Skin Electroporation: Effects on Transgene Expression,DNA Persistence and Local Tissue Environment

Background

Electrical pulses have been used to enhance uptake of molecules into living cells for decades. This technique, often referred to as electroporation, has become an increasingly popular method to enhance in vivo DNA delivery for both gene therapy applications as well as for delivery of vaccines against both infectious diseases and cancer. In vivo electrovaccination (gene delivery followed by electroporation) is currently being investigated in several clinical trials, including DNA delivery to healthy volunteers. However, the mode of action at molecular level is not yet fully understood.

Methodology/Principal Findings

This study investigates intradermal DNA electrovaccination in detail and describes the effects on expression of the vaccine antigen, plasmid persistence and the local tissue environment. Gene profiling of the vaccination site showed that the combination of DNA and electroporation induced a significant up-regulation of pro-inflammatory genes. In vivo imaging of luciferase activity after electrovaccination demonstrated a rapid onset (minutes) and a long duration (months) of transgene expression. However, when the more immunogenic prostate specific antigen (PSA) was co-administered, PSA-specific T cells were induced and concurrently the luciferase expression became undetectable. Electroporation did not affect the long-term persistence of the PSA-expressing plasmid.

Conclusions/Significance

This study provides important insights to how DNA delivery by intradermal electrovaccination affects the local immunological responses of the skin, transgene expression and clearance of the plasmid. As the described vaccination approach is currently being evaluated in clinical trials, the data provided will be of high significance.     

Hamiltonian Monte Carlo methods for efficient parameter estimation in steady state dynamical systems

Background

Parameter estimation for differential equation models of intracellular processes is a highly relevant bu challenging task. The available experimental data do not usually contain enough information to identify all parameters uniquely, resulting in ill-posed estimation problems with often highly correlated parameters. Sampling-based Bayesian statistical approaches are appropriate for tackling this problem. The samples are typically generated via Markov chain Monte Carlo, however such methods are computationally expensive and their convergence may be slow, especially if there are strong correlations between parameters. Monte Carlo methods based on Euclidean or Riemannian Hamiltonian dynamics have been shown to outperform other samplers by making proposal moves that take the local sensitivities of the system’s states into account and accepting these moves with high probability. However, the high computational cost involved with calculating the Hamiltonian trajectories prevents their widespread use for all but the smallest differential equation models. The further development of efficient sampling algorithms is therefore an important step towards improving the statistical analysis of predictive models of intracellular processes.

Results

We show how state of the art Hamiltonian Monte Carlo methods may be significantly improved for steady state dynamical models. We present a novel approach for efficiently calculating the required geometric quantities by tracking steady states across the Hamiltonian trajectories using a Newton-Raphson method and employing local sensitivity information. Using our approach, we compare both Euclidean and Riemannian versions of Hamiltonian Monte Carlo on three models for intracellular processes with real data and demonstrate at least an order of magnitude improvement in the effective sampling speed. We further demonstrate the wider applicability of our approach to other gradient based MCMC methods, such as those based on Langevin diffusions.

Conclusion

Our approach is strictly benefitial in all test cases. The Matlab sources implementing our MCMC methodology is available from https://github.com/a-kramer/ode_rmhmc.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-253) contains supplementary material, which is available to authorized users.     

Parallel Markov chain Monte Carlo - bridging the gap to high-performance Bayesian computation in animal breeding and genetics

Background

Most Bayesian models for the analysis of complex traits are not analytically tractable and inferences are based on computationally intensive techniques. This is true of Bayesian models for genome-enabled selection, which uses whole-genome molecular data to predict the genetic merit of candidate animals for breeding purposes. In this regard, parallel computing can overcome the bottlenecks that can arise from series computing. Hence, a major goal of the present study is to bridge the gap to high-performance Bayesian computation in the context of animal breeding and genetics.

Results

Parallel Monte Carlo Markov chain algorithms and strategies are described in the context of animal breeding and genetics. Parallel Monte Carlo algorithms are introduced as a starting point including their applications to computing single-parameter and certain multiple-parameter models. Then, two basic approaches for parallel Markov chain Monte Carlo are described: one aims at parallelization within a single chain; the other is based on running multiple chains, yet some variants are discussed as well. Features and strategies of the parallel Markov chain Monte Carlo are illustrated using real data, including a large beef cattle dataset with 50K SNP genotypes.

