ResearchPad - normal-distribution https://www.researchpad.co Default RSS Feed en-us © 2020 Newgen KnowledgeWorks <![CDATA[The Childbirth Experience Questionnaire (CEQ)—Validation of its use in a Danish-speaking population of new mothers stimulated with oxytocin during labour]]> https://www.researchpad.co/article/elastic_article_14579 When determining optimal treatment regimens, patient reported outcomes including satisfaction are increasingly appreciated. It is well established that the birth experience may affect the postnatal attachment to the newborn and the management of subsequent pregnancies and deliveries. As we have no robust validated Danish tool to evaluate the childbirth experience exists, we aimed to perform a transcultural adaptation of the Childbirth Experience Questionnaire (CEQ) to a Danish context.MethodsIn accordance with the COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN), we translated the Swedish-CEQ to Danish. The Danish-CEQ was tested for content validity among 10 new mothers. In a population of women who have had their labour induced, we then assessed the electronic questionnaire for validity and reliability using factor analytical design, hypothesis testing, and internal consistency. Based on these data, we determined criterion and construct responsiveness in addition to floor and ceiling effects.ResultsThe content validation resulted in minor adjustments in two items. This improved the comprehensibility. The electronic questionnaire was completed by 377 of 495 women (76.2%). The original Swedish-CEQ was four-dimensional, however an exploratory factor analysis revealed a three-dimensional structure in our Danish population (Own capacity, Participation, and Professional support). Parous women, women who delivered vaginally, and women with a labour duration <12 hours had a higher score in each domain. The internal consistency (Cronbach’s alpha) ranged between 0.75 and 0.89 and the ICC between 0.68–0.93. We found ceiling effects of 57.6% in the domain Professional support and of 25.5% in the domain Participation.ConclusionThis study offers transcultural adaptation of the Swedish-CEQ to a Danish context. The 3-dimensional Danish-CEQ demonstrates construct validity and reliability. Our results revealed significant ceiling effect especially in the domain Professional support, which needs to be acknowledged when considering implementing the Danish-CEQ into trials and clinical practice. ]]> <![CDATA[Class enumeration false positive in skew-t family of continuous growth mixture models]]> https://www.researchpad.co/article/Ne0623f60-4058-4fc0-9606-ac0f597752dc

Growth Mixture Modeling (GMM) has gained great popularity in the last decades as a methodology for longitudinal data analysis. The usual assumption of normally distributed repeated measures has been shown as problematic in real-life data applications. Namely, performing normal GMM on data that is even slightly skewed can lead to an over selection of the number of latent classes. In order to ameliorate this unwanted result, GMM based on the skew t family of continuous distributions has been proposed. This family of distributions includes the normal, skew normal, t, and skew t. This simulation study aims to determine the efficiency of selecting the “true” number of latent groups in GMM based on the skew t family of continuous distributions, using fit indices and likelihood ratio tests. Results show that the skew t GMM was the only model considered that showed fit indices and LRT false positive rates under the 0.05 cutoff value across sample sizes and for normal, and skewed and kurtic data. Simulation results are corroborated by a real educational data application example. These findings favor the development of practical guides of the benefits and risks of using the GMM based on this family of distributions.

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<![CDATA[Intra-individual variation of particles in exhaled air and of the contents of Surfactant protein A and albumin]]> https://www.researchpad.co/article/N3daed577-6f93-4f19-9dc8-54ce3f8d7d6e

Introduction

Particles in exhaled air (PEx) provide samples of respiratory tract lining fluid from small airways containing, for example, Surfactant protein A (SP-A) and albumin, potential biomarkers of small airway disease. We hypothesized that there are differences between morning, noon, and afternoon measurements and that the variability of repeated measurements is larger between days than within days.

Methods

PEx was obtained in sixteen healthy non-smoking adults on 11 occasions, within one day and between days. SP-A and albumin were quantified by ELISA. The coefficient of repeatability (CR), intraclass correlation coefficient (ICC), and coefficient of variation (CV) were used to assess the variation of repeated measurements.

Results

SP-A and albumin increased significantly from morning towards the noon and afternoon by 13% and 25% on average, respectively, whereas PEx number concentration and particle mean mass did not differ significantly between the morning, noon and afternoon. Between-day CRs were not larger than within-day CRs.

Conclusions

Time of the day influences the contents of SP-A and albumin in exhaled particles. The variation of repeated measurements was rather high but was not influenced by the time intervals between measurements.

