bims-exposo 2024-11-17 papers

bims-exposo

Biomed News

on Exposomics

Issue of 2024–11–17
seventeen papers selected by
Yunjia Lai, Columbia University

Supramolecular solvents: a gateway to all-in-one extractions in chemical exposomics.
Insights into the Chemical Exposome during Pregnancy: A Non-Targeted Analysis of Preterm and Term Births.
Prenatal chemical exposures and the methylome: current evidence and opportunities for environmental epigenetics.
Characterization of Chemical Exposome in A Paired Human Preconception Pilot Study.
Applications of artificial intelligence for chemical analysis and monitoring of pharmaceutical and personal care products in water and wastewater: A review.
Triple acquisition mass spectrometry (TRAM) combining targeted and non-targeted metabolomics in a single run.
Suspect and nontarget screening of pesticides and their transformation products in agricultural products using liquid chromatography-high-resolution mass spectrometry.
Multimodal chemical speciation techniques based on simultaneous high resolution molecular/atomic mass spectrometry applied to online target/non-target analysis: A tutorial review.
Chemical migration from reusable plastic bottles: Silicone, polyethylene, and polypropylene show highest hazard potential in LC-HRMS analysis.
Chemical genetic approaches to dissect microbiota mechanisms in health and disease.
Organophosphorus Flame Retardants and Metabolic Disruption: An in Silico, in Vitro, and in Vivo Study Focusing on Adiponectin Receptors.
Cell Painting and Chemical Structure Read-Across Can Complement Each Other for Rat Acute Oral Toxicity Prediction in Chemical Early Derisking.
An intelligent spectral identification approach for the simultaneous detection of endocrine-disrupting chemicals in aquatic environments.
Pregnant women's lifestyles and exposure to endocrine-disrupting chemicals: a machine learning approach.
Polarity Gradient Solvent Confinement Membrane Cartridge to Broaden Metabolite Coverage of Plasma Untargeted Analysis.
Comprehensive Characterization of Oxidative Stress-Modulating Chemicals Using GPT-Based Text Mining.
Machine Learning-Assisted Surface-Enhanced Raman Spectroscopy Detection for Environmental Applications: A Review.

Anal Bioanal Chem. 2024 Nov 07.

Supramolecular solvents: a gateway to all-in-one extractions in chemical exposomics.

Noelia Caballero-Casero, Ana M Ballesteros-Gómez, Soledad Rubio.

  The characterization of the human chemical exposome through daily estimated intakes or biomonitoring has become paramount to understand the causal pathways leading to common diseases. The paradigm shift that has taken place in looking at health has moved research from the classical biomedical model based on "one exposure, one disease" to a more comprehensive approach based on multiple chemicals and low dose effects. For this purpose, untargeted and/or suspect analysis of chemicals based on liquid chromatography and high-resolution mass spectrometry (LC-HRMS) has been proposed as the most relevant strategy for sequencing the exposome. A key aspect in this respect is the development of unbiased sample preparation methods that efficiently concentrate the wide range of untargeted/suspected chemicals while minimizing interference from sample matrices. Here, we aim to critically discuss the potential of tailored supramolecular solvents (SUPRAS) for achieving all-in-one extractions in chemical exposomics, as an alternative to overcome the limitations of the current sample treatment strategies, on the basis of their intrinsic properties and the applications reported so far.

Keywords:  All-in-one extractions; Chemical exposome; Sample preparation; Supramolecular solvents

DOI:  https://doi.org/10.1007/s00216-024-05645-7
Environ Sci Technol. 2024 Nov 11.

Insights into the Chemical Exposome during Pregnancy: A Non-Targeted Analysis of Preterm and Term Births.

Xiaowen Ji, Mathusa Lakuleswaran, Whitney Cowell, Linda G Kahn, Marina Sirota, Dimitri Abrahamsson.

