Perfluoroalkyl acids (PFAAs) are emerging pollutants that endangers food safety. Developing methods for the selective determination of trace PFAAs in complex samples remains challenging. Herein, an ionic liquid modified porous imprinted phenolic resin-dispersive filter extraction-liquid chromatography-tandem mass spectrometry (IL-PIPR-DFE-LC-MS/MS) method was developed for the determination of perfluorooctanoic acid (PFOA) and perfluorooctane sulfonate (PFOS) in eggs. The new IL-PIPR adsorbent was prepared at room temperature, which avoids the disorder and instability of the template at high temperatures. The imprinting factor of IL-PIPR for PFOA and PFOS exceeded 7.3. DFE, combined with IL-PIPR (15 mg), was used to extract PFOA and PFOS from eggs within 15 min. The established method exhibits low limits of detection (0.01-0.02 ng/g) and high recoveries (84.7%-104.7%), which surpass those of previously reported methods. This work offers a new approach to explore advanced imprinted adsorbents for PFAAs, efficient sample pretreatment technique, and analytical method for pollutants in foods.

Polyunsaturated fatty acids (PUFAs) are vital nutrients in human physiology and are implicated in various chronic diseases. However, the relationship between PUFAs and gastric polyps remains unclear. This study employed liquid chromatography-tandem mass spectrometry (LC-MS/MS) to assess PUFA levels in the serum of 350 patients, along with analyzing the ω-6 to ω-3 ratio. The results revealed significant differences in the levels of C16:1, C18:1, C18:2, α-C18:3, γ-C18:3, C20:1, C20:4, C20:5, ω-3-C22:5, ω-6-C22:5, and C22:6, as well as ω-6 to ω-3 ratio between the control and gasteic polyp groups. Moreover, setting the threshold for ω-6: ω-3 at 10 revealed a close correlation between polyp occurrence and this ratio. These findings suggest that PUFAs and the ω-6 to ω-3 ratio hold promise as potential early screening markers for gastric polyps. However, further research is imperative to elucidate the underlying mechanisms and therapeutic potential of PUFAs in managing gastric polyps.

OBJECTIVE: To assess the diagnostic value of combining plasma steroid profiling with machine learning (ML) in differentiating between mild autonomous cortisol secretion (MACS) and nonfunctioning adenoma (NFA) in patients with adrenal incidentalomas.
METHODS: The plasma steroid profiles data in the laboratory information system were screened from January 2021 to December 2023. EXtreme Gradient Boosting was applied to establish diagnostic models using plasma 24-steroid panels and/or clinical characteristics of the subjects. The SHapley Additive exPlanation (SHAP) method was used for explaining the model.
RESULTS: Seventy-six patients with MACS and 86 patients with NFA were included in the development and internal validation cohort while the external validation cohort consisted of 27 MACS and 21 NFA cases. Among 5 ML models evaluated, eXtreme Gradient Boosting demonstrated superior performance with an area under the curve of 0.77 using 24 steroid hormones. The SHAP method identified 5 steroids that exhibited optimal performance in distinguishing MACS from NFA, namely dehydroepiandrosterone, 11-deoxycortisol, 11β-hydroxytestosterone, testosterone, and dehydroepiandrosteronesulfate. Upon incorporating clinical features into the model, the area under the curve increased to 0.88, with a sensitivity of 0.77 and specificity of 0.82. Furthermore, the results obtained through SHAP revealed that lower levels of testosterone, dehydroepiandrosterone, low-density lipoprotein cholesterol, body mass index, and adrenocorticotropic hormone along with higher level of 11-deoxycortisol significantly contributed to the identification of MACS in the model.
CONCLUSIONS: We have elucidated the utilization of ML-based steroid profiling to discriminate between MACS and NFA in patients with adrenal incidentalomas. This approach holds promise for distinguishing these 2 entities through a single blood collection.

BACKGROUND: As a well-known fact to the public, gestational diabetes mellitus (GDM) could bring serious risks for both pregnant women and infants. During this important investigation into the linkage between GDM patients and their altered expression in the serum, proteomics techniques were deployed to detect the differentially expressed proteins (DEPs) of in the serum of GDM patients to further explore its pathogenesis, and find out possible biomarkers to forecast GDM occurrence.
OBJECTIVE: To investigation serum proteins differentially expressed in GDM were assessed using isobaric tag for relative and absolute quantitation (iTRAQ) proteomics and bioinformatics analyses.
METHODS: Subjects were divided into GDM and normal control groups according to the IADPSG diagnostic criteria. Serum samples were randomly selected from four cases in each group at 24-28 wk of gestation, and the blood samples were identified by applying iTRAQ technology combined with liquid chromatography-tandem mass spectrometry. Key proteins and signaling pathways associated with GDM were identified by bioinformatics analysis, and the expression of key proteins in serum from 12 wk to 16 wk of gestation was further verified using enzyme-linked immunosorbent assay (ELISA).
RESULTS: Forty-seven proteins were significantly differentially expressed by analyzing the serum samples between the GDM gravidas as well as the healthy ones. Among them, 31 proteins were found to be upregulated notably and the rest 16 proteins were downregulated remarkably. Bioinformatic data report revealed abnormal expression of proteins associated with lipid metabolism, coagulation cascade activation, complement system and inflammatory response in the GDM group. ELISA results showed that the contents of RBP4, as well as ANGPTL8, increased in the serum of GDM gravidas compared with the healthy ones, and this change was found to initiate from 12 wk to 16 wk of gestation.
CONCLUSIONS: GDM symptoms may involve abnormalities in lipid metabolism, coagulation cascade activation, complement system and inflammatory response. RBP4 and ANGPTL8 are expected to be early predictors of GDM.