Conclusions

Parallel Markov chain Monte Carlo algorithms are useful for computing complex Bayesian models, which does not only lead to a dramatic speedup in computing but can also be used to optimize model parameters in complex Bayesian models. Hence, we anticipate that use of parallel Markov chain Monte Carlo will have a profound impact on revolutionizing the computational tools for genomic selection programs.     

Air kerma strength characterization of a GZP6 Cobalt-60 brachytherapy source

Background

Task group number 40 (TG-40) of the American Association of Physicists in Medicine (AAPM) has recommended calibration of any brachytherapy source before its clinical use. GZP6 afterloading brachytherapy unit is a ⁶⁰Co high dose rate (HDR) system recently being used in some of the Iranian radiotherapy centers.

Aim

In this study air kerma strength (AKS) of ⁶⁰Co source number three of this unit was estimated by Monte Carlo simulation and in air measurements.

Materials and methods

Simulation was performed by employing the MCNP-4C Monte Carlo code. Self-absorption of the source core and its capsule were taken into account when calculating air kerma strength. In-air measurements were performed according to the multiple distance method; where a specially designed jig and a 0.6 cm³ Farmer type ionization chamber were used for the measurements. Monte Carlo simulation, in air measurement and GZP6 treatment planning results were compared for primary air kerma strength (as for November 8th 2005).

Results

Monte Carlo calculated and in air measured air kerma strength were respectively equal to 17240.01 μGym² h⁻¹ and 16991.83 μGym² h⁻¹. The value provided by the GZP6 treatment planning system (TPS) was “15355 μGym² h⁻¹”.

Conclusion

The calculated and measured AKS values are in good agreement. Calculated-TPS and measured-TPS AKS values are also in agreement within the uncertainties related to our calculation, measurements and those certified by the GZP6 manufacturer. Considering the uncertainties, the TPS value for AKS is validated by our calculations and measurements, however, it is incorporated with a large uncertainty.     

Investigation of DNA integration into reproductive organs following intramuscular injection of DNA in mice

Background:

DNA immunization with plasmid DNA encoding bacterial, viral, parasitic, and tumor antigens has been reported to trigger protective immunity. The use of plasmid DNA vaccinations against many diseases has produced promising results in animal and human clinical trials; however, safety concerns about the use of DNA vaccines exist, such as the possibility of integration into the host genome, and elicitation of adverse immune responses.

Methods:

In this study, we examined the potential integration and bio-distribution of pcDNA3.1+PA, a new vaccine candidate with GenBank accession # {"type":"entrez-nucleotide","attrs":{"text":"EF550208","term_id":"147743335","term_text":"EF550208"}}EF550208, encoding the PA63 gene, in reproductive organs of mice; ovaries and uterus in female, and testis in male. Animals of both sexes were injected intramuscularly with pcDNA3.1+PA. Host genome integration and tissue distribution were examined using PCR and RT-PCR two times monthly for six months.

Results:

RT-PCR confirmed that pcDNA3.1+PA was not integrated into the host genome and did not enter reproductive organs.

Conclusions:

This finding has important implications for the use of pcDNA3.1+PA plasmid as a vaccine and opens new perspectives in the DNA vaccine area.Key Words: DNA, Intramuscular injection, Integration, Mice, Reproductive organs     

An updated algorithm for the generation of neutral landscapes by spectral synthesis

Background

Patterns that arise from an ecological process can be driven as much from the landscape over which the process is run as it is by some intrinsic properties of the process itself. The disentanglement of these effects is aided if it possible to run models of the process over artificial landscapes with controllable spatial properties. A number of different methods for the generation of so-called ‘neutral landscapes’ have been developed to provide just such a tool. Of these methods, a particular class that simulate fractional Brownian motion have shown particular promise. The existing methods of simulating fractional Brownian motion suffer from a number of problems however: they are often not easily generalisable to an arbitrary number of dimensions and produce outputs that can exhibit some undesirable artefacts.

Methodology

We describe here an updated algorithm for the generation of neutral landscapes by fractional Brownian motion that do not display such undesirable properties. Using Monte Carlo simulation we assess the anisotropic properties of landscapes generated using the new algorithm described in this paper and compare it against a popular benchmark algorithm.

Conclusion/Significance

The results show that the existing algorithm creates landscapes with values strongly correlated in the diagonal direction and that the new algorithm presented here corrects this artefact. A number of extensions of the algorithm described here are also highlighted: we describe how the algorithm can be employed to generate landscapes that display different properties in different dimensions and how they can be combined with an environmental gradient to produce landscapes that combine environmental variation at the local and macro scales.     