  Human-made chemicals are ubiquitous, leading to chronic exposure to complex mixtures of potentially harmful substances. We investigated chemical exposures in pregnant women in New York City by applying a non-targeted analysis (NTA) workflow to 95 paired prenatal urine and serum samples (35 pairs of preterm birth) collected as part of the New York University Children's Health and Environment Study. We analyzed all samples using liquid chromatography coupled with Orbitrap high-resolution mass spectrometry in both positive and negative electrospray ionization modes, employing full scan and data-dependent MS/MS fragmentation scans. We detected a total of 1524 chemical features for annotation, with 12 chemicals confirmed by authentic standards. Two confirmed chemicals dodecyltrimethylammonium and N,N-dimethyldecylamine N-oxide appear to not have been previously reported in human blood samples. We observed a statistically significant differential enrichment between urine and serum samples, as well as between preterm and term birth (p < 0.0001) in serum samples. When comparing between preterm and term births, an exogenous contaminant, 1,4-cyclohexanedicarboxylic acid (tentative), showed a statistical significance difference (p = 0.003) with more abundance in preterm birth in serum. An example of chemical associations (12 associations in total) observed was between surfactants (tertiary amines) and endogenous metabolites (fatty acid amides).

Keywords:  chemical associations; exogenous chemicals; exposure; high-resolution mass spectrometry; non-targeted analysis; preterm birth

DOI:  https://doi.org/10.1021/acs.est.4c08534
Epigenomics. 2024 Nov 14. 1-9

Prenatal chemical exposures and the methylome: current evidence and opportunities for environmental epigenetics.

Anne K Bozack, Leonardo Trasande.

  Exposure to pollutants and chemicals during critical developmental periods in early life can impact health and disease risk across the life course. Research in environmental epigenetics has provided increasing evidence that prenatal exposures affect epigenetic markers, particularly DNA methylation. In this article, we discuss the role of DNA methylation in early life programming and review evidence linking the intrauterine environment to epigenetic modifications, with a focus on exposure to tobacco smoke, metals, and endocrine-disrupting chemicals. We also discuss challenges and novel approaches in environmental epigenetic research and explore the potential of epigenetic biomarkers in studies of pediatric populations as indicators of exposure and disease risk. Overall, we aim to highlight how advancements in environmental epigenetics may transform our understanding of early-life exposures and inform new approaches for supporting long-term health.

Keywords:  DNA methylation; developmental epigenetics; environmental epigenetics; epigenetic epidemiology; epigenetic programming

DOI:  https://doi.org/10.1080/17501911.2024.2426441
Environ Sci Technol. 2024 Nov 07.

Characterization of Chemical Exposome in A Paired Human Preconception Pilot Study.

Jaye Marchiandi, Sonia Dagnino, Deirdre Zander-Fox, Mark P Green, Bradley O Clarke.

  Parental preconception exposure to synthetic chemicals may have critical influences on fertility and reproduction. Here, we present a robust LC-MS/MS method covering up to 95 diverse xenobiotics in human urine, serum, seminal and follicular fluids to support exposome-wide assessment in reproductive health outcomes. Extraction recoveries of validated analytes ranged from 62% to 137% and limits of quantification from 0.01 to 6.0 ng/mL in all biofluids. We applied the validated method to a preconception cohort of Australian couples (n = 30) receiving fertility treatment. In total, 36 and 38 xenobiotics were detected across the paired biofluids of males and females, respectively, including PFAS, parabens, organic UV-filters, plastic additives, antimicrobials, and other industrial chemicals. Results showed 39% of analytes in males and 37% in females were equally detected in paired serum, urine, and reproductive fluids. The first detection of the sunscreen ingredient avobenzone and the industrial chemical 4-nitrophenol in follicular and seminal fluids suggests it can cross both blood-follicle/testis barriers, indicating potential risks for fertility. Further, the blood-follicle transfer of perfluorobutanoic acid, PFOA, PFHxS, PFOS, and oxybenzone corroborate that serum concentrations can be reliable proxies for assessing exposure within the ovarian microenvironment. In conclusion, we observed significant preconception exposure to multiple endocrine disruptors in couples and identified potential xenobiotics relevant to male and female fertility impairments.

Keywords:  endocrine disrupting chemicals (EDCs); human biomonitoring (HBM); multiclass; per- and polyfluoroalkyl substances (PFAS); phenolics; xenobiotics

DOI:  https://doi.org/10.1021/acs.est.4c04356
Chemosphere. 2024 Nov 14. pii: S0045-6535(24)02592-X. [Epub ahead of print]368 143692

Applications of artificial intelligence for chemical analysis and monitoring of pharmaceutical and personal care products in water and wastewater: A review.

