Identification of the allergenic sensitizing potential of bisphenol A substitutes used in the industry.

BACKGROUND: Bisphenol (BP-)A is a chemical used in Europe to produce polycarbonate plastics and epoxy resin or as colour developer in thermal paper. Due to its toxicity, BPA presence was restricted by European regulations. Therefore, substitute chemicals are replacing BPA. OBJECTIVE: To assess the allergenic sensitizing potential of 27 substitutes to BPA used in the industry. METHODS: The expression of two costimulatory molecules and six cytokines were analysed by flow cytometry in mouse bone marrow-derived dendritic cells (BMDCs) exposed to the chemicals. RESULTS: All substances except one induced overexpression of at least one receptor and were thus identified as having allergenic sensitizing potential. Based on the BMDC model, they were classified as extreme (1 out of 27), strong (20 out of 27) and moderate (5 out of 27) sensitizers. BPA was classified as a moderate sensitizer and BPF was the only substitute classified as a non-sensitizer. The more potent substitutes induced more than 2-fold secretion of CCL3, CCL4 and/or CCL5 by dendritic cells. CONCLUSION: Most of the BPA substitutes tested in this study have an allergenic sensitizing potential; 24 of them being more potent than BPA itself. Only BPE, BPF and 2,4-BPS appeared to be weaker sensitizers than BPA.

The BMDC model, a performant cell-based test to assess the sensitizing potential and potency of chemicals including pre/pro-haptens.

BACKGROUND: Chemical-induced allergies at workplace represent a significant occupational health issue. These substances must be properly identified as sensitizers. In previous studies, an original model using mouse bone marrow-derived dendritic cells (BMDC) was developed for this purpose. OBJECTIVES: The aim of this study was to evaluate the predictive capacity of the BMDC model with a large panel of sensitizers (including pre- and pro-haptens) and non-sensitizers. METHODS: The readout from the BMDC model is based on expression levels of six phenotypic markers measured by flow cytometry. RESULTS: The results indicate that 29 of the 37 non-sensitizers, and 81 of the 86 sensitizers were correctly classified compared to the Local Lymph Node Assay (LLNA). Statistical analysis revealed the BMDC model to have a sensitivity of 94%, a specificity of 78%, and an accuracy of 89%. The EC2 (Effective Concentration) values calculated with this model allow sensitizers to be categorized into four classes: extreme, strong, moderate and weak. CONCLUSIONS: These excellent predictive performances show that the BMDC model discriminates between sensitizers and non-sensitizers with outstanding precision equal to or better than existing validated alternative models. Moreover, this model allows to predict sensitization potency of chemicals. The BMDC test could therefore be proposed as an additional tool to assess the sensitizing potential and potency of chemicals.

Screening methods for linear errors-in-variables models in high dimensions.

Microarray studies, in order to identify genes associated with an outcome of interest, usually produce noisy measurements for a large number of gene expression features from a small number of subjects. One common approach to analyzing such high-dimensional data is to use linear errors-in-variables (EIV) models; however, current methods for fitting such models are computationally expensive. In this paper, we present two efficient screening procedures, namely, corrected penalized marginal screening (PMSc) and corrected sure independence screening (SISc), to reduce the number of variables for final model building. Both screening procedures are based on fitting corrected marginal regression models relating the outcome to each contaminated covariate separately, which can be computed efficiently even with a large number of features. Under mild conditions, we show that these procedures achieve screening consistency and reduce the number of features substantially, even when the number of covariates grows exponentially with sample size. In addition, if the true covariates are weakly correlated, we show that PMSc can achieve full variable selection consistency. Through a simulation study and an analysis of gene expression data for bone mineral density of Norwegian women, we demonstrate that the two new screening procedures make estimation of linear EIV models computationally scalable in high-dimensional settings, and improve finite sample estimation and selection performance compared with estimators that do not employ a screening stage.

A Multi-Step Precision Pathway for Predicting Allograft Survival in Heterogeneous Cohorts of Kidney Transplant Recipients.

