement with the Standard Model expectation. This result is translated into lower limits on the masses of charginos, neutralinos, sleptons, sneutrinos and squarks. For instance, for m_0=500 GeV/c^2 and tan(beta)=sqrt(2) charginos with masses smaller than 81 GeV/c^2 and neutralinos with masses smaller than 29 GeV/c^2 are excluded at the 95% confidence level for any generation structure of the LQDbar coupling.Comment: 32 pages, 30 figure

Search for CP Violation in the Decay Z -> b (b bar) g

About three million hadronic decays of the Z collected by ALEPH in the years 1991-1994 are used to search for anomalous CP violation beyond the Standard Model in the decay Z -> b \bar{b} g. The study is performed by analyzing angular correlations between the two quarks and the gluon in three-jet events and by measuring the differential two-jet rate. No signal of CP violation is found. For the combinations of anomalous CP violating couplings, ${\hat{h}}_b = {\hat{h}}_{Ab}g_{Vb}-{\hat{h}}_{Vb}g_{Ab}$ and $h^{\ast}_b = \sqrt{\hat{h}_{Vb}^{2}+\hat{h}_{Ab}^{2}}$, limits of \hat{h}_b < 0.59$and$h^{\ast}_{b} < 3.02$ are given at 95\% CL.Comment: 8 pages, 1 postscript figure, uses here.sty, epsfig.st

Triple Gauge Boson Couplings

We present the results obtained by the "Triple Gauge Couplings" working group during the LEP2 Workshop (1994-1995). The report concentrates on the measurement of $WW\gamma$ and $WWZ$ couplings in $e^-e^+\to W^-W^+$ or, more generally, four-fermion production at LEP2. In addition the detection of new interactions in the bosonic sector via other production channels is discussed.Comment: 52 pages, LaTeX, 17 PostScript figures. To appear in "Physics at LEP2", G. Altarelli and F. Zwirner eds., CERN Report 1996. Conveners: G. Gounaris, J.-L. Kneur, D. Zeppenfel

Integrative MicroRNA and Proteomic Approaches Identify Novel Osteoarthritis Genes and Their Collaborative Metabolic and Inflammatory Networks

BACKGROUND: Osteoarthritis is a multifactorial disease characterized by destruction of the articular cartilage due to genetic, mechanical and environmental components affecting more than 100 million individuals all over the world. Despite the high prevalence of the disease, the absence of large-scale molecular studies limits our ability to understand the molecular pathobiology of osteoathritis and identify targets for drug development. METHODOLOGY/PRINCIPAL FINDINGS: In this study we integrated genetic, bioinformatic and proteomic approaches in order to identify new genes and their collaborative networks involved in osteoarthritis pathogenesis. MicroRNA profiling of patient-derived osteoarthritic cartilage in comparison to normal cartilage, revealed a 16 microRNA osteoarthritis gene signature. Using reverse-phase protein arrays in the same tissues we detected 76 differentially expressed proteins between osteoarthritic and normal chondrocytes. Proteins such as SOX11, FGF23, KLF6, WWOX and GDF15 not implicated previously in the genesis of osteoarthritis were identified. Integration of microRNA and proteomic data with microRNA gene-target prediction algorithms, generated a potential "interactome" network consisting of 11 microRNAs and 58 proteins linked by 414 potential functional associations. Comparison of the molecular and clinical data, revealed specific microRNAs (miR-22, miR-103) and proteins (PPARA, BMP7, IL1B) to be highly correlated with Body Mass Index (BMI). Experimental validation revealed that miR-22 regulated PPARA and BMP7 expression and its inhibition blocked inflammatory and catabolic changes in osteoarthritic chondrocytes. CONCLUSIONS/SIGNIFICANCE: Our findings indicate that obesity and inflammation are related to osteoarthritis, a metabolic disease affected by microRNA deregulation. Gene network approaches provide new insights for elucidating the complexity of diseases such as osteoarthritis. The integration of microRNA, proteomic and clinical data provides a detailed picture of how a network state is correlated with disease and furthermore leads to the development of new treatments. This strategy will help to improve the understanding of the pathogenesis of multifactorial diseases such as osteoarthritis and provide possible novel therapeutic targets

Integrative MicroRNA and Proteomic Approaches Identify Novel Osteoarthritis Genes and Their Collaborative Metabolic and Inflammatory Networks

BACKGROUND: Osteoarthritis is a multifactorial disease characterized by destruction of the articular cartilage due to genetic, mechanical and environmental components affecting more than 100 million individuals all over the world. Despite the high prevalence of the disease, the absence of large-scale molecular studies limits our ability to understand the molecular pathobiology of osteoathritis and identify targets for drug development. METHODOLOGY/PRINCIPAL FINDINGS: In this study we integrated genetic, bioinformatic and proteomic approaches in order to identify new genes and their collaborative networks involved in osteoarthritis pathogenesis. MicroRNA profiling of patient-derived osteoarthritic cartilage in comparison to normal cartilage, revealed a 16 microRNA osteoarthritis gene signature. Using reverse-phase protein arrays in the same tissues we detected 76 differentially expressed proteins between osteoarthritic and normal chondrocytes. Proteins such as SOX11, FGF23, KLF6, WWOX and GDF15 not implicated previously in the genesis of osteoarthritis were identified. Integration of microRNA and proteomic data with microRNA gene-target prediction algorithms, generated a potential "interactome" network consisting of 11 microRNAs and 58 proteins linked by 414 potential functional associations. Comparison of the molecular and clinical data, revealed specific microRNAs (miR-22, miR-103) and proteins (PPARA, BMP7, IL1B) to be highly correlated with Body Mass Index (BMI). Experimental validation revealed that miR-22 regulated PPARA and BMP7 expression and its inhibition blocked inflammatory and catabolic changes in osteoarthritic chondrocytes. CONCLUSIONS/SIGNIFICANCE: Our findings indicate that obesity and inflammation are related to osteoarthritis, a metabolic disease affected by microRNA deregulation. Gene network approaches provide new insights for elucidating the complexity of diseases such as osteoarthritis. The integration of microRNA, proteomic and clinical data provides a detailed picture of how a network state is correlated with disease and furthermore leads to the development of new treatments. This strategy will help to improve the understanding of the pathogenesis of multifactorial diseases such as osteoarthritis and provide possible novel therapeutic targets