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Global storm‐resolving models (GSRMs) that can explicitly resolve some of deep convection are now being integrated for climate timescales. GSRMs are able to simulate more realistic precipitation distributions relative to traditional Coupled Model Intercomparison Project 6 (CMIP6) models. In this study, we present results from two‐year‐long integrations of a GSRM developed at Geophysical Fluid Dynamics Laboratory, eXperimental System for High‐resolution prediction on Earth‐to‐Local Domains (X‐SHiELD), for the response of precipitation to sea surface temperature warming and an isolated increase in CO2 and compare it to CMIP6 models. At leading order, X‐SHiELD's response is within the range of the CMIP6 models. However, a close examination of the precipitation distribution response reveals that X‐SHiELD has a different response at lower percentiles and the response of the extreme events are at the lower end of the range of CMIP6 models. A regional decomposition reveals that the difference is most pronounced for midlatitude land, where X‐SHiELD shows a lower increase at intermediate percentiles and drying at lower percentiles. Plain Language Summary: The main tool to investigate and make projections of the response of the climate system to warming is global climate models. These models usually have ∼100 km horizontal resolution and as a result need parameterizations in order to account for small‐scale processes. These parameterizations are a large source of uncertainty. Advancements in computational capabilities and technology allow us to run global models with high enough resolutions, called global storm‐resolving models (GSRMs), that some important small‐scale processes are explicitly resolved. In this study, we use two‐year‐long integrations of an atmosphere GSRM in present‐day and warmer climates and compare the precipitation response to more traditional global climate models. We find that the precipitation response of the GSRM lies within the range of the traditional models, except for the low percentiles of precipitation. A closer examination indicates that the main source of differences between the GSRM and traditional models occurs in midlatitude land regions, where the GSRM predicts more extensive drying of low precipitation percentiles compared to the traditional models. The GSRM also has an increase in precipitation extremes with warming that is somewhat weaker than that of traditional global climate models. Key Points: We compare precipitation changes between Coupled Model Intercomparison Project 6 (CMIP6) models and eXperimental System for High‐resolution prediction on Earth‐to‐Local Domains (X‐SHiELD), a global storm resolving model developed at Geophysical Fluid Dynamics LaboratoryX‐SHiELD response is mostly within the range of CMIP6 models, except for more drying of low precipitation percentilesX‐SHiELD precipitation distribution changes over midlatitude land differs significantly from the CMIP6 models [ABSTRACT FROM AUTHOR]

Coarse‐grid weather and climate models rely particularly on parameterizations of cloud fields, and coarse‐grained cloud fields from a fine‐grid reference model are a natural target for a machine‐learned parameterization. We machine‐learn the coarsened‐fine cloud properties as a function of coarse‐grid model state in each grid cell of NOAA's FV3GFS global atmosphere model with 200 km grid spacing, trained using a 3 km fine‐grid reference simulation with a modified version of FV3GFS. The ML outputs are coarsened‐fine fractional cloud cover and liquid and ice cloud condensate mixing ratios, and the inputs are coarse model temperature, pressure, relative humidity, and ice cloud condensate. The predicted fields are skillful and unbiased, but somewhat under‐dispersed, resulting in too many partially cloudy model columns. When the predicted fields are applied diagnostically (offline) in FV3GFS's radiation scheme, they lead to small biases in global‐mean top‐of‐atmosphere (TOA) and surface radiative fluxes. An unbiased global‐mean TOA net radiative flux is obtained by setting to zero any predicted cloud with grid‐cell mean cloud fraction less than a threshold of 6.5%; this does not significantly degrade the ML prediction of cloud properties. The diagnostic, ML‐derived radiative fluxes are far more accurate than those obtained with the existing cloud parameterization in the nudged coarse‐grid model, as they leverage the accuracy of the fine‐grid reference simulation's cloud properties. Plain Language Summary: Weather and climate models typically use simplified means of predicting clouds, and these methods are particularly important for models that run at coarse resolution (200 km pixels) and relatively quickly. Machine learning is a natural way to improve upon the methods of predicting cloud. We first show that detailed cloud information from an accurate model run at much finer resolution (3 km pixels) can produce more accurate radiative energy fluxes at earth's surface and top of atmosphere when used in the coarse model, instead of its own predicted clouds. We show that by training on the temperature, humidity, and other characteristics of the coarse model, machine learning can replicate much of this skill in predicting clouds, and provide better radiation estimates than traditional methods. Because solar and thermal radiation is sensitive to small amounts of cloud, we find that we need to remove small amounts erroneously predicted by machine learning to achieve the right amounts of radiation at earth's surface and top of atmosphere. The machine‐learning based approach could be used as a replacement for the current statistical methods of predicting cloud fields in fast, coarse‐resolution weather and climate models. Key Points: Fine‐grid model clouds, when coarsened, can produce more accurate radiative fluxes than a coarse model's own parameterized cloudsMachine learning can capture the coarsened fine‐grid clouds as functions of coarse model state (temperature, pressure, and humidity)With appropriate post‐processing, the machine‐learned clouds also outperform the coarse model's clouds in terms of diagnostic radiation [ABSTRACT FROM AUTHOR]

Parameterization of subgrid‐scale processes is a major source of uncertainty in global atmospheric model simulations. Global storm‐resolving simulations use a finer grid (less than 5 km) to reduce this uncertainty by explicitly resolving deep convection and details of orography. This study uses machine learning to replace the physical parameterizations of heating and moistening rates, but not wind tendencies, in a coarse‐grid (200 km) global atmosphere model, using training data obtained by spatially coarse‐graining a 40‐day realistic geography global storm‐resolving simulation. The training targets are the three‐dimensional fields of effective heating and moistening rates, including the effect of grid‐scale motions that are resolved but imperfectly simulated by the coarse model. A neural network is trained to predict the time‐dependent heating and moistening rates in each grid column using the coarse‐grained temperature, specific humidity, surface turbulent heat fluxes, cosine of solar zenith angle, land‐sea mask and surface geopotential of that grid column as inputs. The coefficient of determination R2 for offline prediction ranges from 0.4 to 0.8 at most vertical levels and latitudes. Online, we achieve stable 35‐day simulations, with metrics of skill such as the time‐mean pattern of near‐surface temperature and precipitation comparable or slightly better than a baseline simulation with conventional physical parameterizations. However, the structure of tropical circulation and relative humidity in the upper troposphere are unrealistic. Overall, this study shows potential for the replacement of human‐designed parameterizations with data‐driven ones in a realistic setting. Plain Language Summary: Numerical models used for projecting climate change impacts must use ad‐hoc assumptions about the effects of unresolved small‐scale processes. These assumptions contribute to uncertainty in predicting how rainfall and temperature will change in the future. Expensive fine‐grid simulations which eliminate the need for some of these assumptions are possible to run for shorter (month‐to year‐long) duration. We use such a simulation to train a data‐driven representation of the effects of processes, like clouds, which are poorly simulated by a cheaper coarse‐grid model. The data‐driven representation (a neural network) predicts rates of temperature and moisture change in each column using inputs from that grid column. This approach has been previously shown to work for models with idealized boundary conditions, but not for the realistic setting we use. When this neural network is used in a coarse‐resolution model, the realism of many global skill metrics is as good or better than a baseline model with traditional representation of small‐scale processes. However, some features are degraded, such as the time‐evolving pattern of rainfall in the tropics and humidity in the upper atmosphere. This work is a first step toward the use of data‐driven representations of unresolved processes in realistic global atmospheric models. Key Points: Effective sources of heat and moisture are computed from a global storm‐resolving simulation accounting for semi‐resolved dynamicsA neural network is trained to predict columns of the effective sources using profiles of temperature and specific humidityWhen used online, stable month‐long simulations are possible although skill is not yet comparable to a previous corrective approach [ABSTRACT FROM AUTHOR]

