Introduction and Aims: The increase in stress levels, social confinement, and addiction's physical consequences play an essential role in the proliferation of drug abuse. In this context, the Covid-19 pandemic produced remarkable effects on those individuals prone to addictions, especially to alcohol. Alcohol is linked to multiple dangerous conditions such as social issues, severe medical conditions, and road accidents. The determination of ethylglucuronide (EtG) in hair is frequently performed to test and monitor chronic excessive alcohol intake conditions, as it allows differentiation among low-risk/moderate drinkers, and excessive/chronic drinkers. Our study aimed to explore hair EtG levels in a controlled population to assess the impact of Covid-19 lockdown on alcohol intake along March-May 2020.Materials and Methods: EtG levels were measured in all hair samples collected in the months following April 2020 to evaluate the behaviors related to alcohol intake along with the time frame from March to May 2020. The measured concentration distributions for each month were compared with those reported in the same month during the previous 4 years (2016–2019). The dataset was built to highlight possible differences between genders, and the different categories of alcohol consumption, separately.Results: The samples collected from April to August 2020 (500 < N <1,100 per month) showed an increase in the percentage of subjects classified as abstinent/low-risk drinkers (from 60 up to 79%) and a decrease of subjects classified as moderate and chronic drinkers (−12 and −7%, respectively) when compared to the previous 4 years. A decrease in the overall mean value of EtG in the period April–June 2020 was observed, while the EtG levels of both June and July 2020 provided an increasing trend for chronic/excessive consumers (+27 and +19% for June and July 2020, respectively). A peculiar rise in the EtG levels of moderate and chronic/excessive female consumers was observed along April–June 2020, too.Discussion and Conclusions: Behavioral and social studies generally report a decrease in alcohol consumption during the Covid-19 lockdown. However, people already suffering from drug or alcohol addictions before Covid-19 pandemic seemingly enhance their harmful behavior. Our data from April to August 2020 are consistent with both suppositions. Our observations confirm once again the utility of EtG to investigate the patterns of alcohol consumption in the population.

The misuse of fentanyl, and novel synthetic opioids (NSO) in general, has become a public health emergency, especially in the United States. The detection of NSO is often challenged by the limited diagnostic time frame allowed by urine sampling and the wide range of chemically modified analogues, continuously introduced to the recreational drug market. In this study, an untargeted metabolomics approach was developed to obtain a comprehensive “fingerprint” of any anomalous and specific metabolic pattern potentially related to fentanyl exposure. In recent years, in vitro models of drug metabolism have emerged as important tools to overcome the limited access to positive urine samples and uncertainties related to the substances actually taken, the possible combined drug intake, and the ingested dose. In this study, an in vivo experiment was designed by incubating HepG2 cell lines with either fentanyl or common drugs of abuse, creating a cohort of 96 samples. These samples, together with 81 urine samples including negative controls and positive samples obtained from recent users of either fentanyl or “traditional” drugs, were subjected to untargeted analysis using both UHPLC reverse phase and HILIC chromatography combined with QTOF mass spectrometry. Data independent acquisition was performed by SWATH in order to obtain a comprehensive profile of the urinary metabolome. After extensive processing, the resulting datasets were initially subjected to unsupervised exploration by principal component analysis (PCA), yielding clear separation of the fentanyl positive samples with respect to both controls and samples positive to other drugs. The urine datasets were then systematically investigated by supervised classification models based on soft independent modeling by class analogy (SIMCA) algorithms, with the end goal of identifying fentanyl users. A final single-class SIMCA model based on an RP dataset and five PCs yielded 96% sensitivity and 74% specificity. The distinguishable metabolic patterns produced by fentanyl in comparison to other opioids opens up new perspectives in the interpretation of the biological activity of fentanyl.

Early-life exposure occurs during gestation through transfer to the fetus and later, during lactation. Recent monitoring data revealed that the Portuguese population is exposed to mycotoxins, including young children. This study aimed to develop a pilot study to assess the early-life exposure to mycotoxins through a mother–child cohort, and to identify the associated challenges. Participants were recruited during pregnancy (1st trimester) and followed-up in three moments of observation: 2nd trimester of pregnancy (mother), and 1st and 6th month of the child’s life (mother and child), with the collection of biological samples and sociodemographic and food consumption data. The earlyMYCO pilot study enrolled 19 mother–child pairs. The analysis of biological samples from participants revealed the presence of 4 out of 15 and 5 out of 18 mycotoxins’ biomarkers of exposure in urine and breast milk samples, respectively. The main aspects identified as contributors for the successful development of the cohort were the multidisciplinary and dedicated team members in healthcare units, reduced burden of participation, and the availability of healthcare units for the implementation of the fieldwork. Challenges faced, lessons learned, and suggestions were discussed as a contribution for the development of further studies in this area.

Mycotoxin contamination is a global challenge to food safety and population health. A diversity of adverse effects in human health such as organ damage, immunity disorders and carcinogenesis are attributed to acute and chronic exposure to mycotoxins. While there is a high likelihood of mycotoxin co-occurrence in the daily diet, multiple mycotoxin exposures represent a considerable challenge in understanding the accumulative effects of groups of exposures on health outcomes. Nevertheless, previous studies on mycotoxin exposure-health outcome associations have focused on a single or a limited number of exposures. To guide multi-exposure assessment, careful considerations of statistical approaches available are required. In addition, the issue of multicollinearity in high-dimensional settings of multiple exposure analysis underlies the controversy surrounding the reliability and consistency of statistical conclusions about the exposure-health outcome associations. Conventional approaches such as generalised linear regressions (GLR) in conjunction with regularisation methods, including ridge regression, lasso and elastic net, offer some clear advantages in terms of results’ interpretation and model selection. However, when highly-correlated variables are observed, these methods have shown a low specificity in variable selection. Principal component analysis (PCA) that has been widely used as a dimensionality reduction technique also has the limitation to identify important predictor variables as this approach may overlook the associations between certain components and health outcomes. Recently, some alternative approaches have been introduced to address the issues of high dimensionality and highly-correlated data in the context of epidemiological and environmental research. Two of the noticeable approaches are weighted quantile sum regression (WQSR) and Bayesian kernel machine regression (BKMR). Combining different methods of inference allows us to interpret the role of certain exposures, their interactions and the combined effects on human health under diverse statistical perspectives, which ultimately facilitate the construction of the toxicological profile of multiple mycotoxins’ exposure.