Development of a Third Trimester Contingent Prognostic Prediction Scheme for Suspected Early-Onset Pre-Eclampsia

2021 ◽  
pp. 1-9
Author(s):  
Enric Sabrià ◽  
Paula Lafuente-Ganuza ◽  
Paloma Lequerica-Fernández ◽  
Ana Isabel Escudero ◽  
Eduardo Martínez-Morillo ◽  
...  
Keyword(s):  
Early Onset ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
False Positive Rate ◽  
Area Under The Curve ◽  
Prediction Tool ◽  
Model Based ◽  
Prognostic Prediction ◽  
Short Term Prediction ◽  
Plgf Ratio

<b><i>Introduction:</i></b> Short-term prediction of pre-eclampsia (PE) using soluble FMS-like tyrosine kinase-1 (sFlt-1)/ placental growth factor (PlGF) ratio has high false-positive rate. Therefore, we developed a prognostic prediction tool that predicts early-onset PE leading to delivery within 1 week on pregnancies with an sFlt-1/PlGF ratio above 38 and compared it with an analogous model based on sFlt-1/PlGF ratio and with the 655 sFlt-1/PlGF ratio cutoff. <b><i>Methods:</i></b> Cohort study of 363 singleton pregnancies with clinical suspicion of PE before 34 weeks of gestation, allowing repeated assessments (522). 213 samples with an sFlt-1/PlGF ratio above 38 were assessed to construct and identify the best-fit linear mixed model. N-terminal pro-B-type natriuretic peptide (NT-proBNP), sFlt-1 MoM, PlGF MoM, and sFlt-1/PlGF ratio combined with gestational age (GA) were assessed. <b><i>Results:</i></b> None of the pregnancies with an sFlt-1/PlGF ratio of 38 or below developed early-onset PE (309 samples from 240 pregnancies). Conversely, 47 women of 213 assessments (22.1%) with an sFlt-1/PlGF ratio above 38 developed the assessed outcome. The selected model included sFlt-1 MoM, NT-proBNP, and GA. Differences in area under the curve were observed between the selected model and the GA + sFlt-1/PlGF model (<i>p</i> = 0.04). At an sFlt-1/PlGF ratio cutoff of 655, detection rate was 31.9% (15/47), while the selected model detection was 55.3% (26/47) (<i>p</i> = 0.008). <b><i>Discussion:</i></b> Considering repeated assessments, the sFlt-1/PlGF ratio of 38 or below adequately ruled out early-onset PE, leading to delivery within 1 week. However, when sFlt-1/PlGF ratio is above 38, the prediction tool derived from linear mixed model based on GA, NT-proBNP, and sFlt-1 MoM, provided a better prognosis prediction than the sFlt-1/PlGF ratio.

scholarly journals The Design of Early-Stage Plant Breeding Trials Using Genetic Relatedness

2020 ◽  
Vol 25 (4) ◽  
pp. 553-578 ◽  
Author(s):  
Brian R. Cullis ◽  
Alison B. Smith ◽  
Nicole A. Cocks ◽  
David G. Butler
Keyword(s):  
Plant Breeding ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Genetic Relatedness ◽  
Early Stage ◽  
Genetic Effects ◽  
Breeding Programs ◽  
Model Based ◽  
Selection Decisions ◽  
Design Generation

