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Negative ions, like fluorine, are produced post-plasma in the interface of an ICPMS in contrast to positive ions.

This article describes a fast, sensitive, selective, and robust method for determining triphenylmethane dyes and their metabolites in animal tissue samples by liquid chromatography with tandem mass spectrometry (LC–MS/MS). The separation of analytes from matrix-interfering components was performed using a C18 column with a mobile phase composition of water and methanol containing 0.1% formic acid and 5 mM ammonium formate as additives in both phases. To minimize sample matrix effects and analyte loss during sample preparation, extracted-matrix calibration in combination with isotope dilution was used for analyte quantification. There were two MS/MS transitions selected for each analyte for analyte confirmation, further enhancing the method’s selectivity and accuracy. The method was validated using salmon and chicken tissue samples.

Analysis of biofluids, such as plasma, can be used to investigate occupational pesticide exposure in the agricultural industry. Considering the chemical complexity and variability of plasma samples, any protocol for pesticide analysis should achieve efficient sample cleanup to minimize matrix effects and enhance method sensitivity through analyte pre-concentration. In this work, a high-throughput method was developed for analysis of 79 pesticides, commonly used in agricultural practices, in human plasma, using biocompatible solid-phase microextraction (SPME) coupled to liquid chromatography-tandem mass spectrometry. An SPME method was developed using abiocompatible hydrophilic-lipophilic balance/polyacrylonitrile (HLB/PAN) extraction phase and demonstrated negligible matrix effects. The performance of the developed SPME method was compared to a QuEChERS-Quick, Easy, Cheap, Effective, Rugged, and Safe- method, the most common sample preparation and cleanup approach for pesticide analysis in complex matrices. Comparable accuracy and precision were achieved for both methods, with accuracy values within 70-120% and relative standard deviation < 15%. Overall, the developed SPME and QuEChERS methods extracted 79 out of 82 monitored pesticides in human plasma. The SPME protocol demonstrated higher sensitivity than the QuEChERS method and a drastic reduction of matrix effects.

We report on the absolute quantification of nanoparticle interactions with individual human B cells using quadrupole-based inductively coupled plasma mass spectrometry (ICP-MS). This method enables the quantification of nanoparticle-cell interactions at single nanoparticle and single cell levels. We demonstrate the efficient and accurate detection of individually suspended B cells and found an ∼100-fold higher association of colloidally stable positively charged nanoparticles with single B cells than neutrally charged nanoparticles. We confirmed that these nanoparticles were internalized by individual B cells and determined that the internalization occurred via energy-dependent pathways consistent with endocytosis. Using dual analyte ICP-MS, we determined that >80% of single B cells were positive for nanoparticles. Our study demonstrates an ICP-MS workflow for the absolute quantification of nanoparticle-cell interactions with single cell and single nanoparticle resolution. This unique workflow could inform the rational design of various nanomaterials for controlling cellular interactions, including immune cell-nanoparticle interactions.

The increasing prevalence of products that incorporate engineered nanoparticles (ENPs) has prompted efforts to investigate the potential release, environmental fate, and exposure of the ENPs. However, the investigation of cerium dioxide nanoparticles (CeO2 NPs) in soil has remained limited, owing to the analytical challenge from the soil's complex nature. In this study, this challenge was overcome by applying a novel single particle-inductively coupled plasma-mass spectrometry (SP-ICP-MS) methodology to detect CeO2 NPs extracted from soil, utilizing tetrasodium pyrophosphate (TSPP) aqueous solution as an extractant. This method is highly sensitive for determining CeO2 NPs in soil, with detection limits of size and concentration of 15 nm and 194 NPs mL-1, respectively. Extraction efficiency was sufficient in the tested TSPP concentration range from 1 mM to 10 mM at a soil-to-extractant ratio 1:100 (g mL-1) for the extraction of CeO2 NPs from the soil spiked with CeO2 NPs. The aging study demonstrated that particle size, size distribution, and particle concentration underwent no significant change in the aged soils for a short period of one month. This study showed an efficient method capable of extracting and accurately determining CeO2 NPs in soil matrices. The method can serve as a useful tool for nanoparticle analysis in routine soil tests and soil research.

Abstract. The formation of organic nitrates (ON) in the gas phase and their impact on mass formation of Secondary Organic Aerosol (SOA) was investigated in a laboratory study for α-pinene and β-pinene photo-oxidation. Focus was the elucidation of those mechanisms that cause the often observed suppression of SOA mass formation by NOx, and therein the role of highly oxygenated multifunctional molecules (HOM). We observed that with increasing NOx (a) the portion of HOM organic nitrates (HOM-ON) increased, (b) the fraction of accretion products (HOM-ACC) decreased and (c) HOM-ACC contained on average smaller carbon numbers. Specifically, we investigated HOM organic nitrates (HOM-ON), arising from the termination reactions of HOM peroxy radicals with NOx, and HOM permutation products (HOM-PP), such as ketones, alcohols or hydroperoxides, formed by other termination reactions. Effective uptake coefficients γeff of HOM on particles were determined. HOM with more than 6 O-atoms efficiently condensed on particles (γeff &gt; 0.5 in average) and for HOM containing more than 8 O-atoms, every collision led to loss. There was no systematic difference in γeff for HOM-ON and HOM-PP arising from the same HOM peroxy radicals. This similarity is attributed to the multifunctional character of the HOM: as functional groups in HOM arising from the same precursor HOM peroxy radical are identical, vapor pressures should not strongly depend on the character the final termination group. As a consequence, the suppressing effect of NOx on SOA formation cannot be simply explained by replacement of terminal functional groups by organic nitrate groups. The fraction of organic bound nitrate (OrgNO3) stored in gas-phase HOM-ON appeared to be substantially higher than the fraction of particulate OrgNO3 observed by aerosol mass spectrometry. This result suggests losses of OrgNO3 for organic nitrates in particles, probably due to hydrolysis of OrgNO3 that releases HNO3 into the gas phase but leaves behind the organic rest in the particulate phase. However, the loss of HNO3 alone, could not explain the observed suppressing effect of NOx on particle mass formation from α-pinene and β-pinene. We therefore attributed most of the reduction in SOA mass yields with increasing NOx to the significant suppression of gas-phase HOM-ACC which have high molecular mass and are potentially important for SOA mass formation at low NOx conditions.