Babak Kavianpour, Farzad Piadeh, Mohammad Gheibi, Atiyeh Ardakanian, Kourosh Behzadian, Luiza C Campos.

  Specifying and interpreting the occurrence of emerging pollutants is essential for assessing treatment processes and plants, conducting wastewater-based epidemiology, and advancing environmental toxicology research. In recent years, artificial intelligence (AI) has been increasingly applied to enhance chemical analysis and monitoring of contaminants in environmental water and wastewater. However, their specific roles targeting pharmaceuticals and personal care products (PPCPs) have not been reviewed sufficiently. This review aims to narrow the gap by highlighting, scoping, and discussing the incorporation of AI during the detection and quantification of PPCPs when utilising chemical analysis equipment and interpreting their monitoring data for the first time. In the chemical analysis of PPCPs, AI-assisted prediction of chromatographic retention times and collision cross-sections (CCS) in suspect and non-target screenings using high-resolution mass spectrometry (HRMS) enhances detection confidence, reduces analysis time, and lowers costs. AI also aids in interpreting spectroscopic analysis results. However, this approach still cannot be applied in all matrices, as it offers lower sensitivity than liquid chromatography coupled with tandem or HRMS. For the interpretation of monitoring of PPCPs, unsupervised AI methods have recently presented the capacity to survey regional or national community health and socioeconomic factors. Nevertheless, as a challenge, long-term monitoring data sources are not given in the literature, and more comparative AI studies are needed for both chemical analysis and monitoring. Finally, AI assistance anticipates more frequent applications of CCS prediction to enhance detection confidence and the use of AI methods in data processing for wastewater-based epidemiology and community health surveillance.

Keywords:  Artificial intelligence; Contaminants of emerging concern; High-resolution mass spectrometry; PPCPs; Quantitative structure retention relationship; Suspect and non-targeted screening; Wastewater-based epidemiology

DOI:  https://doi.org/10.1016/j.chemosphere.2024.143692
Anal Chim Acta. 2024 Dec 01. pii: S0003-2670(24)01115-2. [Epub ahead of print]1331 343314

Triple acquisition mass spectrometry (TRAM) combining targeted and non-targeted metabolomics in a single run.

Lisa Panzenboeck, Harald Schoeny, Bruno Stelzer, Elisabeth Foels, Marvin Glas, Marlene Pühringer, Dorian Hirschmann, Daniela Loetsch, Christian Dorfer, Evelyn Rampler, Gunda Koellensperger.

   BACKGROUND: We introduce TRAM, a triple acquisition strategy on a high-speed quadrupole time-of-flight mass spectrometer for merging non-targeted and targeted metabolomics into one run. TRAM stands for "quasi-simultaneous" acquisition of (1) a full scan MS1, (2) top 30 data-dependent MS2 (DDA), and (3) targeted scheduled MS2 for multiple reaction monitoring (MRM) within measurement cycles of ∼1 s. TRAM combines the selectivity and sensitivity of state-of-the-art targeted MRM-based methods with the full scope of non-targeted analysis enabled by high-resolution mass spectrometry.
RESULTS: In this work, we deploy a workflow based on hydrophilic interaction liquid chromatography (HILIC). For a broad panel of metabolites, we provide chromatographic retention times, and optimized conditions as a basis for targeted MRM experiments, listing accurate masses and sum formulas for fragment ions (including fully 13C labeled analogs). Validation experiments showed that TRAM offered (1) linear working ranges and limits of quantification comparable to MRM-only methods, (2) enabled accurate quantification in SRM 1950 human plasma reference material, and (3) was equivalent to DDA-only approaches in non-targeted metabolomics. Metabolomics in human cerebrospinal fluid showcased the power of the strategy, emphasizing the need for high coverage/high throughput metabolomics in clinical studies.
SIGNIFICANCE: Acquiring up to 30 data-dependent spectra per MS cycle while still offering gold standard absolute quantification down to low nanomolar concentrations, TRAM allows in-depth profiling and reduces required sample volume, time, cost, and environmental impact.