Accurate prediction of allograft survival after kidney transplantation allows early identification of at-risk recipients for adverse outcomes and initiation of preventive interventions to optimize post-transplant care. Many prediction algorithms do not model cohort heterogeneity and may lead to inaccurate assessment of longer-term graft outcomes among minority groups. Using data from a national Australian kidney transplant cohort (2008-2017) as the derivation set, we developed P-Cube, a multi-step precision prediction pathway model for predicting overall graft survival in three ethnic subgroups: European Australians, Asian Australians and Aboriginal and Torres Strait Islander Peoples. The concordance index for the European Australians, Asian Australians, and Aboriginal and Torres Strait Islander Peoples subpopulations were 0.99 (0.98-0.99), 0.93 (0.92-0.94) and 0.92 (0.91-0.93), respectively. Similar findings were observed when validating P-cube using an external dataset [Scientific Registry of Transplant Recipient Registry (2006-2020)]. Six sub-categories of recipients with distinct risk factor profiles were identified. Some factors such as blood group compatibility were considered important across the entire transplant population. Other factors such as human leukocyte antigen (HLA)-DR mismatches were unique to older recipients. The P-cube model identifies allograft survival specific risk factors within a heterogenous population and offers personalized survival predictions in a diverse cohort.

Quantification of global orbital shape variation.

The complex anatomy of the orbit generates a complex orbital shape that can only be quantified approximatively by classic linear measurements such as maximum width and height. There is no global three-dimensional quantification of variations in orbital shape. The purpose of this study was to develop a method to quantify a global three-dimensional orbital shape variation in a healthy population and to test a series of explanatory factors. We investigated the hypotheses that orbital shape is related to gender(H1), orbital size(H2) and/or age(H3). Medical computed tomography(CT) images of 60 adult individuals were studied. The study sample consisted of 30 males and 30 females with a mean age of 25.1 years. Four anatomical landmarks and 140 semi-landmarks were measured on both positive and negative 3D reconstructed orbits and analyzed with geometric morphometrics. A principal component analysis(PCA) was computed to define a morphological space. Shape variation was visualized using vector distance maps and diagrams. The greatest variation was seen in the length of the superior orbital fissure. There was a gradient in terms of orbital shape ranging from short, wide orbits to tall, narrow orbits. The analysis did not highlight any significant age-, gender- or size-related impact in terms of orbital shape variation. Future avenues to explore include the study of other potential explanatory factors such as the different embryological origins of the orbital bones, the passage of vessels and nerves, and ethnic origins. This method can also be applied to the study of pathological orbits.

Prediction modeling-part 2: using machine learning strategies to improve transplantation outcomes.

Kidney transplant recipients and transplant physicians face important clinical questions where machine learning methods may help improve the decision-making process. This mini-review explores potential applications of machine learning methods to key stages of a kidney transplant recipient's journey, from initial waitlisting and donor selection, to personalization of immunosuppression and prediction of post-transplantation events. Both unsupervised and supervised machine learning methods are presented, including k-means clustering, principal components analysis, k-nearest neighbors, and random forests. The various challenges of these approaches are also discussed.

svReg: Structural varying-coefficient regression to differentiate how regional brain atrophy affects motor impairment for Huntington disease severity groups.

For Huntington disease, identification of brain regions related to motor impairment can be useful for developing interventions to alleviate the motor symptom, the major symptom of the disease. However, the effects from the brain regions to motor impairment may vary for different groups of patients. Hence, our interest is not only to identify the brain regions but also to understand how their effects on motor impairment differ by patient groups. This can be cast as a model selection problem for a varying-coefficient regression. However, this is challenging when there is a pre-specified group structure among variables. We propose a novel variable selection method for a varying-coefficient regression with such structured variables and provide a publicly available R package svreg for implementation of our method. Our method is empirically shown to select relevant variables consistently. Also, our method screens irrelevant variables better than existing methods. Hence, our method leads to a model with higher sensitivity, lower false discovery rate and higher prediction accuracy than the existing methods. Finally, we found that the effects from the brain regions to motor impairment differ by disease severity of the patients. To the best of our knowledge, our study is the first to identify such interaction effects between the disease severity and brain regions, which indicates the need for customized intervention by disease severity.

