A Chemical Structure and Machine Learning Approach to Assess the Potential Bioactivity of Endogenous Metabolites and Their Association with Early Childhood Systemic Inflammation.

Metabolomics has gained much attention due to its potential to reveal molecular disease mechanisms and present viable biomarkers. This work uses a panel of untargeted serum metabolomes from 602 children from the COPSAC2010 mother-child cohort. The annotated part of the metabolome consists of 517 chemical compounds curated using automated procedures. We created a filtering method for the quantified metabolites using predicted quantitative structure-bioactivity relationships for the Tox21 database on nuclear receptors and stress response in cell lines. The metabolites measured in the children's serums are predicted to affect specific targeted models, known for their significance in inflammation, immune function, and health outcomes. The targets from Tox21 have been used as targets with quantitative structure-activity relationships (QSARs). They were trained for ~7000 structures, saved as models, and then applied to the annotated metabolites to predict their potential bioactivities. The models were selected based on strict accuracy criteria surpassing random effects. After application, 52 metabolites showed potential bioactivity based on structural similarity with known active compounds from the Tox21 set. The filtered compounds were subsequently used and weighted by their bioactive potential to show an association with early childhood hs-CRP levels at six months in a linear model supporting a physiological adverse effect on systemic low-grade inflammation.

Vertical Metabolome Transfer from Mother to Child: An Explainable Machine Learning Method for Detecting Metabolomic Heritability.

Vertical transmission of metabolic constituents from mother to child contributes to the manifestation of disease phenotypes in early life. This study probes the vertical transmission of metabolites from mothers to offspring by utilizing machine learning techniques to differentiate between true mother-child dyads and randomly paired non-dyads. Employing random forests (RF), light gradient boosting machine (LGBM), and logistic regression (Elasticnet) models, we analyzed metabolite concentration discrepancies in mother-child pairs, with maternal plasma sampled at 24 weeks of gestation and children's plasma at 6 months. The propensity of vertical transfer was quantified, reflecting the likelihood of accurate mother-child matching. Our findings were substantiated against an external test set and further verified through statistical tests, while the models were explained using permutation importance and SHapley Additive exPlanations (SHAP). The best model was achieved using RF, while xenobiotics were shown to be highly relevant in transfer. The study reaffirms the transmission of certain metabolites, such as perfluorooctanoic acid (PFOA), but also reveals additional insights into the maternal influence on the child's metabolome. We also discuss the multifaceted nature of vertical transfer. These machine learning-driven insights complement conventional epidemiological findings and offer a novel perspective on using machine learning as a methodology for understanding metabolic interactions.

A chemical structure and machine learning approach to assess the potential bioactivity of endogenous metabolites and their association with early-childhood hs-CRP levels.

Metabolomics has gained much attraction due to its potential to reveal molecular disease mechanisms and present viable biomarkers. In this work we used a panel of untargeted serum metabolomes in 602 childhood patients of the COPSAC2010 mother-child cohort. The annotated part of the metabolome consists of 493 chemical compounds curated using automated procedures. Using predicted quantitative-structure-bioactivity relationships for the Tox21 database on nuclear receptors and stress response in cell lines, we created a filtering method for the vast number of quantified metabolites. The metabolites measured in children's serums used here have predicted potential against the chosen target modelled targets. The targets from Tox21 have been used with quantitative structure-activity relationships (QSARs) and were trained for ~7000 structures, saved as models, and then applied to 493 metabolites to predict their potential bioactivities. The models were selected based on strict accuracy criteria surpassing random effects. After application, 52 metabolites showed potential bioactivity based on structural similarity with known active compounds from the Tox21 set. The filtered compounds were subsequently used and weighted by their bioactive potential to show an association with early childhood hs-CRP levels at six months in a linear model supporting a physiological adverse effect on systemic low-grade inflammation. The significant metabolites were reported.

A historical perspective of biomedical explainable AI research.