Estimating the power of indirect comparisons: a simulation study

Background

Indirect comparisons are becoming increasingly popular for evaluating medical treatments that have not been compared head-to-head in randomized clinical trials (RCTs). While indirect methods have grown in popularity and acceptance, little is known about the fragility of confidence interval estimations and hypothesis testing relying on this method.

Methods

We present the findings of a simulation study that examined the fragility of indirect confidence interval estimation and hypothesis testing relying on the adjusted indirect method.

Findings

Our results suggest that, for the settings considered in this study, indirect confidence interval estimation suffers from under-coverage while indirect hypothesis testing suffers from low power in the presence of moderate to large between-study heterogeneity. In addition, the risk of overestimation is large when the indirect comparison of interest relies on just one trial for one of the two direct comparisons.

Interpretation

Indirect comparisons typically suffer from low power. The risk of imprecision is increased when comparisons are unbalanced.     

Clinical Management and Burden of Prostate Cancer: A Markov Monte Carlo Model

Background

Prostate cancer (PCa) is the most common non-skin cancer among men in developed countries. Several novel treatments have been adopted by healthcare systems to manage PCa. Most of the observational studies and randomized trials on PCa have concurrently evaluated fewer treatments over short follow-up. Further, preceding decision analytic models on PCa management have not evaluated various contemporary management options. Therefore, a contemporary decision analytic model was necessary to address limitations to the literature by synthesizing the evidence on novel treatments thereby forecasting short and long-term clinical outcomes.

Objectives

To develop and validate a Markov Monte Carlo model for the contemporary clinical management of PCa, and to assess the clinical burden of the disease from diagnosis to end-of-life.

Methods

A Markov Monte Carlo model was developed to simulate the management of PCa in men 65 years and older from diagnosis to end-of-life. Health states modeled were: risk at diagnosis, active surveillance, active treatment, PCa recurrence, PCa recurrence free, metastatic castrate resistant prostate cancer, overall and PCa death. Treatment trajectories were based on state transition probabilities derived from the literature. Validation and sensitivity analyses assessed the accuracy and robustness of model predicted outcomes.

Results

Validation indicated model predicted rates were comparable to observed rates in the published literature. The simulated distribution of clinical outcomes for the base case was consistent with sensitivity analyses. Predicted rate of clinical outcomes and mortality varied across risk groups. Life expectancy and health adjusted life expectancy predicted for the simulated cohort was 20.9 years (95%CI 20.5–21.3) and 18.2 years (95% CI 17.9–18.5), respectively.

Conclusion

Study findings indicated contemporary management strategies improved survival and quality of life in patients with PCa. This model could be used to compare long-term outcomes and life expectancy conferred of PCa management paradigms.     

Cost-Minimization Analysis Favours Intravenous Ferric Carboxymaltose over Ferric Sucrose for the Ambulatory Treatment of Severe Iron Deficiency

Objective

Intravenous iron is widely used to treat iron deficiency in day-care units. Ferric carboxymaltose (FCM) allows administration of larger iron doses than iron sucrose (IS) in each infusion (1000 mg vs. 200 mg). As FCM reduces the number of infusions required but is more expensive, we performed a cost-minimization analysis to compare the cost impact of the two drugs.

Materials and Methods

The number of infusions and the iron dose of 111 consecutive patients who received intravenous iron at a gastrointestinal diseases day-care unit from 8/2007 to 7/2008 were retrospectively obtained. Costs of intravenous iron drugs were obtained from the Spanish regulatory agencies. The accounting department of the Hospital determined hospital direct and indirect costs for outpatient iron infusion. Non-hospital direct costs were calculated on the basis of patient interviews. In the pharmacoeconomic model, base case mean costs per patient were calculated for administering 1000 mg of iron per infusion using FCM or 200 mg using IS. Sensitivity analysis and Monte Carlo simulation were performed.

Results

Under baseline assumptions, the estimated cost of iron infusion per patient and year was €304 for IS and €274 for FCM, a difference of €30 in favour of FCM. Adding non-hospital direct costs to the model increased the difference to €67 (€354 for IS vs. €287 for FCM). A Monte Carlo simulation taking into account non-hospital direct costs favoured the use of FCM in 97% of simulations.