Abstract Metallic based cancer therapy drugs have been around for several years, the most widely used being platinum-based drugs, however these come with severe side effects due to the non-specific targeting of these drugs. Recently a number of nanoparticle-based cancer therapeutics have been approved for clinical use or are currently under development. Advantages that engineered nanoparticles may offer over conventional small molecule drugs include: (i) prolonged circulation time in the body; (ii) reduction of nonspecific cellular uptake along with undesirable off-target and other side effects; and (iii) improvement in cellular interactions through specific cancer cell targeting moieties. The therapeutic effect of cancer treatment is related to the amount of drug that interacts with each individual cancer cell. Traditional drug research techniques, such as conventional inductively coupled plasma mass spectrometry (ICP-MS), have been limited to cell ensemble measurements, which require homogenization of a given cell population for quantitative analysis. All cells within this population are assumed to be similar and therefore assumed to interact with the same amount of drug, however, recent studies demonstrate that cell populations are heterogeneous, and differences exist even for cells from the same cell population and cell line. For example, gene expression measurements based on homogenized cell populations are misleading as they only provide averaged results and do not account for the small but critical changes occurring in individual cells such as size, protein levels, and expressed RNA transcripts. These variations are key aspects when answering previously unsolvable questions in cancer research, stem cell biology, immunology, developmental biology, and neurology. To overcome these limitations, PerkinElmer developed Single-Cell (SC) ICP-MS, which allows the rapid analysis of a large number of individual cells rather than a cell population as a whole or only a few cells. This allows for the quantification of the metal mass in individual cells, resulting in a histogram of the population showing not only the most frequent and mean masses of drug in the population but also the distribution throughout the population. Here we will show results for both cisplatin uptake and surface modified gold nanoparticle uptake into cancer cells. The first resulting in a wide distribution in the amount of platinum measured per cell over time with differences in resistant and non-resistant cancer cells. While the latter shows the ability of this technique to quantify the number for modified Au nanoparticles per cell as well as the number of cells containing the drug. Note: This abstract was not presented at the meeting. Citation Format: Chady Stephan, Ruth Merrifield, Stefan Wilhelm. Quantifying the Uptake of Metal Based Cancer Therapy Drugs Using Single Cell ICP-MS [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2988.

ObjectiveTo examine whether metformin use was associated with knee cartilage volume loss over 4 years and risk of total knee replacement over 6 years in obese individuals with knee osteoarthritis.MethodsThis study analysed the Osteoarthritis Initiative participants with radiographic knee osteoarthritis (Kellgren-Lawrence grade ≥ 2) who were obese (body mass index [BMI] ≥ 30 kg/m2). Participants were classified as metformin users if they self-reported regular metformin use at baseline, 1-year and 2-year follow-up (n = 56). Non-users of metformin were defined as participants who did not report the use of metformin at any visit from baseline to 4-year follow-up (n = 762). Medial and lateral cartilage volume (femoral condyle and tibial plateau) were assessed using magnetic resonance imaging at baseline and 4 years. Total knee replacement over 6 years was assessed. General linear model and binary logistic regression were used for statistical analyses.ResultsThe rate of medial cartilage volume loss was lower in metformin users compared with non-users (0.71% vs. 1.57% per annum), with a difference of − 0.86% per annum (95% CI − 1.58% to − 0.15%, p = 0.02), after adjustment for age, gender, BMI, pain score, Kellgren-Lawrence grade, self-reported diabetes, and weight change over 4 years. Metformin use was associated with a trend towards a significant reduction in risk of total knee replacement over 6 years (odds ratio 0.30, 95% CI 0.07–1.30, p = 0.11), after adjustment for age, gender, BMI, Kellgren-Lawrence grade, pain score, and self-reported diabetes.ConclusionsThese data suggest that metformin use may have a beneficial effect on long-term knee joint outcomes in those with knee osteoarthritis and obesity. Randomised controlled trials are needed to confirm these findings and determine whether metformin would be a potential disease-modifying drug for knee osteoarthritis with the obese phenotype.

In this work, a dense recurrent convolutional neural network (DRCNN) was constructed to detect sleep arousals using available Polysomnography (PSG) measurement channels provided in the 2018 Physionet challenge database. Our model structure is composed of multiple dense convolutional units (DCU) followed by a bidirectional long-short term memory (LSTM) layer followed by a softmax output layer. The sleep events including sleep stages, arousal regions and multiple types of apnea-hypopnea/normal are manually annotated in 2018 Physionet challenge database which enable us to train our proposed network using a multi-task learning mechanism. Three binary cross-entropy loss functions corresponding to sleep/wake, arousal presence/absence and apnea/hypopnea presence/absence detections are summed up to generate our overall network loss function that is optimized using the Adam method. Our model performance was evaluated using two metrics: the area under the precision-recall curve (AUPRC) and the area under the receiver operating characteristic curve (AUROC). To measure our model generalization, 4-fold cross-validation was also performed. For training, full night recording data was applied to our model. Finally, our proposed algorithm achieves the first place in the official stage of the Physionet challenge with AUPRC of 0.54 on the blind testing dataset.