The black-box nature of most artificial intelligence (AI) models encourages the development of explainability methods to engender trust into the AI decision-making process. Such methods can be broadly categorized into two main types: post hoc explanations and inherently interpretable algorithms. We aimed at analyzing the possible associations between COVID-19 and the push of explainable AI (XAI) to the forefront of biomedical research. We automatically extracted from the PubMed database biomedical XAI studies related to concepts of causality or explainability and manually labeled 1,603 papers with respect to XAI categories. To compare the trends pre- and post-COVID-19, we fit a change point detection model and evaluated significant changes in publication rates. We show that the advent of COVID-19 in the beginning of 2020 could be the driving factor behind an increased focus concerning XAI, playing a crucial role in accelerating an already evolving trend. Finally, we present a discussion with future societal use and impact of XAI technologies and potential future directions for those who pursue fostering clinical trust with interpretable machine learning models.

Linking infant size and early growth with maternal lifestyle and breastfeeding - the first year of life in the CRIBS cohort.

BACKGROUND: Every third child in Croatia is classed as overweight or obese. Infant growth can represent early warning signs for obesity. AIM: To detect early risk factors for obesity by investigating infant size and early growth trajectories and their association with maternal lifestyle and breastfeeding. SUBJECTS AND METHODS: Ninety-eight mother-child pairs from the Croatian Islands' Birth Cohort Study (CRIBS) cohort were included in the study. Data were collected from questionnaires and medical records. Growth data were converted to Z-scores using World Health Organisation (WHO) standards and used as the primary outcome. RESULTS: Z-score trajectories in the first year of life were in line with WHO standards. A direct link between infant size and maternal socioeconomic status (SES) or breastfeeding was not detected. However, child weight gain in the first 6 months was associated with maternal body mass index (BMI) before pregnancy (p < 0.01). A positive association was also established between breastfeeding and maternal SES and mothers that report an unhealthy diet have heavier children (p < 0.05, respectively). CONCLUSION: Infant size and early growth in Croatia is in line with WHO standards and risk factors for obesity development were detectable in the first year of life, but not highly pronounced. However, more effective BMI monitoring and promotion of a healthy diet and lifestyle of women before and during pregnancy is needed.

Rapid growth of Aitken-mode particles during Arctic summer by fog chemical processing and its implication.

In the Arctic, new particle formation (NPF) and subsequent growth processes are the keys to produce Aitken-mode particles, which under certain conditions can act as cloud condensation nuclei (CCNs). The activation of Aitken-mode particles increases the CCN budget of Arctic low-level clouds and, accordingly, affects Arctic climate forcing. However, the growth mechanism of Aitken-mode particles from NPF into CCN range in the summertime Arctic boundary layer remains a subject of current research. In this combined Arctic cruise field and modeling study, we investigated Aitken-mode particle growth to sizes above 80 nm. A mechanism is suggested that explains how Aitken-mode particles can become CCN without requiring high water vapor supersaturation. Model simulations suggest the formation of semivolatile compounds, such as methanesulfonic acid (MSA) in fog droplets. When the fog droplets evaporate, these compounds repartition from CCNs into the gas phase and into the condensed phase of nonactivated Aitken-mode particles. For MSA, a mass increase factor of 18 is modeled. The postfog redistribution mechanism of semivolatile acidic and basic compounds could explain the observed growth of >20 nm h-1 for 60-nm particles to sizes above 100 nm. Overall, this study implies that the increasing frequency of NPF and fog-related particle processing can affect Arctic cloud properties in the summertime boundary layer.

Machine Learning and Meteorological Normalization for Assessment of Particulate Matter Changes during the COVID-19 Lockdown in Zagreb, Croatia.