Conclusion

In this pharmacoeconomic analysis, FCM infusion reduced the costs of iron infusion at a gastrointestinal day-care unit.     

Efficacy of New Generation Antidepressants: Differences Seem Illusory

Background

Recently, Cipriani and colleagues examined the relative efficacy of 12 new-generation antidepressants on major depression using network meta-analytic methods. They found that some of these medications outperformed others in patient response to treatment. However, several methodological criticisms have been raised about network meta-analysis and Cipriani''s analysis in particular which creates the concern that the stated superiority of some antidepressants relative to others may be unwarranted.

Materials and Methods

A Monte Carlo simulation was conducted which involved replicating Cipriani''s network meta-analysis under the null hypothesis (i.e., no true differences between antidepressants). The following simulation strategy was implemented: (1) 1000 simulations were generated under the null hypothesis (i.e., under the assumption that there were no differences among the 12 antidepressants), (2) each of the 1000 simulations were network meta-analyzed, and (3) the total number of false positive results from the network meta-analyses were calculated.

Findings

Greater than 7 times out of 10, the network meta-analysis resulted in one or more comparisons that indicated the superiority of at least one antidepressant when no such true differences among them existed.

Interpretation

Based on our simulation study, the results indicated that under identical conditions to those of the 117 RCTs with 236 treatment arms contained in Cipriani et al.''s meta-analysis, one or more false claims about the relative efficacy of antidepressants will be made over 70% of the time. As others have shown as well, there is little evidence in these trials that any antidepressant is more effective than another. The tendency of network meta-analyses to generate false positive results should be considered when conducting multiple comparison analyses.     

A Monte Carlo study on dose distribution validation of GZP6 60Co stepping source

Aim

Stepping source in brachytherapy systems is used to treat a target lesion longer than the effective treatment length of the source. Cancerous lesions in the cervix, esophagus and rectum are examples of such a target lesion.

Background

In this study, the stepping source of a GZP6 afterloading intracavitary brachytherapy unit was simulated using Monte Carlo (MC) simulation and the results were used for the validation of the GZP6 treatment planning system (TPS).

Materials and methods

The stepping source was simulated using MCNPX Monte Carlo code. Dose distributions in the longitudinal plane were obtained by using a matrix shift method for esophageal tumor lengths of 8 and 10 cm. A mesh tally has been employed for the absorbed dose calculation in a cylindrical water phantom. A total of 5 × 10⁸ photon histories were scored and the MC statistical error obtained was at the range of 0.008–3.5%, an average of 0.2%.

Results

The acquired MC and TPS isodose curves were compared and it was shown that the dose distributions in the longitudinal plane were relatively coincidental. In the transverse direction, a maximum dose difference of 7% and 5% was observed for tumor lengths of 8 and 10 cm, respectively.

Conclusion

Considering that the certified source activity is given with ±10% uncertainty, the obtained difference is reasonable. It can be concluded that the accuracy of the dose distributions produced by GZP6 TPS for the stepping source is acceptable for its clinical applications.     

Construction of an expression plasmid (vector) encoding Brucella melitensis outer membrane protein,a candidate for DNA vaccine

Background:

DNA vaccination with plasmid encoding bacterial, viral, and parasitic immunogens has been shown to be an attractive method to induce efficient immune responses. Bacteria of the genus Brucella are facultative intracellular pathogens for which new and efficient vaccines are needed.

Methods:

To evaluate the use of a DNA immunization strategy for protection against brucellosis, a plasmid containing the DNA encoding the Brucella melitensis (B. melitensis) 31 kDa outer membrane protein, as a potent immunogenic target, was constructed.

Results:

The constructed plasmid, pcDNA3.1+omp31, was injected intramuscularly into mice and the expression of omp31 RNA was assessed by RT-PCR. The integrity of the pcDNA3.1+omp31 construct was confirmed with restriction analysis and sequencing. Omp31 mRNA expression was verified by RT-PCR.

Conclusion:

Our results indicate that the pcDNA3.1+omp31 eukaryotic expression vector expresses omp31 mRNA and could be useful as a vaccine candidate.Key Words: Brucella melitensis, omp31, DNA Vaccine, pcDNA3.1     

CRANKITE: A fast polypeptide backbone conformation sampler

Background

CRANKITE is a suite of programs for simulating backbone conformations of polypeptides and proteins. The core of the suite is an efficient Metropolis Monte Carlo sampler of backbone conformations in continuous three-dimensional space in atomic details.