High‐dimensional immunoprofiling is essential for studying host response to immunotherapy, infection, and disease in murine model systems. However, the difficulty of multiparameter panel design combined with a lack of existing murine tools has prevented the comprehensive study of all major leukocyte phenotypes in a single assay. Herein, we present a 40‐color flow cytometry panel for deep immunophenotyping of murine lymphoid tissues, including the spleen, blood, Peyer's patches, inguinal lymph nodes, bone marrow, and thymus. This panel uses a robust set of surface markers capable of differentiating leukocyte subsets without the use of intracellular staining, thus allowing for the use of cells in downstream functional experiments or multiomic analyses. Our panel classifies T cells, B cells, natural killer cells, innate lymphoid cells, monocytes, macrophages, dendritic cells, basophils, neutrophils, eosinophils, progenitors, and their functional subsets by using a series of co‐stimulatory, checkpoint, activation, migration, and maturation markers. This tool has a multitude of systems immunology applications ranging from serial monitoring of circulating blood signatures to complex endpoint analysis, especially in pre‐clinical settings where treatments can modulate leukocyte abundance and/or function. Ultimately, this 40‐color panel resolves a diverse array of immune cells on the axes of time, tissue, and treatment, filling the niche for a modern tool dedicated to murine immunophenotyping. [ABSTRACT FROM AUTHOR]

Using machine learning (ML) for the online correction of coarse‐resolution atmospheric models has proven effective in reducing biases in near‐surface temperature and precipitation rate. However, ML corrections often introduce new biases in the upper atmosphere and causes inconsistent model performance across different random seeds. Furthermore, they produce profiles that are outside the distribution of samples used in training, which can interfere with the baseline physics of the atmospheric model and reduce model reliability. This study introduces the use of a novelty detector to mask ML corrections when the atmospheric state is deemed out‐of‐sample. The novelty detector is trained on profiles of temperature and specific humidity in a semi‐supervised fashion using samples from the coarsened reference fine‐resolution simulation. The novelty detector responds to particularly biased simulations relative to the reference simulation by categorizing more columns as out‐of‐sample. Without novelty detection, corrective ML occasionally causes undesirably large climate biases. When coupled to a running year‐long coarse‐grid simulation, novelty detection deems about 21% of columns to be novelties. This identification reduces the spread in the root‐mean‐square error (RMSE) of time‐mean spatial patterns of surface temperature and precipitation rate across a random seed ensemble. In particular, the random seed with the worst RMSE is improved by up to 60% (depending on the variable) while the best seed maintains its low RMSE. By reducing the variance in quality of ML‐corrected climate models, novelty detection offers reliability without compromising prediction quality in atmospheric models. Plain Language Summary: Fine‐grid global storm‐resolving models produce more accurate rainfall and temperature forecasts than coarse‐grid climate models, but are too computationally expensive to run for many years. Corrective machine learning (ML) can help coarse‐grid climate models act more like fine‐grid models, but also makes them more vulnerable to inputs lying outside the range of training data for the ML algorithm. For such "out‐of‐sample" inputs, the ML may give unreliable results. Using a separate ML scheme, we identify out‐of‐sample data and disable the ML correction for these cases. We find that this robustly improves the time‐mean temperature and precipitation patterns predicted by ML‐corrected climate simulations to be 30%–50% better than similar simulations without ML. Incorporating novelty detectors into ML‐corrected simulations can improve their prediction skill by helping them avoid drifting into "out‐of‐sample" states. Key Points: A novelty detector is trained to classify whether an atmospheric profile belongs to a high‐resolution model's distribution of profilesThe detector classifies more profiles as novelties in machine‐learning corrected simulations that drift further from a reference simulationUsing the detector to turn off corrections for novel profiles leads to corrected simulations with more consistent and less biased climates [ABSTRACT FROM AUTHOR]

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One approach to improving the accuracy of a coarse‐grid global climate model is to add machine‐learned (ML) state‐dependent corrections to the prognosed model tendencies, such that the climate model evolves more like a reference fine‐grid global storm‐resolving model (GSRM). Our past work demonstrating this approach was trained with short (40‐day) simulations of GFDL's X‐SHiELD GSRM with 3 km global horizontal grid spacing. Here, we extend this approach to span the full annual cycle by training and testing our ML using a new year‐long GSRM simulation. Our corrective ML models are trained by learning the state‐dependent tendencies of temperature and humidity and surface radiative fluxes needed to nudge a closely related 200 km grid coarse model, FV3GFS, to the GSRM evolution. Coarse‐grid simulations adding these learned ML corrections run stably for multiple years. Compared to a no‐ML baseline, the time‐mean spatial pattern errors with respect to the fine‐grid target are reduced by 6%–26% for land surface temperature and 9%–25% for land surface precipitation. The ML‐corrected simulations develop other biases in climate and circulation that differ from, but have comparable amplitude to, the no‐ML baseline simulation. Plain Language Summary: A recent vein of research uses machine learning (ML) to try and improve the predictive accuracy of climate models. Fine‐resolution global storm‐resolving simulations make more accurate rainfall and temperature predictions than climate models, but computers take too long to finish many‐year simulations. We use data from a year‐long high quality reference simulation to train ML models, which are then used in a lower quality but faster climate model. The ML applies corrections continually during the faster simulation to make it act more like the slow, high quality model. This improves the faster model's predictions for rainfall and temperature over land but also has unintended side effects of drying out the atmosphere and changing its circulation and tropical rainfall patterns. Key Points: Machine‐learned (ML) corrective tendencies and radiative fluxes for a coarse‐grid global model are trained using a year‐long global storm‐resolving simulationThe ML corrections reduce systematic errors of land surface temperature and precipitation in multiyear coarse‐model simulationsThe ML corrections nevertheless induce systematic biases in the simulated Hadley circulation [ABSTRACT FROM AUTHOR]