In this paper, the authors investigated changes in mass concentrations of particulate matter (PM) during the Coronavirus Disease of 2019 (COVID-19) lockdown. Daily samples of PM1, PM2.5 and PM10 fractions were measured at an urban background sampling site in Zagreb, Croatia from 2009 to late 2020. For the purpose of meteorological normalization, the mass concentrations were fed alongside meteorological and temporal data to Random Forest (RF) and LightGBM (LGB) models tuned by Bayesian optimization. The models' predictions were subsequently de-weathered by meteorological normalization using repeated random resampling of all predictive variables except the trend variable. Three pollution periods in 2020 were examined in detail: January and February, as pre-lockdown, the month of April as the lockdown period, as well as June and July as the "new normal". An evaluation using normalized mass concentrations of particulate matter and Analysis of variance (ANOVA) was conducted. The results showed that no significant differences were observed for PM1, PM2.5 and PM10 in April 2020-compared to the same period in 2018 and 2019. No significant changes were observed for the "new normal" as well. The results thus indicate that a reduction in mobility during COVID-19 lockdown in Zagreb, Croatia, did not significantly affect particulate matter concentration in the long-term..

COVID-19 Lockdowns-Effect on Concentration of Pharmaceuticals and Illicit Drugs in Two Major Croatian Rivers.

In order to prevent the spread of COVID-19, contingency measures in the form of lockdowns were implemented all over the world, including in Croatia. The aim of this study was to detect if those severe, imposed restrictions of social interactions reflected on the water quality of rivers receiving wastewaters from urban areas. A total of 18 different pharmaceuticals (PhACs) and illicit drugs (IDrgs), as well as their metabolites, were measured for 16 months (January 2020-April 2021) in 12 different locations at in the Sava and Drava Rivers, Croatia, using UHPLC coupled to LCMS. This period encompassed two major Covid lockdowns (March-May 2020 and October 2020-March 2021). Several PhACs more than halved in river water mass flow during the lockdowns. The results of this study confirm that Covid lockdowns caused lower cumulative concentrations and mass flow of measured PhACs/IDrgs in the Sava and Drava Rivers. This was not influenced by the increased use of drugs for the treatment of the COVID-19, like antibiotics and steroidal anti-inflammatory drugs. The decreases in measured PhACs/IDrgs concentrations and mass flows were more pronounced during the first lockdown, which was stricter than the second.

Combining short-term bioassays using fish and crustacean model organisms with ToxCast in vitro data and broad-spectrum chemical analysis for environmental risk assessment of the river water (Sava, Croatia).

This study focused on the short-term whole organism bioassays (WOBs) on fish (Danio rerio) and crustaceans (Gammarus fossarum and Daphnia magna) to assess the negative biological effects of water from the major European River Sava and the comparison of the obtained results with in vitro toxicity data (ToxCast database) and Risk Quotient (RQ) methodology. Pollution profiles of five sampling sites along the River Sava were assessed by simultaneous chemical analysis of 562 organic contaminants (OCs) of which 476 were detected. At each sampling site, pharmaceuticals/illicit drugs category was mostly represented by their cumulative concentration, followed by categories industrial chemicals, pesticides and hormones. An exposure-activity ratio (EAR) approach based on ToxCast data highlighted steroidal anti-inflammatory drugs, antibiotics, antiepileptics/neuroleptics, industrial chemicals and hormones as compounds with the highest biological potential. Summed EAR-based prediction of toxicity showed a good correlation with the estimated toxicity of assessed sampling sites using WOBs. WOBs did not exhibit increased mortality but caused various sub-lethal biological responses that were dependant relative to the sampling site pollution intensity as well as species sensitivity. Exposure of G. fossarum and D. magna to river water-induced lower feeding rates increased GST activity and TBARS levels. Zebrafish D. rerio embryo exhibited a significant decrease in heartbeat rate, failure in pigmentation formation, as well as inhibition of ABC transporters. Nuclear receptor activation was indicated as the biological target of greatest concern based on the EAR approach. A combined approach of short-term WOBs, with a special emphasis on sub-lethal endpoints, and chemical characterization of water samples compared against in vitro toxicity data from the ToxCast database and RQs can provide a comprehensive insight into the negative effect of pollutants on aquatic organisms.

A Generation Shift in Mediterranean Diet Adherence and Its Association with Biological Markers and Health in Dalmatia, Croatia.