Methods

In contrast to other programs relying on local Metropolis moves in the space of dihedral angles, our sampler utilizes local crankshaft rotations of rigid peptide bonds in Cartesian space.

Results

The sampler allows fast simulation and analysis of secondary structure formation and conformational changes for proteins of average length.     

Exploration of the association rules mining technique for the signal detection of adverse drug events in spontaneous reporting systems

Background

The detection of signals of adverse drug events (ADEs) has increased because of the use of data mining algorithms in spontaneous reporting systems (SRSs). However, different data mining algorithms have different traits and conditions for application. The objective of our study was to explore the application of association rule (AR) mining in ADE signal detection and to compare its performance with that of other algorithms.

Methodology/Principal Findings

Monte Carlo simulation was applied to generate drug-ADE reports randomly according to the characteristics of SRS datasets. Thousand simulated datasets were mined by AR and other algorithms. On average, 108,337 reports were generated by the Monte Carlo simulation. Based on the predefined criterion that 10% of the drug-ADE combinations were true signals, with RR equaling to 10, 4.9, 1.5, and 1.2, AR detected, on average, 284 suspected associations with a minimum support of 3 and a minimum lift of 1.2. The area under the receiver operating characteristic (ROC) curve of the AR was 0.788, which was equivalent to that shown for other algorithms. Additionally, AR was applied to reports submitted to the Shanghai SRS in 2009. Five hundred seventy combinations were detected using AR from 24,297 SRS reports, and they were compared with recognized ADEs identified by clinical experts and various other sources.

Conclusions/Significance

AR appears to be an effective method for ADE signal detection, both in simulated and real SRS datasets. The limitations of this method exposed in our study, i.e., a non-uniform thresholds setting and redundant rules, require further research.     

A Rapid and Economic In-House DNA Purification Method Using Glass Syringe Filters

Background

Purity, yield, speed and cost are important considerations in plasmid purification, but it is difficult to achieve all of these at the same time. Currently, there are many protocols and kits for DNA purification, however none maximize all four considerations.

Methodology/Principal Findings

We now describe a fast, efficient and economic in-house protocol for plasmid preparation using glass syringe filters. Plasmid yield and quality as determined by enzyme digestion and transfection efficiency were equivalent to the expensive commercial kits. Importantly, the time required for purification was much less than that required using a commercial kit.

Conclusions/Significance

This method provides DNA yield and quality similar to that obtained with commercial kits, but is more rapid and less costly.     

Monte Carlo dose calculation of GZP6 60Co stepping source based on a matrix shift technique

Background

As a routine method for stepping source simulation, a Monte Carlo program is run according to the number of steps and then the summation of dose from each run is taken to obtain total dose distribution. This method is time consuming.

Aim

As an alternative method, a matrix shift based technique was applied to simulate a stepping source for brachytherapy.

Materials and methods

The stepping source of GZP6 brachytherapy unit was simulated. In a matrix shift method, it is assumed that a radiation source is stationary and instead the data matrix is shifted based on the number of steps. In this study, by running MCNPX program for one point and calculation of the dose matrix using the matrix shift method, the isodose curves for the esophageal cancer tumor lengths of 4 and 6 cm were obtained and compared with the isodose curves obtained by running MCNPX programs in each step position separately (15 and 23 steps for esophageal cancer tumor lengths of 4 and 6 cm, respectively).

Results

The difference between the two dose matrixes for the stepping and matrix shift methods based on the average dose differences are 3.85 × 10⁻⁴ Gy and 5.19 × 10⁻⁴ Gy for treatment length of 4 cm and 6 cm, respectively. Dose differences are insignificant and these two methods are equally valid.

Conclusions

The matrix shift method presented in this study can be used for calculation of dose distribution for a brachytherapy stepping source as a quicker tool compared to other routine Monte Carlo based methods.     

Safe bunker designing for the 18?MV Varian 2100 Clinac: a comparison between Monte Carlo simulation based upon data and new protocol recommendations

Aim

The aim of this study was to compare two bunkers designed by only protocols recommendations and Monte Carlo (MC) based upon data derived for an 18 MV Varian 2100Clinac accelerator.

Background

High energy radiation therapy is associated with fast and thermal photoneutrons. Adequate shielding against the contaminant neutron has been recommended by IAEA and NCRP new protocols.

