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1. Metabolomic and elemental profiling of blood serum in bladder cancer

   https://doi.org/10.1016/j.jpha.2022.08.004  

   Ossoliński, Krzysztof; Ruman, Tomasz; Copié, Valérie; Tripet, Brian P.; Nogueira, Leonardo B.; Nogueira, Katiane O.P.C.; Kołodziej, Artur; Płaza-Altamer, Aneta; Ossolińska, Anna; Ossoliński, Tadeusz; et al (September 2022, Journal of Pharmaceutical Analysis)

   Bladder cancer (BC) is one of the most frequently diagnosed types of urinary cancer. Despite advances in treatment methods, no specific biomarkers are currently in use. Targeted and untargeted profiling of metabolites and elements of human blood serum from 100 BC patients and the same number of normal controls (NCs), with external validation, was attempted using three analytical methods, i.e., nuclear magnetic resonance, gold and silver-109 nanoparticle-based laser desorption/ionization mass spec- trometry (LDI-MS), and inductively coupled plasma optical emission spectrometry (ICP-OES). All results were subjected to multivariate statistical analysis. Four potential serum biomarkers of BC, namely, iso- butyrate, pyroglutamate, choline, and acetate, were quantified with proton nuclear magnetic resonance, which had excellent predictive ability as judged by the area under the curve (AUC) value of 0.999. Two elements, Li and Fe, were also found to distinguish between cancer and control samples, as judged from ICP-OES data and AUC of 0.807 (in validation set). Twenty-five putatively identified compounds, mostly related to glycans and lipids, differentiated BC from NCs, as detected using LDI-MS. Five serum metab- olites were found to discriminate between tumor grades and nine metabolites between tumor stages. The results from three different analytical platforms demonstrate that the identified distinct serum metabolites and metal elements have potential to be used for noninvasive detection, staging, and grading of BC. 

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2. Assessment of urinary biomarkers of mycotoxin exposure in adults from Cameroon

   https://doi.org/10.1163/18750796-bja10010  

   Abia, WA; Tchana, AN; Djonabaye, D; Šarkanj, B; Mfopou, EY; Ezekiel, CN; Sulyok, M; Turner, PC; Elliott, CT; Warth, B; et al (November 2024, World Mycotoxin Journal)

   Abstract In Cameroon, dietary staples are frequently contaminated with diverse toxic fungal metabolites, known as mycotoxins. Aflatoxins and fumonisins are a public health concern, particularly concerning cancer and/or early life stunting. Mycotoxin mixtures are predicted from food measures; and this study reports the levels and frequencies of urinary mycotoxin biomarkers in Cameroonian adults. A single first void urine sample was collected from 89 adults from Yaoundé, Cameroon. Urine samples were tested for eight distinct mycotoxins using measures of both parent compounds and/or their metabolites by liquid chromatography-tandem mass spectrometry (LC-MS/MS). Altogether, seven distinct mycotoxins, aflatoxin, fumonisin, deoxynivalenol, zearalenone, nivalenol, ochratoxin A, and citrinin, (or their metabolites) were observed in urine samples. At least one mycotoxin was detected in all of the urine samples, 87 (98%) of which were above the limit of quantitation. Aflatoxin M₁was detected in 42% (n.d.-0.21 μg/l) of samples of which about a quarter additionally contained fumonisin B₁. Of the remaining toxins deoxynivalenol (78%), zearalenone (99%), ochratoxin A (95%), nivalenol (53%), and citrinin (87%) were present in the samples. Alternariol was not detected in any sample. Mixtures of mycotoxins in the samples were frequently observed with 64 samples (72%) containing more than five mycotoxin exposure biomarkers. Estimates of intake exceeded the TDIs for fumonisin B₁(n = 4), deoxynivalenol (n = 1) and zearalenone (n = 2), no TDI is set for aflatoxin. This study reveals frequent co-exposure of Cameroonian individuals to a complex mixture of toxic and carcinogenic mycotoxins, with mixtures of aflatoxin and fumonisin being a particular priority from a public health standpoint. 