Interindividual exposure differences have been identified in oral targeted antineoplastic drugs (OADs) owing to the pharmacogenetic background of the patients and their susceptibility to multiple factors, resulting in insufficient efficacy or adverse effects. Therapeutic drug monitoring (TDM) can prevent sub-optimal concentrations of OADs and improve their clinical treatment. This study aimed to develop and validate an LC-MS/MS method for the simultaneous quantification of 11 OADs (gefitinib, imatinib, lenvatinib, regorafenib, everolimus, osimertinib, sunitinib, tamoxifen, lapatinib, fruquintinib and sorafenib) and 2 active metabolites (N-desethyl sunitinib and Z-endoxifen) in human plasma. Protein precipitation was used to extract OADs from the plasma samples. Chromatographic separation was performed using an Eclipse XDB-C18 (4.6 × 150 mm, 5 μm) column with a gradient elution of the mobile phase composed of 2 mM ammonium acetate with 0.1 % formic acid in water (solvent A) and methanol (solvent B) at a flow rate of 0.8 mL/min. Mass analysis was performed using positive ion mode electrospray ionization in multiple-reaction monitoring mode. The developed method was validated following FDA bioanalytical guidelines. The calibration curves were linear over the range of 2-400 ng/mL for gefitinib, imatinib, lenvatinib, regorafenib, and everolimus; 1-200 ng/mL for osimertinib, sunitinib, N-desethyl sunitinib, tamoxifen, and Z-endoxifen; and 5-1000 ng/mL for lapatinib, fruquintinib, and sorafenib, with all coefficients of correlation above 0.99. The intra- and inter-day imprecision was below 12.81 %. This method was successfully applied to the routine TDM of gefitinib, lenvatinib, regorafenib, osimertinib, fruquintinib, and sorafenib to optimize the dosage regimens.

The extensive consumption of antibiotics has been reported to significantly promote the generation of antibiotic resistance (ABR), however, a quantitative causal relationship between antibiotic exposure and ABR response is absent. This study aimed to pinpoint the accurate regulatory concentration of fluoroquinolones (FQs) and to understand the biochemical mechanism of the mutual action between FQ exposure and FQ resistance response. Highly sensitive analytical methods were developed by using UPLC-MS/MS to determine the total residual, extracellular residual, total intracellular, intracellular residual and intracellular degraded concentration of three representative FQs, including ciprofloxacin (CIP), ofloxacin (OFL) and norfloxacin (NOR), with detection limits in the range of 0.002-0.057 μg/L, and recoveries in the range of 80-93%. The MICs of Escherichia coli (E. coli) were 7.0-31.4-fold of the respective MIC0 after 40-day FQ exposure, and significant negative associations were discovered between the intracellular (residual, degraded or the sum) FQ concentrations and FQ resistance. Transcriptional expression and whole-genome sequencing results indicated that reduced membrane permeability and enhanced multi-drug efflux pumps contributed to the decreasing intracellular concentration. These results unveiled the pivotal role of intracellular concentration in triggering FQ resistance, providing important information to understand the dose-response relationship between FQ exposure and FQ resistance response, and ascertain the target dose metric of FQs for eliminating FQ resistance crisis.

Accurate quantification of mycotoxin in cereals is crucial for ensuring food safety and human health. However, the preparation of traditional multisample external calibration curves (MSCCs) is labor-intensive and error-prone. Here, a multiple isotopologue reaction-monitoring (MIRM)-LC-MS/MS method for accurate quantitation of ten major mycotoxins in cereals was successfully developed and validated, where a novel one-sample multipoint calibration curve (OSCC) strategy is used instead of MSCCs. The OSCC can be established by examining the correlation between the calculated theoretical isotopic abundances and the measured abundance across various MIRM channels. In comparison to the MSCC, the OSCC strategy exhibits outstanding performance including superior selectivity, accuracy (78.4-108.6%), and precision (<12.5%). Furthermore, the proposed OSCC-MIRM-LC-MS/MS method was successfully applied to investigate mycotoxin contamination in cereal samples in China. Considering the advantages of simplified workflows and improved throughput, the OSCC-MIRM-LC-MS/MS methodology holds great promise for accurately quantifying chemical contaminants in foods.