Bretherton et al. (2022, https://doi.org/10.1029/2021MS002794) demonstrated a successful approach for using machine learning (ML) to help a coarse‐resolution global atmosphere model with real geography (a ∼200 km version of NOAA's FV3GFS) evolve more like a fine‐resolution model, at the scales resolved by both. This study extends that work for application in multiple climates and multi‐year ML‐corrected simulations. Here four fine‐resolution (∼25 km) 2 year reference simulations are run using FV3GFS with climatological sea surface temperatures perturbed uniformly by −4, 0, +4, and +8 K. A data set of state‐dependent corrective tendencies is then derived through nudging the ∼200 km model to the coarsened state of the fine‐resolution simulations in each climate. Along with the surface radiative fluxes, the corrective tendencies of temperature and specific humidity are machine‐learned as functions of the column state. ML predictions for the fluxes and corrective tendencies are applied in 5.25 years ∼200 km resolution simulations in each climate, and improve the spatial pattern errors of land precipitation by 8%–28% and land surface temperature by 19%–25% across the four climates. The ML has a neutral impact on the pattern error of oceanic precipitation. Plain Language Summary: Previous work demonstrated how to use machine learning to help a computationally efficient coarse‐grid climate model behave like a more realistic, but expensive, fine‐grid reference simulation that we could only afford to run for 40 days. The machine learning was interpreted as correcting errors in the representation of uncertain small‐scale cloud, precipitation, and turbulence processes on the model simulations. By using a fine‐grid model with a grid spacing eight times as large as our previous reference that runs tens of times faster, we extend that approach to multi‐year coarse‐grid simulations of a range of climates, both warmer and colder than the present day. Different random starting guesses ("seeds") lead to slightly different machine learning corrections even with exactly the same training protocol. When applied interactively in 1 year coarse‐grid simulations, the machine learning corrections consistently improve the time‐mean pattern of rainfall and surface temperature over land versus fine‐grid reference simulations in each of the climates we trained against. These machine learning models can be used successfully to enhance the accuracy of 5 year simulations in all climates. Key Points: Machine learning models trained from coarsened fine‐grid outputs correct the evolution of a coarse‐grid model in four climatesAblating upper level inputs and outputs of machine learning models robustly stabilizes multi‐year simulationsTrained models reduce rainfall and surface temperature errors over land in 5 year simulations in each climate [ABSTRACT FROM AUTHOR]

Global atmospheric "storm‐resolving" models with horizontal grid spacing of less than 5 km resolve deep cumulus convection and flow in complex terrain. They promise to be reference models that could be used to improve computationally affordable coarse‐grid global climate models across a range of climates, reducing uncertainties in regional precipitation and temperature trends. Here, machine learning of nudging tendencies as functions of column state is used to correct the physical parameterization tendencies of temperature, humidity, and optionally winds, in a real‐geography coarse‐grid model (FV3GFS with a 200 km grid) to be closer to those of a 40‐day reference simulation using X‐SHiELD, a modified version of FV3GFS with a 3 km grid. Both simulations specify the same historical sea‐surface temperature fields. This methodology builds on a prior study using a global observational analysis as the reference. The coarse‐grid model without machine learning corrections has too few clouds, causing too much daytime heating of land surfaces that creates excessive surface latent heat flux and rainfall. This bias is avoided by learning downwelling radiative flux from the fine‐grid model. The best configuration uses learned nudging tendencies for temperature and humidity but not winds. Neural nets slightly outperform random forests. Forecasts of 850 hPa temperature gain 18 hr of skill at 3–7 days leads and time‐mean precipitation patterns are improved 30% by applying the ML correction. Adding machine‐learned wind tendencies improves 500 hPa height skill for the first five days of forecasts but degrades time‐mean upper tropospheric temperature and zonal wind patterns thereafter. Plain Language Summary: Global weather and climate models can be made more realistic by using a finer computational grid, but this is too expensive for routine use. We design a machine‐learned correction to make a more economical coarse‐grid model better track a fine‐grid reference version of this model. The correction is trained using a limited, computationally affordable, period of fine‐grid model output. It is applied interactively during the coarse‐grid simulation. As desired, adding the correction substantially improves how well weather forecasts and time‐mean rainfall patterns with the coarse‐grid model match the fine‐grid reference. Key Points: With a state‐dependent machine learning correction, a coarse‐grid global atmospheric model evolves more like a fine‐grid model versionThe skill of coarse‐grid weather forecasts and time‐mean rainfall are improved compared to the fine‐grid storm‐resolving reference modelAccounting for cloud biases by correcting surface downwelling radiation improves surface turbulent fluxes and precipitation over land [ABSTRACT FROM AUTHOR]

Due to limited resolution and inaccurate physical parameterizations, weather and climate models consistently develop biases compared to the observed atmosphere. Using the FV3GFS model at coarse resolution, we propose a method of machine learning corrective tendencies from a hindcast simulation nudged toward observational analysis. We show that a random forest can predict the nudging tendencies from this hindcast simulation with moderate skill using only the model state as input. This random forest is then coupled to FV3GFS, adding corrective tendencies of temperature, specific humidity and horizontal winds at each timestep. The coupled model shows no signs of instability in year‐long simulations and has significant reductions in short‐term forecast error for 500 hPa height, surface pressure and near‐surface temperature. Furthermore, the root mean square error of the annual‐mean precipitation is reduced by about 20%. Biases of other variables remain similar or in some cases, like upper‐atmospheric temperature, increase in the year‐long simulations. Plain Language Summary: After initialization from a realistic snapshot of the atmosphere, weather and climate models inevitably develop prediction errors compared to the real world. This decreases the usefulness of forecasts. These errors arise from the coarse resolution of the numerical models and from the uncertain treatment of small‐scale processes. We propose a method to reduce these errors by training a machine learning model to correct for them as the atmospheric model proceeds. We show that a random forest can make reasonably skillful predictions of the required correction using a snapshot of the model state as input. When we make a forecast with the machine‐learning corrected model, equally skillful predictions of important midtropospheric and surface variables are possible half‐a‐day to a day further into the future. The pattern of precipitation predicted by the machine learning corrected model is also more realistic, with a decrease in excessive rainfall over high mountains. On the other hand, the corrected model develops larger errors in temperature in the high latitudes, particularly in the lower stratosphere. Key Points: Nudging an atmospheric model toward observations is a good way to estimate state‐dependent biasesMachine learning of state‐dependent biases improves hindcast skill of a coarse‐resolution general circulation model (GCM)Bias‐corrected year‐long simulations are stable and reduce time‐mean precipitation pattern errors [ABSTRACT FROM AUTHOR]