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3. Detection of Volatile Organic Compounds (VOCs) in Urine via Gas Chromatography-Mass Spectrometry QTOF to Differentiate Between Localized and Metastatic Models of Breast Cancer

   https://doi.org/10.1038/s41598-019-38920-0  

   Woollam, Mark; Teli, Meghana; Angarita-Rivera, Paula; Liu, Shengzhi; Siegel, Amanda P.; Yokota, Hiroki; Agarwal, Mangilal (February 2019, Scientific Reports)

   Abstract Breast cancer is the most common cancer detected in women and current screening methods for the disease are not sensitive. Volatile organic compounds (VOCs) include endogenous metabolites that provide information about health and disease which might be useful to develop a better screening method for breast cancer. The goal of this study was to classify mice with and without tumors and compare tumors localized to the mammary pad and tumor cells injected into the iliac artery by differences in VOCs in urine. After 4T1.2 tumor cells were injected into BALB/c mice either in the mammary pad or into the iliac artery, urine was collected, VOCs from urine headspace were concentrated by solid phase microextraction and results were analyzed by gas chromatography-mass spectrometry quadrupole time-of-flight. Multivariate and univariate statistical analyses were employed to find potential biomarkers for breast cancer and metastatic breast cancer in mice models. A set of six VOCs classified mice with and without tumors with an area under the receiver operator characteristic (ROC AUC) of 0.98 (95% confidence interval [0.85, 1.00]) via five-fold cross validation. Classification of mice with tumors in the mammary pad and iliac artery was executed utilizing a different set of six VOCs, with a ROC AUC of 0.96 (95% confidence interval [0.75, 1.00]). 

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4. Towards Optimizing Neural Network-Based Quantification for NMR Metabolomics

   https://doi.org/10.3390/metabo15040249  

   Johnson, Hayden; Tipirneni-Sajja, Aaryani (April 2025, Metabolites)

   Background: Quantification of metabolites from nuclear magnetic resonance (NMR) spectra in an accurate, high-throughput manner requires effective data processing tools. Neural networks are relatively underexplored in quantitative NMR metabolomics despite impressive speed and throughput compared to more conventional peak-fitting metabolomics software. Methods: This work investigates practices for dataset and model development in the task of metabolite quantification directly from simulated NMR spectra for three neural network models: the multi-layered perceptron, the convolutional neural network, and the transformer. Model architectures, training parameters, and training datasets are optimized before comparing each model on simulated 400-MHz 1H-NMR spectra of complex mixtures with 8, 44, or 86 metabolites to quantify in spectra ranging from simple to highly complex and overlapping peaks. The optimized models were further validated on spectra at 100- and 800-MHz. Results: The transformer was the most effective network for NMR metabolite quantification, especially as the number of metabolites per spectra increased or target concentrations were low or had a large dynamic range. Further, the transformer was able to accurately quantify metabolites in simulated spectra from 100-MHz up to 800-MHz. Conclusions: The methods developed in this work reveal that transformers have the potential to accurately perform fully automated metabolite quantification in real-time and, with further development with experimental data, could be the basis for automated quantitative NMR metabolomics software. 

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5. Peptide-Mediated Targeting Mesoporous Silica Nanoparticles: A Novel Tool for Fighting Bladder Cancer

   Sweeney, Sean K; Luo, Yi; O'Donnell, Michael A; Assouline, Jose G (February 2017, Journal of biomedical nanotechnology)

   Transitional cell carcinoma of the bladder is particularly devastating due to its high rate of recurrence and difficulty in retention of treatments within the bladder. Current cystoscopic approaches to detect and stage the tumor are limited by the penetrative depth of the cystoscope light source, and intravesical dyes that highlight tumors for surgical resection are non-specific. To address the needs for improved specificity in tumor detection and follow-up, we report on a novel technology relying on the engineered core of mesoporous silica (MSN) with surface modifications that generate contrast in fluorescence and magnetic resonance imaging (MRI). The particle surface was further functionalized to include a bladder cancer cell specific peptide, Cyc6, identified via phage display. This peptide possesses nanomolar specificity for bladder cancer cells and homology across multiple species including mouse, canine, and human. Our study takes advantage of its target expression in bladder tumor which is not expressed in normal bladder wall. When functionalized to MSN, the Cyc6 improved binding efficiency and specificity for bladder cancer cells in vitro. In an in vivo model, MSN instilled into bladders of tumor-bearing mice enhanced T 1- and T 2-weighted MRI signals, improving the detection of the tumor boundaries. These findings support the notion that our targeted nanomaterial presents new options for early detection and eventual therapeutic intervention. Ultimately, the combination of real-time and repeated MRI evaluation of the tumors enhanced by nanoparticle contrast have the potential for translation into human clinical studies for tumor staging, therapeutic monitoring, and drug delivery. 

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