Simultaneous detection of influenza A, B and respiratory syncytial virus in wastewater samples by one-step multiplex RT-ddPCR assay.

BACKGROUND: After the occurrence of the COVID-19 pandemic, detection of other disseminated respiratory viruses using highly sensitive molecular methods was declared essential for monitoring the spread of health-threatening viruses in communities. The development of multiplex molecular assays are essential for the simultaneous detection of such viruses even at low concentrations. In the present study, a highly sensitive and specific multiplex one-step droplet digital PCR (RT-ddPCR) assay was developed for the simultaneous detection and absolute quantification of influenza A (IAV), influenza B (IBV), respiratory syncytial virus (RSV), and beta-2-microglobulin transcript as an endogenous internal control (IC B2M). RESULTS: The assay was first evaluated for analytical sensitivity and specificity, linearity, reproducibility, and recovery rates with excellent performance characteristics and then applied to 37 wastewater samples previously evaluated with commercially available and in-house quantitative real-time reverse transcription PCR (RT-qPCR) assays. IAV was detected in 16/37 (43%), IBV in 19/37 (51%), and RSV in 10/37 (27%) of the wastewater samples. Direct comparison of the developed assay with real-time RT-qPCR assays showed statistically significant high agreement in the detection of IAV (kappa Cohen's correlation coefficient: 0.834, p = 0.001) and RSV (kappa: 0.773, p = 0.001) viruses between the two assays, while the results for the detection of IBV (kappa: 0.355, p = 0.27) showed good agreement without statistical significance. CONCLUSIONS: Overall, the developed one-step multiplex ddPCR assay is cost-effective, highly sensitive and specific, and can simultaneously detect three common respiratory viruses in the complex matrix of wastewater samples even at low concentrations. Due to its high sensitivity and resistance to PCR inhibitors, the developed assay could be further used as an early warning system for wastewater monitoring.

Aggregation-Driven Photoinduced α-C(sp3)-H Bond Hydroxylation/C(sp3)-C(sp3) Coupling of Boron Dipyrromethene Dye in Water Reported by Near-Infrared Emission.

Molecular aggregation is a powerful tool for tuning advanced materials' photophysical and electronic properties. Here we present a novel potential for the aqueous-solvated aggregated state of boron dipyrromethene (BODIPY) to facilitate phototransformations otherwise achievable only under harsh chemical conditions. We show that the photoinduced symmetry-breaking charge separation state can itself initiate catalyst-free redox chemistry, leading to selective α-C(sp3)-H bond activation/Csp3-Csp3 coupling on the BODIPY backbone. The photoproduction progress was tracked by monitoring the evolution of the strong Stokes-shifted near-infrared emission, resulting from selective self-assembly of the terminal heterodimeric photoproduct into well-ordered J-aggregates, as revealed by X-ray structural analysis. These findings provide a facile and green route to further explore the promising frontier of packing-triggered selective photoconversions via supramolecular engineering.

Alzheimer's disease: using gene/protein network machine learning for molecule discovery in olive oil.

Alzheimer's disease (AD) poses a profound human, social, and economic burden. Previous studies suggest that extra virgin olive oil (EVOO) may be helpful in preventing cognitive decline. Here, we present a network machine learning method for identifying bioactive phytochemicals in EVOO with the highest potential to impact the protein network linked to the development and progression of the AD. A balanced classification accuracy of 70.3 ± 2.6% was achieved in fivefold cross-validation settings for predicting late-stage experimental drugs targeting AD from other clinically approved drugs. The calibrated machine learning algorithm was then used to predict the likelihood of existing drugs and known EVOO phytochemicals to be similar in action to the drugs impacting AD protein networks. These analyses identified the following ten EVOO phytochemicals with the highest likelihood of being active against AD: quercetin, genistein, luteolin, palmitoleate, stearic acid, apigenin, epicatechin, kaempferol, squalene, and daidzein (in the order from the highest to the lowest likelihood). This in silico study presents a framework that brings together artificial intelligence, analytical chemistry, and omics studies to identify unique therapeutic agents. It provides new insights into how EVOO constituents may help treat or prevent AD and potentially provide a basis for consideration in future clinical studies.

