In vivo alkaline comet assay: Statistical considerations on historical negative and positive control data.

The alkaline comet assay is frequently used as in vivo follow-up test within different regulatory environments to characterize the DNA-damaging potential of different test items. The corresponding OECD Test guideline 489 highlights the importance of statistical analyses and historical control data (HCD) but does not provide detailed procedures. Therefore, the working group "Statistics" of the German-speaking Society for Environmental Mutation Research (GUM) collected HCD from five laboratories and >200 comet assay studies and performed several statistical analyses. Key results included that (I) observed large inter-laboratory effects argue against the use of absolute quality thresholds, (II) > 50% zero values on a slide are considered problematic, due to their influence on slide or animal summary statistics, (III) the type of summarizing measure for single-cell data (e.g., median, arithmetic and geometric mean) may lead to extreme differences in resulting animal tail intensities and study outcome in the HCD. These summarizing values increase the reliability of analysis results by better meeting statistical model assumptions, but at the cost of information loss. Furthermore, the relation between negative and positive control groups in the data set was always satisfactorily (or sufficiently) based on ratio, difference and quantile analyses.

Polymorphisms in genes of melatonin biosynthesis and signaling support the light-at-night hypothesis for breast cancer.

Light-at-night triggers the decline of pineal gland melatonin biosynthesis and secretion and is an IARC-classified probable breast-cancer risk factor. We applied a large-scale molecular epidemiology approach to shed light on the putative role of melatonin in breast cancer. We investigated associations between breast-cancer risk and polymorphisms at genes of melatonin biosynthesis/signaling using a study population of 44,405 women from the Breast Cancer Association Consortium (22,992 cases, 21,413 population-based controls). Genotype data of 97 candidate single nucleotide polymorphisms (SNPs) at 18 defined gene regions were investigated for breast-cancer risk effects. We calculated adjusted odds ratios (ORs) and 95% confidence intervals (CI) by logistic regression for the main-effect analysis as well as stratified analyses by estrogen- and progesterone-receptor (ER, PR) status. SNP-SNP interactions were analyzed via a two-step procedure based on logic regression. The Bayesian false-discovery probability (BFDP) was used for all analyses to account for multiple testing. Noteworthy associations (BFDP < 0.8) included 10 linked SNPs in tryptophan hydroxylase 2 (TPH2) (e.g. rs1386492: OR = 1.07, 95% CI 1.02-1.12), and a SNP in the mitogen-activated protein kinase 8 (MAPK8) (rs10857561: OR = 1.11, 95% CI 1.04-1.18). The SNP-SNP interaction analysis revealed noteworthy interaction terms with TPH2- and MAPK-related SNPs (e.g. rs1386483R ∧ rs1473473D ∧ rs3729931D: OR = 1.20, 95% CI 1.09-1.32). In line with the light-at-night hypothesis that links shift work with elevated breast-cancer risks our results point to SNPs in TPH2 and MAPK-genes that may impact the intricate network of circadian regulation.

The Role of Traffic Volume on Sound Pressure Level Reduction before and during COVID-19 Lockdown Measures-A Case Study in Bochum, Germany.

During the SARS-CoV-2 pandemic, sound pressure levels (SPL) decreased because of lockdown measures all over the world. This study aims to describe SPL changes over varying lockdown measure timeframes and estimate the role of traffic on SPL variations. To account for different COVID-19 lockdown measures, the timeframe during the pandemic was segmented into four phases. To analyze the association between a-weighted decibels (dB(A)) and lockdown phases relative to the pre-lockdown timeframe, we calculated a linear mixed model, using 36,710 h of recording time. Regression coefficients depicting SPL changes were compared, while the model was subsequently adjusted for wind speed, rainfall, and traffic volume. The relative adjusted reduction of during pandemic phases to pre-pandemic levels ranged from -0.99 dB(A) (CI: -1.45; -0.53) to -0.25 dB(A) (CI: -0.96; 0.46). After controlling for traffic volume, we observed little to no reduction (-0.16 dB(A) (CI: -0.77; 0.45)) and even an increase of 0.75 dB(A) (CI: 0.18; 1.31) during the different lockdown phases. These results showcase the major role of traffic regarding the observed reduction. The findings can be useful in assessing measures to decrease noise pollution for necessary future population-based prevention.

Regularization approaches in clinical biostatistics: A review of methods and their applications.