Toluene and methylethylketone: effect of combined exposure on their metabolism in rat.

1. Multiple exposures are ubiquitous in industrial environments. In this article, we highlight the risks faced by workers and complete the data available on the metabolic impact of a common mixture: toluene (TOL) and methylethylketone (MEK). 2. Rats were exposed by inhalation under controlled conditions either to each solvent individually, or to mixtures of the two. How the interaction between the two solvents affected their fate in the blood and brain, their main relevant urinary metabolites (o-cresol, benzylmercapturic acid for TOL and 2,3-butanediols for MEK) and their hepatic metabolism were investigated. 3. Although the cytochrome P450 concentration was unchanged, and the activities of CYP1A2 and CYP2E1 isoforms were not additively or synergistically induced by co-exposure, TOL metabolism was inhibited by the presence of MEK (and vice versa). Depending on the relative proportions of each compound in the mixture, this sometimes resulted in a large increase in blood and brain concentrations. Apart from extreme cases (unbalanced mixtures), the amount of o-cresol and benzylmercapturic acid (and to a lesser extent 2,3-butanediols) excreted were proportional to the blood solvent concentrations. 4. In a co-exposure context, ortho-cresol and benzylmercapturic acid can be used as urinary biomarkers in biomonitoring for employees to relatively accurately assess TOL exposure.

Activation of T cells by dendritic cells exposed to a reference sensitizer: Towards a promising model to assess the allergenic potential of chemicals.

BACKGROUND: Low molecular weight chemicals constitute one of the major causes of occupational allergies. European legislation on chemicals recommends limiting the use of in vivo models for assessing the sensitizing potential of chemicals, and encourages the development of integrated alternative methods. An in vitro mouse model of bone marrow-derived dendritic cells (BMDCs) that showed good accuracy (75%) and sensitivity (69%) has previously been developed to assess the sensitizing potential of chemicals. OBJECTIVE: To assess the ability of BMDCs to activate T cells (TCs) in vitro. METHODS: BMDCs pre-exposed to the reference sensitizer ammonium hexachloroplatinate (AHCP) were co-cultured with different subpopulations of TCs. TC activation was assessed by surface marker expression, proliferation, and cytokine release. RESULTS: The results showed significant activation of TCs co-cultured with dendritic cells pre-exposed to AHCP as evaluated by CD124 expression, proliferation, and cytokine secretion. Moreover, the response of TCs appeared to be Th2-oriented. Naive TCs were shown to be involved in this response, and the removal of regulatory TCs did not improve the cell response. CONCLUSIONS: The BMDCs used in this previously developed model appear to have the ability to activate TCs, confirming that the BMDC model represents a reliable assay for assessing the sensitizing potential of chemicals.

Metabolism of inhaled methylethylketone in rats.

Methylethylketone (MEK) is widely used in industry, often in combination with other compounds. Although nontoxic, it can make other chemicals harmful. This study investigates the fate of MEK in rat blood, brain and urine as well as its hepatic metabolism following inhalation over 1 month (at 20, 200 or 1400 ppm). MEK did not significantly accumulate in the organism: blood concentrations were similar after six-hour or 1-month inhalation periods, and brain concentrations only increased slightly after 1 month's exposure. Urinary excretion, based on the major metabolites, 2,3-butanediols (± and meso forms), accounted for less than 2.4% of the amount inhaled. 2-Butanol, 3-hydroxy-2-butanone and MEK itself were only detectable in urine in the highest concentration conditions investigated, when metabolic saturation occurred. Although MEK exposure did not alter the total cytochrome P450 concentration, it induced activation of both CYP1A2 and CYP2E1 enzymes. In addition, the liver glutathione concentration (reduced and oxidized forms) decreased, as did glutathione S-transferase (GST) activity (at exposure levels over 200 ppm). These metabolic data could be useful for pharmacokinetic model development and/or verification and suggest the ability of MEK to influence the metabolism (and potentiate the toxicity) of other substances.

