N-nitrosodimethylamine (NDMA) and N-nitrosodiethylamine (NDEA), group 2A carcinogens, were detected in finished drug products, including metformin, ranitidine, sartans and other drugs which caused multiple recalls in the USA and Europe. Important studies also reported the formation of NDMA when ranitidine and nitrite were added to simulated gastric fluid. Our objective was to screen finished drug products from Europe and USA for nitrosamine impurities and investigate the formation of NDMA in metformin finished drug products when added to simulated gastric fluid. One dosage unit of 30 different commercially available drugs, including metformin, sartans, and ranitidine were tested for NDMA, NDEA, and dimethylformamide (DMF) impurities, using a liquid chromatography-mass spectrometry (LC-MS) method. Then, 6 metformin finished drug products were tested in stomach conditions for 2 h at 37 °C in a 100 mL solution with a pH of 2.5 and different nitrite concentrations (40, 10, 1, 0.1 mM) and tested for NDMA, and DMF using LC-MS. We measured NDMA, NDEA, and DMF in 30 finished drug products. NDMA and DMF were quantified for metformin drug products in simulated gastric fluid with different nitrite concentrations. None of the 30 drugs showed concerning levels of NDMA, NDEA, or DMF when tested as single tablets. However, when metformin tablets are added to simulated gastric fluid solutions with high nitrite concentrations (40 mM and 10 mM), NDMA can reach amounts of thousands of nanograms per tablet. At the closest concentration to physiologic conditions we used, 1 mM, NDMA is still present in the hundreds of nanograms in some metformin products. In this in vitro study, nitrite concentration had a very important effect on NDMA quantification in metformin tablets added to simulated gastric fluid. 1 mM nitrite caused an increase above the acceptable daily intake set by the U.S. Food and Drug Administration (FDA) for some of the metformin drugs. 10 mM, 40 mM nitrite solutions generated NDMA amounts exceeding by more than a hundred times the acceptable daily intake set by the FDA of 96 nanograms. These findings suggest that metformin can react with nitrite in gastric-like conditions and generate NDMA. Thus, patients taking metformin could be exposed to NDMA when high nitrite levels are present in their stomach, and we recommend including a statement within the Patient Package Inserts/Instructions for use.

The matrix effects (ME) in simultaneous analysis of pesticide residue using liquid chromatography-tandem mass spectrometry (LC-MS/MS) were evaluated by comparing the slopes of matrix-matched and reagent-only calibrations of four types of vegetable samples. Both the sampling and measurement variances of the ME were also determined using one-way analysis of variance. Substantial ion suppression (ME<-20%) was observed in komatsuna, spinach, and tomato when a modified Japanese official method was implemented. The ME magnitude varied significantly due to sample variability for some pesticides, but it varied by no more than 4% as a result of analytical procedure variance. This study also showed that the addition of stable isotope-labeled internal standards at low concentrations improved the recovery of pesticides from samples at various residue levels. The findings of this study highlight the importance and practical application of internal standards and the matrix-matched calibration method in residue analysis using LC-MS/MS.

Stress can affect different groups of plant metabolites and multiple signaling pathways. Untargeted metabolomics enables the collection of whole-spectrum data for the entire metabolite content present in plant tissues at that point in time. We present a thorough approach for large-scale, untargeted metabolomics of plant tissues using reverse-phase liquid chromatography connected to high-resolution mass spectrometry (LC-MS) of dilute methanolic extract. MZmine is a specialized computer software that automates the alignment and baseline modification of all derived mass peaks across all samples, resulting in precise information on the relative abundance of hundreds of metabolites reflected by thousands of mass signals. Further processing with statistic and bioinformatic techniques will provide a comprehensive perspective of the variations and connections among groups of samples.