A range of regularization approaches have been proposed in the data sciences to overcome overfitting, to exploit sparsity or to improve prediction. Using a broad definition of regularization, namely controlling model complexity by adding information in order to solve ill-posed problems or to prevent overfitting, we review a range of approaches within this framework including penalization, early stopping, ensembling and model averaging. Aspects of their practical implementation are discussed including available R-packages and examples are provided. To assess the extent to which these approaches are used in medicine, we conducted a review of three general medical journals. It revealed that regularization approaches are rarely applied in practical clinical applications, with the exception of random effects models. Hence, we suggest a more frequent use of regularization approaches in medical research. In situations where also other approaches work well, the only downside of the regularization approaches is increased complexity in the conduct of the analyses which can pose challenges in terms of computational resources and expertise on the side of the data analyst. In our view, both can and should be overcome by investments in appropriate computing facilities and educational resources.

Rapid detection and online analysis of microbial changes through flow cytometry.

Short-read 16 S rRNA gene sequencing is the dominating technology to profile microbial communities in different habitats. Its uncontested taxonomic resolution paved the way for major contributions to the field. Sample measurement and analysis, that is, sequencing, is rather slow-in order of days. Alternatively, flow cytometry can be used to profile the microbiota of various sources within a few minutes per sample. To keep up with high measurement speed, we developed the open source-analyzing tool FlowSoFine. To validate the ability to distinguish microbial profiles, we examined human skin samples of three body sites (N = 3 × 54) with flow cytometry and 16 S rRNA gene amplicon sequencing. Confirmed by sequencing of the very same samples, body site was found to be significantly different by flow cytometry. For a proof-of-principle multidimensional approach, using stool samples of patients (N = 40) with/without inflammatory bowel diseases, we could discriminate the health status by their bacterial patterns. In conclusion, FlowSoFine enables the generation and comparison of cytometric fingerprints of microbial communities from different sources. The implemented interface supports the user through all analytical steps to work out the biological relevant signals from raw measurements to publication ready figures. Furthermore, we present flow cytometry as a valid method for skin microbiota analysis.

Navigating chemical reaction space - application to DNA-encoded chemistry.

Databases contain millions of reactions for compound synthesis, rendering selection of reactions for forward synthetic design of small molecule screening libraries, such as DNA-encoded libraries (DELs), a big data challenge. To support reaction space navigation, we developed the computational workflow Reaction Navigator. Reaction files from a large chemistry database were processed using the open-source KNIME Analytics Platform. Initial processing steps included a customizable filtering cascade that removed reactions with a high probability to be incompatible with DEL, as they would e.g. damage the genetic barcode, to arrive at a comprehensive list of transformations for DEL design with applicability potential. These reactions were displayed and clustered by user-defined molecular reaction descriptors which are independent of reaction core substitution patterns. Thanks to clustering, these can be searched manually to identify reactions for DEL synthesis according to desired reaction criteria, such as ring formation or sp3 content. The workflow was initially applied for mapping chemical reaction space for aromatic aldehydes as an exemplary functional group often used in DEL synthesis. Exemplary reactions have been successfully translated to DNA-tagged substrates and can be applied to library synthesis. The versatility of the Reaction Navigator was then shown by mapping reaction space for different reaction conditions, for amines as a second set of starting materials, and for data from a second database.

Identifying treatment effect heterogeneity in dose-finding trials using Bayesian hierarchical models: Identifying treatment effect heterogeneity in dose-finding trials.

An important task in drug development is to identify patients, which respond better or worse to an experimental treatment. Identifying predictive covariates, which influence the treatment effect and can be used to define subgroups of patients, is a key aspect of this task. Analyses of treatment effect heterogeneity are however known to be challenging, since the number of possible covariates or subgroups is often large, while samples sizes in earlier phases of drug development are often small. In addition, distinguishing predictive covariates from prognostic covariates, which influence the response independent of the given treatment, can often be difficult. While many approaches for these types of problems have been proposed, most of them focus on the two-arm clinical trial setting, where patients are given either the treatment or a control. In this article we consider parallel groups dose-finding trials, in which patients are administered different doses of the same treatment. To investigate treatment effect heterogeneity in this setting we propose a Bayesian hierarchical dose-response model with covariate effects on dose-response parameters. We make use of shrinkage priors to prevent overfitting, which can easily occur, when the number of considered covariates is large and sample sizes are small. We compare several such priors in simulations and also investigate dependent modeling of prognostic and predictive effects to better distinguish these two types of effects. We illustrate the use of our proposed approach using a Phase II dose-finding trial and show how it can be used to identify predictive covariates and subgroups of patients with increased treatment effects.