Keywords:  Absolute quantification; HILIC; Liquid chromatography-mass spectrometry; Meningioma; Non-targeted metabolomics; Targeted metabolomics; ZenoTOF 7600

DOI:  https://doi.org/10.1016/j.aca.2024.343314
Talanta. 2024 Nov 04. pii: S0039-9140(24)01533-9. [Epub ahead of print]283 127154

Suspect and nontarget screening of pesticides and their transformation products in agricultural products using liquid chromatography-high-resolution mass spectrometry.

Tiantian Chen, Yujie Zhang, Yuting Wang, Wenying Liang, Zengqi Yan, Xin Lu, Xinyu Liu, Chunxia Zhao, Guowang Xu.

  Intensive agricultural production involves the extensive use of chemicals, leading to the presence of pesticides and their transformation products (TPs) in agricultural products. Our study developed a high-coverage method to map the occurrence of pesticides and their transformation products in agricultural products using liquid chromatography-high-resolution mass spectrometry (LC-HRMS). Initially, a suspect list of 1265 pesticides was compiled based on in-house standards and online databases to identify potential parent pesticides. Besides, the reported and predicted TPs, as well as the multi-class characteristic fragment ions (CFIs) of pesticides, were summarized. Subsequently, nontarget features were identified by matching with 10226 TPs and 39-classes of CFIs. Both known and unknown parent pesticides and their TPs can be identified via suspect and nontarget screening procedures. Ultimately, the proposed method was applied to strawberry samples to demonstrate its effectiveness. We identified 67 parent pesticides and 57 TPs in 107 samples, with the majority at low concentrations, and preliminary traceability suggesting they may migrate from soil. The findings suggest that our method can enable suspect and nontarget screening of pesticides and their TPs, and it is also applicable to other food matrices. This method may facilitate regulatory agencies in strengthening the supervision of unknown risk substances or TPs, thereby comprehensively safeguarding consumer health.

Keywords:  Agricultural products; LC-HRMS; Nontarget screening; Pesticide residues; Suspect screening; Transformation products

DOI:  https://doi.org/10.1016/j.talanta.2024.127154
Anal Chim Acta. 2024 Dec 01. pii: S0003-2670(24)00885-7. [Epub ahead of print]1331 343084

Multimodal chemical speciation techniques based on simultaneous high resolution molecular/atomic mass spectrometry applied to online target/non-target analysis: A tutorial review.

Lilian Seiko Kato, Vinnícius Henrique Cerqueira da Silva, Diego Campaci de Andrade, Guilherme Cruz, Jorge Henrique Pedrobom, Andrea Raab, Jörg Feldmann, Marco Aurélio Zezzi Arruda.

   BACKGROUND: If identifying target species is challenging regarding chemical speciation, non-target species present even more significant difficulties. Thus, to improve the performance of the methods, multimodal online coupling involving atomic and molecular mass spectrometry (LC-ICP-MS-ESI-HRMS) is an advance in this direction. Then, this kind of coupling is highlighted in this Tutorial Review, as well as some references emphasizing its potentialities and possible limitations. Some crucial definitions of speciomics, chemical speciation, and others are also included.
RESULTS: The main parameters that influence the coupling of an inductively coupled plasma mass spectrometer with a high-resolution mass spectrometer through a chromatographic system are critically commented on, and a diversity of results is demonstrated by using a turtle liver (Caretta caretta) as a model sample. The parameters were discussed in detail in a step-by-step manner: ICP-MS/MS acquisition modes and instrumental parameters, HRMS acquisition modes and instrumental parameters, and data processing strategies (Full MS - Top N, All Ion Fragmentation - AIF, Parallel Reaction Monitoring - PRM). Additionally, this Tutorial Review also demonstrates a diversity of results through target and non-target analysis.
SIGNIFICANCE: Constituting a guide for those who are interested in a non-targeted analysis of molecular non-volatile/semi-volatile compounds, this Tutorial Review presents trans and multidisciplinary proposals for those communities involving chemistry, biochemistry, medicine, biology, environmental, pharmaceutical, food safety, and omics, among others, where metal (also metalloids or semi-metals and non-metals or heteroatoms) and molecular species are necessary for a good understanding of the studied system. This kind of coupling also allows the discovery of novel biological active elemental species in diverse matrices.