Previous studies have confirmed the beneficial effect of a Mediterranean diet in mitigating health issues related to cardiovascular disease, diabetes and obesity. However, rapid changes in the traditional way of life and the "westernization" of the diet in Mediterranean populations, especially in younger generations, has led to progressive abandonment of healthy dietary patterns. In order to investigate the generation shift in dietary patterns and lifestyle habits in the Mediterranean part of Croatia, we compared two cohorts of 610 women (266 pregnant and 344 non-pregnant) from the same region, but from different age groups. The MDSS score was derived from food frequency questionnaires. The results showed that the young, reproductively active generation (pregnant women) in Dalmatia, Croatia, although having a higher education and socioeconomic status, exhibits a more adverse eating behaviour (lower adherence to the Mediterranean diet) and lifestyle (excessive smoking in pregnancy) than the older population from the same region. Lower MDSS scores across aggregated age groups in both cohorts showed significant association with higher blood lipid levels and higher smoking frequency. In conclusion, Mediterranean diet adherence is associated with biological markers (age, lipid profile) and lifestyle (smoking) in our study, with a more adverse trend observed in the younger generation.

Should We Embed in Chemistry? A Comparison of Unsupervised Transfer Learning with PCA, UMAP, and VAE on Molecular Fingerprints.

Methods for dimensionality reduction are showing significant contributions to knowledge generation in high-dimensional modeling scenarios throughout many disciplines. By achieving a lower dimensional representation (also called embedding), fewer computing resources are needed in downstream machine learning tasks, thus leading to a faster training time, lower complexity, and statistical flexibility. In this work, we investigate the utility of three prominent unsupervised embedding techniques (principal component analysis-PCA, uniform manifold approximation and projection-UMAP, and variational autoencoders-VAEs) for solving classification tasks in the domain of toxicology. To this end, we compare these embedding techniques against a set of molecular fingerprint-based models that do not utilize additional pre-preprocessing of features. Inspired by the success of transfer learning in several fields, we further study the performance of embedders when trained on an external dataset of chemical compounds. To gain a better understanding of their characteristics, we evaluate the embedders with different embedding dimensionalities, and with different sizes of the external dataset. Our findings show that the recently popularized UMAP approach can be utilized alongside known techniques such as PCA and VAE as a pre-compression technique in the toxicology domain. Nevertheless, the generative model of VAE shows an advantage in pre-compressing the data with respect to classification accuracy.

Treatment outcome clustering patterns correspond to discrete asthma phenotypes in children.

Despite widely and regularly used therapy asthma in children is not fully controlled. Recognizing the complexity of asthma phenotypes and endotypes imposed the concept of precision medicine in asthma treatment. By applying machine learning algorithms assessed with respect to their accuracy in predicting treatment outcome, we have successfully identified 4 distinct clusters in a pediatric asthma cohort with specific treatment outcome patterns according to changes in lung function (FEV1 and MEF50), airway inflammation (FENO) and disease control likely affected by discrete phenotypes at initial disease presentation, differing in the type and level of inflammation, age of onset, comorbidities, certain genetic and other physiologic traits. The smallest and the largest of the 4 clusters- 1 (N = 58) and 3 (N = 138) had better treatment outcomes compared to clusters 2 and 4 and were characterized by more prominent atopic markers and a predominant allelic (A allele) effect for rs37973 in the GLCCI1 gene previously associated with positive treatment outcomes in asthmatics. These patients also had a relatively later onset of disease (6 + yrs). Clusters 2 (N = 87) and 4 (N = 64) had poorer treatment success, but varied in the type of inflammation (predominantly neutrophilic for cluster 4 and likely mixed-type for cluster 2), comorbidities (obesity for cluster 2), level of systemic inflammation (highest hsCRP for cluster 2) and platelet count (lowest for cluster 4). The results of this study emphasize the issues in asthma management due to the overgeneralized approach to the disease, not taking into account specific disease phenotypes.

Predicting Treatment Outcomes Using Explainable Machine Learning in Children with Asthma.

