ArticleToxics2026
Development and Validation of a Method for Quantitative UPLC-MS/MS Determination of Selected Perfluorocarboxylic and Perfluorosulfonic Acids in Human Urine.
Article in Toxics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
0 citing papers in PubMed.
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Per- and polyfluoroalkyl substances (PFASs) are a large class of thousands of synthetic organofluorine chemical compounds used for many industrial applications. Humans are exposed to PFASs mainly through diet and contaminated drinking water. Studies show that PFASs induce several adverse effects on humans. A great number of human biomonitoring studies have been widely conducted with the aim of estimating exposure to PFASs. The matrices mainly investigated are blood, serum and breast milk. However, in many cases, the need for non-invasive sampling methods with a minimal impact on donors has become paramount to comply with modern ethical standards and regulations. For this reason, we developed a streamlined and efficient method for the analysis of eight perfluorocarboxylic and perfluorosulfonic acids (PFHpA; PFHxS; PFOA; PFHpS; PFNA; PFOS; PFDA; and PFUdA) in human urine samples by UPLC chromatography tandem mass spectrometry. Chromatographic and MS parameters were optimized; the method was validated for: repeatability (<20%), within-lab reproducibility (<20%), trueness (within the set 20% variation limit of agreement between the mean of the data set and the true value), efficiency (51-97%), linearity (R
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