Keywords:  Arsenobetaine; Chemical speciation; ESI-HRMS; ICP-MS/MS; Marine turtle liver; Metallobiomolecules; Speciomics

DOI:  https://doi.org/10.1016/j.aca.2024.343084
J Hazard Mater. 2024 Nov 12. pii: S0304-3894(24)02970-4. [Epub ahead of print]480 136391

Chemical migration from reusable plastic bottles: Silicone, polyethylene, and polypropylene show highest hazard potential in LC-HRMS analysis.

Selina Tisler, Nastacha Kristiansen, Jan H Christensen.

  Estimating the chemical hazards of drinking water stored in reusable plastic bottles is challenging due to the numerous intentionally and unintentionally added chemicals. To address this, we developed a broad screening strategy using evaporation enrichment and liquid chromatography high-resolution mass spectrometry (LC-HRMS) to evaluate migration of non-volatile chemicals from various reusable plastic bottles. The study analyzed a wide range of materials, revealing significant variability in chemical profiles across different bottle types. Over 70 % of nearly 1000 unknown compounds were unique to specific bottles. Silicone, HDPE, LDPE, and PP bottles showed the highest migration rates, with silicone releasing the most unknowns, but also phthalates and plasticizers. PP bottles exhibited concerning migration of clarifying agents and bisphenol A derivatives. In contrast, PS, PET, PETG, and PCTG had minimal migration, indicating lower health risks. These findings highlight the need for comprehensive assessments of plastic materials to improve consumer safety.

Keywords:  Bisphenol; Clarifying agents; Drinking water; Food contact materials; Non-intentionally added substances; Non-target screening; Phthalates

DOI:  https://doi.org/10.1016/j.jhazmat.2024.136391
Science. 2024 Nov 15. 386(6723): eado8548

Chemical genetic approaches to dissect microbiota mechanisms in health and disease.

Xinglin Yang, Howard C Hang.

Advances in genomics, proteomics, and metabolomics have revealed associations between specific microbiota species in health and disease. However, the precise mechanism(s) of action for many microbiota species and molecules have not been fully elucidated, limiting the development of microbiota-based diagnostics and therapeutics. In this Review, we highlight innovative chemical and genetic approaches that are enabling the dissection of microbiota mechanisms and providing causation in health and disease. Although specific microbiota molecules and mechanisms have begun to emerge, new approaches are still needed to go beyond phenotypic associations and translate microbiota discoveries into actionable targets and therapeutic leads to prevent and treat diseases.

DOI: https://doi.org/10.1126/science.ado8548
Environ Health Perspect. 2024 Nov;132(11): 117003

Organophosphorus Flame Retardants and Metabolic Disruption: An in Silico, in Vitro, and in Vivo Study Focusing on Adiponectin Receptors.

Ying Liu, Xiaochun Ma, Yifei Le, Jiafan Feng, Mengting Xu, Wanyue Wang, Cui Wang.

BACKGROUND: Environmental chemical exposures have been associated with metabolic outcomes, and typically, their binding to nuclear hormone receptors is considered the molecular initiating event (MIE) for a number of outcomes. However, more studies are needed to understand the influence of such exposures on cell membrane-bound adiponectin receptors (AdipoRs), which are critical metabolic regulators.
OBJECTIVE: We aimed to clarify the potential interactions between AdipoRs and environmental chemicals, specifically organophosphorus flame retardants (OPFRs), and the resultant effects.
METHODS: Employing in silico simulation, cell thermal shift, and noncompetitive binding assays, we screened eight OPFRs for interactions with AdipoR1 and AdipoR2. We tested two key events underlying AdipoR modulation upon OPFR exposure in a liver cell model. The Toxicological Prioritization Index (ToxPi)scoring scheme was used to rank OPFRs according to their potential to disrupt AdipoR-associated metabolism. We further examined the inhibitory effect of OPFRs on AdipoR signaling activation in mouse models.
RESULTS: Analyses identified pi-pi stacking and pi-sulfur interactions between the aryl-OPFRs 2-ethylhexyl diphenyl phosphate (EHDPP), triphenyl phosphate (TPhP), and tricresyl phosphate (TCP) and the transmembrane cavities of AdipoR1 and AdipoR2. Cell thermal shift assays showed a >3°C rightward shift in the AdipoR proteins' melting curves upon exposure to these three compounds. Although the binding sites differed from adiponectin, results suggest that aryl-OPFRs noncompetitively inhibited the binding of the endogenous peptide ligand ADP355 to the receptors. Analyses of key events underlying AdipoR modulation revealed that glucose uptake was notably lower, whereas lipid content was higher in cells exposed to aryl-OPFRs. EHDPP, TCP, and TPhP were ranked as the top three disruptors according to the ToxPi scores. A noncompetitive binding between these aryl-OPFRs and AdipoRs was also observed in wild-type (WT) mice. In db/db mice, the finding of lower blood glucose levels after ADP355 injection was diminished in the presence of a typical aryl-OPFR (TCP). WT mice exposed to TCP demonstrated lower AdipoR1 signaling, which was marked by lower phosphorylated AMP-activated protein kinase (pAMPK) and a higher expression of gluconeogenesis-related genes. Moreover, WT mice exposed to ADP355 demonstrated higher levels of pAMPK protein and peroxisome proliferator-activated receptor-α messenger RNA. This was accompanied by higher glucose disposal and by lower levels of long-chain fatty acids and hepatic triglycerides; these metabolic improvements were negated upon TCP co-treatment.
CONCLUSIONS: In silico, in vitro, and in vivo assays suggest that aryl-OPFRs act as noncompetitive inhibitors of AdipoRs, preventing their activation by adiponectin, and thus function as antagonists to these receptors. Our study describes a novel MIE for chemical-induced metabolic disturbances and highlights a new pathway for environmental impact on metabolic health. https://doi.org/10.1289/EHP14634.