Abstract The use of appropriate statistical methods has a key role in improving the accuracy of selection decisions in a plant breeding program. This is particularly important in the early stages of testing in which selections are based on data from a limited number of field trials that include large numbers of breeding lines with minimal replication. The method of analysis currently recommended for early-stage trials in Australia involves a linear mixed model that includes genetic relatedness via ancestral information: non-genetic effects that reflect the experimental design and a residual model that accommodates spatial dependence. Such analyses have been widely accepted as they have been found to produce accurate predictions of both additive and total genetic effects, the latter providing the basis for selection decisions. In this paper, we present the results of a case study of 34 early-stage trials to demonstrate this type of analysis and to reinforce the importance of including information on genetic relatedness. In addition to the application of a superior method of analysis, it is also critical to ensure the use of sound experimental designs. Recently, model-based designs have become popular in Australian plant breeding programs. Within this paradigm, the design search would ideally be based on a linear mixed model that matches, as closely as possible, the model used for analysis. Therefore, in this paper, we propose the use of models for design generation that include information on genetic relatedness and also include non-genetic and residual models based on the analysis of historic data for individual breeding programs. At present, the most commonly used design generation model omits genetic relatedness information and uses non-genetic and residual models that are supplied as default models in the associated software packages. The major reasons for this are that preexisting software is unacceptably slow for designs incorporating genetic relatedness and the accuracy gains resulting from the use of genetic relatedness have not been quantified. Both of these issues are addressed in the current paper. An updating scheme for calculating the optimality criterion in the design search is presented and is shown to afford prodigious computational savings. An in silico study that compares three types of design function across a range of ancillary treatments shows the gains in accuracy for the prediction of total genetic effects (and thence selection) achieved from model-based designs using genetic relatedness and program specific non-genetic and residual models. Supplementary materials accompanying this paper appear online.

Blood-to-saliva glucose time lag in sedated healthy dogs

2021 ◽  
pp. 104063872110353
Author(s):  
Antonia Ioannou ◽  
Heidi Phillips ◽  
Stephanie Keating ◽  
Anne Barger ◽  
Nicolas Lopez-Villalobos ◽  
...  
Keyword(s):  
Glucose Concentration ◽  
Repeated Measures ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Time Lag ◽  
Area Under The Curve ◽  
Oral Bacteria ◽  
Glucose Levels ◽  
Healthy Dogs ◽  
Paired Serum

The management of diabetes mellitus mandates measurement of blood glucose. Saliva offers an alternative to blood sampling, but measurement of the salivary glucose concentration is difficult, and the blood-to-saliva glucose time lag is uncertain. We aimed to determine the serum–saliva glucose time lag in the saliva of healthy dogs. The combined duct of the mandibular and sublingual salivary glands of 6 dogs was cannulated to collect saliva and prevent glucose degradation by oral bacteria. Following a 0.25 g/kg IV bolus of dextrose, paired serum–saliva samples were collected at baseline and in twelve 5-min blocks over 60 min. Serum and salivary glucose levels were analyzed with a linear mixed model for repeated measures with a compound symmetry error structure. Mean (±SD) saliva production was 10.3 ± 2.9 µL/kg/min, and the area under the curve (AUCglucose)saliva/serum ratio was 0.006, which highlights the magnitude of the large difference in glucose concentration between the 2 compartments. The serum–saliva glucose time lag was 30–40 min.

scholarly journals Gonadotropin Response to Insulin and Lipid Infusion Reproduces the Reprometabolic Syndrome of Obesity in Lean Women

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. A732-A733
Author(s):  
Nanette F Santoro ◽  
Nanette F Santoro
Keyword(s):  
Mixed Model ◽  
Linear Mixed Model ◽  
Statistical Significance ◽  
Area Under The Curve ◽  
Pulse Amplitude ◽  
Normal Weight ◽  
Obese Women ◽  
Mixed Model Analysis ◽  
Early Follicular Phase ◽  
Very High

Abstract Introduction: The reprometabolic syndrome of obesity is associated with reduced gonadotropins and impaired LH and FSH response to gonadotropin releasing hormone (GnRH). We sought to reproductive the reprometabolic syndrome in normal weight, eumenorrheic women by infusing a combination of insulin and lipid. Materials and Methods: 15 women, mean age 32 (IQR 26,36) and BMI 21.9 (20.2, 22.9) were recruited with intent to perform early follicular phase, 6-hour infusions of insulin (20-40mg/mU/m2/min) and lipid (Intralipid) or saline infusion (controls); 12 women completed both intended studies and an additional 3 women completed only one of the two studies. The first 4 hours of each study assessed endogenous gonadotropins; at 4hrs, a 75 ng/kg GnRH bolus was administered and sampling continued until 6hrs. Linear mixed model analysis was used to determine differences between Intralipid versus saline on endogenous LH pulse amplitude (primary outcomes), mean FSH, and area under the curve (AUC) response to GnRH (secondary outcomes). Results: LH pulse amplitude, mean FSH, and both AUC responses to GnRH were all reduced by Intralipid/insulin; mean FSH (P=0.03) and AUC for LH (P=0.05) were at or near statistical significance. LH pulse amplitude and response to GnRH were significantly reduced (P=0.04 and 0.02) when one participant with very high LH and AMH levels was excluded. Discussion: Acute infusion of insulin/lipid to eumenorrheic, normal weight women recapitulated the reprometabolic syndrome of obesity. These findings imply that specific circulating factors in obese women contribute to their sub fertility and thus may be amenable to discovery and treatment.