Asthma in children is a heterogeneous disease manifested by various phenotypes and endotypes. The level of disease control, as well as the effectiveness of anti-inflammatory treatment, is variable and inadequate in a significant portion of patients. By applying machine learning algorithms, we aimed to predict the treatment success in a pediatric asthma cohort and to identify the key variables for understanding the underlying mechanisms. We predicted the treatment outcomes in children with mild to severe asthma (N = 365), according to changes in asthma control, lung function (FEV1 and MEF50) and FENO values after 6 months of controller medication use, using Random Forest and AdaBoost classifiers. The highest prediction power is achieved for control- and, to a lower extent, for FENO-related treatment outcomes, especially in younger children. The most predictive variables for asthma control are related to asthma severity and the total IgE, which were also predictive for FENO-based outcomes. MEF50-related treatment outcomes were better predicted than the FEV1-based response, and one of the best predictive variables for this response was hsCRP, emphasizing the involvement of the distal airways in childhood asthma. Our results suggest that asthma control- and FENO-based outcomes can be more accurately predicted using machine learning than the outcomes according to FEV1 and MEF50. This supports the symptom control-based asthma management approach and its complementary FENO-guided tool in children. T2-high asthma seemed to respond best to the anti-inflammatory treatment. The results of this study in predicting the treatment success will help to enable treatment optimization and to implement the concept of precision medicine in pediatric asthma treatment.

Predictive Capability of QSAR Models Based on the CompTox Zebrafish Embryo Assays: An Imbalanced Classification Problem.

The CompTox Chemistry Dashboard (ToxCast) contains one of the largest public databases on Zebrafish (Danio rerio) developmental toxicity. The data consists of 19 toxicological endpoints on unique 1018 compounds measured in relatively low concentration ranges. The endpoints are related to developmental effects occurring in dechorionated zebrafish embryos for 120 hours post fertilization and monitored via gross malformations and mortality. We report the predictive capability of 209 quantitative structure-activity relationship (QSAR) models developed by machine learning methods using penalization techniques and diverse model quality metrics to cope with the imbalanced endpoints. All these QSAR models were generated to test how the imbalanced classification (toxic or non-toxic) endpoints could be predicted regardless which of three algorithms is used: logistic regression, multi-layer perceptron, or random forests. Additionally, QSAR toxicity models are developed starting from sets of classical molecular descriptors, structural fingerprints and their combinations. Only 8 out of 209 models passed the 0.20 Matthew's correlation coefficient value defined a priori as a threshold for acceptable model quality on the test sets. The best models were obtained for endpoints mortality (MORT), ActivityScore and JAW (deformation). The low predictability of the QSAR model developed from the zebrafish embryotoxicity data in the database is mainly due to a higher sensitivity of 19 measurements of endpoints carried out on dechorionated embryos at low concentrations.

Understanding the true effects of the COVID-19 lockdown on air pollution by means of machine learning.

During March 2020, most European countries implemented lockdowns to restrict the transmission of SARS-CoV-2, the virus which causes COVID-19 through their populations. These restrictions had positive impacts for air quality due to a dramatic reduction of economic activity and atmospheric emissions. In this work, a machine learning approach was designed and implemented to analyze local air quality improvements during the COVID-19 lockdown in Graz, Austria. The machine learning approach was used as a robust alternative to simple, historical measurement comparisons for various individual pollutants. Concentrations of NO2 (nitrogen dioxide), PM10 (particulate matter), O3 (ozone) and Ox (total oxidant) were selected from five measurement sites in Graz and were set as target variables for random forest regression models to predict their expected values during the city's lockdown period. The true vs. expected difference is presented here as an indicator of true pollution during the lockdown. The machine learning models showed a high level of generalization for predicting the concentrations. Therefore, the approach was suitable for analyzing reductions in pollution concentrations. The analysis indicated that the city's average concentration reductions for the lockdown period were: -36.9 to -41.6%, and -6.6 to -14.2% for NO2 and PM10, respectively. However, an increase of 11.6-33.8% for O3 was estimated. The reduction in pollutant concentration, especially NO2 can be explained by significant drops in traffic-flows during the lockdown period (-51.6 to -43.9%). The results presented give a real-world example of what pollutant concentration reductions can be achieved by reducing traffic-flows and other economic activities.

