Maternal exposure to SSRIs or SNRIs and the risk of congenital abnormalities in offspring: A systematic review and meta-analysis.

BACKGROUND: The association of maternal exposure to selective serotonin reuptake inhibitors (SSRIs) or serotonin and norepinephrine reuptake inhibitors (SNRIs) with the risk of system-specific congenital malformations in offspring remains unclear. We conducted a meta-analysis to examine this association and the risk difference between these two types of inhibitors. METHODS: A literature search was performed from January 2000 to May 2023 using PubMed and Web of Science databases. Cohort and case-control studies that assess the association of maternal exposure to SSRIs or SNRIs with the risk of congenital abnormalities were eligible for the study. RESULTS: Twenty-one cohort studies and seven case-control studies were included in the meta-analysis. Compared to non-exposure, maternal exposure to SNRIs is associated with a higher risk of congenital cardiovascular abnormalities (pooled OR: 1.64 with 95% CI: 1.36, 1.97), anomalies of the kidney and urinary tract (pooled OR: 1.63 with 95% CI: 1.21, 2.20), malformations of nervous system (pooled OR: 2.28 with 95% CI: 1.50, 3.45), anomalies of digestive system (pooled OR: 2.05 with 95% CI: 1.60, 2.64) and abdominal birth defects (pooled OR: 2.91 with 95%CI: 1.98, 4.28), while maternal exposure to SSRIs is associated with a higher risk of congenital cardiovascular abnormalities (pooled OR: 1.25 with 95%CI: 1.20, 1.30), anomalies of the kidney and urinary tract (pooled OR: 1.14 with 95%CI: 1.02, 1.27), anomalies of digestive system (pooled OR: 1.11 with 95%CI: 1.01, 1.21), abdominal birth defects (pooled OR: 1.33 with 95%CI: 1.16, 1.53) and musculoskeletal malformations (pooled OR: 1.44 with 95%CI: 1.32, 1.56). CONCLUSIONS: SSRIs and SNRIs have various teratogenic risks. Clinicians must consider risk-benefit ratios and patient history when prescribing medicines.

Single-cell transcriptomics shows dose-dependent disruption of hepatic zonation by TCDD in mice.

2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD) dose-dependently induces the development of hepatic fat accumulation and inflammation with fibrosis in mice initially in the portal region. Conversely, differential gene and protein expression is first detected in the central region. To further investigate cell-specific and spatially resolved dose-dependent changes in gene expression elicited by TCDD, single-nuclei RNA sequencing and spatial transcriptomics were used for livers of male mice gavaged with TCDD every 4 days for 28 days. The proportion of 11 cell (sub)types across 131 613 nuclei dose-dependently changed with 68% of all portal and central hepatocyte nuclei in control mice being overtaken by macrophages following TCDD treatment. We identified 368 (portal fibroblasts) to 1339 (macrophages) differentially expressed genes. Spatial analyses revealed initial loss of portal identity that eventually spanned the entire liver lobule with increasing dose. Induction of R-spondin 3 (Rspo3) and pericentral Apc, suggested dysregulation of the Wnt/ß-catenin signaling cascade in zonally resolved steatosis. Collectively, the integrated results suggest disruption of zonation contributes to the pattern of TCDD-elicited NAFLD pathologies.

Fitness cost conferred by the novel erm(51) and rpoB mutation on environmental multidrug resistant-Rhodococcus equi.

Rhodococcus equi is a common cause of severe pneumonia in foals. Emergence of macrolide-resistant R. equi isolated from foals and their environment has been reported in the United States. A novel erm(51) gene was recently identified in R. equi in soil from horse farms in Kentucky. Our objective was to determine the effect of the erm(51) gene and associated rpoB mutation on the fitness of multidrug resistant-R. equi (MDR-R. equierm(51)+, rpoB+) under different nutrient conditions. Bacterial growth curves were generated for 3 MDR-R. equierm(51)+, rpoB+ isolates and 3 wild-type (WTN) R. equi isolates recovered from environmental samples of farms in central Kentucky. Growth was measured over 30.5 h in brain-heart infusion broth (BHI), minimal medium (MM), and minimal medium without iron (MM-I). All isolates had significantly (P < 0.05) higher growth in BHI compared to either MM or MM-I. MDR-R. equierm(51)+, rpoB+ exhibited significantly lower growth compared to WTN isolates in BHI (nutrient-rich condition), but not in either MM or MM-I (nutrient-restricted conditions). This study indicates that under nutrient-rich conditions fitness of MDR-R. equierm(51)+, rpoB+ is reduced relative to susceptible isolates; however, under nutrient-restricted conditions MDR-R. equierm(51)+, rpoB+ isolates grow similarly to susceptible isolates. These findings indicate that MDR-R. equierm(51)+, rpoB+ might be outcompeted by susceptible isolates in nature when practices to reduce antimicrobial pressure, such as reducing antimicrobial use in foals, are implemented. But it also raises the concern that these resistant genotypes might persist in the environment of horse-breeding farms in the face of selective pressures such as antimicrobials or nutrient restriction.