An AI-powered patient triage platform for future viral outbreaks using COVID-19 as a disease model.

Over the last century, outbreaks and pandemics have occurred with disturbing regularity, necessitating advance preparation and large-scale, coordinated response. Here, we developed a machine learning predictive model of disease severity and length of hospitalization for COVID-19, which can be utilized as a platform for future unknown viral outbreaks. We combined untargeted metabolomics on plasma data obtained from COVID-19 patients (n = 111) during hospitalization and healthy controls (n = 342), clinical and comorbidity data (n = 508) to build this patient triage platform, which consists of three parts: (i) the clinical decision tree, which amongst other biomarkers showed that patients with increased eosinophils have worse disease prognosis and can serve as a new potential biomarker with high accuracy (AUC = 0.974), (ii) the estimation of patient hospitalization length with ± 5 days error (R2 = 0.9765) and (iii) the prediction of the disease severity and the need of patient transfer to the intensive care unit. We report a significant decrease in serotonin levels in patients who needed positive airway pressure oxygen and/or were intubated. Furthermore, 5-hydroxy tryptophan, allantoin, and glucuronic acid metabolites were increased in COVID-19 patients and collectively they can serve as biomarkers to predict disease progression. The ability to quickly identify which patients will develop life-threatening illness would allow the efficient allocation of medical resources and implementation of the most effective medical interventions. We would advocate that the same approach could be utilized in future viral outbreaks to help hospitals triage patients more effectively and improve patient outcomes while optimizing healthcare resources.

Significant Turning Point: Common Buzzard (Buteo buteo) Exposure to Second-Generation Anticoagulant Rodenticides in the United Kingdom.

Second-generation anticoagulant rodenticides (SGARs) are widely used to control rodent populations, resulting in the serious secondary exposure of predators to these contaminants. In the United Kingdom (UK), professional use and purchase of SGARs were revised in the 2010s. Certain highly toxic SGARs have been authorized since then to be used outdoors around buildings as resistance-breaking chemicals under risk mitigation procedures. However, it is still uncertain whether and how these regulatory changes have influenced the secondary exposure of birds of prey to SGARs. Based on biomonitoring of the UK Common Buzzard (Buteo buteo) collected from 2001 to 2019, we assessed the temporal trend of exposure to SGARs and statistically determined potential turning points. The magnitude of difenacoum decreased over time with a seasonal fluctuation, while the magnitude and prevalence of more toxic brodifacoum, authorized to be used outdoors around buildings after the regulatory changes, increased. The summer of 2016 was statistically identified as a turning point for exposure to brodifacoum and summed SGARs that increased after this point. This time point coincided with the aforementioned regulatory changes. Our findings suggest a possible shift in SGAR use to brodifacoum from difenacoum over the decades, which may pose higher risks of impacts on wildlife.

A critical review of wastewater-based epidemiology as a tool to evaluate the unintentional human exposure to potentially harmful chemicals.

Wastewater-based epidemiology (WBE) is a powerful tool to gather epidemiological insights at the community level, providing objective data on population exposure to harmful substances. A considerable portion of the human exposure to these potentially harmful chemicals occurs unintentionally, unlike substances such as pharmaceuticals, illicit drugs, or alcohol. In this context, this comprehensive review analyzes WBE studies focused on classes of organic chemicals to which humans are unintentionally exposed, namely organophosphorus flame retardants, per- and polyfluoroalkyl substances (PFAS), benzotriazoles and benzothiazoles, phthalates and terephthalates, benzophenones, pesticides, bisphenols, and parabens. The review highlights some advantages of WBE for public health surveillance, e.g., non-invasive analysis, predictive capability, nearly real-time data, population-wide insights, no ethical approval, and unbiased sampling. It also discusses challenges and future research directions in WBE regarding exposure to harmful chemicals from various sources. The review emphasizes the critical role of wastewater sampling, sample preparation, quality control, and instrumental analysis in achieving accurate and reliable results. Furthermore, it examines the selection of human biomarkers for WBE studies and explores strategies to link WBE with human biomonitoring (HBM), which together enhance both the precision and effectiveness of exposure assessments.