Objective: Women with pre-eclampsia have elevated circulating levels of soluble fms-like tyrosine kinase-1 (sFlt-1). Statins can reduce sFlt-1 from cultured cells and improve pregnancy outcome in animals with a pre-eclampsia-like syndrome. We investigated the effect of pravastatin on plasma sFlt-1 levels during pre-eclampsia.Design: Blinded (clinician and participant), proof of principle, placebo-controlled trial.Setting: Fifteen UK maternity units.Population: We used a minimisation algorithm to assign 62 women with early-onset pre-eclampsia (24+0 -31+6  weeks of gestation) to receive pravastatin 40 mg daily (n = 30) or matched placebo (n = 32), from randomisation to childbirth.Primary Outcome: Difference in mean plasma sFlt-1 levels over the first 3 days following randomisation.Results: The difference in the mean maternal plasma sFlt-1 levels over the first 3 days after randomisation between the pravastatin (n = 27) and placebo (n = 29) groups was 292 pg/ml (95% CI -1175 to 592; P = 0.5), and over days 1-14 was 48 pg/ml (95% CI -1009 to 913; P = 0.9). Women who received pravastatin had a similar length of pregnancy following randomisation compared with those who received placebo (hazard ratio 0.84; 95% CI 0.50-1.40; P = 0.6). The median time from randomisation to childbirth was 9 days (interquartile range [IQR] 5-14 days) for the pravastatin group and 7 days (IQR 4-11 days) for the placebo group. There were three perinatal deaths in the placebo-treated group and no deaths or serious adverse events attributable to pravastatin.Conclusions: We found no evidence that pravastatin lowered maternal plasma sFlt-1 levels once early-onset pre-eclampsia had developed. Pravastatin appears to have no adverse perinatal effects.Tweetable Abstract: Pravastatin does not improve maternal plasma sFlt-1 or placental growth factor levels following a diagnosis of early preterm pre-eclampsia #clinicaltrial finds. [ABSTRACT FROM AUTHOR]

Fragile X syndrome (FXS) is caused by silencing of the FMR1 gene and consequent absence of its protein product, fragile X mental retardation protein (FMRP). FMRP is an RNA‐binding protein that can suppress translation. The absence of FMRP leads to symptoms of FXS including intellectual disability and has been proposed to lead to abnormalities in synaptic plasticity. Synaptic plasticity, protein synthesis, and cellular growth pathways have been studied extensively in hippocampal slices from a mouse model of FXS (Fmr1 KO). Enhanced metabotropic glutamate receptor 5 (mGluR5)‐dependent long‐term depression (LTD), increased rates of protein synthesis, and effects on signaling molecules have been reported. These phenotypes were found under amino acid starvation, a condition that has widespread, powerful effects on activation and translation of proteins involved in regulating protein synthesis. We asked if this non‐physiological condition could have effects on Fmr1 KO phenotypes reported in hippocampal slices. We performed hippocampal slice experiments in the presence and absence of amino acids. We measured rates of incorporation of a radiolabeled amino acid into protein to determine protein synthesis rates. By means of western blots, we assessed relative levels of total and phosphorylated forms of proteins involved in signaling pathways regulating translation. We measured evoked field potentials in area CA1 to assess the strength of the long‐term depression response to mGluR activation. In the absence of amino acids, we replicate many of the reported findings in Fmr1 KO hippocampal slices, but in the more physiological condition of inclusion of amino acids in the medium, we did not find evidence of enhanced mGluR5‐dependent LTD. Activation of mGluR5 increased protein synthesis in both wild type and Fmr1 KO. Moreover, mGluR5 activation increased eIF2α phosphorylation and decreased phosphorylation of p70S6k in slices from Fmr1 KO. We propose that the eIF2α response is a cellular attempt to compensate for the lack of regulation of translation by FMRP. Our findings call for a re‐examination of the mGluR theory of FXS. [ABSTRACT FROM AUTHOR]

A previous proteomic study examining the plasma acute‐phase response of rainbow trout to sterile inflammation highlighted an unidentified 9.5‐kDa spot using 2D‐PAGE, which was dramatically increased. The 15 amino acid sequence obtained from this protein spot allowed rapid amplification of cDNA ends PCR to generate a 443‐bp nucleotide sequence that was 98.6% similar to type‐4 ice‐structuring protein LS‐12 from Atlantic salmon Salmo salar Linnaeus. Quantitative reverse translation PCR and an ELISA were used to measure gene expression and plasma concentrations of LS‐12 following experimental intraperitoneal injection of rainbow trout with either 106 or 108 colony‐forming units (CFU) of Flavobacterium psychrophilum. There was no significant change in the plasma concentration of LS‐12 up to 15 days post‐infection in any group. Hepatic LS‐12 gene expression was significantly reduced at 3 and 6 days (p < 0.001) post‐infection in fish injected with 108 CFU of F. psychrophilum relative to control fish, while branchial or head kidney expression was unchanged. Infected fish had significantly increased hepatic gene expression of serum amyloid A, confirming an acute‐phase response. Under the conditions used, LS‐12 is not a positive acute‐phase protein in rainbow trout. [ABSTRACT FROM AUTHOR]

The atrophic and edentulous jaw can pose a number of challenges for the implant clinician. In simple terms, the amount of bone that remains is insufficient for the conventional placement of a dental implant. A variety of treatment strategies can be employed to enable implants to be placed despite the paucity of bone stock in either the mandible or the maxilla. Conceptually these strategies follow one of two pathways: either augmentation of the bone, or the novel utilization of the remaining bone. This article will discuss patient assessment, treatment planning, and the range of contemporary options available to enable fixed implant based rehabilitation of each jaw. "The edentulous patient is an amputee, an oral invalid, to whom we should pay total respect and rehabilitation ambitions" (P-I Branemark, September 2005). [ABSTRACT FROM AUTHOR]

We surveyed the treatment of taxonomic information in 567 papers published in nine entomological journals in 2016., The proportion of papers that provide taxonomic data in sufficient detail to permit precise validation of taxonomic identifications is vanishingly small: most did not cite identification methods, most did not state whether identified material had been vouchered, and taxon concepts were almost universally absent in non-taxonomic papers. Overall, the combination of all three factors was provided less than 2% of the time and almost two-thirds of all papers provided none of the three., We suggest that journals should modify the templates used by editors and reviewers by overtly including the following questions: Are Order and Family named in the title, abstract or keywords?, Are the methods used for identification of all studied taxa stated clearly?, Is it clear who did the identifications, are they named and is their contact information and/or institutional affiliation provided?, Is the literature whereupon these identifications are based cited appropriately? This would include some reference to as thorough a revisional taxon concept statement as possible, preferably from recent revisions if available., Are exemplars of all focal species (or all sampled individuals) vouchered in a named repository (ideally with contact person name and accession numbers or other means of ready detection)?, , Accurate and replicable taxonomic identification is the cornerstone of biology, without which entomological research risks becoming irreproducible and thus not scientific. [ABSTRACT FROM AUTHOR]

Exposure to stress during early development can permanently influence an individual's physiology and behaviour, and affect its subsequent health. The extent to which elevated glucocorticoids cause such long-term 'programming' remains largely untested. In the present study, using the Japanese quail as our study species, we independently manipulated exposure to corticosterone during pre- and/or post-natal development and investigated the subsequent effects on global gene expression profiles within the hippocampus and hypothalamus upon achieving adulthood. Our results showed that the changes in transcriptome profiles in response to corticosterone exposure clearly differed between the hippocampus and the hypothalamus. We also showed that these effects depended on the developmental timing of exposure and identified brain-region specific gene expression patterns that were either: (i) similarly altered by corticosterone regardless of the developmental stage in which hormonal exposure occurred or (ii) specifically and uniquely altered by either pre-natal or post-natal exposure to corticosterone. Corticosterone-treated birds showed alterations in networks of genes that included known markers of the programming actions of early-life adversity (e.g. brain-derived neurotrophic factor and mineralocorticoid receptor within the hippocampus; corticotrophin-releasing hormone and serotonin receptors in the hypothalamus). Altogether, for the first time, these findings provide experimental support for the hypothesis that exposure to elevated glucocorticoids during development may be a key hormonal signalling pathway through which the long-term phenotypic effects associated with early-life adversity emerge and potentially persist throughout the lifespan. These data also highlight that stressors might have different long-lasting impacts on the brain transcriptome depending on the developmental stage in which they are experienced; more work is now required to relate these mechanisms to organismal phenotypic differences. [ABSTRACT FROM AUTHOR]

An abstract of the article "Low 1st trimester PAPP-A and 2nd trimester uterine artery Dopplers in antenatal detection of SGA babies <10th centile" by O.M. Thompson and others is presented.