Influence of breast cancer risk factors on proliferation and DNA damage in human breast glandular tissues: role of intracellular estrogen levels, oxidative stress and estrogen biotransformation.

Breast cancer etiology is associated with both proliferation and DNA damage induced by estrogens. Breast cancer risk factors (BCRF) such as body mass index (BMI), smoking, and intake of estrogen-active drugs were recently shown to influence intratissue estrogen levels. Thus, the aim of the present study was to investigate the influence of BCRF on estrogen-induced proliferation and DNA damage in 41 well-characterized breast glandular tissues derived from women without breast cancer. Influence of intramammary estrogen levels and BCRF on estrogen receptor (ESR) activation, ESR-related proliferation (indicated by levels of marker transcripts), oxidative stress (indicated by levels of GCLC transcript and oxidative derivatives of cholesterol), and levels of transcripts encoding enzymes involved in estrogen biotransformation was identified by multiple linear regression models. Metabolic fluxes to adducts of estrogens with DNA (E-DNA) were assessed by a metabolic network model (MNM) which was validated by comparison of calculated fluxes with data on methoxylated and glucuronidated estrogens determined by GC- and UHPLC-MS/MS. Intratissue estrogen levels significantly influenced ESR activation and fluxes to E-DNA within the MNM. Likewise, all BCRF directly and/or indirectly influenced ESR activation, proliferation, and key flux constraints influencing E-DNA (i.e., levels of estrogens, CYP1B1, SULT1A1, SULT1A2, and GSTP1). However, no unambiguous total effect of BCRF on proliferation became apparent. Furthermore, BMI was the only BCRF to indeed influence fluxes to E-DNA (via congruent adverse influence on levels of estrogens, CYP1B1 and SULT1A2).

Combining heterogeneous subgroups with graph-structured variable selection priors for Cox regression.

BACKGROUND: Important objectives in cancer research are the prediction of a patient's risk based on molecular measurements such as gene expression data and the identification of new prognostic biomarkers (e.g. genes). In clinical practice, this is often challenging because patient cohorts are typically small and can be heterogeneous. In classical subgroup analysis, a separate prediction model is fitted using only the data of one specific cohort. However, this can lead to a loss of power when the sample size is small. Simple pooling of all cohorts, on the other hand, can lead to biased results, especially when the cohorts are heterogeneous. RESULTS: We propose a new Bayesian approach suitable for continuous molecular measurements and survival outcome that identifies the important predictors and provides a separate risk prediction model for each cohort. It allows sharing information between cohorts to increase power by assuming a graph linking predictors within and across different cohorts. The graph helps to identify pathways of functionally related genes and genes that are simultaneously prognostic in different cohorts. CONCLUSIONS: Results demonstrate that our proposed approach is superior to the standard approaches in terms of prediction performance and increased power in variable selection when the sample size is small.

Exercises and Skin Physiology During International Space Station Expeditions.

BACKGROUND: The first skin physiological pilot experiment (SkinA) on a single astronaut showed a deterioration of the skin. In a follow-up experiment (SkinB) we showed that skin physiological parameters improved on average. However, it is well known that sports have positive effects on the skin, that astronauts prefer special sports devices, and do sports with different intensity. The aim of this study was to analyze the different sports activities of SkinB astronauts and to find out whether they have an influence on the skin physiological parameters.METHODS: The cumulative distance covered on the treadmill and on the cycle ergometer as well as the repetition of arm-related exercises have been calculated and possible correlation between sports activities and skin physiological parameters have been analyzed.RESULTS: The average distance covered for all six astronauts per day is 1364 AU on the treadmill T2, and 11,077 AU on the cycle ergometer CEVIS. In addition, the astronauts performed an average of about 73 repetitions of all arm-related exercises daily. Here, we were able to show very well how differently the astronauts on the ISS train. In addition, a decreasing trend in skin volume can be observed in astronauts with increasing activity on the bicycle and more repetitions on arm-related exercises.CONCLUSION: Increased activity on the cycle ergometer and increased arm-related exercises have a medium negative impact on the parameter skin volume and thus reflects more fluid content in the skin. No correlations between sports activities and skin moisture/skin barrier function could be found.Braun N, Hunsdieck B, Theek C, Ickstadt K, Heinrich U. Exercises and skin physiology during International Space Station expeditions. Aerosp Med Hum Perform. 2021; 92(3):160166.