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<![CDATA[Cross-comparative analysis of evacuation behavior after earthquakes using mobile phone data]]> https://www.researchpad.co/article/5c76fe4dd5eed0c484e5b867

Despite the importance of predicting evacuation mobility dynamics after large scale disasters for effective first response and disaster relief, our general understanding of evacuation behavior remains limited because of the lack of empirical evidence on the evacuation movement of individuals across multiple disaster instances. Here we investigate the GPS trajectories of a total of more than 1 million anonymized mobile phone users whose positions were tracked for a period of 2 months before and after four of the major earthquakes that occurred in Japan. Through a cross comparative analysis between the four disaster instances, we find that in contrast to the assumed complexity of evacuation decision making mechanisms in crisis situations, an individual’s evacuation probability is strongly dependent on the seismic intensity that they experience. In fact, we show that the evacuation probabilities in all earthquakes collapse into a similar pattern, with a critical threshold at around seismic intensity 5.5. This indicates that despite the diversity in the earthquakes profiles and urban characteristics, evacuation behavior is similarly dependent on seismic intensity. Moreover, we found that probability density functions of the distances that individuals evacuate are not dependent on seismic intensities that individuals experience. These insights from empirical analysis on evacuation from multiple earthquake instances using large scale mobility data contributes to a deeper understanding of how people react to earthquakes, and can potentially assist decision makers to simulate and predict the number of evacuees in urban areas with little computational time and cost. This can be achieved by utilizing only the information on population density distribution and seismic intensity distribution, which can be observed instantaneously after the shock.

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<![CDATA[Identification of movement synchrony: Validation of windowed cross-lagged correlation and -regression with peak-picking algorithm]]> https://www.researchpad.co/article/5c6b26b9d5eed0c484289f1e

In psychotherapy, movement synchrony seems to be associated with higher patient satisfaction and treatment outcome. However, it remains unclear whether movement synchrony rated by humans and movement synchrony identified by automated methods reflect the same construct. To address this issue, video sequences showing movement synchrony of patients and therapists (N = 10) or not (N = 10), were analyzed using motion energy analysis. Three different synchrony conditions with varying levels of complexity (naturally embedded, naturally isolated, and artificial) were generated for time series analysis with windowed cross-lagged correlation/ -regression (WCLC, WCLR). The concordance of ratings (human rating vs. automatic assessment) was computed for 600 different parameter configurations of the WCLC/WCLR to identify the parameter settings that measure movement synchrony best. A parameter configuration was rated as having a good identification rate if it yields high concordance with human-rated intervals (Cohen’s kappa) and a low amount of over-identified data points. Results indicate that 76 configurations had a good identification rate (IR) in the least complex condition (artificial). Two had an acceptable IR with regard to the naturally isolated condition. Concordance was low with regard to the most complex (naturally embedded) condition. A valid identification of movement synchrony strongly depends on parameter configuration and goes beyond the identification of synchrony by human raters. Differences between human-rated synchrony and nonverbal synchrony measured by algorithms are discussed.

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<![CDATA[Testing of library preparation methods for transcriptome sequencing of real life glioblastoma and brain tissue specimens: A comparative study with special focus on long non-coding RNAs]]> https://www.researchpad.co/article/5c6b26afd5eed0c484289e7d

Current progress in the field of next-generation transcriptome sequencing have contributed significantly to the study of various malignancies including glioblastoma multiforme (GBM). Differential sequencing of transcriptomes of patients and non-tumor controls has a potential to reveal novel transcripts with significant role in GBM. One such candidate group of molecules are long non-coding RNAs (lncRNAs) which have been proved to be involved in processes such as carcinogenesis, epigenetic modifications and resistance to various therapeutic approaches. To maximize the value of transcriptome sequencing, a proper protocol for library preparation from tissue-derived RNA needs to be found which would produce high quality transcriptome sequencing data and increase the number of detected lncRNAs. It is important to mention that success of library preparation is determined by the quality of input RNA, which is in case of real-life tissue specimens very often altered in comparison to high quality RNA commonly used by manufacturers for development of library preparation chemistry. In the present study, we used GBM and non-tumor brain tissue specimens and compared three different commercial library preparation kits, namely NEXTflex Rapid Directional qRNA-Seq Kit (Bioo Scientific), SENSE Total RNA-Seq Library Prep Kit (Lexogen) and NEBNext Ultra II Directional RNA Library Prep Kit for Illumina (NEB). Libraries generated using SENSE kit were characterized by the most normal distribution of normalized average GC content, the least amount of over-represented sequences and the percentage of ribosomal RNA reads (0.3–1.5%) and highest numbers of uniquely mapped reads and reads aligning to coding regions. However, NEBNext kit performed better having relatively low duplication rates, even transcript coverage and the highest number of hits in Ensembl database for every biotype of our interest including lncRNAs. Our results indicate that out of three approaches the NEBNext library preparation kit was most suitable for the study of lncRNAs via transcriptome sequencing. This was further confirmed by highly consistent data reached in an independent validation on an expanded cohort.