Objective To examine whether differences exist in routine first trimester maternal serum screening analyte measurements between normal pregnancies, placenta praevia and abnormally invasive placentation ( AIP). Design Multidisciplinary audit. Setting Associated university teaching hospital with 9000 annual deliveries. Population Five hundred and sixteen pregnancies in total, including 344 normal controls, 17 with AIP and 155 placenta praevia cases. Methods Comparison of maternal serum free β hCG and PAPP-A MoMs distribution in pregnancies with abnormally invasive placentation, placenta praevia and normal controls, after correcting for known confounding factors between October 2005 and September 2013. Data from a previously published first trimester AIP and biochemistry study were combined with our study data and compared in the above way to complete the analysis. Main outcome measures Differences in first trimester maternal serum PAPP-A and free β hCG in AIP, placenta praevia, and normal pregnancies. Results Median free β hCG MoM in the control group was 1.04, and 1.08 ( P = 0.859) in the placenta praevia group compared with 0.81 in the AIP group ( P = 0.06). Median PAPP-A MoM was 1.01 in the control group and 1.05 ( P = 0.83) in praevia, compared with 1.22 in AIP cases (0.16). The combined AIP dataset gave an overall PAPP-A median MoM of 1.40, and free β hCG of 0.85. Both markers showed a significantly different distribution from controls ( PAPP-A P = 0.002 and free β hCG P = 0.031). Conclusions There may be differences between first trimester maternal serum biochemical markers between normal pregnancies and those complicated by abnormally invasive placentation. If upheld, this may provide useful information for the early identification of abnormally invasive placentation. More studies are required. [ABSTRACT FROM AUTHOR]

Stress exposure during early-life development can programme individual brain and physiology. The hypothalamic-pituitary-adrenal (HPA) axis is one of the primary targets of this programming, which is generally associated with a hyperactive HPA axis, indicative of a reduced negative-feedback. This reduced feedback efficiency usually results from a reduced level of the glucocorticoid receptor (GR) and/or the mineralocorticoid receptor (MR) within the HPA axis. However, a few studies have shown that early-life stress exposure results in an attenuated physiological stress response, suggesting an enhance feedback efficiency. In the present study, we aimed to determine whether early-life stress had long-term consequences on GR and MR levels in quail and whether the effects on the physiological response to acute stress observed in prenatally stressed individuals were underpinned by changes in GR and/or MR levels in one or more HPA axis components. We determined GR and MR mRNA expression in the hippocampus, hypothalamus and pituitary gland in quail exposed to elevated corticosterone during prenatal development, postnatal development, or both, and in control individuals exposed to none of the stressors. We showed that prenatal stress increased the GR:MR ratio in the hippocampus, GR and MR expression in the hypothalamus and GR expression in the pituitary gland. Postnatal stress resulted in a reduced MR expression in the hippocampus. Both early-life treatments permanently affected the expression of both receptor types in HPA axis regions. The effects of prenatal stress are in accordance with a more efficient negative-feedback within the HPA axis and thus can explain the attenuated stress response observed in these birds. Therefore, these changes in receptor density or number as a consequence of early-life stress exposure might be the mechanism that allows an adaptive response to later-life stressful conditions. [ABSTRACT FROM AUTHOR]

Anionic polyacrylamide (PAM) products are commonly used to remove suspended materials from turbid waters and to help mitigate soil erosion. In the present study, juvenile rainbow trout ( Oncorhynchus mykiss) were exposed to 3 mg/L to 300 mg/L of 10 commercially available PAM products (Clearflow Water Lynx Polymer Log and Clearflow Soil Lynx Granular Polymer; Clearflow Enviro Systems Group), and gill histological parameters were measured following either 7 d or 30 d of polymer exposure. A cationic polymer product (≤0.38 mg/L MagnaFloc 368; Ciba Specialty Chemical) was also tested for comparison. Mild gill lesions were observed in fish exposed to polymer products. Lamellar fusion, interlamellar hyperplasia, epithelial lifting, mucous cell metaplasia, and cell counts of epithelial swelling and necrosis/apoptosis were minimal in fish exposed to environmentally relevant concentrations of anionic polymer (≤30 mg/L). Gill morphology was largely unaffected by exposure to concentrations up to 300 mg/L of many PAM products. Several anionic polymer products noticeably affected gill tissue by increasing epithelial hypertrophy, interlamellar hyperplasia, mucous cell metaplasia, and the frequency of necrotic cells. The severity of the lesions lessened with time, suggesting that fish may have experienced a short-term irritant effect. Similar levels of gill pathology were frequently observed in fish exposed to cationic polymer MagnaFloc 368 despite the concentration being 1000-fold lower than that of the PAM products. These observations highlight the increased toxicity of cationic polymers to aquatic life compared with anionic PAMs. Environ Toxicol Chem 2014;33:1552-1562. © 2014 SETAC [ABSTRACT FROM AUTHOR]

ABSTRACT Objectives To investigate the levels of tumour markers CA 19-9 and CA 15-3 in the first trimester maternal serum of euploid control and trisomy 21 pregnancies. Methods Maternal serum marker levels of 69 trisomy 21 and 388 euploid controls were quantified by the Kryptor analyser, and levels were compared between the two groups after analysis for confounding factors. Monte Carlo simulation was carried out to determine the effect of adding potential markers to the combined test. Results Neither marker was affected by gestational age; however, CA 19-9 required correction for maternal weight. CA 19-9 was significantly increased in trisomy 21 pregnancies (0.98 MoM in euploid, 1.16 MoM in trisomy 21, p = 0.024). Levels of CA 15-3 were not found to differ significantly (1.03 MoM in euploid, 1.09 in trisomy 21, p = 0.130). Detection rates were unaffected by addition of CA 19-9 to the combined test. Conclusion Although a small significant increase in CA 19-9 levels was found in trisomy 21 group, it is unlikely to be of any use as part of a trisomy 21 screening tool. © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