Interval estimation of the overall treatment effect in random-effects meta-analyses: Recommendations from a simulation study comparing frequentist, Bayesian, and bootstrap methods.

There exists a variety of interval estimators for the overall treatment effect in a random-effects meta-analysis. A recent literature review summarizing existing methods suggested that in most situations, the Hartung-Knapp/Sidik-Jonkman (HKSJ) method was preferable. However, a quantitative comparison of those methods in a common simulation study is still lacking. Thus, we conduct such a simulation study for continuous and binary outcomes, focusing on the medical field for application. Based on the literature review and some new theoretical considerations, a practicable number of interval estimators is selected for this comparison: the classical normal-approximation interval using the DerSimonian-Laird heterogeneity estimator, the HKSJ interval using either the Paule-Mandel or the Sidik-Jonkman heterogeneity estimator, the Skovgaard higher-order profile likelihood interval, a parametric bootstrap interval, and a Bayesian interval using different priors. We evaluate the performance measures (coverage and interval length) at specific points in the parameter space, that is, not averaging over a prior distribution. In this sense, our study is conducted from a frequentist point of view. We confirm the main finding of the literature review, the general recommendation of the HKSJ method (here with the Sidik-Jonkman heterogeneity estimator). For meta-analyses including only two studies, the high length of the HKSJ interval limits its practical usage. In this case, the Bayesian interval using a weakly informative prior for the heterogeneity may help. Our recommendations are illustrated using a real-world meta-analysis dealing with the efficacy of an intramyocardial bone marrow stem cell transplantation during coronary artery bypass grafting.

Statistical analysis of in vivo alkaline comet assay data - Comparison of median and geometric mean as centrality measures.

The comet assay is one of the standard tests for evaluating the genotoxic potential of a test item able to detect DNA strand breaks in cells or isolated nuclei from various tissues. The in vivo alkaline comet assay is part of the standard test battery, given in option 2 of the ICH guidance S2 (R1) and a follow-up test in the EFSA framework on genotoxicity testing. The current OECD guideline for the testing of chemicals No. 489 directly affects the statistical analysis of comet data as it suggests using the median per slide and the mean of all medians per animal. However, alternative approaches can be used if scientifically justified. In this work, we demonstrated that the selection of different centrality measures to describe an average value per slide may lead to fundamentally different statistical test results and contradicting interpretations. Our focus was on geometric means and medians per slide for the primary endpoint "tail intensity". We compared both strategies using original and simulated data in different experimental settings incl. a varying number of animals, slides and cells per slide. In general, it turned out that the chosen centrality measure has an immense impact on the final statistical test result.

Zero-cell corrections in random-effects meta-analyses.

The standard estimator for the log odds ratio (the unconditional maximum likelihood estimator) and the delta-method estimator for its standard error are not defined if the corresponding 2 × 2 table contains at least one "zero cell". This is also an issue when estimating the overall log odds ratio in a meta-analysis. It is well known that correcting for zero cells by adding a small increment should be avoided. Nevertheless, these zero-cell corrections continue to be used. With this Brief Method Note, we want to warn of a particularly bad zero-cell correction. For this, we conduct a simulation study comparing the following two zero-cell corrections under the ordinary random-effects model: (a) adding 1 2 to all cells of all the individual studies' 2 × 2 tables independently of any zero-cell occurrences and (b) adding 1 2 to all cells of only those 2 × 2 tables containing at least one zero cell. The main finding is that correction (a) performs worse than correction (b). Thus, we strongly discourage the use of correction (a).

Influence of breast cancer risk factors and intramammary biotransformation on estrogen homeostasis in the human breast.