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<![CDATA[A novel scale-space approach for multinormality testing and the k-sample problem in the high dimension low sample size scenario]]> https://www.researchpad.co/article/5c50c44ed5eed0c4845e84bb

Two classical multivariate statistical problems, testing of multivariate normality and the k-sample problem, are explored by a novel analysis on several resolutions simultaneously. The presented methods do not invert any estimated covariance matrix. Thereby, the methods work in the High Dimension Low Sample Size situation, i.e. when np. The output, a significance map, is produced by doing a one-dimensional test for all possible resolution/position pairs. The significance map shows for which resolution/position pairs the null hypothesis is rejected. For the testing of multinormality, the Anderson-Darling test is utilized to detect potential departures from multinormality at different combinations of resolutions and positions. In the k-sample case, it is tested whether k data sets can be said to originate from the same unspecified discrete or continuous multivariate distribution. This is done by testing the k vectors corresponding to the same resolution/position pair of the k different data sets through the k-sample Anderson-Darling test. Successful demonstrations of the new methodology on artificial and real data sets are presented, and a feature selection scheme is demonstrated.

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<![CDATA[Clustering algorithms: A comparative approach]]> https://www.researchpad.co/article/5c478c94d5eed0c484bd335e

Many real-world systems can be studied in terms of pattern recognition tasks, so that proper use (and understanding) of machine learning methods in practical applications becomes essential. While many classification methods have been proposed, there is no consensus on which methods are more suitable for a given dataset. As a consequence, it is important to comprehensively compare methods in many possible scenarios. In this context, we performed a systematic comparison of 9 well-known clustering methods available in the R language assuming normally distributed data. In order to account for the many possible variations of data, we considered artificial datasets with several tunable properties (number of classes, separation between classes, etc). In addition, we also evaluated the sensitivity of the clustering methods with regard to their parameters configuration. The results revealed that, when considering the default configurations of the adopted methods, the spectral approach tended to present particularly good performance. We also found that the default configuration of the adopted implementations was not always accurate. In these cases, a simple approach based on random selection of parameters values proved to be a good alternative to improve the performance. All in all, the reported approach provides subsidies guiding the choice of clustering algorithms.

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<![CDATA[Bayesian multiple logistic regression for case-control GWAS]]> https://www.researchpad.co/article/5c6059c9d5eed0c4847cbe79

Genetic variants in genome-wide association studies (GWAS) are tested for disease association mostly using simple regression, one variant at a time. Standard approaches to improve power in detecting disease-associated SNPs use multiple regression with Bayesian variable selection in which a sparsity-enforcing prior on effect sizes is used to avoid overtraining and all effect sizes are integrated out for posterior inference. For binary traits, the logistic model has not yielded clear improvements over the linear model. For multi-SNP analysis, the logistic model required costly and technically challenging MCMC sampling to perform the integration. Here, we introduce the quasi-Laplace approximation to solve the integral and avoid MCMC sampling. We expect the logistic model to perform much better than multiple linear regression except when predicted disease risks are spread closely around 0.5, because only close to its inflection point can the logistic function be well approximated by a linear function. Indeed, in extensive benchmarks with simulated phenotypes and real genotypes, our Bayesian multiple LOgistic REgression method (B-LORE) showed considerable improvements (1) when regressing on many variants in multiple loci at heritabilities ≥ 0.4 and (2) for unbalanced case-control ratios. B-LORE also enables meta-analysis by approximating the likelihood functions of individual studies by multivariate normal distributions, using their means and covariance matrices as summary statistics. Our work should make sparse multiple logistic regression attractive also for other applications with binary target variables. B-LORE is freely available from: https://github.com/soedinglab/b-lore.