ABSTRACT Objective To investigate the levels of placental growth factor (PlGF) in second trimester maternal serum in trisomy 21 cases and euploid controls - an unclear subject in the published literature. Methods Second trimester maternal serum samples from 17 recent (since 2005) and 74 older trisomy 21 cases and 542 euploid controls were extracted from frozen storage and retrospectively analysed for PlGF using a DELFIA Xpress immunoassay platform. Results were converted to multiples of median (MoM) for comparison. Results The control group had a PlGF MoM of 0.99, the recent trisomy 21 cases had a PlGF MoM of 1.13 and the older cases a PlGF MoM of 1.11; however, the differences between trisomy 21 cases and controls were not significant. Conclusion Although we have found no significant change in the second trimester levels of PlGF in trisomy 21 pregnancies, there remains wide disagreement within the literature on the behaviour of this marker during pregnancies of this syndrome. © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Background Placental growth factor (PlGF) is a proposed first-trimester screening marker for pre-eclampsia. This study investigates the stability of PlGF in serum and whole blood at typical routine storage temperatures. Methods Serum pools were stored at refrigerator temperature, room temperature or 30 °C for up to 30 days, or exposed to up to six freeze-thaw cycles. Whole blood was stored at room temperature or 30 °C for up to 6 days. PlGF was quantified using a DELFIA Xpress analyser. Results Placental growth factor levels increased over time, seemingly because of the dissociation of PlGF bound to a soluble binding protein, sFlt-1. Increase was slow in serum at refrigerator temperature, remaining stable (less than 10% change from start point) for at least 30 days. At room temperature PlGF was stable for 3.3 days and at 30 °C for 1 day. Serum PlGF remained stable for at least six freeze-thaw cycles. In whole blood, instability was worse, being stable for only 19.4 h at room temperature and just 3.3 h at 30 °C. Conclusion Routine screening of sample handling requires careful monitoring. However, no extra precautions need to be taken when PlGF is used for pre-eclampsia screening run alongside existing first trimester aneuploidy screening programs that include hCGβ. Copyright © 2011 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Objectives To measure maternal serum placental growth factor (PlGF) levels in trisomy 21 cases and controls in samples drawn before 11 weeks' gestation. Methods Early first-trimester maternal serum samples, drawn between 8 + 0 and 10 + 6 weeks' gestation, for 37 trisomy 21 cases and 244 unaffected controls were retrieved from frozen storage, and PlGF was retrospectively measured using a DELFIA Xpress immunoassay platform. PlGF levels were converted to multiples of the median (MoM), and trisomy 21 and unaffected groups were compared. Results Raw PlGF and MoM levels were significantly higher in the maternal serum of trisomy 21 cases than in controls over the 3-week gestational window (unaffected 1.0MoM compared with trisomy 21 1.3MoM (P < 0.0001)). However at 8 completedweeks of gestation the increase was most significant and at 10 completed weeks there was no significant difference between trisomy 21 and unaffected PlGF levels. Conclusions Early PlGF levels in maternal serum in trisomy 21 cases may be increased relative to unaffected controls, however, the relationship between PlGF levels and gestational age in trisomy 21 and unaffected pregnancies in the first two trimesters of pregnancy appears to be complex and requires further study. [ABSTRACT FROM AUTHOR]

OBJECTIVES: To determine the stability of first trimester free-[beta] human chorionic gonadotrophin (free-[beta] hCG) and pregnancy-associated plasma protein-A (PAPP-A) in dried blood spots (DBSs) under typical storage conditions. METHODS: First trimester maternal blood was spotted onto filter paper and left to dry. DBSs were analysed for PAPP-A and free-[beta] hCG using an AutoDELFIA dual assay at t = 0. Cards were stored at one of - 20 °C, refrigerator temperature, room temperature or 30 °C and reanalysed at set future time points. RESULTS: Free-[beta] hCG was stable (<10% change in concentration) under all temperatures tested for at least 35 days. PAPP-A was stable at - 20 °C and refrigerator temperature for at least 35 days. However, PAPP-A levels decreased by 10% at 4.1 days at room temperature and at 3.9 days at 30 °C. Longer-term storage at - 20 °C and refrigerator temperature showed that both PAPP-A and free-[beta] hCG levels were significantly decreased by 107 and 244 days. CONCLUSIONS: Free-[beta] hCG stability is greatly improved in DBS compared to serum storage; however PAPP-A stability is decreased in the DBS medium. Despite this DBS, screening may not necessitate such strict storage and transportation rules compared to serum screening programmes. Copyright © 2011 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Objective To estimate the difference between levels of the two biochemical markers pregnancy-associated plasma protein-A (PAPP-A) and maternal serum free β-human chorionic gonadotropin (free β-hCG) in twin pregnancies relative to singleton pregnancies and establish an improved screening procedure for chromosomal abnormalities such as trisomy 21 in twin pregnancies. Methods 4843 unaffected and 47 trisomy 21-affected twin pregnancies were included in the study. Chorionicity-specific medians were generated for PAPP-A and free β-hCG from gestational ages 8 to 14 weeks. Multiple of the median values for each of the biochemical markers were calculated. Detection rates and false-positive rates were estimated for screening tests incorporating nuchal translucency and maternal age, with and without biochemistry. Results Medians for the two biochemical markers for monochorionic and dichorionic twins in unaffected pregnancies show a gestational age-specific increase relative to singleton medians. Allowing for gestation and chorionicity, twin pregnancies affected with trisomy 21 had higher levels of free β-hCG and lower levels of PAPP-A. Adding biochemistry into the risk assessment using a fixed risk cut-off of 1 in 100 increased the detection rate for fetal trisomy 21 in dizygotic twin pregnancies from 78 to 90%, and decreased the false-positive rate from 8.0 to 5.9%. Conclusion Generation of chorionicity-specific medians for the biochemical markers and their use in risk assessment can improve the performance of first-trimester screening for chromosomal abnormalities in twins to a level comparable with that in singleton pregnancies. Copyright © 2010 ISUOG. Published by John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The article presents a research study which aims to lay down an algorithm for first-trimester combined screening for trisomy 21 at 7-14 weeks gestation. It states that inorder to produce log multiples of the median (MoM) values for pregnancy-associated plasma protein-A (PAPP-A) and free B-human chronic gonadotrophin (B-hCG), the study used a multiple regression modeling of log-transformed marker values. It concludes that the performance of biochemical screening is best at 9-10 weeks.

