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ObjectsTo evaluate the hsa_circ_0001445 level in peripheral blood leukocytes of patients with coronary heart disease (CHD) and its related clinical factors, and predict its circRNA-miRNA-mRNA regulatory network in CHD pathogenesis via bioinformatics analysis.MethodsPeripheral blood leukocytes were isolated from the whole blood samples of 94 CHD patients (aged 65.96 ± 9.78 years old) and 126 healthy controls (aged 60.75 ± 8.81 years old). qRT-PCR was used to quantify the expression level of circRNA and subsequently analyze its association with CHD clinical parameters. Via bioinformatics algorithm and GEO datasets, differential miRNA expression was evaluated using the Limma package. A miRNA-mRNA regulatory network was predicted by cyTargetLinker. ClusterProfiler was employed to perform functional enrichment analysis of the circRNA network to investigate its role in CHD pathogenesis.ResultsThe expression of hsa_circ_0001445 in peripheral blood leukocytes of CHD patients was downregulated compared with that of healthy controls. Positive correlations were evident between hsa_circ_0001445 expression level and the levels of hemoglobin, triglycerides, high- and low-density lipoprotein cholesterol. A significant negative correlation was also found between hsa_circ_0001445 expression level and age and the neutrophil level. Low expression of hsa_circ_0001445 exhibited a discriminatory ability between CHD patients and healthy controls with a sensitivity of 67.5% and a specificity of 76.6% (p

We develop a probability model for evaluating long-term outcomes due to regular screening that incorporates the effects of prior screening examinations. Previous models assume that individuals have no prior screening examinations at their current ages. Due to current widespread medical emphasis on screening, the consideration of screening histories is essential, particularly in assessing the benefit of future screening examinations given a certain number of previous negative screens. Screening participants are categorized into four mutually exclusive groups: symptom-free-life, no-early-detection, true-early-detection, and overdiagnosis. For each case, we develop models that incorporate a person’s current age, screening history, expected future screening frequency, screening test sensitivity, and other factors, and derive the probabilities of occurrence for the four groups. The probability of overdiagnosis among screen-detected cases is derived and estimated. The model applies to screening for any disease or condition; for concreteness, we focus on female breast cancer and use data from the study conducted by the Health Insurance Plan of Greater New York (HIP) to estimate these probabilities and corresponding credible intervals. The model can provide policy makers with important information regarding ranges of expected lives saved and percentages of true-early-detection and overdiagnosis among the screen-detected cases.

Early detection combined with effective treatments are the only ways to fight against cancer, and cancer screening is the primary technique for early detection. Although mass cancer screening has been carried out for decades, there are many unsolved problems, and the statistical theory of cancer screening is still under developed. Screening sensitivity, time duration in the preclinical state, and time duration in the disease free state are the three key parameters, which are critical in cancer screening, since all other estimates are functions of the three key parameters. Lead time is the diagnosis time advanced by screening, and it serves as a measurement of effectiveness of screening programs. In this article, we provide a review for major probability models and statistical methodologies that have been developed on the estimation of the three key parameters and the lead time distributions. These methods can be applied to screening of other chronic diseases after slight modifications.

Problem statement: Lung cancer screening using X-rays has been controversial for many years. A major concern is whether lung cancer screening really brings any survival benefits, which depends on effective treatment after early detection. The problem was analyzed from a different point of view and estimates were presented of the projected lead time for participants in a lung cancer screening program using the Johns Hopkins Lung Project (JHLP) data. Method: The newly developed method of lead time estimation was applied where the lifetime T was treated as a random variable rather than a fixed value, resulting in the number of future screenings for a given individual is a random variable. Using the actuarial life table available from the United States Social Security Administration, the lifetime distribution was first obtained, then the lead time distribution was projected using the JHLP data. Results: The data analysis with the JHLP data shows that, for a male heavy smoker with initial screening ages at 50, 60, and 70, the probability of no-early-detection with semiannual screens will be 32.16%, 32.45%, and 33.17%, respectively; while the mean lead time is 1.36, 1.33 and 1.23 years. The probability of no-early-detection increases monotonically when the screening interval increases, and it increases slightly as the initial age increases for the same screening interval. The mean lead time and its standard error decrease when the screening interval increases for all age groups, and both decrease when initial age increases with the same screening interval. Conclusion: The overall mean lead time estimated with a random lifetime T is slightly less than that with a fixed value of T. This result is hoped to be of benefit to improve current screening programs.

Normalization is an essential step with considerable impact on high-throughput RNA sequencing (RNA-seq) data analysis. Although there are numerous methods for read count normalization, it remains a challenge to choose an optimal method due to multiple factors contributing to read count variability that affects the overall sensitivity and specificity. In order to properly determine the most appropriate normalization methods, it is critical to compare the performance and shortcomings of a representative set of normalization routines based on different dataset characteristics. Therefore, we set out to evaluate the performance of the commonly used methods (DESeq, TMM-edgeR, FPKM-CuffDiff, TC, Med UQ and FQ) and two new methods we propose: Med-pgQ2 and UQ-pgQ2 (per-gene normalization after per-sample median or upper-quartile global scaling). Our per-gene normalization approach allows for comparisons between conditions based on similar count levels. Using the benchmark Microarray Quality Control Project (MAQC) and simulated datasets, we performed differential gene expression analysis to evaluate these methods. When evaluating MAQC2 with two replicates, we observed that Med-pgQ2 and UQ-pgQ2 achieved a slightly higher area under the Receiver Operating Characteristic Curve (AUC), a specificity rate > 85%, the detection power > 92% and an actual false discovery rate (FDR) under 0.06 given the nominal FDR (≤0.05). Although the top commonly used methods (DESeq and TMM-edgeR) yield a higher power (>93%) for MAQC2 data, they trade off with a reduced specificity (

Breast cancer screening programs have been effective in detecting tumors prior to symptoms. Recently, there has been concern over the issue of over-diagnosis, that is, diagnosis of a breast cancer that does not manifest prior to death. Estimates for over-diagnosis vary, ranging from 7 to 52%. This variability may be due partially to issues associated with bias and/or incorrect inferences associated with the lack of probability modeling. A critical issue is how to evaluate the long-term effects due to continued screening. Participants in a periodic screening program can be classified into four mutually exclusive groups depending on whether individuals are diagnosed and whether their symptoms appear prior to death: True-earlydetection; No-early-detection; Over-diagnosis; and Not-sonecessary. All initially superficially healthy people will eventually fall into one of these four categories. This manuscript reviews the major methodologies associated with the over-diagnosis and long-term effects of breast cancer screening.