Benchmarking of a Bayesian single cell RNAseq differential gene expression test for dose-response study designs.

The application of single-cell RNA sequencing (scRNAseq) for the evaluation of chemicals, drugs, and food contaminants presents the opportunity to consider cellular heterogeneity in pharmacological and toxicological responses. Current differential gene expression analysis (DGEA) methods focus primarily on two group comparisons, not multi-group dose-response study designs used in safety assessments. To benchmark DGEA methods for dose-response scRNAseq experiments, we proposed a multiplicity corrected Bayesian testing approach and compare it against 8 other methods including two frequentist fit-for-purpose tests using simulated and experimental data. Our Bayesian test method outperformed all other tests for a broad range of accuracy metrics including control of false positive error rates. Most notable, the fit-for-purpose and standard multiple group DGEA methods were superior to the two group scRNAseq methods for dose-response study designs. Collectively, our benchmarking of DGEA methods demonstrates the importance in considering study design when determining the most appropriate test methods.

Identification of miR-203a, mir-10a, and miR-194 as predictors for risk of lymphovascular invasion in head and neck cancers.

Lymphovascular invasion (LVI) is an important prognostic indicator of lymph node metastasis and disease aggressiveness but clear molecular mechanisms mediating this in head and neck cancers (HNSC) remain undefined. To identify important microRNAs (miRNAs) in HNSC that associate with and are also predictive of increased risk of LVI, we used a combination of clustering algorithms, multiple regression analyses and machine learning approaches and analyzed miRNA expression profiles in the TCGA HNSC database. As the first step, we identified miRNAs with increased association with LVI as a binary variable. In order to determine whether the identified miRNAs would show functional clusters that are also indicative of increased risk for LVI, we carried out unsupervised as well as supervised clustering. Our results identified distinct clusters of miRNAs that are predictive of increased LVI. We further refined these findings using a Random forest approach, and miR-203a-3p, mir-10a-5p, and miR-194-5p to be most strongly associated with LVI. Pathway enrichment analysis showed these miRNAs targeted genes involved in Hippo signaling and fatty acid oxidation pathways that are mediators of lymph node metastasis. Specific association was also identified between the miRNAs associated with LVI and expression of several lymphangiogenic genes that could be critical for determination of therapeutic strategies.

Matched case-control data with a misclassified exposure: what can be done with instrumental variables?

Matched case-control studies are used for finding the association between a disease and an exposure after controlling the effect of important confounding variables. It is a known fact that the disease-exposure association parameter estimators are biased when the exposure is misclassified, and a matched case-control study is of no exception. Any bias correction method relies on validation data that contain the true exposure and the misclassified exposure value, and in turn the validation data help to estimate the misclassification probabilities. The question is what we can do when there are no validation data and no prior knowledge on the misclassification probabilities, but some instrumental variables are observed. To answer this unexplored and unanswered question, we propose two methods of reducing the exposure misclassification bias in the analysis of a matched case-control data when instrumental variables are measured for each subject of the study. The significance of these approaches is that the proposed methods are designed to work without any validation data that often are not available when the true exposure is impossible or too costly to measure. A simulation study explores different types of instrumental variable scenarios and investigates when the proposed methods work, and how much bias can be reduced. For the purpose of illustration, we apply the methods to a nested case-control data sampled from the 1989 US birth registry.

CXCL11-CXCR3 Axis Mediates Tumor Lymphatic Cross Talk and Inflammation-Induced Tumor, Promoting Pathways in Head and Neck Cancers.