Objective To evaluate the influence of maternal insulin-dependent diabetes mellitus (IDDM) on maternal serum free ß-hCG, pregnancy-associated plasma protein-A (PAPP-A) and fetal nuchal translucency (NT) thickness from 11 weeks to 13 weeks 6 days of gestation in a large cohort of women screened prospectively for chromosomal anomalies. Methods Information on maternal IDDM status, maternal serum biochemical marker levels and fetal NT were collected from the prenatal screening computer records. On total, the control group included 83 972 and the IDDM group included 489 pregnancies. The median-corrected free β-hCG and PAPP-A, expressed as MoM, and fetal NT, expressed as delta values, in the IDDM and non-IDDM groups were compared. Results There were no significant differences between the IDDM and non-IDDM groups in median-corrected free β-hCG (IDDM 1.01 MoM, non-IDDM 1.01 MoM; p = 0.970), or mean delta NT (IDDM 0.00 mm, non-IDDM 0.02 mm; p = 0.412). However, the median-corrected PAPP-A was significantly lower (IDDM 0.88 MoM, non-IDDM 1.03 MoM; p < 0.0001). Conclusions In pregnancies with maternal IDDM, first-trimester screening for chromosomal defects does not require adjustments for the measured fetal NT and maternal serum free ß-hCG. However, for PAPP-A the 15% reduction is large enough to require correction in the calculation of risks for chromosomal defects. Copyright © 2010 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Background Maternal serum A Disintergrin And Metaloprotease-12 (ADAM-12) has been proposed as a marker for prenatal screening of chromosomal abnormalities, pre-eclampsia and other adverse pregnancy outcomes. In this study, we examine the stability of ADAM-12 with time and at different temperatures. Methods Maternal serum and whole blood pools were stored at 30 °C, room temperature and refrigerator temperature or subjected to repeated freeze-thaw cycles. ADAM-12 was measured at set time points using an automated DELFIA® research assay. Results Using a 10% change in concentration as a limit of stability, ADAM-12 is stable in serum for less than 15 h at 30 °C, less than 20 h at room temperature and for 51 h at refrigerator temperature. ADAM-12 levels are not altered following three − 20 °C to room temperature freeze-thaw cycles. The stability of ADAM-12 in whole blood appears similar to that in serum. Conclusions The findings of this study suggest that ADAM-12 may be unstable under many routine laboratory conditions, and the marker's instability may also be partly responsible for the discrepancies in the literature. Copyright © 2010 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Objective To assess the impact of early vaginal bleeding on the levels of markers used in first trimester screening for aneuploidy. Methods A retrospective analysis was carried out on the free beta human chorionic gonadotrophin (β-hCG) and pregnancy associated plasma protein-A (PAPP-A) levels and nuchal translucency thickness in 49 653 women with a normal singleton fetus who had first trimester combined screening for Down Syndrome in three centres. Median MoMs and the distribution of log MoMs of the two markers were compared in two groups-7470 women who self-reported vaginal bleeding and 42 183 women who reported no vaginal bleeding at any stage prior to the screening test. Results The overall median MoM free β-hCG and that in the bleeding and non-bleeding group were 0.9854, 1.0012 and 0.9832, and for PAPP-A were 1.0407, 1.0413 and 1.037. There was no significant difference between the bleeding and non-bleeding group by median test ( p = 0.080) or by t-test comparing log MoMs ( p = 0.1305) for free β-hCG and for PAPP-A with median test ( p = 0.5071) or by t-test comparing log MoMs ( p = 0.1740). For delta nuchal translucency (NT) there was also no significant difference between the bleeding and non-bleeding group ( p = 0.055). Conclusion Vaginal bleeding has little or no impact on first trimester marker levels and no correction is necessary. Copyright © 2010 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Objective The objective of this study was to examine first-trimester maternal serum placental growth factor (PlGF) levels in pregnancies which later develop hypertensive and growth complications. Methods In this case-control study, PlGF levels were measured by AutoDELFIA immunoassay platform. There were 47 cases of at least one of the following adverse outcomes: pre-eclampsia (PE), small for gestational age (SGA), haemolysis elevated liver enzymes and low platelets (HELLP) and gestational hypertension (GH) and 452 matched controls. Results PlGF levels were significantly lower in cases of all PE, early PE, HELLP, all SGA, early SGA and SGA without PE, but not in GH, late PE, late SGA, PE with SGA or PE without SGA or HELLP. Conclusion Low levels of first-trimester PlGF provide a good indicator of SGA complications and some hypertensive disorders, in particular severe cases of PE such as early onset and HELLP syndrome. Copyright © 2010 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Background Maternal serum placental protein 13 (PP13) has been proposed as a marker for prenatal screening of pre-eclampsia (PE) and other adverse pregnancy outcomes. This study aims to examine the stability of PP13 at different routine temperatures. Methods Maternal serum pools and whole blood samples were stored at '30 °C', room temperature or refrigerator temperature. Further, serum pools were also subjected to repeated freeze-thaw cycles. PP13 was measured at set time points using an AutoDELFIA® research assay. Results Levels of PP13 are stable, defined as less than 10% change in concentration, in serum for 17.4 h at '30 °C', 3.4 days at room temperature and for at least 34 days at refrigerator temperature. PP13 concentration is not altered following three −20 °C to room temperature freeze-thaw cycles. PP13 is stable in whole blood for at least 3 days at all three temperatures studied. Conclusions PP13 is a suitably stable molecule and can be treated under routine laboratory and normal transport temperatures. Copyright © 2010 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The article presents a study on pre-eclampsia markers in the first-trimester pregnancy. It reports that pre-eclampsia is a disorder that develops in early pregnancy, linked with failure of trophoblast of the maternal spiral arteries. It also presents charts on baseline values, clinical and delivery data of the pregnant women with pre-eclampsia.

Objective To examine placental growth factor (PlGF) levels in first trimester maternal serum in trisomy 21 pregnancies and to investigate the potential value of PlGF in a first trimester screening test. Methods First trimester maternal serum from 70 trisomy 21 cases and 375 euploid controls were retrospectively analyzed for PlGF using a DELFIA® Xpress immunoassay platform. Results were expressed as multiples of medians (MoM) for comparison. Results PlGF levels were significantly decreased in pregnancies with trisomy 21, 0.76 MoM versus 0.98 MoM in controls. Inclusion of PlGF into the first trimester combined test [maternal age, pregnancy associated plasma protein-A (PAPP-A), free-β human chorionic gonadotrophin (β-hCG) and nuchal translucency] would increase the detection rate by 0.5% at a 5% false positive rate. Conclusion PlGF at 11 weeks to 13 weeks 6 days has the potential to be included as a marker for the detection of pregnancies with trisomy 21. Copyright © 2010 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Objective To assess the impact of a vanishing twin on the levels of the biochemical markers used in the first trimester aneuploidy screening. Methods A retrospective analysis of free β-hCG and PAPP-A levels in 270 women with a normal singleton fetus with ultrasound evidence of a vanishing twin pregnancy. Marker levels (as MoM) were compared in three groups-76 women with a second empty gestational sac, 194 women with a second gestational sac containing a dead fetus with a measurable crown rump length (CRL), and 1360 matched singleton pregnancies. Results In women with a second empty gestational sac, the median free β-hCG and PAPP-A MoMs (0.968 and 1.040, respectively) were not significantly different from the 1.0 MoM in singleton pregnancies. In the group with a vanished twin with a measurable-CRL-there was a significantly increased median PAPP-A MoM (1.317) but the median free β-hCG MoM was not changed (1.024). Modelling this bias in PAPP-A MoM the detection rate for trisomy 21 would fall from 85 to 75%. Conclusion First trimester screening in the presence of a vanishing twin may lead to errors in risk estimation. In such circumstances it may be advisable to restrict screening to the use of nuchal translucency (NT) alone. Copyright © 2009 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