DOI: https://doi.org/10.1289/EHP14634
Chem Res Toxicol. 2024 Nov 08.

Cell Painting and Chemical Structure Read-Across Can Complement Each Other for Rat Acute Oral Toxicity Prediction in Chemical Early Derisking.

Fabrice Camilleri, Joanna M Wenda, Claire Pecoraro-Mercier, Jean-Paul Comet, David Rouquié.

Early derisking decisions in the development of new chemical compounds enable the identification of novel chemical candidates with improved safety profiles. In vivo studies are traditionally conducted in the early assessment of acute oral toxicity of crop protection products to avoid compounds, which are considered "very acutely toxic", with an in vivo lethal dose of 50% (LD50) ≤ 60 mg/kg body weight. Those studies are lengthy and costly and raise ethical concerns, catalyzing the use of nonanimal alternatives. The objective of our analysis was to assess the predictive efficacy of read-across approaches for acute oral toxicity in rats, comparing the use of chemical structure information, in vitro biological data derived from the Cell Painting profiling assay on U2OS cells, or the combination of both. Our findings indicate that the classification of compounds as very acute oral toxic (LD50 ≤ 60 mg/kg) or not is possible using a read-across approach, with chemical structure information, morphological profiles, or a combination of both. When classifying compounds structurally similar to those in the training set, the chemical structure was more predictive (balanced accuracy of 0.82). Conversely, when the compounds to be classified were structurally different from those in the training set, the morphological profiles were more predictive (balanced accuracy of 0.72). Combining the two models allowed for the classification of compounds structurally similar to those in the training set to slightly improve the predictions (balanced accuracy of 0.85).

DOI: https://doi.org/10.1021/acs.chemrestox.4c00169
Environ Res. 2024 Nov 13. pii: S0013-9351(24)02275-8. [Epub ahead of print] 120368

An intelligent spectral identification approach for the simultaneous detection of endocrine-disrupting chemicals in aquatic environments.

Liulu Yao, Zhizhi Fu, Qiannan Duan, Mingzhe Wu, Fan Song, Haoyu Wang, Yiheng Qin, Yonghui Bai, Chi Zhou, Xudong Quan, Jianchao Lee.