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<![CDATA[Mixed effects approach to the analysis of the stepped wedge cluster randomised trial—Investigating the confounding effect of time through simulation]]> https://www.researchpad.co/article/5c1c0af1d5eed0c484426f5a

Background

A stepped wedge cluster randomised trial (SWCRT) is a multicentred study which allows an intervention to be rolled out at sites in a random order. Once the intervention is initiated at a site, all participants within that site remain exposed to the intervention for the remainder of the study.

The time since the start of the study (“calendar time”) may affect outcome measures through underlying time trends or periodicity. The time since the intervention was introduced to a site (“exposure time”) may also affect outcomes cumulatively for successful interventions, possibly in addition to a step change when the intervention began.

Methods

Motivated by a SWCRT of self-monitoring for bipolar disorder, we conducted a simulation study to compare model formulations to analyse data from a SWCRT under 36 different scenarios in which time was related to the outcome (improvement in mood score). The aim was to find a model specification that would produce reliable estimates of intervention effects under different scenarios. Nine different formulations of a linear mixed effects model were fitted to these datasets. These models varied in the specification of calendar and exposure times.

Results

Modelling the effects of the intervention was best accomplished by including terms for both calendar time and exposure time. Treating time as categorical (a separate parameter for each measurement time-step) achieved the best coverage probabilities and low bias, but at a cost of wider confidence intervals compared to simpler models for those scenarios which were sufficiently modelled by fewer parameters. Treating time as continuous and including a quadratic time term performed similarly well, with slightly larger variations in coverage probability, but narrower confidence intervals and in some cases lower bias. The impact of misspecifying the covariance structure was comparatively small.

Conclusions

We recommend that unless there is a priori information to indicate the form of the relationship between time and outcomes, data from SWCRTs should be analysed with a linear mixed effects model that includes separate categorical terms for calendar time and exposure time. Prespecified sensitivity analyses should consider the different formulations of these time effects in the model, to assess their impact on estimates of intervention effects.

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<![CDATA[An evaluation roadmap for critical quality attributes from tier 1 in analytical similarity assessment]]> https://www.researchpad.co/article/5c12cf7fd5eed0c48491478e

Analytical similarity assessment of critical quality attributes (CQAs) serves as a foundation for the development of biosimilar products and facilitates an abbreviated subsequent clinical evaluation. In this study, we establish a statistical evaluation roadmap with statistical approaches for some selected CQAs from Tier 1, because they are most relevant to clinical outcomes and require the most rigorous statistical methods. In the roadmap, we incorporate 3 methods—ranking and tier assignment of quality attributes, the equivalence test, and the Mann–Whitney test for equivalence—that are important to determine analytical similarity between the reference and biosimilar products. For the equivalence test, we develop a power calculation formula based on the two one-sided tests procedure. Exact sample sizes can be numerically calculated. Then, we propose a flexible idea for selecting the number of reference lots (nR) and the number of biosimilar lots (nT) to adjust for serious unbalanced sample sizes. From results of extensive simulations under various parameter settings, we obtain a workable strategy to determine the optimum sample size combination (nT, nR) for the equivalence test of CQAs from Tier 1. R codes are provided to facilitate implementation of the roadmap and corresponding methods in practice.

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<![CDATA[Modelling structural determinants of ventilation heterogeneity: A perturbative approach]]> https://www.researchpad.co/article/5c09944dd5eed0c4842ae9c5

We have developed a computational model of gas mixing and ventilation in the human lung represented as a bifurcating network. We have simulated multiple-breath washout (MBW), a clinical test for measuring ventilation heterogeneity (VH) in patients with obstructive lung conditions. By applying airway constrictions inter-regionally, we have predicted the response of MBW indices to obstructions and found that they detect a narrow range of severe constrictions that reduce airway radius to 10%–30% of healthy values. These results help to explain the success of the MBW test to distinguish obstructive lung conditions from healthy controls. Further, we have used a perturbative approach to account for intra-regional airway heterogeneity that avoids modelling each airway individually. We have found, for random airway heterogeneity, that the variance in MBW indices is greater when indices are already elevated due to constrictions. By quantifying this effect, we have shown that variability in lung structure and mechanical properties alone can lead to clinically significant variability in MBW indices (specifically the Lung Clearance Index—LCI, and the gradient of phase-III slopes—Scond), but only in cases simulating obstructive lung conditions. This method is a computationally efficient way to probe the lung’s sensitivity to structural changes, and to quantify uncertainty in predictions due to random variations in lung mechanical and structural properties.