scholarly journals Development of a KRAS-Associated Metabolic Risk Model for Prognostic Prediction in Pancreatic Cancer

2021 ◽  
Vol 2021 ◽  
pp. 1-21
Author(s):  
Zuyi Ma ◽  
Zhenchong Li ◽  
Zuguang Ma ◽  
Zixuan Zhou ◽  
Hongkai Zhuang ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Risk Model ◽  
Area Under The Curve ◽  
Metabolic Reprogramming ◽  
Bioinformatics Analyses ◽  
Mutation Status ◽  
Metabolic Characteristics ◽  
Model Based ◽  
Metabolic Genes ◽  
Prognostic Prediction

Background. KRAS was reported to affect some metabolic genes and promote metabolic reprogramming in solid tumors. However, there was no comprehensive analysis to explore KRAS-associated metabolic signature or risk model for pancreatic cancer (PC). Methods. In the current study, multiple bioinformatics analyses were used to identify differentially expressed metabolic genes based on KRAS mutation status in PC. Then, we developed and validated a prognostic risk model based on the selected KRAS-associated metabolic genes. Besides, we explored the association between the risk model and the metabolic characteristics as well as gemcitabine-associated chemoresistance in PC. Results. 6 KRAS-associated metabolic genes (i.e., CYP2S1, GPX3, FTCD, ENPP2, UGT1A10, and XDH) were selected and enrolled to establish a prognostic risk model. The prognostic model had a high C-index of 0.733 for overall survival (OS) in TCGA pancreatic cancer database. The area under the curve (AUC) values of 1- and 3-year survival were both greater than 0.70. Then, the risk model was validated in two GEO datasets and also presented a satisfactory discrimination and calibration performance. Further, we found that the expression of some KRAS-driven glycolysis-associated genes (PKM, GLUT1, HK2, and LDHA) and gemcitabine-associated chemoresistance genes (i.e., CDA and RMM2) was significantly upregulated in high-risk PC patients evaluated by the risk model. Conclusions. We constructed a risk model based on 6 KRAS-associated metabolic genes, which predicted patients’ survival with high accuracy and reflected tumor metabolic characteristics and gemcitabine-associated chemoresistance in PC.

On the analysis of cubic smoothing spline-based stem curve prediction for forest harvesters

2006 ◽  
Vol 36 (11) ◽  
pp. 2909-2919 ◽  
Author(s):  
Laura Koskela ◽  
Tapio Nummi ◽  
Simone Wenzel ◽  
Veli-Pekka Kivinen
Keyword(s):  
Variable Exponent ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Prediction Method ◽  
Smoothing Spline ◽  
Model Based ◽  
Cutting Pattern ◽  
Taper Equation

In the cut-to-length (CTL) harvesting system the felling, delimbing, and bucking processes take place at the harvesting site. The optimal cutting points along the stem can be determined if the whole stem curve is known. In practice, however, it is not economically feasible to measure the whole stem first before crosscutting, and hence the first cutting decisions are usually made when only a short part of the stem is known. Predictions are used to determine the cutting pattern to compensate for the unknown part of the stem. In this paper our interest focuses on stem curve prediction in a harvesting situation and we study a modified version of a cubic smoothing spline-based prediction method devised by Nummi and Mottonen (T. Nummi and J. Mottonen. 2004. J. Appl. Stat. 31: 105–114). The method's performance was assessed in five different final felling stands of spruce and pine, collected by harvesters in southern Finland. The results for the spline approach are very promising and show the superiority of the method over the linear mixed-model-based approach of Liski and Nummi (E. Liski and T. Nummi. 1995. Scand. J. Stat. 22: 255–269) and also over the approach based on the variable-exponent taper equation of Kozak (A. Kozak. 1988. Can. J. For. Res. 18: 1363–1368).