  With the rapid progression of industrialization, the application and release of endocrine disruptors (EDCs), including bisphenol A (BPA), octylphenol and nonylphenol have significantly increased, presenting substantial health hazards. Conventional analytical techniques, such as high-performance liquid chromatography and gas chromatography-mass spectrometry, are highly sophisticated but suffer from complex procedures and high costs. To overcome these limitations, this study introduces an innovative spectral methodology for the simultaneous detection of multiple aquatic multicomponent EDCs. By leveraging chemical machine vision, specifically with convolutional neural network (CNN) models, we employed a long-path holographic spectrometer for rapid, cost-effective identification of BPA, 4-tert-octylphenol, and 4-nonylphenol in aqueous samples. The CNN, refined with the ResNet-50 architecture, demonstrated superior predictive performance, achieving detection limits as low as 3.34, 3.71 and 4.36 μg/L, respectively. The sensitivity and quantification capability of our approach were confirmed through the analysis of spectral image Euclidean distances, while its universality and resistance properties were validated by assessments of environmental samples. This technology offers significantly advantages over conventional techniques in terms of efficiency and cost, offering a novel solution for EDC monitoring in aquatic environments. The implications of this research extend beyond improved detection speed and cost reduction, presenting new methodologies for analyzing complex chemical systems and contributing to environmental protection and public health.

Keywords:  Chemical machine vision; Convolutional neural network; Deep Learning; Endocrine-disrupting chemicals; Spectral analysis

DOI:  https://doi.org/10.1016/j.envres.2024.120368
Environ Pollut. 2024 Nov 12. pii: S0269-7491(24)02026-8. [Epub ahead of print] 125309

Pregnant women's lifestyles and exposure to endocrine-disrupting chemicals: a machine learning approach.

Ko-CHENS study group

Women have ubiquitous exposure to various endocrine disrupting chemicals (EDCs) present in personal care products, food packaging, and processing. Pregnancy is a phase of increased vulnerability to environmental stressors. Therefore, we aimed to identify questionnaire based variables of pregnant women's lifestyle factors affecting the prenatal concentrations of EDCs: bis-phenol A (BPA), triclosan (TCS), parabens, and phthalates. We also aimed to explore the association between these lifestyle factors and EDC exposure in pregnant women in South Korea. This study is a part of Korean CHildren's ENvironmental health Study (Ko-CHENS). The following lifestyle factors: usage of personal care products, eating habits, cooking practices, food storage practices, and chemical exposure were evaluated through questionnaire. We examined prenatal EDCs: phenols (BPA), TCS, parabens (MEP, ETP, and PRP), and phthalates (MEHHP, MEOHP, MECPP, MBZP, MCOP, MCPP, MCNP, and MNBP). The random forest and least absolute shrinkage and selection operator regression machine learning models were used to predict the important lifestyle factors affecting the prenatal EDC concentrations in pregnant women. Next, we calculated the lifestyle score and evaluated its association with prenatal EDCs, respectively. Our results show that pregnant women who used makeup [β: 1.01, 95% C.I.: 0.01,2.00] >6 times/week had a significant increase in early-pregnancy (EP) ΣParaben exposure. Using perfume up to 3 times/month was significantly associated with EP TCS exposure (β: 0.05, 95% C.I.: 0.01,0.23). While, using perfume >6 times/week was significantly associated to late-pregnancy (LP) ΣParaben exposure, and consuming cup noodles significantly increased LP ΣDEHP exposure. Linear model analysis showed that the lifestyle score significantly increased the EP (β: 0.24, 95% C.I.: 0.07,0.40) and LP (β:0.10, 95% C.I.: 0.01,0.20) ΣParaben exposure. Therefore, pregnant women's lifestyle factors, such as using makeup and perfume and eating habits (e.g., cup noodle consumption), were associated with prenatal EDC exposure.

DOI: https://doi.org/10.1016/j.envpol.2024.125309
Anal Chem. 2024 Nov 12.

Polarity Gradient Solvent Confinement Membrane Cartridge to Broaden Metabolite Coverage of Plasma Untargeted Analysis.

Chunyu Yu, Jiaxin Zhang, Xiaohan Zong, Xiangzi Jin, Lu Liu, Yilin Zou, Yifan Jiao, Meihui Tong, Meiyu Cui, Huwei Liu, Donghao Li.