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<![CDATA[Dual effects of leptin in perioperative gas exchange of morbidly obese patients]]> https://www.researchpad.co/article/5b4a1930463d7e428027f893

Leptin has shown positive effects on respiratory function in experimental settings. The role of leptin on perioperative respiratory function in morbidly obese patients has not been established. We performed a retrospective analysis of morbidly obese patients undergoing laparoscopic sleeve gastrectomy. Fasting serum leptin and interleukin (IL)-6 were measured preoperatively, and arterial blood gases were obtained pre- and postoperatively. Outcome variables were arterial partial pressure of oxygen (PaO2), arterial partial pressure of carbon dioxide (PaCO2), and differences in PaO2 and PaCO2 between pre- and postoperative values (ΔPaO2, ΔPaCO2; postoperative minus preoperative). Patients with lower (<40 μg/L) and higher (≥40 μg/L) leptin levels were compared. Bravais-Pearson’s correlation, multiple linear regression, and logistic regression analysis were performed. A total of 112 morbidly obese patients were included. Serum leptin was significantly higher in females than in males (42.86±12.89 vs. 30.67±13.39 μg/L, p<0.0001). Leptin was positively correlated with body mass index (r = 0.238; p = 0.011), IL-6 (r = 0.473; p<0.0001), and ΔPaO2 (r = 0.312; p = 0.0008). Leptin was negatively correlated with preoperative PaO2 (r = -0.199; p = 0.035). Preoperative PaO2 was lower, ΔPaCO2 was smaller, and ΔPaO2 was greater in the high leptin group than in the low leptin group. In multiple regression analysis, leptin was negatively associated with preoperative PaO2 (estimate coefficient = -0.147; p = 0.023). In logistic regression analysis, leptin was associated with improved ΔPaO2 (odds ratio [OR] = 1.104; p = 0.0138) and ΔPaCO2 (OR = 0.968; p = 0.0334). Leptin appears to have dual effects related to perioperative gas exchange in obese patients undergoing bariatric surgery. It is associated with worse preoperative oxygenation but improved respiratory function after surgery.

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<![CDATA[Instance-based generalization for human judgments about uncertainty]]> https://www.researchpad.co/article/5b28b5e2463d7e1340e24748

While previous studies have shown that human behavior adjusts in response to uncertainty, it is still not well understood how uncertainty is estimated and represented. As probability distributions are high dimensional objects, only constrained families of distributions with a low number of parameters can be specified from finite data. However, it is unknown what the structural assumptions are that the brain uses to estimate them. We introduce a novel paradigm that requires human participants of either sex to explicitly estimate the dispersion of a distribution over future observations. Judgments are based on a very small sample from a centered, normally distributed random variable that was suggested by the framing of the task. This probability density estimation task could optimally be solved by inferring the dispersion parameter of a normal distribution. We find that although behavior closely tracks uncertainty on a trial-by-trial basis and resists an explanation with simple heuristics, it is hardly consistent with parametric inference of a normal distribution. Despite the transparency of the simple generating process, participants estimate a distribution biased towards the observed instances while still strongly generalizing beyond the sample. The inferred internal distributions can be well approximated by a nonparametric mixture of spatially extended basis distributions. Thus, our results suggest that fluctuations have an excessive effect on human uncertainty judgments because of representations that can adapt overly flexibly to the sample. This might be of greater utility in more general conditions in structurally uncertain environments.

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<![CDATA[A Molecular Clock Infers Heterogeneous Tissue Age Among Patients with Barrett’s Esophagus]]> https://www.researchpad.co/article/5989da70ab0ee8fa60b94b76