Two new analytical methods, applying absolute 1H qNMR, were developed to monitor product yield and quantify unreacted carbohydrate and fatty acid reactants, in the synthesis of carbohydrate fatty acid esters (CFAE). These methods provide a mass balance of the crude reaction mixtures and diversify the analytical screening and quantitation approaches available within the synthesis of these molecules. Both methods were validated for the model reaction of methyl α-d-glucopyranoside (MAG) and lauric acid (LA) to form the mono ester product, methyl 6-O-dodecanoyl-α-d-glucopyranoside. Analysis in CD3OD by 1H qNMR, with fumaric acid (FA) as an internal standard (IS), allowed monitoring of all reaction components. Alternatively, using CDCl3 and (E)-stilbene as IS enabled the analysis of CFAE and fatty acid. Parameters calculated for method validation included specificity and selectivity, linearity, accuracy, intermediate precision, limit of detection (LOD), limit of quantification (LOQ) and robustness. Both methods provided excellent linearity with R2 > 0.997. The accuracy, precision, and robustness of the method in CD3OD was <2 % uncertainty making it suitable for complete reaction analysis. The method completed in CDCl3 resulted in accuracy, intermediate precision, and robustness of <5 %, except for accuracy in the lowest levels of concentration (>5 %). For all related analytes in the CD3OD and CDCl3 methods, the LOD and LOQ were determined to ensure applicability for the intended use in the assessment of reaction crude composition. Finally, the system suitability was assessed in a scaled lipase catalysed CFAE synthetic reaction. The determined qNMR product yields were verified against isolated purified product yields with <5 % uncertainty.

Among various biomimetic polymer materials, polydimethylsiloxane (PDMS) stands out as an ideal matrix for surface-enhanced Raman scattering (SERS) due to its unique intrinsic Raman signal and tenacity. In order to realize the precise detection of prostate-specific antigen (PSA), we proposed a sandwich-type SERS-active immunostructure composed of PDMS@silver nanoparticles (Ag NPs)@ZIF-67 biomimetic film as the immunosubstrate and gold nanorods (Au NRs) as immunoprobes. Due to the synergistic effect of electromagnetic enhancement facilitated by biomimetic surfaces and chemical enhancement achieved by ZIF-67, this structure enabled an ultrasensitive and selective detection of PSA across a broad range from 10-3 to 10-9 mg/mL. The achieved limit of detection was as low as 3.0 × 10-10 mg/mL. Particularly, the intrinsic Raman signal of PDMS matrix at 2905 cm-1 was employed as a potential internal standard (IS) in the detection, achieving a high coefficient of determination (R2) value of 0.996. This multifunctional SERS substrate-mediated immunoassay holds vast potential for early diagnosis of prostate cancer, offering promising prospects for clinical applications.

Molecular methods are currently some of the best-suited technologies for implementation in insect monitoring. However, the field is developing rapidly and lacks agreement on methodology or community standards. To apply DNA-based methods in large-scale monitoring, and to gain insight across commensurate data, we need easy-to-implement standards that improve data comparability. Here, we provide three recommendations for how to improve and harmonize efforts in biodiversity assessment and monitoring via metabarcoding: (i) we should adopt the use of synthetic spike-ins, which will act as positive controls and internal standards; (ii) we should consider using several markers through a multiplex polymerase chain reaction (PCR) approach; and (iii) we should commit to the publication and transparency of all protocol-associated metadata in a standardized fashion. For (i), we provide a ready-to-use recipe for synthetic cytochrome c oxidase spike-ins, which enable between-sample comparisons. For (ii), we propose two gene regions for the implementation of multiplex PCR approaches, thereby achieving a more comprehensive community description. For (iii), we offer guidelines for transparent and unified reporting of field, wet-laboratory and dry-laboratory procedures, as a key to making comparisons between studies. Together, we feel that these three advances will result in joint quality and calibration standards rather than the current laboratory-specific proof of concepts. This article is part of the theme issue \'Towards a toolkit for global insect biodiversity monitoring\'.

In vivo ultrafiltration has been used in veterinary pharmacokinetics since the early 2000\'s as an improvement on the tissue cage model which enables sampling of fluids from extra-circulatory compartments. Variability in analyte recovery from ultrafiltration samples, due to membrane fouling or tissue inflammation, has been a concern for this technique. Internal standards may be used to scale or verify the unknown result, such as is common in analytical extractions and in vivo microdialysis. Eight merino sheep were implanted with subcutaneous tissue cages and 2 weeks prior to the initiation of the study the sheep were injected with 0.2 mg/kg moxidectin subcutaneously. On the day of the study ultrafiltration probes were inserted subcutaneously. At time zero 4 mg/kg of carprofen was injected intravenously. Plasma, tissue cage, and ultrafiltration samples were taken 30 min before and 0.5, 1, 2, 3, 4, 5, 7, 24, 36, 48, 72 h after dosing. Carprofen and moxidectin concentrations were measured by LC-MS/MS. Pharmacokinetic parameters were estimated using Monolix for both the carprofen concentrations and the moxidectin corrected carprofen concentrations. The ultrafiltration probes failed to consistently produce enough sample volume to analyse. Moxidectin concentrations in the plasma and tissue cage fluid were stable throughout the 72 h sampling window. Moxidectin proved to be suitable as an in vivo internal standard for pharmacokinetic research using, tissue cages, plasma sampling and ultrafiltration probes, but the application of ultrafiltration techniques requires refinement.