Understanding intramammary estrogen homeostasis constitutes the basis of understanding the role of lifestyle factors in breast cancer etiology. Thus, the aim of the present study was to identify variables influencing levels of the estrogens present in normal breast glandular and adipose tissues (GLT and ADT, i.e., 17ß-estradiol, estrone, estrone-3-sulfate, and 2-methoxy-estrone) by multiple linear regression models. Explanatory variables (exVARs) considered were (a) levels of metabolic precursors as well as levels of transcripts encoding proteins involved in estrogen (biotrans)formation, (b) data on breast cancer risk factors (i.e., body mass index, BMI, intake of estrogen-active drugs, and smoking) collected by questionnaire, and (c) tissue characteristics (i.e., mass percentage of oil, oil%, and lobule type of the GLT). Levels of estrogens in GLT and ADT were influenced by both extramammary production (menopausal status, intake of estrogen-active drugs, and BMI) thus showing that variables known to affect levels of circulating estrogens influence estrogen levels in breast tissues as well for the first time. Moreover, intratissue (biotrans)formation (by aromatase, hydroxysteroid-17beta-dehydrogenase 2, and beta-glucuronidase) influenced intratissue estrogen levels, as well. Distinct differences were observed between the exVARs exhibiting significant influence on (a) levels of specific estrogens and (b) the same dependent variables in GLT and ADT. Since oil% and lobule type of GLT influenced levels of some estrogens, these variables may be included in tissue characterization to prevent sample bias. In conclusion, evidence for the intracrine activity of the human breast supports biotransformation-based strategies for breast cancer prevention. The susceptibility of estrogen homeostasis to systemic and tissue-specific modulation renders both beneficial and adverse effects of further variables associated with lifestyle and the environment possible.

Qualitative and quantitative differences in estrogen biotransformation in human breast glandular and adipose tissues: implications for studies using mammary biospecimens.

Because of its assumed role in breast cancer etiology, estrogen biotransformation (and interaction of compounds therewith) has been investigated in human biospecimens for decades. However, little attention has been paid to the well-known fact that large inter-individual variations exist in the proportion of breast glandular (GLT) and adipose (ADT) tissues and less to adequate tissue characterization. To assess the relevance of this, the present study compares estrogen biotransformation in GLT and ADT. GLT and ADT were isolated from 47 reduction mammoplasty specimens derived from women without breast cancer and were characterized histologically and by their percentages of oil. Levels of 12 unconjugated and five conjugated estrogens were analyzed by GC- and UHPLC-MS/MS, respectively, and levels of 27 transcripts encoding proteins involved in estrogen biotransformation by Taqman® probe-based PCR. Unexpectedly, one-third of specimens provided neat GLT only after cryosection. Whereas 17ß-estradiol, estrone, and estrone-3-sulfate were detected in both tissues, estrone-3-glucuronide and 2-methoxy-estrone were detected predominately in GLT and ADT, respectively. Estrogen levels as well as ratios 17ß-estradiol/estrone and estrone-3-sulfate/estrone differed significantly between GLT and ADT, yet less than between individuals. Furthermore, estrogen levels in GLT and ADT correlated significantly with each other. In contrast, levels of most transcripts encoding enzymes involved in biotransformation differed more than between individuals and did not correlate between ADT and GLT. Thus, mixed breast tissues (and plasma) will not provide meaningful information on local estrogen biotransformation (and interaction of compounds therewith) whereas relative changes in 17ß-estradiol levels may be investigated in the more abundant ADT.

Evaluation of the DNA damage in lymphocytes, sperm and buccal cells of workers under environmental and occupational boron exposure conditions.

Industrial production and use of boron compounds have increased during the last decades, especially for the manufacture of borosilicate glass, fiberglass, metal alloys and flame retardants. This study was conducted in two districts of Balikesir; Bandirma and Bigadic, which geographically belong to the Marmara Region of Turkey. Bandirma is the production and exportation zone for the produced boric acid and some borates and Bigadic has the largest B deposits in Turkey. 102 male workers who were occupationally exposed to boron from Bandirma and 110 workers who were occupationally and environmentally exposed to boron from Bigadic participated to our study. In this study the DNA damage in the sperm, blood and buccal cells of 212 males was evaluated by comet and micronucleus assays. No significant increase in the DNA damage in blood, sperm and buccal cells was observed in the residents exposed to boron both occupationally and environmentally (p = 0.861) for Comet test in the sperm samples, p = 0.116 for Comet test in the lymphocyte samples, p = 0.042 for micronucleus (MN) test, p = 0.955 for binucleated cells (BN), p = 1.486 for condensed chromatin (CC), p = 0.455 for karyorrhectic cells (KHC), p = 0.541 for karyolitic cells (KLY), p = 1.057 for pyknotic cells (PHC), p = 0.331 for nuclear bud (NBUD)). No correlations were seen between blood boron levels and tail intensity values of the sperm samples, lymphocyte samples, frequencies of MN, BN, KHC, KYL, PHC and NBUD. The results of this study came to the same conclusions of the previous studies that boron does not induce DNA damage even under extreme exposure conditions.