Materials and methods

The latest protocols released by the IAEA (safety report No. 47) and NCRP report No. 151 were used for the bunker designing calculations. MC method based upon data was also derived. Two bunkers using protocols and MC upon data were designed and discussed.

Results

From designed door''s thickness, the door designed by the MC simulation and Wu–McGinley analytical method was closer in both BPE and lead thickness. In the case of the primary and secondary barriers, MC simulation resulted in 440.11 mm for the ordinary concrete, total concrete thickness of 1709 mm was required. Calculating the same parameters value with the recommended analytical methods resulted in 1762 mm for the required thickness using 445 mm as recommended by TVL for the concrete. Additionally, for the secondary barrier the thickness of 752.05 mm was obtained.

Conclusion

Our results showed MC simulation and the followed protocols recommendations in dose calculation are in good agreement in the radiation contamination dose calculation. Difference between the two analytical and MC simulation methods revealed that the application of only one method for the bunker design may lead to underestimation or overestimation in dose and shielding calculations.     

Regulatory T Cells Suppress Natural Killer Cells during Plasmid DNA Vaccination in Mice,Blunting the CD8+ T Cell Immune Response by the Cytokine TGFβ

Background

CD4⁺CD25⁺ regulatory T cells (Tregs) suppress adaptive T cell-mediated immune responses to self- and foreign-antigens. Tregs may also suppress early innate immune responses to vaccine antigens and might decrease vaccine efficacy. NK and NKT cells are the first responders after plasmid DNA vaccination and are found at the site of inoculation. Earlier reports demonstrated that NKT cells could improve plasmid DNA efficacy, a phenomenon not found for NK cells. In fact, it has been shown that under certain disease conditions, NK cells are suppressed by Tregs via their release of IL-10 and/or TGFβ. Therefore, we tested the hypothesis that NK cell function is suppressed by Tregs in the setting of plasmid DNA vaccination.

Methodology/Principal Findings

In this study we show that Tregs directly inhibit NK cell function during plasmid DNA vaccination by suppressing the potentially 10-fold, NK cell-mediated, augmentation of plasmid DNA antigen-specific CD8⁺ T cells. We found that this phenomenon is dependent on the secretion of cytokine TGFβ by Tregs, and independent of IL-10.

Conclusions

Our data indicate a crucial function for Tregs in blocking plasmid DNA vaccine-elicited immune responses, revealing potentially novel strategies for improving the efficiency of plasmid DNA vaccines including chemical- or antibody-induced localized blockage of Treg-mediated suppression of NK cells at the site of plasmid DNA vaccine inoculation.     

Antenatal Screening for Down Syndrome Using Serum Placental Growth Factor with the Combined,Quadruple, Serum Integrated and Integrated Tests

Objective

To estimate the value of first or second trimester placental growth factor (PlGF) as an additional antenatal screening marker for Down syndrome.

Design

Nested case-control study.

Setting

Antenatal screening service.

Population or Sample

532 Down syndrome pregnancies and 1,155 matched unaffected pregnancies.

Methods

Stored maternal serum samples (−40°C) were assayed for PlGF. Monte Carlo simulation was used to estimate the screening performance of PlGF with the Combined, Quadruple, serum Integrated and Integrated tests.

Main Outcome Measures

Median PlGF levels in affected and unaffected pregnancies and screening performance (detection rates [DR] for specified false-positive rates [FPR] and vice versa).

Results

First trimester median PlGF was 15%, 28% and 39% lower in Down syndrome than unaffected pregnancies at 11, 12 and 13 completed weeks’ gestation respectively (all p<0.001). Second trimester median PlGF was 31% lower at 14 weeks (p<0.001), and the difference decreased (6% lower at 17 weeks). At a 90% DR with first trimester markers measured at 13 weeks, adding PlGF decreased the FPR from 11.1 to 5.1% using the Combined test, 9.3% to 4.5% using the serum Integrated test, and 3.4% to 1.5% using the Integrated test (or 1.5 to 1.4% with first trimester markers measured at 11 weeks). Adding PlGF to the Quadruple test (measured at 15 weeks) decreased the FPR from 10.0% to 9.6% at a 90% DR.

Conclusions

First trimester PlGF measurements improve the performance of antenatal screening for Down syndrome using the Combined, serum Integrated and Integrated tests. Second trimester PlGF measurements are of limited value.