Biomarkers that drift differentially with age between normal and premalignant tissues, such as Barrett’s esophagus (BE), have the potential to improve the assessment of a patient’s cancer risk by providing quantitative information about how long a patient has lived with the precursor (i.e., dwell time). In the case of BE, which is a metaplastic precursor to esophageal adenocarcinoma (EAC), such biomarkers would be particularly useful because EAC risk may change with BE dwell time and it is generally not known how long a patient has lived with BE when a patient is first diagnosed with this condition. In this study we first describe a statistical analysis of DNA methylation data (both cross-sectional and longitudinal) derived from tissue samples from 50 BE patients to identify and validate a set of 67 CpG dinucleotides in 51 CpG islands that undergo age-related methylomic drift. Next, we describe how this information can be used to estimate a patient’s BE dwell time. We introduce a Bayesian model that incorporates longitudinal methylomic drift rates, patient age, and methylation data from individually paired BE and normal squamous tissue samples to estimate patient-specific BE onset times. Our application of the model to 30 sporadic BE patients’ methylomic profiles first exposes a wide heterogeneity in patient-specific BE onset times. Furthermore, independent application of this method to a cohort of 22 familial BE (FBE) patients reveals significantly earlier mean BE onset times. Our analysis supports the conjecture that differential methylomic drift occurs in BE (relative to normal squamous tissue) and hence allows quantitative estimation of the time that a BE patient has lived with BE.

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<![CDATA[Genomic Prediction of Seed Quality Traits Using Advanced Barley Breeding Lines]]> https://www.researchpad.co/article/5989d9e0ab0ee8fa60b69733

Genomic selection was recently introduced in plant breeding. The objective of this study was to develop genomic prediction for important seed quality parameters in spring barley. The aim was to predict breeding values without expensive phenotyping of large sets of lines. A total number of 309 advanced spring barley lines tested at two locations each with three replicates were phenotyped and each line was genotyped by Illumina iSelect 9Kbarley chip. The population originated from two different breeding sets, which were phenotyped in two different years. Phenotypic measurements considered were: seed size, protein content, protein yield, test weight and ergosterol content. A leave-one-out cross-validation strategy revealed high prediction accuracies ranging between 0.40 and 0.83. Prediction across breeding sets resulted in reduced accuracies compared to the leave-one-out strategy. Furthermore, predicting across full and half-sib-families resulted in reduced prediction accuracies. Additionally, predictions were performed using reduced marker sets and reduced training population sets. In conclusion, using less than 200 lines in the training set can result in low prediction accuracy, and the accuracy will then be highly dependent on the family structure of the selected training set. However, the results also indicate that relatively small training sets (200 lines) are sufficient for genomic prediction in commercial barley breeding. In addition, our results indicate a minimum marker set of 1,000 to decrease the risk of low prediction accuracy for some traits or some families.

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<![CDATA[IVGTT-based simple assessment of glucose tolerance in the Zucker fatty rat: Validation against minimal models]]> https://www.researchpad.co/article/5989db4fab0ee8fa60bdbcc5

For the assessment of glucose tolerance from IVGTT data in Zucker rat, minimal model methodology is reliable but time- and money-consuming. This study aimed to validate for the first time in Zucker rat, simple surrogate indexes of insulin sensitivity and secretion against the glucose-minimal-model insulin sensitivity index (SI) and against first- (Φ1) and second-phase (Φ2) β-cell responsiveness indexes provided by C-peptide minimal model. Validation of the surrogate insulin sensitivity index (ISI) and of two sets of coupled insulin-based indexes for insulin secretion, differing from the cut-off point between phases (FPIR3-SPIR3, t = 3 min and FPIR5-SPIR5, t = 5 min), was carried out in a population of ten Zucker fatty rats (ZFR) and ten Zucker lean rats (ZLR). Considering the whole rat population (ZLR+ZFR), ISI showed a significant strong correlation with SI (Spearman’s correlation coefficient, r = 0.88; P<0.001). Both FPIR3 and FPIR5 showed a significant (P<0.001) strong correlation with Φ1 (r = 0.76 and r = 0.75, respectively). Both SPIR3 and SPIR5 showed a significant (P<0.001) strong correlation with Φ2 (r = 0.85 and r = 0.83, respectively). ISI is able to detect (P<0.001) the well-recognized reduction in insulin sensitivity in ZFRs, compared to ZLRs. The insulin-based indexes of insulin secretion are able to detect in ZFRs (P<0.001) the compensatory increase of first- and second-phase secretion, associated to the insulin-resistant state. The ability of the surrogate indexes in describing glucose tolerance in the ZFRs was confirmed by the Disposition Index analysis. The model-based validation performed in the present study supports the utilization of low-cost, insulin-based indexes for the assessment of glucose tolerance in Zucker rat, reliable animal model of human metabolic syndrome.