Tumor metastasis to the draining lymph nodes is critical in patient prognosis and is tightly regulated by molecular interactions mediated by lymphatic endothelial cells (LECs). The underlying mechanisms remain undefined in the head and neck squamous cell carcinomas (HNSCCs). Using HNSCC cells and LECs we determined the mechanisms mediating tumor-lymphatic cross talk. The effects of a pentacyclic triterpenoid, methyl 2-trifluoromethyl-3,11-dioxoolean-1,12-dien-30-oate (CF3DODA-Me), a potent anticancer agent, were studied on cancer-lymphatic interactions. In response to inflammation, LECs induced the chemokine (C-X-C motif) ligand 9/10/11 chemokines with a concomitant increase in the chemokine (C-X-C motif) receptor 3 (CXCR3) in tumor cells. CF3DODA-Me showed antiproliferative effects on tumor cells, altered cellular bioenergetics, suppressed matrix metalloproteinases and chemokine receptors, and the induction of CXCL11-CXCR3 axis and phosphatidylinositol 3-kinase/AKT pathways. Tumor cell migration to LECs was inhibited by blocking CXCL11 whereas recombinant CXCL11 significantly induced tumor migration, epithelial-to-mesenchymal transition, and matrix remodeling. Immunohistochemical analysis of HNSCC tumor arrays showed enhanced expression of CXCR3 and increased lymphatic vessel infiltration. Furthermore, The Cancer Genome Atlas RNA-sequencing data from HNSCC patients also showed a positive correlation between CXCR3 expression and lymphovascular invasion. Collectively, our data suggest a novel mechanism for cross talk between the LECs and HNSCC tumors through the CXCR3-CXCL11 axis and elucidate the role of the triterpenoid CF3DODA-Me in abrogating several of these tumor-promoting pathways.

A test of homogeneity of distributions when observations are subject to measurement errors.

When the observed data are contaminated with errors, the standard two-sample testing approaches that ignore measurement errors may produce misleading results, including a higher type-I error rate than the nominal level. To tackle this inconsistency, a nonparametric test is proposed for testing equality of two distributions when the observed contaminated data follow the classical additive measurement error model. The proposed test takes into account the presence of errors in the observed data, and the test statistic is defined in terms of the (deconvoluted) characteristic functions of the latent variables. Proposed method is applicable to a wide range of scenarios as no parametric restrictions are imposed either on the distribution of the underlying latent variables or on the distribution of the measurement errors. Asymptotic null distribution of the test statistic is derived, which is given by an integral of a squared Gaussian process with a complicated covariance structure. For data-based calibration of the test, a new nonparametric Bootstrap method is developed under the two-sample measurement error framework and its validity is established. Finite sample performance of the proposed test is investigated through simulation studies, and the results show superior performance of the proposed method than the standard tests that exhibit inconsistent behavior. Finally, the proposed method was applied to real data sets from the National Health and Nutrition Examination Survey. An R package MEtest is available through CRAN.

Searching for Beyond the Standard Model Physics with COHERENT Energy and Timing Data.

We search for beyond the standard model physics by combining COHERENT Collaboration energy and timing data. Focusing on light, ≲GeV mediators, we find the data favor a ∼10-1000 MeV mediator, as compared to the standard model best fit at the ≲2σ level. The best-fit coupling range is g∼10^{-5}-10^{-3}. The timing data provide statistical information on the neutrino flavor distributions that is not attainable from the nuclear recoil energies alone. This result accounts for uncertainty in the effective size of the neutron distribution, and highlights the power of including uncertainties on experimental backgrounds, nuclear structure, and beyond the standard model physics.

A consistent estimator for logistic mixed effect models.

We propose a consistent and locally efficient estimator to estimate the model parameters for a logistic mixed effect model with random slopes. Our approach relaxes two typical assumptions: the random effects being normally distributed, and the covariates and random effects being independent of each other. Adhering to these assumptions is particularly difficult in health studies where in many cases we have limited resources to design experiments and gather data in long-term studies, while new findings from other fields might emerge, suggesting the violation of such assumptions. So it is crucial if we could have an estimator robust to such violations and then we could make better use of current data harvested using various valuable resources. Our method generalizes the framework presented in Garcia & Ma (2016) which also deals with a logistic mixed effect model but only considers a random intercept. A simulation study reveals that our proposed estimator remains consistent even when the independence and normality assumptions are violated. This contrasts from the traditional maximum likelihood estimator which is likely to be inconsistent when there is dependence between the covariates and random effects. Application of this work to a Huntington disease study reveals that disease diagnosis can be further improved using assessments of cognitive performance.

