A retrospective cross-sectional study: Fresh cycle endometrial thickness is a sensitive predictor of inadequate endometrial thickness in frozen embryo transfer cycles

BACKGROUND: The purpose of this study is to assess predictors of inadequate endometrial cavity thickness (ECT), defined as < 8 mm, in frozen embryo transfer (FET) cycles. METHODS: This is a retrospective cross-sectional study at an academic fertility center including 274 women who underwent their first endometrial preparation with estradiol for autologous FET in our center from 2001-2009. Multivariable logistic regression was performed to determine predictors of inadequate endometrial development in FET cycles. RESULTS: Neither age nor duration of estrogen supplementation were associated with FET endometrial thickness. Lower body mass index, nulliparity, previous operative hysteroscopy and thinner fresh cycle endometrial lining were associated with inadequate endometrial thickness in FET cycles. A maximum thickness of 11.5 mm in a fresh cycle was 80% sensitive and 70% specific for inadequate frozen cycle thickness. CONCLUSIONS: Previous fresh cycle endometrial cavity thickness is associated with subsequent FET cycle endometrial cavity thickness. Women with a fresh cycle thickness of 11.5 mm or less may require additional intervention to achieve adequate endometrial thickness in preparation for a frozen cycle

Sperm centriole assessment identifies male factor infertility in couples with unexplained infertility - a pilot study

Unexplained infertility affects about one-third of infertile couples and is defined as the failure to identify the cause of infertility despite extensive evaluation of the male and female partners. Therefore, there is a need for a multiparametric approach to study sperm function. Recently, we developed a Fluorescence-Based Ratiometric Analysis of Sperm Centrioles (FRAC) assay to determine sperm centriole quality. Here, we perform a pilot study of sperm from 10 fertile men and 10 men in couples with unexplained infertility, using three centriolar biomarkers measured at three sperm locations from two sperm fractions, representing high and low sperm quality. We found that FRAC can identify men from couples with unexplained infertility as the likely source of infertility. Higher quality fractions from 10 fertile individuals were the reference population. All 180 studied FRAC values in the 10 fertile individuals fell within the reference population range. Eleven of the 180 studied FRAC values in the 10 infertile patients were outliers beyond the 95% confidence intervals (P = 0.0008). Three men with unexplained infertility had outlier FRAC values in their higher quality sperm fraction, while four had outlier FRAC values in their lower quality sperm fraction (3/10 and 4/10, P = 0.060 and P = 0.025, respectively), suggesting that these four individuals are infertile due, in part, to centriolar defects. We propose that a larger scale study should be performed to determine the ability of FRAC to identify male factor infertility and its potential contribution to sperm multiparametric analysis

The database of the PREDICTS (Projecting Responses of Ecological Diversity In Changing Terrestrial Systems) project

The PREDICTS project—Projecting Responses of Ecological Diversity In Changing Terrestrial Systems (www.predicts.org.uk)—has collated from published studies a large, reasonably representative database of comparable samples of biodiversity from multiple sites that differ in the nature or intensity of human impacts relating to land use. We have used this evidence base to develop global and regional statistical models of how local biodiversity responds to these measures. We describe and make freely available this 2016 release of the database, containing more than 3.2 million records sampled at over 26,000 locations and representing over 47,000 species. We outline how the database can help in answering a range of questions in ecology and conservation biology. To our knowledge, this is the largest and most geographically and taxonomically representative database of spatial comparisons of biodiversity that has been collated to date; it will be useful to researchers and international efforts wishing to model and understand the global status of biodiversity

Whole-genome sequencing reveals host factors underlying critical COVID-19

Altres ajuts: Department of Health and Social Care (DHSC); Illumina; LifeArc; Medical Research Council (MRC); UKRI; Sepsis Research (the Fiona Elizabeth Agnew Trust); the Intensive Care Society, Wellcome Trust Senior Research Fellowship (223164/Z/21/Z); BBSRC Institute Program Support Grant to the Roslin Institute (BBS/E/D/20002172, BBS/E/D/10002070, BBS/E/D/30002275); UKRI grants (MC_PC_20004, MC_PC_19025, MC_PC_1905, MRNO2995X/1); UK Research and Innovation (MC_PC_20029); the Wellcome PhD training fellowship for clinicians (204979/Z/16/Z); the Edinburgh Clinical Academic Track (ECAT) programme; the National Institute for Health Research, the Wellcome Trust; the MRC; Cancer Research UK; the DHSC; NHS England; the Smilow family; the National Center for Advancing Translational Sciences of the National Institutes of Health (CTSA award number UL1TR001878); the Perelman School of Medicine at the University of Pennsylvania; National Institute on Aging (NIA U01AG009740); the National Institute on Aging (RC2 AG036495, RC4 AG039029); the Common Fund of the Office of the Director of the National Institutes of Health; NCI; NHGRI; NHLBI; NIDA; NIMH; NINDS.Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care or hospitalization after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes-including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)-in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease