Identification and Prioritization of Environmental Organic Pollutants: From an Analytical and Toxicological Perspective

Chemical Reviews, Journal Year: 2023, Volume and Issue: 123(17), P. 10584 - 10640

Published: Aug. 2, 2023

Exposure to environmental organic pollutants has triggered significant ecological impacts and adverse health outcomes, which have been received substantial increasing attention. The contribution of unidentified chemical components is considered as the most knowledge gap in understanding combined effects pollutant mixtures. To address this issue, remarkable analytical breakthroughs recently made. In review, basic principles on recognition are overviewed. Complementary methodologies (i.e., quantitative structure–activity relationship prediction, mass spectrometric nontarget screening, effect-directed analysis) experimental platforms briefly described. stages technique development and/or essential parts workflow for each then reviewed. Finally, plausible paths applications future screening methods, interdisciplinary techniques achieving toxicant identification, burgeoning strategies risk assessment cocktails discussed.

Language: Английский

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Microfluidics in environmental analysis: advancements, challenges, and future prospects for rapid and efficient monitoring

Lab on a Chip, Journal Year: 2024, Volume and Issue: 24(5), P. 1175 - 1206

Published: Jan. 1, 2024

This review highlights the latest advancements in microfluidic devices environmental monitoring during last 5 years. We also emphasize current limitations and propose effective strategies to improve monitoring.

Language: Английский

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Current advances in solid-phase microextraction technique as a green analytical sample preparation approach

Green Chemistry Letters and Reviews, Journal Year: 2025, Volume and Issue: 18(1)

Published: April 3, 2025

Language: Английский

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Green microextraction methodologies for sample preparations

Green Analytical Chemistry, Journal Year: 2022, Volume and Issue: 3, P. 100023 - 100023

Published: July 26, 2022

Language: Английский

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Advancements, Applications, and prospects of Metal-Organic frameworks and their derivatives as distinct sorbents in exhaustive and non-exhaustive extraction strategies

Microchemical Journal, Journal Year: 2024, Volume and Issue: 198, P. 110158 - 110158

Published: Feb. 16, 2024

Language: Английский

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Unveiling the Chemistry of Citrus Peel: Insights into Nutraceutical Potential and Therapeutic Applications

Foods, Journal Year: 2024, Volume and Issue: 13(11), P. 1681 - 1681

Published: May 27, 2024

The recent millennium has witnessed a notable shift in consumer focus towards natural products for addressing lifestyle-related disorders, driven by their safety and cost-effectiveness. Nutraceuticals functional foods play an imperative role meeting nutritional needs offering medicinal benefits. With increased scientific knowledge awareness, the significance of healthy lifestyle, including diet, reducing disease risk is widely acknowledged, facilitating access to diverse safer diet longevity. Plant-based rich phytochemicals are increasingly popular effectively utilized management. Agricultural waste from plant-based being recognized as valuable source nutraceuticals dietary interventions. Citrus peels, known flavonoids, emerging promising health-promoting ingredient. Globally, citrus production yields approximately 15 million tons by-products annually, highlighting substantial potential utilizing phyto-therapeutic nutraceutical applications. peels with concentrations ranging 2.5 5.5 g/100 g dry weight, depending on variety. most abundant flavonoids peel include hesperidin naringin, well essential oils monoterpenes like limonene. extracts exhibit high antioxidant capacity, DPPH radical scavenging activities 70 90%, comparable synthetic antioxidants BHA BHT. Additionally, present have been found properties, which can help reduce oxidative stress 30% cardiovascular 25%. Potent anti-inflammatory effects also demonstrated, inflammatory markers such IL-6 TNF-α up 40% cell culture studies. These findings highlight diet-based therapies.

Language: Английский

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SERS-based detection of the antibiotic ceftriaxone in spiked fresh plasma and microdialysate matrix by using silver-functionalized silicon nanowire substrates

Talanta, Journal Year: 2024, Volume and Issue: 271, P. 125697 - 125697

Published: Jan. 18, 2024

Language: Английский

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Recent Advancements in Metabolomics: Applications and Insights into Functional Beverages

Current Food Science and Technology Reports, Journal Year: 2025, Volume and Issue: 3(1)

Published: Jan. 23, 2025

Language: Английский

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Non-destructive origin and ginsenoside analysis of American ginseng via NIR and deep learning

Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy, Journal Year: 2025, Volume and Issue: 334, P. 125913 - 125913

Published: Feb. 17, 2025

Language: Английский

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From Reverse Phase Chromatography to HILIC: Graph Transformers Power Method-Independent Machine Learning of Retention Times

Analytical Chemistry, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 19, 2025

Liquid chromatography (LC) is a cornerstone of analytical separations, but comparing the retention times (RTs) across different LC methods challenging because variations in experimental parameters such as column type and solvent gradient. Nevertheless, RTs are powerful metrics tandem mass spectrometry (MS2) that can reduce false positive rates for metabolite annotation, differentiate isobaric species, improve peptide identification. Here, we present Graphormer-RT, novel graph transformer performs first single-model method-independent prediction RTs. We use RepoRT data set, which contains 142,688 reverse phase (RP) (from 191 methods) 4,373 HILIC 49 methods). Our best RP model (trained tested on achieved test set mean average error (MAE) 29.3 ± 0.6 s, comparable performance to state-of-the-art was only trained single method. best-performing MAE = 42.4 2.9 s. expect Graphormer-RT be used an "foundation model", where transfer learning amount training needed highly accurate "specialist" models applied method-specific tasks. These frameworks could enable machine optimization automated workflows, improved filtration candidate structures using predicted RTs, silico annotation unknown analytes LC-MS2 measurements.

Language: Английский

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