Analysis of linear transformation models with covariate measurement error and interval censoring.

Among several semiparametric models, the Cox proportional hazard model is widely used to assess the association between covariates and the time-to-event when the observed time-to-event is interval-censored. Often, covariates are measured with error. To handle this covariate uncertainty in the Cox proportional hazard model with the interval-censored data, flexible approaches have been proposed. To fill a gap and broaden the scope of statistical applications to analyze time-to-event data with different models, in this paper, a general approach is proposed for fitting the semiparametric linear transformation model to interval-censored data when a covariate is measured with error. The semiparametric linear transformation model is a broad class of models that includes the proportional hazard model and the proportional odds model as special cases. The proposed method relies on a set of estimating equations to estimate the regression parameters and the infinite-dimensional parameter. For handling interval censoring and covariate measurement error, a flexible imputation technique is used. Finite sample performance of the proposed method is judged via simulation studies. Finally, the suggested method is applied to analyze a real data set from an AIDS clinical trial.

Bayesian variable selection in the accelerated failure time model with an application to the surveillance, epidemiology, and end results breast cancer data.

Accelerated failure time model is a popular model to analyze censored time-to-event data. Analysis of this model without assuming any parametric distribution for the model error is challenging, and the model complexity is enhanced in the presence of large number of covariates. We developed a nonparametric Bayesian method for regularized estimation of the regression parameters in a flexible accelerated failure time model. The novelties of our method lie in modeling the error distribution of the accelerated failure time nonparametrically, modeling the variance as a function of the mean, and adopting a variable selection technique in modeling the mean. The proposed method allowed for identifying a set of important regression parameters, estimating survival probabilities, and constructing credible intervals of the survival probabilities. We evaluated operating characteristics of the proposed method via simulation studies. Finally, we apply our new comprehensive method to analyze the motivating breast cancer data from the Surveillance, Epidemiology, and End Results Program, and estimate the five-year survival probabilities for women included in the Surveillance, Epidemiology, and End Results database who were diagnosed with breast cancer between 1990 and 2000.

Two Wrongs Make a Right: Addressing Underreporting in Binary Data from Multiple Sources.

Media-based event data-i.e., data comprised from reporting by media outlets-are widely used in political science research. However, events of interest (e.g., strikes, protests, conflict) are often underreported by these primary and secondary sources, producing incomplete data that risks inconsistency and bias in subsequent analysis. While general strategies exist to help ameliorate this bias, these methods do not make full use of the information often available to researchers. Specifically, much of the event data used in the social sciences is drawn from multiple, overlapping news sources (e.g., Agence France-Presse, Reuters). Therefore, we propose a novel maximum likelihood estimator that corrects for misclassification in data arising from multiple sources. In the most general formulation of our estimator, researchers can specify separate sets of predictors for the true-event model and each of the misclassification models characterizing whether a source fails to report on an event. As such, researchers are able to accurately test theories on both the causes of and reporting on an event of interest. Simulations evidence that our technique regularly out performs current strategies that either neglect misclassification, the unique features of the data-generating process, or both. We also illustrate the utility of this method with a model of repression using the Social Conflict in Africa Database.

Frequentist Standard Errors of Bayes Estimators.

Frequentist standard errors are a measure of uncertainty of an estimator, and the basis for statistical inferences. Frequestist standard errors can also be derived for Bayes estimators. However, except in special cases, the computation of the standard error of Bayesian estimators requires bootstrapping, which in combination with Markov chain Monte Carlo (MCMC) can be highly time consuming. We discuss an alternative approach for computing frequentist standard errors of Bayesian estimators, including importance sampling. Through several numerical examples we show that our approach can be much more computationally efficient than the standard bootstrap.

Semiparametric Bayesian analysis of censored linear regression with errors-in-covariates.

The accelerated failure time (AFT) model is a well-known alternative to the Cox proportional hazard model for analyzing time-to-event data. In this paper we consider fitting an AFT model to right censored data when a predictor variable is subject to measurement errors. First, without measurement errors, estimation of the model parameters in the AFT model is a challenging task due to the presence of censoring, especially when no specific assumption is made regarding the distribution of the logarithm of the time-to-event. The model complexity increases when a predictor is measured with error. We propose a non-parametric Bayesian method for analyzing such data. The novel component of our approach is to model (1) the distribution of the time-to-event, (2) the distribution of the unobserved true predictor, and (3) the distribution of the measurement errors all non-parametrically using mixtures of the Dirichlet process priors. Along with the parameter estimation we also prescribe how to estimate survival probabilities of the time-to-event. Some operating characteristics of the proposed approach are judged via finite sample simulation studies. We illustrate the proposed method by analyzing a data set from an AIDS clinical trial study.