Benefits of improved air quality on ageing lungs: impacts of genetics and obesity.

INTRODUCTION: The beneficial effect of improving air quality on lung function in the elderly remains unclear. We examined associations between decline in air pollutants and lung function, and effect modifications by genetics and body mass index (BMI), in elderly German women. METHODS: Data were analysed from the prospective SALIA (Study on the influence of Air pollution on Lung function, Inflammation and Aging) study (n=601). Spirometry was conducted at baseline (1985-1994; age 55 years), in 2007-2010 and in 2012-2013. Air pollution concentrations at home addresses were determined for each time-point using land-use regression models. Global Lung Initiative 2012 z-scores were calculated. Weighted genetic risk scores (GRSs) were determined from lung function-related risk alleles and used to investigate interactions with improved air quality. Multiple linear mixed models were fitted. RESULTS: Air pollution levels decreased substantially during the study period. Reduction of air pollution was associated with an increase in z-scores for forced expiratory volume in 1 s (FEV1) and the FEV1/forced vital capacity ratio. For a decrease of 10 µg·m-3 in nitrogen dioxide (NO2), the z-score for FEV1 increased by 0.14 (95% CI 0.01-0.26). However, with an increasing number of lung function-related risk alleles, the benefit from improved air quality decreased (GRS×NO2 interaction: p=0.029). Interactions with BMI were not significant. CONCLUSIONS: Reduction of air pollution is associated with a relative improvement of lung function in elderly women, but also depends on their genetic make-up.

Identification of interactions of binary variables associated with survival time using survivalFS.

Many medical studies aim to identify factors associated with a time to an event such as survival time or time to relapse. Often, in particular, when binary variables are considered in such studies, interactions of these variables might be the actual relevant factors for predicting, e.g., the time to recurrence of a disease. Testing all possible interactions is often not possible, so that procedures such as logic regression are required that avoid such an exhaustive search. In this article, we present an ensemble method based on logic regression that can cope with the instability of the regression models generated by logic regression. This procedure called survivalFS also provides measures for quantifying the importance of the interactions forming the logic regression models on the time to an event and for the assessment of the individual variables that take the multivariate data structure into account. In this context, we introduce a new performance measure, which is an adaptation of Harrel's concordance index. The performance of survivalFS and the proposed importance measures is evaluated in a simulation study as well as in an application to genotype data from a urinary bladder cancer study. Furthermore, we compare the performance of survivalFS and its importance measures for the individual variables with the variable importance measure used in random survival forests, a popular procedure for the analysis of survival data. These applications show that survivalFS is able to identify interactions associated with time to an event and to outperform random survival forests.

Boron-exposed male workers in Turkey: no change in sperm Y:X chromosome ratio and in offspring's sex ratio.

Boron-associated shifts in sex ratios at birth were suggested earlier and attributed to a decrease in Y- vs. X-bearing sperm cells. As the matter is pivotal in the discussion of reproductive toxicity of boron/borates, re-investigation in a highly borate-exposed population was required. In the present study, 304 male workers in Bandirma and Bigadic (Turkey) with different degrees of occupational and environmental exposure to boron were investigated. Boron was quantified in blood, urine and semen, and the persons were allocated to exposure groups along B blood levels. In the highest ("extreme") exposure group (n = 69), calculated mean daily boron exposures, semen boron and blood boron concentrations were 44.91 ± 18.32 mg B/day, 1643.23 ± 965.44 ng B/g semen and 553.83 ± 149.52 ng B/g blood, respectively. Overall, an association between boron exposure and Y:X sperm ratios in semen was not statistically significant (p > 0.05). Also, the mean Y:X sperm ratios in semen samples of workers allocated to the different exposure groups were statistically not different in pairwise comparisons (p > 0.05). Additionally, a boron-associated shift in sex ratio at birth towards female offspring was not visible. In essence, the present results do not support an association between boron exposure and decreased Y:X sperm ratio in males, even under extreme boron exposure conditions.