Evaluation of the Prognostic Value of the sFlt-1/PlGF Ratio in Early-Onset Preeclampsia

2020 ◽  
Author(s):  
Oriane Tasta ◽  
Olivier Parant ◽  
Safouane M. Hamdi ◽  
Mickael Allouche ◽  
Christophe Vayssiere ◽  
...  
Keyword(s):  
Early Onset ◽  
Prognostic Value ◽  
Area Under The Curve ◽  
Expectant Management ◽  
Bivariate Analysis ◽  
Additional Tool ◽  
Elevated Liver Enzymes ◽  
Increased Risk ◽  
The Difference ◽  
Plgf Ratio

Objective Increased expression of soluble fms-like tyrosine kinase 1 (sFlt-1), associated with a decrease in placental growth factor (PlGF), plays a key role in the pathogenesis of preeclampsia (PE). We evaluated the prognostic value of the sFlt-1/PlGF ratio for the onset of adverse maternofetal outcomes (AMFO) in case of early-onset PE with attempted expectant management. Study Design From October 2016 through November 2018, all singleton pregnancies complicated by early-onset PE (before 34 weeks of gestation) were included in a cohort study. The plasma levels of sFlt-1 and PlGF were blindly measured on admission. For the statistical analysis, we performed a bivariate analysis, a comparison of the receiving operating characteristic curves and a survival analysis estimated by the Kaplan–Meier method. Results Among 109 early PE, AMFO occurred in 87 pregnancies (79.8%), mainly hemolysis, elevated liver enzymes, and low platelet count syndrome and severe fetal heart rate abnormalities requiring urgent delivery. The area under the curve (AUC) of sFlt-1/PlGF ratio was 0.82 (95% confidence interval [CI]: 0.73–0.88) for the risk of AMFO and the difference between the AUCs was significant for each separate standard parameter (p = 0.018 for initial diastolic blood pressure, p = 0.013 for alanine aminotransferase, p < 0.001 for uric acid). Pregnancies were best classified by a cutoff ratio of 293, with a sensitivity of 95% and a specificity of 50%. With a ratio value less than 293, no pregnancy was complicated or had been stopped during the first 5 days. A ratio more than 293 was associated with an increased risk of AMFO onset (hazard ratio [HR]: 3.61; 95% CI: 2.13–6.10; p < 0.001) and had a significant association with the length of time between the diagnosis of PE and delivery (HR: 2.49; 95% CI: 1.56–3.96; p < 0.001). Conclusion The sFlt-1/PlGF ratio is an additional tool in the prediction of AMFO in proven early-onset PE, which is likely to improve care by anticipating severe complications. Key Points

scholarly journals Evaluating Postprandial Lipid Response Following Chardonnay Grape Marc Powder Supplementation

2020 ◽  
Vol 4 (Supplement_2) ◽  
pp. 640-640
Author(s):  
Fanny Lee ◽  
Sridevi Krishnan ◽  
Aneeta Vedula ◽  
Leslie Woodhouse ◽  
Torey Arvik ◽  
...  
Keyword(s):  
Mixed Model ◽  
Linear Mixed Model ◽  
Area Under The Curve ◽  
Calculated Data ◽  
Seed Extract ◽  
Innovation Research ◽  
Grape Marc ◽  
Lipid Response ◽  
Significant Difference ◽  
Postprandial Lipid