Functional Mixed Effects Model for Small Area Estimation.

Functional data analysis has become an important area of research due to its ability of handling high dimensional and complex data structures. However, the development is limited in the context of linear mixed effect models, and in particular, for small area estimation. The linear mixed effect models are the backbone of small area estimation. In this article, we consider area level data, and fit a varying coefficient linear mixed effect model where the varying coefficients are semi-parametrically modeled via B-splines. We propose a method of estimating the fixed effect parameters and consider prediction of random effects that can be implemented using a standard software. For measuring prediction uncertainties, we derive an analytical expression for the mean squared errors, and propose a method of estimating the mean squared errors. The procedure is illustrated via a real data example, and operating characteristics of the method are judged using finite sample simulation studies.

Analysis of Multivariate Disease Classification Data in the Presence of Partially Missing Disease Traits.

In modern cancer epidemiology, diseases are classified based on pathologic and molecular traits, and different combinations of these traits give rise to many disease subtypes. The effect of predictor variables can be measured by fitting a polytomous logistic model to such data. The differences (heterogeneity) among the relative risk parameters associated with subtypes are of great interest to better understand disease etiology. Due to the heterogeneity of the relative risk parameters, when a risk factor is changed, the prevalence of one subtype may change more than that of another subtype does. Estimation of the heterogeneity parameters is difficult when disease trait information is only partially observed and the number of disease subtypes is large. We consider a robust semiparametric approach based on the pseudo-conditional likelihood for estimating these heterogeneity parameters. Through simulation studies, we compare the robustness and efficiency of our approach with that of the maximum likelihood approach. The method is then applied to analyze the associations of weight gain with risk of breast cancer subtypes using data from the American Cancer Society Cancer Prevention Study II Nutrition Cohort.

Semiparametric approach for non-monotone missing covariates in a parametric regression model.

Missing covariate data often arise in biomedical studies, and analysis of such data that ignores subjects with incomplete information may lead to inefficient and possibly biased estimates. A great deal of attention has been paid to handling a single missing covariate or a monotone pattern of missing data when the missingness mechanism is missing at random. In this article, we propose a semiparametric method for handling non-monotone patterns of missing data. The proposed method relies on the assumption that the missingness mechanism of a variable does not depend on the missing variable itself but may depend on the other missing variables. This mechanism is somewhat less general than the completely non-ignorable mechanism but is sometimes more flexible than the missing at random mechanism where the missingness mechansim is allowed to depend only on the completely observed variables. The proposed approach is robust to misspecification of the distribution of the missing covariates, and the proposed mechanism helps to nullify (or reduce) the problems due to non-identifiability that result from the non-ignorable missingness mechanism. The asymptotic properties of the proposed estimator are derived. Finite sample performance is assessed through simulation studies. Finally, for the purpose of illustration we analyze an endometrial cancer dataset and a hip fracture dataset.

Semiparametric analysis of linear transformation models with covariate measurement errors.

We take a semiparametric approach in fitting a linear transformation model to a right censored data when predictive variables are subject to measurement errors. We construct consistent estimating equations when repeated measurements of a surrogate of the unobserved true predictor are available. The proposed approach applies under minimal assumptions on the distributions of the true covariate or the measurement errors. We derive the asymptotic properties of the estimator and illustrate the characteristics of the estimator in finite sample performance via simulation studies. We apply the method to analyze an AIDS clinical trial data set that motivated the work.

Bias reduction in conditional logistic regression.

We employ a general bias preventive approach developed by Firth (Biometrika 1993; 80:27-38) to reduce the bias of an estimator of the log-odds ratio parameter in a matched case-control study by solving a modified score equation. We also propose a method to calculate the standard error of the resultant estimator. A closed-form expression for the estimator of the log-odds ratio parameter is derived in the case of a dichotomous exposure variable. Finite sample properties of the estimator are investigated via a simulation study. Finally, we apply the method to analyze a matched case-control data from a low birthweight study.