In vitro detection of chemical allergens: an optimized assay using mouse bone marrow-derived dendritic cells.

BACKGROUND: Identification of the allergenic potency of chemicals is a key step in the safety assessment process. Predictive assays that require no or few animals are needed. OBJECTIVES: To develop an alternative in vitro mouse bone marrow-derived dendritic cell (BMDC) assay to determine the allergenic potential of chemicals. METHODS: BMDCs were exposed to well-known allergens and to non-allergenic chemicals. Surface marker expression and cytokine release of BMDCs were analysed after treatment. RESULTS: Eleven tested chemicals showed a significant stimulation index (SI) of >1.5 (accuracy, 75%; sensitivity, 69%). The four non-allergens all showed a SI of <1.5. Eight contact allergens tested showed a significant SI of >1.5 (accuracy, 92%; sensitivity, 89%), whereas only two respiratory allergens showed a significant SI of >1.5 (accuracy, 60%; sensitivity, 33%). CONCLUSIONS: The results indicate that the BMDC assay could become a reliable test for assessment of the allergenic potential of chemicals. The next step should include the testing of further chemicals, with the aim of integrating this assay into the toolbox of in vitro methods for the evaluation of the allergenic potential of chemicals.

Inferring data-specific micro-RNA function through the joint ranking of micro-RNA and pathways from matched micro-RNA and gene expression data.

MOTIVATION: In practice, identifying and interpreting the functional impacts of the regulatory relationships between micro-RNA and messenger-RNA is non-trivial. The sheer scale of possible micro-RNA and messenger-RNA interactions can make the interpretation of results difficult. RESULTS: We propose a supervised framework, pMim, built upon concepts of significance combination, for jointly ranking regulatory micro-RNA and their potential functional impacts with respect to a condition of interest. Here, pMim directly tests if a micro-RNA is differentially expressed and if its predicted targets, which lie in a common biological pathway, have changed in the opposite direction. We leverage the information within existing micro-RNA target and pathway databases to stabilize the estimation and annotation of micro-RNA regulation making our approach suitable for datasets with small sample sizes. In addition to outputting meaningful and interpretable results, we demonstrate in a variety of datasets that the micro-RNA identified by pMim, in comparison to simpler existing approaches, are also more concordant with what is described in the literature. AVAILABILITY AND IMPLEMENTATION: This framework is implemented as an R function, pMim, in the package sydSeq available from http://www.ellispatrick.com/r-packages. CONTACT: jean.yang@sydney.edu.au SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Vibrio Mimicus as the Rare Cause of Acute Diarrheal Illness.

Vibrio mimicus, although named for having many of the same virulent factors as Vibrio cholera, is a rare cause of significant gastrointestinal illness. Like many of the Vibrio species, the strongest risk factor for V. mimicus infection is seafood consumption. After consuming crabs, a 64-year-old male presented with a three day history of voluminous, non-bloody, water diarrhea. The severity of the diarrhea caused the patient to have orthostatic hypotension and acute kidney injury, which improved with fluid resuscitation. The diarrhea resolved in 24-hours, and the patient was discharged without medications. Stool studies later returned positive for V. mimicus. Clinicians, especially those in coastal regions, should consider this rare pathogen in the setting of refractory diarrhea and a history significant for seafood consumption. With early clinical suspicion, clinicians can focus on volume repletion while limiting the use of anti-microbials.

Determination of prognosis in metastatic melanoma through integration of clinico-pathologic, mutation, mRNA, microRNA, and protein information.