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<![CDATA[A Missing Data Approach to Correct for Direct and Indirect Range Restrictions with a Dichotomous Criterion: A Simulation Study]]> https://www.researchpad.co/article/5989da7cab0ee8fa60b98b56

A recurring methodological problem in the evaluation of the predictive validity of selection methods is that the values of the criterion variable are available for selected applicants only. This so-called range restriction problem causes biased population estimates. Correction methods for direct and indirect range restriction scenarios have widely studied for continuous criterion variables but not for dichotomous ones. The few existing approaches are inapplicable because they do not consider the unknown base rate of success. Hence, there is a lack of scientific research on suitable correction methods and the systematic analysis of their accuracies in the cases of a naturally or artificially dichotomous criterion. We aim to overcome this deficiency by viewing the range restriction problem as a missing data mechanism. We used multiple imputation by chained equations to generate complete criterion data before estimating the predictive validity and the base rate of success. Monte Carlo simulations were conducted to investigate the accuracy of the proposed correction in dependence of selection ratio, predictive validity, and base rate of success in an experimental design. In addition, we compared our proposed missing data approach with Thorndike’s well-known correction formulas that have only been used in the case of continuous criterion variables so far. The results show that the missing data approach is more accurate in estimating the predictive validity than Thorndike’s correction formulas. The accuracy of our proposed correction increases as the selection ratio and the correlation between predictor and criterion increase. Furthermore, the missing data approach provides a valid estimate of the unknown base rate of success. On the basis of our findings, we argue for the use of multiple imputation by chained equations in the evaluation of the predictive validity of selection methods when the criterion is dichotomous.

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<![CDATA[An Advanced Model to Precisely Estimate the Cell-Free Fetal DNA Concentration in Maternal Plasma]]> https://www.researchpad.co/article/5989da39ab0ee8fa60b875c6

Background

With the speedy development of sequencing technologies, noninvasive prenatal testing (NIPT) has been widely applied in clinical practice for testing for fetal aneuploidy. The cell-free fetal DNA (cffDNA) concentration in maternal plasma is the most critical parameter for this technology because it affects the accuracy of NIPT-based sequencing for fetal trisomies 21, 18 and 13. Several approaches have been developed to calculate the cffDNA fraction of the total cell-free DNA in the maternal plasma. However, most approaches depend on specific single nucleotide polymorphism (SNP) allele information or are restricted to male fetuses.

Methods

In this study, we present an innovative method to accurately deduce the concentration of the cffDNA fraction using only maternal plasma DNA. SNPs were classified into four maternal-fetal genotype combinations and three boundaries were added to capture effective SNP loci in which the mother was homozygous and the fetus was heterozygous. The median value of the concentration of the fetal DNA fraction was estimated using the effective SNPs. A depth-bias correction was performed using simulated data and corresponding regression equations for adjustments when the depth of the sequencing data was below 100-fold or the cffDNA fraction is less than 10%.

Results

Using our approach, the median of the relative bias was 0.4% in 18 maternal plasma samples with a median sequencing depth of 125-fold. There was a significant association (r = 0.935) between our estimations and the estimations inferred from the Y chromosome. Furthermore, this approach could precisely estimate a cffDNA fraction as low as 3%, using only maternal plasma DNA at the targeted region with a sequencing depth of 65-fold. We also used PCR instead of parallel sequencing to calculate the cffDNA fraction. There was a significant association (r = 98.2%) between our estimations and those inferred from the Y chromosome.

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<![CDATA[Angiopoietin-like-4 and minimal change disease]]> https://www.researchpad.co/article/5989db59ab0ee8fa60bdf0bb

Background

Minimal Change Disease (MCD) is the most common type of nephrotic syndrome in children. Angiopoietin-like-4 (Angplt4) has been proposed as mediator of proteinuria in MCD. The aim of this study was to evaluate the role of Angptl4 as a biomarker in MCD.

Methods

Patients with biopsy-proven primary MCD, focal segmental glomerulosclerosis, membranous nephropathy (60, 52 and 52 respectively) and 18 control subjects had urinary and serum Angptl4 measured by Elisa. Frozen kidney tissue sections were stained for Angptl4.

Results

Angptl4 was not identified in glomeruli of MCD patients in relapse. Urinary Angptl4 levels were elevated in MCD in relapse as well as in patients with massive proteinuria due to other glomerular diseases.

Conclusion

Neither serum nor urine Angptl4 appear to be good biomarkers in MCD. Elevated urinary Angptl4 n glomerular disease appears to reflect the degree of proteinuria rather than any specific disease.

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