Surface-enhanced Raman spectroscopy (SERS) can quickly identify molecular fingerprints and has been widely used in the field of rapid detection. However, the non-uniformity inherent in SERS substrate signals, coupled with the finite nature of the detection object, significantly hampers the advancement of SERS. Nowadays, the existing mature immunochromatographic assay (ICA) method is usually combined with SERS technology to address the defects of SERS detection. Nevertheless, the porous structure of the strip will also affect the signal uniformity during detection. Obviously, a method using SERS-ICA is needed to effectively solve signal fluctuations, improve detection accuracy, and has certain versatility. This paper introduces an internal standard method combining deep learning to predict and process Raman data. Based on the signal fluctuation of single-antigen SERS-ICA test strip, the double-antigen SERS-ICA test strip was constructed. The full spectrum Raman data of double-antigen SERS-ICA test strip was normalized by the sum of two characteristic peaks of internal standard molecules, and then processed by deep learning algorithm. The Relative Standard Deviation (RSD) of Raman data of bisphenol A was compared before and after internal standard normalization of double-antigen SERS-ICA test strip. The RSD processed by this method was increased by 3.8 times. After normalization, the prediction accuracy of Root Mean Square Error (RMSE) is improved by 2.66 times, and the prediction accuracy of R-square (R2) is increased from 0.961 to 0.994. The results showed that RMSE and R2 were used to comprehensively predict the collected data of double-antigen SERS-ICA test strip, which could effectively improve the prediction accuracy. The internal standard algorithm can effectively solve the challenges of uneven hot spots and poor signal reproducibility on the test strip to a certain extent, so as to improve the semi-quantitative accuracy.

Simultaneous, reliable, and ultra-sensitive analysis of promising miRNA biomarkers of colorectal cancer (CRC) in serum is critical for early diagnosis and prognosis of CRC. In this work, we proposed a novel 3D hierarchic assembly clusters-based SERS strategy with dual enrichment and enhancement designed for the ultrasensitive and quantitative analysis of two upregulated CRC-related miRNAs (miR-21 and miR-31). The biosensor contains the following: (1) SERS probe, Au nanocage@Au nanoparticles (AuNC@Au NPs) labeled with Raman reporters (RaRs). (2) magnetic capture unit, Ag-coated Fe3O4 magnetic nanoparticles (AgMNPs) modified with internal standard (IS). (3) signal amplify probes (SA probes) for the formation of hierarchic assembly clusters. Based on this sensing strategy, the intensity ratio IRaRs/IIS with Lg miRNAs presents a wide linear range (10 aM-100 pM) with a limit of detection of 3.46 aM for miR-21, 6.49 aM for miR-31, respectively. Moreover, the biosensor shows good specificity and anti-interference ability, and the reliability and repeatability of the strategy were then verified by practical detection of clinical serum. Finally, the biosensor can distinguish CRC cancer subjects from normal ones and guide the distinct tumor, lymph node, and metastasis (TNM) stages. Overall, benefiting from the face-to-face coupling of hierarchic assembly clusters, rapid magnetic enrichment and IS signal calibration of AgMNPs, the established biosensor achieves ultra-sensitive and simultaneous detection of dual miRNAs and opens potential avenues for prediction and staging of CRC.

Nitrites are ubiquitous in food and pose a serious threat to human health. Therefore, the rapid and accurate determination of nitrite ion concentration in food is a prerequisite for eliminating the damage of nitrites. In this study, a robust, rapid, and sensitive method is proposed for nitrite detection in pickled food, in which Au@Ag nanoparticles are used as a reliable surface-enhanced Raman spectroscopy (SERS) substrate taking advantage of the high enhancement effect of silver and the good stability of gold. Nitrites were anchored to the surface of the SERS substrate by bridging with 4-aminophenylthiophenol (PATP). With Raman scattering cross-section amplification and internal calibration by PATP, a satisfactory linear relationship (R2 = 0.987) was established for nitrite detection in the concentration range of 5.00-100.00 μM, and the limit of detection (LOD) was 0.17 μM. This SERS-based method demonstrated high selectivity, good precision (RSD < 7.00 %), and satisfying recovery rates (101.42-107.35 %) in real samples, thus improving the determination method for nitrites. Therefore, this method has application potential in food safety and supervision.