In patients with metastatic melanoma, the identification and validation of accurate prognostic biomarkers will assist rational treatment planning. Studies based on "-omics" technologies have focussed on a single high-throughput data type such as gene or microRNA transcripts. Occasionally, these features have been evaluated in conjunction with limited clinico-pathologic data. With the increased availability of multiple data types, there is a pressing need to tease apart which of these sources contain the most valuable prognostic information. We evaluated and integrated several data types derived from the same tumor specimens in AJCC stage III melanoma patients-gene, protein, and microRNA expression as well as clinical, pathologic and mutation information-to determine their relative impact on prognosis. We used classification frameworks based on pre-validation and bootstrap multiple imputation to compare the prognostic power of each data source, both individually as well as integratively. We found that the prognostic utility of clinico-pathologic information was not out-performed by any of the various "-omics" platforms. Rather, a combination of clinico-pathologic variables and mRNA expression data performed best. Furthermore, a patient-based classification analysis revealed that the prognostic accuracy of various data types was not the same for different patients. This indicates that ongoing development in the individualized evaluation of melanoma patients must take account of the value of both traditional and novel "-omics" measurements.

Identification of important regressor groups, subgroups and individuals via regularization methods: application to gut microbiome data.

MOTIVATION: Gut microbiota can be classified at multiple taxonomy levels. Strategies to use changes in microbiota composition to effect health improvements require knowing at which taxonomy level interventions should be aimed. Identifying these important levels is difficult, however, because most statistical methods only consider when the microbiota are classified at one taxonomy level, not multiple. RESULTS: Using L1 and L2 regularizations, we developed a new variable selection method that identifies important features at multiple taxonomy levels. The regularization parameters are chosen by a new, data-adaptive, repeated cross-validation approach, which performed well. In simulation studies, our method outperformed competing methods: it more often selected significant variables, and had small false discovery rates and acceptable false-positive rates. Applying our method to gut microbiota data, we found which taxonomic levels were most altered by specific interventions or physiological status. AVAILABILITY: The new approach is implemented in an R package, which is freely available from the corresponding author. CONTACT: tpgarcia@srph.tamhsc.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Controlling the local false discovery rate in the adaptive Lasso.

The Lasso shrinkage procedure achieved its popularity, in part, by its tendency to shrink estimated coefficients to zero, and its ability to serve as a variable selection procedure. Using data-adaptive weights, the adaptive Lasso modified the original procedure to increase the penalty terms for those variables estimated to be less important by ordinary least squares. Although this modified procedure attained the oracle properties, the resulting models tend to include a large number of "false positives" in practice. Here, we adapt the concept of local false discovery rates (lFDRs) so that it applies to the sequence, λn, of smoothing parameters for the adaptive Lasso. We define the lFDR for a given λn to be the probability that the variable added to the model by decreasing λn to λn-δ is not associated with the outcome, where δ is a small value. We derive the relationship between the lFDR and λn, show lFDR =1 for traditional smoothing parameters, and show how to select λn so as to achieve a desired lFDR. We compare the smoothing parameters chosen to achieve a specified lFDR and those chosen to achieve the oracle properties, as well as their resulting estimates for model coefficients, with both simulation and an example from a genetic study of prostate specific antigen.

Structured variable selection with q-values.

When some of the regressors can act on both the response and other explanatory variables, the already challenging problem of selecting variables when the number of covariates exceeds the sample size becomes more difficult. A motivating example is a metabolic study in mice that has diet groups and gut microbial percentages that may affect changes in multiple phenotypes related to body weight regulation. The data have more variables than observations and diet is known to act directly on the phenotypes as well as on some or potentially all of the microbial percentages. Interest lies in determining which gut microflora influence the phenotypes while accounting for the direct relationship between diet and the other variables A new methodology for variable selection in this context is presented that links the concept of q-values from multiple hypothesis testing to the recently developed weighted Lasso.

Exploring multicollinearity using a random matrix theory approach.