Plant resistome profiling in evolutionary old bog vegetation provides new clues to understand emergence of multi-resistance.

The expanding antibiotic resistance crisis calls for a more in depth understanding of the importance of antimicrobial resistance genes (ARGs) in pristine environments. We, therefore, studied the microbiome associated with Sphagnum moss forming the main vegetation in undomesticated, evolutionary old bog ecosystems. In our complementary analysis of culture collections, metagenomic data and a fosmid library from different geographic sites in Europe, we identified a low abundant but highly diverse pool of resistance determinants, which targets an unexpectedly broad range of 29 antibiotics including natural and synthetic compounds. This derives both, from the extraordinarily high abundance of efflux pumps (up to 96%), and the unexpectedly versatile set of ARGs underlying all major resistance mechanisms. Multi-resistance was frequently observed among bacterial isolates, e.g. in Serratia, Rouxiella, Pandoraea, Paraburkholderia and Pseudomonas. In a search for novel ARGs, we identified the new class A ß-lactamase Mm3. The native Sphagnum resistome comprising a highly diversified and partially novel set of ARGs contributes to the bog ecosystem´s plasticity. Our results reinforce the ecological link between natural and clinically relevant resistomes and thereby shed light onto this link from the aspect of pristine plants. Moreover, they underline that diverse resistomes are an intrinsic characteristic of plant-associated microbial communities, they naturally harbour many resistances including genes with potential clinical relevance.

Pollution Sources and Carcinogenic Risk of PAHs in PM1 Particle Fraction in an Urban Area.

Airborne particles are composed of inorganic species and organic compounds. PM1 particles, with an aerodynamic diameter smaller than 1 µm, are considered to be important in the context of adverse health effects. Many compounds bound to particulate matter, such as polycyclic aromatic hydrocarbons (PAH), are suspected to be genotoxic, mutagenic, and carcinogenic. In this study, PAHs in the PM1 particle fraction were measured for one year (1/1/2018-31/12/2018). The measuring station was located in the northern residential part of Zagreb, the Croatian capital, close to a street with modest traffic. Significant differences were found between PAH concentrations during cold (January-March, October-December) and warm (April-September) periods of the year. In general, the mass concentrations of PAHs characteristic for car exhausts (benzo(ghi)perylene (BghiP), indeno(1,2,3-cd)pyrene (IP), and benzo(b)fluoranthene (BbF)) were higher during the whole year than concentrations of fluoranthene (Flu) and pyrene (Pyr), which originated mostly from domestic heating and biomass burning. Combustion of diesel and gasoline from vehicles was found to be one of the main PAH sources. The incremental lifetime cancer risk (ILCR) was estimated for three age groups of populations and the results were much lower than the acceptable risk level (1 × 10-6). However, more than ten times higher PAH concentrations in the cold part of the year, as well as associated health risk, emphasize the need for monitoring of PAHs in PM1. These data represent a valuable tool in future plans and actions to control PAH sources and to improve the quality of life of urban populations.

Affinity of Antifungal Isoxazolo[3,4-b]pyridine-3(1H)-Ones to Phospholipids in Immobilized Artificial Membrane (IAM) Chromatography.