BACKGROUND: In this study we aim to investigate the stability of free-beta-hCG and PAPP-A over time in serum and whole blood in typical routine temperatures. METHODS: Serum pools were stored under the following temperatures: 30 degrees C, room temperature, refrigerator temperature and -20 degrees C, for up to 240 days. Stability of the markers in whole blood was examined in a shorter study and compared to serum. Samples were analysed using the AutoDELFIA and DELFIA Xpress analysers. RESULTS: On the AutoDELFIA, considering a 10% change acceptable, PAPP-A levels are stable in serum for 142 days at refrigerator temperature, 37 days at room temperature and 20 days at 30 degrees C. Free-beta hCG is stable in serum for 94 days at refrigerator temperature, 3 days at room temperature and 12 h at 30 degrees C. There was no significant change with either analyte after -20 degrees C storage for up to 240 days or after six repeated freeze-thaw cycles. In whole blood, free-beta hCG levels increased more rapidly compared to serum, especially at 30 degrees C. CONCLUSION: Normal handling of samples is only likely to minimally effect the risk assessment of chromosomal anomalies. However, careful attention should be paid to minimise the increase of free-beta hCG levels in samples shipped as whole blood. [ABSTRACT FROM AUTHOR]

OBJECTIVE: To asses whether supra elevated levels of maternal serum free beta hCG in the first trimester are associated with impaired renal function. METHOD: A cohort of 553 women with maternal serum free beta-hCG greater than 5 multiple of median (MoM) with a single euploid fetus was matched with a control of the same maternal age (+/-1 year), ethnic origin and with a free beta-hCG within the range 0.50-1.50 MoM. Screening samples were analysed for serum creatinine and estimated glomerular filtration rate was calculated. Renal function in the two groups was compared. The database was examined to find outcomes registered as known renal disease amongst the high free beta-hCG group. RESULTS: In the group with a supra elevated free beta-hCG MoM there was a significant reduction in estimated glomerular filtration rate (eGFR) (122.85, 95% CI 120.43-124.74 vs 118.80, 95% CI 114.90-121.59; p = 0.009) suggesting a small but increased risk of renal disease. CONCLUSIONS: The presence of renal disease should also be considered as one potential cause of supra elevated levels of free beta-hCG in addition to the possibility of paternally derived triploidy and trisomy 21. [ABSTRACT FROM AUTHOR]

Objective: To investigate if fetal sex has an impact on 1st trimester combined screening for aenuploidy. Methods: We studied the first trimester PAPP-A, free beta-human chorionic gonadatropin (beta-hCG) and nuchal translucency levels in 56,024 normal, singleton pregnancies with known fetal sex at birth. We also examined the distributions in 722 pregnancies with trisomy 21 of known fetal sex. Results: We have found a 14.74% increase in first trimester maternal serum (MS) median free beta-hCG MoM, 6.25% increase of PAPP-A and a 9.41% decrease in delta NT, when the fetus was female. Analysis of data has shown that women carrying a female fetus were 1.084 times more likely to be in the 'at risk' group than those carrying a male fetus. In examining data from 722 pregnancies in which the fetus was affected by trisomy 21, we observed a similar 20.8% increase in free beta-hCG MoM, 5.7% increase in PAPP-A and a 12% decrease in delta NT when the fetus was female. Amongst the trisomy 21 cases, 88.8% of male trisomy 21 cases were detected compared with 91.2% in female cases, this difference was not statistically significant. Correcting for fetal sex redressed the balance in screen-positive rate between the sexes and had a minimal impact on detection rate. Conclusion: Correcting for fetal sex may be a worthwhile consideration. A cost-benefit analysis would be required to determine if it is feasible to introduce fetal gender assignment into the routine first trimester scan for the purpose of marker correction and whether this would have any significant impact. [ABSTRACT FROM AUTHOR]

The article discusses a study which evaluates the use of maternal serum in predicting if pregnant women may develop pre-eclampsia. Samples of serum inhibin-An and activin-A were collected for the first-trimester prenatal chromosomal anomaly screening program. Patients underwent uterine artery Doppler flow velocimetry to determine pulsatility index during pregnancy. Researchers found that increased level of maternal serum is seen to be too low to contribute to screening for pre-eclampsia.

The article investigates the contribution of maternal variables that influence the measured concentration of free beta-human chorionic gonadotropin (hCG) and pregnancy-associated plasma protein-A (PAPP-A), and the interaction between these covariates, in initial-trimester biochemical screening for trisomy 21. Results suggest it is important to adjust the measured values of free beta-hCG and PAPA-A for maternal and pregnancy characteristics in first-trimester biochemical screening for trisomy 21.

The article investigates the clinical utility of the first-trimester markers of aneuploidy in their ability to predict preterm delivery. Results reveal that low levels of maternal serum pregnancy-associated plasma protein-A (PAPP-A) are related to an increased risk of preterm or early preterm delivery. The likelihood ratios (LR) profiles offered at various levels of PAPP-A may be helpful in counseling women with such results.

The article presents a study that examines the clinical utility of the first-trimester biochemical markers of aneuploidy in their ability to predict subsequent delivery of a small-for-gestational age infant. The methods and results of the study are discussed. In conclusion, low levels of maternal serum PAPP-A are associated with increased risk for subsequent delivery of an small-for-gestational infant.

Objective To examine whether in smokers there is a significant dose dependency between the number of cigarettes per day and levels of free ß-hCG and pregnancy-associated plasma protein A (PAPP-A) at 11-13+6 weeks of gestation. Methods This was a retrospective analysis of the maternal serum free ß-hCG and PAPP-A levels in relation to the maternal smoking status in 109 263 chromosomally normal singleton pregnancies that had undergone first-trimester screening for Down syndrome by a combination of fetal nuchal translucency thickness and maternal serum biochemistry. Results There were 95 287 nonsmokers and 13 976 cigarette smokers. The overall median PAPP-A MoM among cigarette smokers was 0.827, which was 19.6% lower than the value of 1.029 in nonsmokers ( p < 0.0001 for log10 MoM). The respective values for β-hCG MoM were 1.003 for smokers and 1.035 for nonsmokers ( p < 0.0001 for log10 MoM) which corresponds to a reduction of 3.1%. There was a significant inverse relationship between the number of cigarettes per day and the level of PAPP-A MoM ( r = 0.989, p < 0.0001) but not the level of free β-hCG MoM ( r = 0.733; p = 0.098). Using a statistical modeling approach we found that the screen-positive rate when correcting the PAPP-A MoM by an all or nil smoking factor was reduced by only 0.1% (3.75 vs 3.85%) when compared to correcting with a factor related to the smoking dose per day. Conclusion In first-trimester screening for Down syndrome by maternal serum PAPP-A and free β-hCG the impact of correcting for the dose dependant rather than the all or nil effect of smoking is marginal. However, a dose dependent correction improves the accuracy of the individual patient-specific risk. Copyright © 2007 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]