Clustering of gene expression data is often done with the latent aim of dimension reduction, by finding groups of genes that have a common response to potentially unknown stimuli. However, what is poorly understood to date is the behaviour of a low dimensional signal embedded in high dimensions. This paper introduces a multicollinear model which is based on random matrix theory results, and shows potential for the characterisation of a gene cluster's correlation matrix. This model projects a one dimensional signal into many dimensions and is based on the spiked covariance model, but rather characterises the behaviour of the corresponding correlation matrix. The eigenspectrum of the correlation matrix is empirically examined by simulation, under the addition of noise to the original signal. The simulation results are then used to propose a dimension estimation procedure of clusters from data. Moreover, the simulation results warn against considering pairwise correlations in isolation, as the model provides a mechanism whereby a pair of genes with `low' correlation may simply be due to the interaction of high dimension and noise. Instead, collective information about all the variables is given by the eigenspectrum.

A prediction model for viability at the end of the first trimester after a single early pregnancy evaluation.

OBJECTIVE: The aim was to develop a new model to predict the outcome at the end of the 1st trimester after a single visit to the early pregnancy unit (EPU). METHODS: Prospective observational study in the EPU at Nepean Hospital, between November 2006 and February 2009. Data were collected from all women in the 1st trimester of their pregnancy who had a live intrauterine pregnancy (IUP) at the 1st transvaginal ultrasound scan (TVS). 29 historical, clinical and ultrasound end points were recorded. Women were followed until the final diagnosis was established at the end of the 1st trimester: viability or nonviability. A multinomial logistic regression model was developed. The performance of this model was evaluated using receiver operating characteristic (ROC) curves. RESULTS: Data from 416 pregnancies were included: 92.1% were live beyond the 1st trimester, and 7.9% had miscarried. The most useful prognostic variables for developing the logistic regression model were gestational age by dates, vaginal (PV) bleeding, PV clots, gestational age by TVS, consistency with menstrual dates, mean gestational sac (GS) size, mean yolk sac (YS) size and number of previous caesarean sections. Used retrospectively on 416 women based on 25 imputations, the model gave an AUC of 0.88. Based on cross-validation, the independent predictive power obtained an AUC of 0.78. CONCLUSIONS: We have developed a new model to predict the outcome of the 1st trimester in women with live IUP at the 1st scan.

Preoperative Transfusion in Sickle Cell Disease Children Undergoing Adenotonsillectomy.

Objective: To perform a systematic review and meta-analysis comparing two pre-operative transfusion regimens (conservative versus aggressive) in children with sickle cell disease(SCD) undergoing adenotonsillectomy in terms of post-operative complications, complications related to SCD and transfusion related complications. Data Sources and Review Methods: A literature review was performed through PubMed, EMBASE, Cochrane, and Ovid databases using the following phrases: (Adenotonsillectomy OR Tonsillectomy) AND (Sickle Cell Disease OR Sickle Cell Trait). Using predetermined inclusion and exclusion criteria, seven articles were selected for systemic review and two control trials were included in meta-analysis. Results: Out of a total of 3,146 results, seven articles were selected for review and two controlled trials were included in the meta-analysis. There was no statistically significant difference in the rate of primary and secondary hemorrhage between the aggressive and conservative transfusion regimens (RR = 3.1, CI = 0.84-11.4, p-value = 0.089). The rate of sickle cell disease related complications including vaso-occlusive crisis and acute chest syndrome was also not statistically significant between the two transfusion groups (RR = 1.4, CI = 0.7-2.8, p-value = 0.339). Even though, the transfusion related complications did not reach statistical significance, there was a higher complication rate in the group receiving aggressive blood transfusion. Conclusion: In SCD children undergoing adenotonsillectomy, an aggressive transfusion regimen that focuses on reducing the Hemoglobin S ratio to below 30% has not been shown to be more effective in reducing post-operative complications when compared to a conservative transfusion regimen. Therefore, it is reasonable to utilize a conservative transfusion regimen, thereby reducing the transfusion-associated risks.