High throughput RNA sequencing (RNA-seq) technology is increasingly used in disease-related biomarker studies. A negative binomial distribution has become the popular choice for modeling read counts of genes in RNA-seq data due to over-dispersed read counts. In this study, we propose two explicit sample size calculation methods for RNA-seq data using a negative binomial regression model. To derive these new sample size formulas, the common dispersion parameter and the size factor as an offset via a natural logarithm link function are incorporated. A two-sided Wald test statistic derived from the coefficient parameter is used for testing a single gene at a nominal significance level 0.05 and multiple genes at a false discovery rate 0.05. The variance for the Wald test is computed from the variance-covariance matrix with the parameters estimated from the maximum likelihood estimates under the unrestricted and constrained scenarios. The performance and a sideby- side comparison of our new formulas with three existing methods with a Wald test, a likelihood ratio test or an exact test are evaluated via simulation studies. Since other methods are much computationally extensive, we recommend our M1 method for quick and direct estimation of sample sizes in an experimental design. Finally, we illustrate sample sizes estimation using an existing breast cancer RNA-seq data. [ABSTRACT FROM AUTHOR]

We estimated the effects on the lead time for women participating in a long-term breast cancer screening program when the screening sensitivities and disease progression are independent of age. The lead time, or time by which a diagnosis is advanced by screening, is one of the major concerns of any cancer screening program, which we consider to include both a mammogram and physical examination. Using estimates of test sensitivities and mean sojourn times previously calculated by other authors from observed data, we estimated properties of the lead time. We utilized the model for the lead time derived by other authors and ran simulations for different screening program designs, concentrating on screening interval lengths of 0.5 years, 1 year, 1.5 years, and 2 years. These estimates were based on a long-term screening program from age 50 to 80. For each six-month decrease in screening interval length, we estimated the percent increase in mean lead time, as well as the percent increase in the proportion of clinical patients who will have their cancer detected at a screening exam. [ABSTRACT FROM AUTHOR]

Abstract  The objective of this study was to investigate the moisture absorption process for wood-based composites subjected to over-saturated moisture conditions. Two stages are comprised in the moisture transfer process at the over-saturated moisture conditions, an initial stage which is the moisture transfer process mainly under fiber saturation point (FSP), and a second stage which is the moisture transfer process beyond the FSP. A model was developed based on two-part equations to describe the process, from which three coefficients (k 1 , k 21 , and k 22) can be used to quantitatively describe the moisture transfer process under the conditions. Two different wood-based composites, wood fiberboard and wood fiber/polymer composites (polymer content: 30%), were used to test the model at four different ambient temperatures (30, 45, 62, and 80°C). It was shown that the two-part equation can accurately describe the moisture absorption process under over-saturated moisture conditions. The moisture absorption rate in the initial stage was about 30–60% greater than that in the second stage for most of the cases evaluated in this study. The higher the temperature, the greater moisture absorption parameters were obtained. At both moisture absorption stages (below FSP and above FSP), the calculated activation energy for the moisture absorption rate of wood fiberboard was very close to that of wood fiber/polymer composites. [ABSTRACT FROM AUTHOR]

In addition to additive (A) and dominant (D) genetic effects, the A X A interaction (or A X A epislatic) effects that control many quantitative traits are important for genetic and breeding studies. To estimate these genetic variance components, including genotype X environment (G X E) interaction, one usually expects to have data from at least two generations (i.e., F1 and F2) and parents with the same entries in all environments. Practical difficulties may arise in implementing such a design. In this study, we performed Monte Carlo simulations to compare the estimated variance components between four partial and two complete genetic designs (GDs) using the mixed linear model approach. Our definition for GD is different from the traditional definitions of genetic mating designs. Simulation results showed that the estimated genetic variance components for A, A X E, A X A epistatic, and A x A X E effects were unbiased for the six designs. Among four partial designs, two provided the comparable results for D and D X E effects compared with the complete GDs, but with slightly larger mean square errors (MSEs), indicating that some partial GDs can be used when the genetic resources are limited. [ABSTRACT FROM AUTHOR]

This article extends previous probability models for periodic breast cancer screening examinations. The specific aim is to provide statistical inference for age dependence of sensitivity and the transition probability from the disease free to the preclinical state. The setting is a periodic screening program in which a cohort of initially asymptomatic women undergo a sequence of breast cancer screening exams. We use age as a covariate in the estimation of screening sensitivity and the transition probability simultaneously, both from a frequentist point of view and within a Bayesian framework. We apply our method to the Health Insurance Plan of Greater New York study of female breast cancer and give age-dependent sensitivity and transition probability density estimates. The inferential methodology we develop is also applicable when analyzing studies of modalities for early detection of other types of progressive chronic diseases. [ABSTRACT FROM AUTHOR]

Examines the NORM method for finding threshold values in wavelet regression. Minimization of the average mean-squared error; Types of noise; Accuracy of the method for estimating noise level.