Background: Parabens are a group of esters of para-hydroxybenzoic acid utilized as antimicrobial preservatives in many personal care products. Epidemiological studies regarding the adverse effects of parabens on fetuses are limited. The aim of this study was to determine the association between placental paraben exposure and birth outcomes. We assessed paraben concentrations in placental tissue, which potentially gives a better understanding of fetal exposure than the maternal urinary concentrations which are the current golden standard. Methods: Placental tissue was collected immediately after birth from 142 mother-child pairs from the ENVIRONAGE birth cohort. The placental concentrations of four parabens (methyl (MeP), ethyl (EtP), propyl (PrP), and butyl (BuP)) were determined by ultra-performance liquid chromatography coupled with tandem mass-spectrometry. Generalized linear regression models were used to determine the association between paraben exposure levels and birth outcomes. Results: The geometric means of placental MeP, EtP, PrP, and BuP were 1.84, 2.16, 1.68 and 0.05 ng/g tissue, respectively. The sum of parabens (∑ parabens, including MeP, EtP and PrP) was negatively associated with birth weight in newborn girls (− 166 g, 95% CI: − 322, − 8.6, p = 0.04) after adjustment for a priori selected covariates. The sum of parabens was negatively associated with head circumference (− 0.6 cm, 95% CI: − 1.1, − 0.2, p = 0.008) and borderline associated with birth length (− 0.6 cm, 95% CI:-1.3, 0.1, p = 0.08). In newborn girls the placental concentration of EtP was negatively associated with head circumference (− 0.6 cm, 95% CI:-1.1, − 0.1, p = 0.01) and borderline significantly associated with birth weight and birth length. Lastly, placental EtP and ∑parabens were negatively associated with placental weight in newborn girls but not in newborn boys (− 45.3 g, 95% CI:-86.2, − 4.4, p = 0.03). Conclusion: The negative association between maternal paraben exposure and birth outcomes warrants further research and follow-up over time to determine long term effects of gestational exposure to parabens.

Respiratory health of children is a health priority. Club cell protein (CC16) is an interesting biomarker of lung diseases and adverse effects towards the airway epithelium integrity. Osteopontin (OPN) and nuclear factor-kappa B (NF-κB) also play a role in respiratory health. The use of urine as biomarker source is useful in studies involving children but necessitates proper adjustment for physiological confounders influencing the urinary excretion, potentially characterized with beta-2-microglobulin (β2M), retinol binding protein 4 (RBP4) or myoglobin (MYO), as well as adjustment for possible renal dysfunction, characterized by human serum albumin (HSA). The simultaneous quantification of all these proteins in urine could facilitate children’s health monitoring. A multiple reaction monitoring method (MRM) was developed and validated for the relative quantification of the seven mentioned urinary proteins. A total of nine proteotypic peptides were selected and used for the relative quantification of the seven proteins. The MRM method was completely validated for all proteins and partially for OPN. LOQ’s ranged from 0.3 to 42.8 ng/ml, a good reproducibility and a good linearity were obtained across the analytical measurement range (r² > 0.98). The method yielded varying correlations (r² of 0.78, 0.71, 0.34 and 0.15 for CC16, β2M, RBP4 and HSA respectively) with available immunoassay data. It also allowed the identification and successful quantification of β2M and RBP4 as a protein candidate for adjustment of renal handling and dysfunction. All proteins were detected in the urine samples except for MYO and NF-κB. Our validated MRM-method is able to simultaneously quantify in urine biomarkers of airway epithelium integrity and biomarkers of variation in renal function and urinary dilution. This will allow to investigate further in future studies if urine can be used as a good surrogate source for biomarkers of airway epithelium integrity, and to understand the complex relationship between cause and effect in children’s respiratory health monitoring.

Air pollution exposure can lead to exacerbation of respiratory disorders in children. Using sensitive biomarkers helps to assess the impact of air pollution on children’s respiratory health and combining protein, genetic and epigenetic biomarkers gives insights on their interrelatedness. Most studies do not contain such an integrated approach and investigate these biomarkers individually in blood, although its collection in children is challenging. Our study aimed at assessing the feasibility of conducting future integrated larger-scale studies evaluating respiratory health risks of air pollution episodes in children, based on a qualitative analysis of the technical and logistic aspects of a small-scale field study involving 42 children. This included the preparation, collection and storage of non-invasive samples (urine, saliva), the measurement of general and respiratory health parameters and the measurement of specific biomarkers (genetic, protein, epigenetic) of respiratory health and air pollution exposure. Bottlenecks were identified and modifications were proposed to expand this integrated study to a higher number of children, time points and locations. This would allow for non-invasive assessment of the impact of air pollution exposure on the respiratory health of children in future larger-scale studies, which is critical for the development of policies or measures at the population level.

Mass spectrometric (MS) fingerprinting coupled with chemometrics for the detection of plants in plant mixtures is sparsely researched. This paper aims to check its value for herbal adulteration concerning plants with slimming as an indication. Moreover, it is among the first to exploit the full three-dimensional dataset (i.e., time × intensity × mass) obtained with liquid chromatography hyphenated with MS for herbal fingerprinting purposes. The MS parameters were optimized to achieve highly specific fingerprints. Trituration’s (total 55), blanks (total 11) and reference plants were injected in the MS system to generate the dataset. The dataset was complex and humongous, necessitating the application of compression techniques. After compression, Partial Least Squares-Discriminant Analysis (PLS-DA) was performed to generate models validated for accuracy using cross-validation and an external test set. Confusion matrices were constructed to provide insight into the modeling predictions. A complimentary evaluation between data obtained using a previously developed Diode Array Detection (DAD) method and the MS data was performed by data fusion techniques and newly generated models. The fused dataset models were comparable to MS models. For ease of application, MS modeling was deemed to be superior. The future market studies would adopt MS modeling as the preferred choice. A proof of concept was carried out on 10 real-life samples obtained from illegal sources. The results indicated the need for stronger monitoring of (illegal) plant food supplements entering the market, especially via the internet.