Various polarity chemicals exist in complex samples, such as plasma; nontargeted comprehensive analysis naturally requires multiple polar-extracted solvents; consequently, the polarity of the solvent plays a crucial role in the extraction efficiency of analytes from complex samples. In the present study, based on the diffusion behavior and nanoconfinement effect of solvents in the nanoconfined space, the polarity gradient solvent confinement liquid-phase nanoextraction (PGSC-NLPNE) protocol aimed to perform a one-step nontargeted analysis of a wide range of metabolites in plasma was established. The continuously wide range of extracted solvent polarities on carbon nanofibers/carbon fiber (CNFs/CF) membranes was achieved using a mixture of hexane, dichloromethane, methanol, and water as nanoconfined solvents. The polarities (Log P) of gradient solvents ranged from -1.38 to 3.94. Correlational analyses indicated that metabolites with Log P values ranging from -1.90 to 3.84 were closely related according to similarity-intermiscibility theory. Coupled with a homemade modified guard column device, CNFs/CF membrane cartridge (CCMC), a PGSC-NLPNE-UHPLC-MS online protocol was established and applied in plasma untargeted analysis. By comparing metabolome coverage, reproducibility, and extraction recovery with protein precipitation and two-step liquid-liquid extraction commonly used in untargeted analysis, the PGSC-NLPNE-CCMC protocol demonstrated higher reproducibility and recovery. This protocol has shown great potential for ultrafast analysis of plasma untargeted metabolomics with broader metabolome coverage. It could be a potential tool to rapidly screen out valuable biomarkers related to diseases in the clinic.

DOI: https://doi.org/10.1021/acs.analchem.4c04400
Environ Sci Technol. 2024 Nov 08.

Comprehensive Characterization of Oxidative Stress-Modulating Chemicals Using GPT-Based Text Mining.

Wenqing Liang, Wenyuan Su, Laijin Zhong, Zhendong Yang, Tingyu Li, Yong Liang, Ting Ruan, Guibin Jiang.

  The screening of hazardous environmental pollutants is hindered by the limited availability of toxicological databases. Large language model (LLM)-based text mining holds the potential to automatically extract complex toxicological information from the literature. Due to its relevance to diseases and the challenge of comprehensive characterization, oxidative stress serves as a suitable case for research by texting mining. In this study, a robust workflow utilizing a LLM (i.e., GPT-4) was developed to extract information on oxidative stress tests, including data collection, text preprocessing, prompt engineering, and performance evaluation procedures. A total of 17,780 relevant records were extracted from 7166 articles, covering 2558 unique compounds. A rising interest in oxidative stress was observed over the past two decades. A list of known prooxidants (n = 1416) and antioxidants (n = 1102) was established, with the leading chemical categories being pharmaceuticals, pesticides, and metals for prooxidants and pharmaceuticals and flavonoids for antioxidants. Structural alert analysis identified potential prooxidant (e.g., chlorobenzene, nitrobenzene, and tertiary amines) and antioxidant (e.g., flavonoid and thiol) substructures. These findings illustrate the feasibility of building toxicological databases through LLM-based text mining in a cost-efficient manner, and the information obtained from the technique holds significant promise for future applications in environmental and health research.

Keywords:  8-hydroxy-2′-deoxyguanosine; antioxidants; artificial intelligence; database; malondialdehyde; prooxidants; reactive oxygen species; structural alerts

DOI:  https://doi.org/10.1021/acs.est.4c07390
Environ Sci Technol. 2024 Nov 13.

Machine Learning-Assisted Surface-Enhanced Raman Spectroscopy Detection for Environmental Applications: A Review.

Sonali Srivastava, Wei Wang, Wei Zhou, Ming Jin, Peter J Vikesland.

  Surface-enhanced Raman spectroscopy (SERS) has gained significant attention for its ability to detect environmental contaminants with high sensitivity and specificity. The cost-effectiveness and potential portability of the technique further enhance its appeal for widespread application. However, challenges such as the management of voluminous quantities of high-dimensional data, its capacity to detect low-concentration targets in the presence of environmental interferents, and the navigation of the complex relationships arising from overlapping spectral peaks have emerged. In response, there is a growing trend toward the use of machine learning (ML) approaches that encompass multivariate tools for effective SERS data analysis. This comprehensive review delves into the detailed steps needed to be considered when applying ML techniques for SERS analysis. Additionally, we explored a range of environmental applications where different ML tools were integrated with SERS for the detection of pathogens and (in)organic pollutants in environmental samples. We sought to comprehend the intricate considerations and benefits associated with ML in these contexts. Additionally, the review explores the future potential of synergizing SERS with ML for real-world applications.

Keywords:  Environmental Pollutants; Machine Learning; Surface-Enhanced Raman Spectroscopy

DOI:  https://doi.org/10.1021/acs.est.4c06737