Abstract Objectives Chardonnay grape marc is the remaining skins and seeds after pressing and is a byproduct of winemaking that offers a rich source of phytonutrients. The objective was to evaluate postprandial lipid response following adding Chardonnay grape marc powder to the diet. Methods Overweight or obese men and women with hyperlipidemia between 35–65 y were recruited for this randomized, double-blinded 16-week crossover study. Subjects consumed 1500 mg of 1) placebo, 2) high Chardonnay seed extract, low Chardonnay marc blend (HE) or 3) high chardonnay marc, low chardonnay seed extract blend (HM) in a randomized order. Each intervention arm lasted 3 weeks and included two 3-week washout periods. Following each intervention, fasting blood was drawn, then subjects consumed a high fat challenge meal. Postprandial blood was subsequently drawn at 1, 2 and 3 hours. Incremental area under the curve (iAUC) was also calculated. Data were not normally distributed thus were log transformed before conducting analyses. Analyses were conducted with 24 completed subjects and intention to treat for 3 subjects who withdrew using linear mixed model ANOVA. Results Incremental area under the triglyceride curve response to a meal showed a significant effect of intervention (P = 0.05). Pairwise comparisons showed a significant difference between HE and HM (P = 0.04). Conclusions Following HM intervention, there was a significantly lower triglyceride iAUC compared to the HE intervention, but no difference compared to placebo. HM intervention resulted in better triglyceride clearance compared to the HE intervention. Funding Sources NIFA Phase II Small Business Innovation Research Grant awarded to Sonomaceuticals, LLC.

scholarly journals New prognostic prediction tool for suspected early-onset pre-eclampsia: model development and performance evaluation.

Authorea ◽  
2020 ◽  
Author(s):  
Enric Sabri Bach ◽  
Paula Lafuente Ganuza ◽  
Paloma Lequerica Fern ndez ◽  
Ana In s Escudero ◽  
Eduardo Mart nez Morillo ◽  
...  
Keyword(s):  
Performance Evaluation ◽  
Early Onset ◽  
Model Development ◽  
Prediction Tool ◽  
And Performance ◽  
Prognostic Prediction

scholarly journals Development of a KRAS-Associated Metabolic Risk Model for Prognostic Prediction in Pancreatic Cancer

2020 ◽  
Author(s):  
Zuyi Ma ◽  
Zhenchong Li ◽  
Zixuan Zhou ◽  
Hongkai Zhuang ◽  
Chunsheng Liu ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Risk Model ◽  
Area Under The Curve ◽  
Metabolic Reprogramming ◽  
Bioinformatics Analyses ◽  
Mutation Status ◽  
Metabolic Characteristics ◽  
Model Based ◽  
Metabolic Genes ◽  
Prognostic Prediction

Abstract Background: KRAS was reported to affect some metabolic genes and promote metabolic reprogramming in solid tumors. However, there is no comprehensive analysis to explore KRAS associated metabolic signature or risk model for Pancreatic cancer (PC).Methods: In current study, multiple bioinformatics analyses were used to identify differentially expressed metabolic genes based on KRAS mutation status in PC. Then we developed and validated a prognostic risk model based on the selected KRAS-associated metabolic genes. Besides, we explored the association of the risk model and the metabolic characteristics as well as Gemcitabine associated chemoresistance in PC.Results: 6 KRAS-associated metabolic genes (i.e. CYP2S1, GPX3, FTCD, ENPP2, UGT1A10, and XDH) were selected and were enrolled to establish a prognostic risk model. The prognostic model had a high C-index of 0.733 for overall survival (OS) in the TCGA pancreatic cancer database. The area under the curve (AUC) values of 1- and 3-year survival were both greater than 0.70. Then the risk model was validated in two GEO datasets and also presented a satisfactory discrimination and calibration performance. Further, we found that the expression of some KRAS-driven glycolysis associated genes (PKM, GLUT1, HK2, and LDHA) and Gemcitabine associated chemoresistance genes (i.e. CDA and RMM2) were significantly up-regulated in high-risk PC patients evaluated by the risk model.Conclusions: We constructed a risk model based on 6 KRAS associated metabolic genes, which predicts patients' survival with high accuracy and reflects tumor metabolic characteristics and Gemcitabine associated chemoresistance in PC.

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