Currently, rapid evaluation of the physicochemical parameters of drug candidates, such as lipophilicity, is in high demand owing to it enabling the approximation of the processes of absorption, distribution, metabolism, and elimination. Although the lipophilicity of drug candidates is determined using the shake flash method (n-octanol/water system) or reversed phase liquid chromatography (RP-LC), more biosimilar alternatives to classical lipophilicity measurement are currently available. One of the alternatives is immobilized artificial membrane (IAM) chromatography. The present study is a continuation of our research focused on physiochemical characterization of biologically active derivatives of isoxazolo[3,4-b]pyridine-3(1H)-ones. The main goal of this study was to assess the affinity of isoxazolones to phospholipids using IAM chromatography and compare it with the lipophilicity parameters established by reversed phase chromatography. Quantitative structure-retention relationship (QSRR) modeling of IAM retention using differential evolution coupled with partial least squares (DE-PLS) regression was performed. The results indicate that in the studied group of structurally related isoxazolone derivatives, discrepancies occur between the retention under IAM and RP-LC conditions. Although some correlation between these two chromatographic methods can be found, lipophilicity does not fully explain the affinities of the investigated molecules to phospholipids. QSRR analysis also shows common factors that contribute to retention under IAM and RP-LC conditions. In this context, the significant influences of WHIM and GETAWAY descriptors in all the obtained models should be highlighted.

Toxicity prediction and effect characterization of 90 pharmaceuticals and illicit drugs measured in plasma of fish from a major European river (Sava, Croatia).

Chemical analysis of plasma samples of wild fish from the Sava River (Croatia) revealed the presence of 90 different pharmaceuticals/illicit drugs and their metabolites (PhACs/IDrgs). The concentrations of these PhACs/IDrgs in plasma were 10 to 1000 times higher than their concentrations in river water. Antibiotics, allergy/cold medications and analgesics were categories with the highest plasma concentrations. Fifty PhACs/IDrgs were identified as chemicals of concern based on the fish plasma model (FPM) effect ratios (ER) and their potential to activate evolutionary conserved biological targets. Chemicals of concern were also prioritized by calculating exposure-activity ratios (EARs) where plasma concentrations of chemicals were compared to their bioactivities in comprehensive ToxCast suite of in vitro assays. Overall, the applied prioritization methods indicated stimulants (nicotine, cotinine) and allergy/cold medications (prednisolone, dexamethasone) as having the highest potential biological impact on fish. The FPM model pointed to psychoactive substances (hallucinogens/stimulants and opioids) and psychotropic substances in the cannabinoids category (i.e. CBD and THC). EAR confirmed above and singled out additional chemicals of concern - anticholesteremic simvastatin and antiepileptic haloperidol. Present study demonstrates how the use of a combination of chemical analyses, and bio-effects based risk predictions with multiple criteria can help identify priority contaminants in freshwaters. The results reveal a widespread exposure of fish to complex mixtures of PhACs/IDrgs, which may target common molecular targets. While many of the prioritized chemicals occurred at low concentrations, their adverse effect on aquatic communities, due to continuous chronic exposure and additive effects, should not be neglected.

Adherence to Mediterranean Diet and Maternal Lifestyle during Pregnancy: Island-Mainland Differentiation in the CRIBS Birth Cohort.

Maternal nutrition and lifestyle in pregnancy are important modifiable factors for both maternal and offspring's health. Although the Mediterranean diet has beneficial effects on health, recent studies have shown low adherence in Europe. This study aimed to assess the Mediterranean diet adherence in 266 pregnant women from Dalmatia, Croatia and to investigate their lifestyle habits and regional differences. Adherence to the Mediterranean diet was assessed through two Mediterranean diet scores. Differences in maternal characteristics (diet, education, income, parity, smoking, pre-pregnancy body mass index (BMI), physical activity, contraception) with regards to location and dietary habits were analyzed using the non-parametric Mann-Whitney U test. The machine learning approach was used to reveal other potential non-linear relationships. The results showed that adherence to the Mediterranean diet was low to moderate among the pregnant women in this study, with no significant mainland-island differences. The highest adherence was observed among wealthier women with generally healthier lifestyle choices. The most significant mainland-island differences were observed for lifestyle and socioeconomic factors (income, education, physical activity). The machine learning approach confirmed the findings of the conventional statistical method. We can conclude that adverse socioeconomic and lifestyle conditions were more pronounced in the island population, which, together with the observed non-Mediterranean dietary pattern, calls for more effective intervention strategies.