Ruthenium p-Cymene Complexes Incorporating Substituted Pyridine-Quinoline-Based Ligands: Synthesis, Characterization, and Cytotoxic Properties.

Organometallic complexes of the formula [Ru(N^N)(p-cymene)Cl][X] (N^N = bidentate polypyridyl ligands, p-cymene = 1-methyl-4-(1-methylethyl)-benzene, X = counter anion), are currently studied as possible candidates for the potential treatment of cancer. Searching for new organometallic compounds with good to moderate cytotoxic activities, a series of mononuclear water-soluble ruthenium(II)-arene complexes incorporating substituted pyridine-quinoline ligands, with pending -CH2OH, -CO2H and -CO2Me groups in the 4-position of quinoline ring, were synthesized, for the first time, to study their possible effect to modulate the activity of the ruthenium p-cymene complexes. These include the [Ru(η6-p-cymene)(pqhyme)Cl][X] (X = Cl- (1-Cl), PF6- (1-PF6), pqhyme = 4-hydroxymethyl-2-(pyridin-2-yl)quinoline), [Ru(η6-p-cymene)(pqca)Cl][Cl] ((2-Cl), pqca = 4-carboxy-2-(pyridin-2-yl)quinoline), and [Ru(η6-p-cymene)(pqcame)Cl][X] (X = Cl- (3-Cl), PF6- (3-PF6), pqcame = 4-carboxymethyl-2-(pyridin-2-yl)quinoline) complexes, respectively. Identification of the complexes was based on multinuclear NMR and ATR-IR spectroscopic methods, elemental analysis, conductivity measurements, UV-Vis spectroscopic, and ESI-HRMS techniques. The solid-state structures of 1-PF6 and 3-PF6 have been elucidated by single-crystal X-ray diffraction revealing a three-legged piano stool geometry. This is the first time that the in vitro cytotoxic activities of these complexes are studied. These were conducted in HEK293T (human embryonic kidney cells) and HeLa cells (cervical cancer cells) via the MTT assay. The results show poor in vitro anticancer activities for the HeLa cancer cell lines and 3-Cl proved to be the most potent (IC50 > 80 µΜ). In both cell lines, the cytotoxicity of the ligand precursor pqhyme is significantly higher than that of cisplatin.

Comprehensive profiling and semi-quantification of exogenous chemicals in human urine using HRMS-based strategies.

Chemicals infiltrate our daily experiences through multiple exposure pathways. Human biomonitoring (HBM) is routinely used to comprehensively understand these chemical interactions. Historically, HBM depended on targeted screening methods limited to a relatively small set of chemicals with triple quadrupole instruments typically. However, recent advances in high-resolution mass spectrometry (HRMS) have facilitated the use of broad-scope target, suspect, and non-target strategies, enhancing chemical exposome characterization within acceptable detection limits. Despite these advancements, establishing robust and efficient sample treatment protocols is still essential for trustworthy broad-range chemical analysis. This study sought to validate a methodology leveraging HRMS-based strategies for accurate profiling of exogenous chemicals and related metabolites in urine samples. We evaluated five extraction protocols, each encompassing various chemical classes, such as pharmaceuticals, plastic additives, personal care products, and pesticides, in terms of their extraction recoveries, linearity, matrix effect, sensitivity, and reproducibility. The most effective protocol was extensively validated and subsequently applied to 10 real human urine samples using wide-scope target analysis encompassing over 2000 chemicals. We successfully identified and semi-quantified a total of 36 chemicals using an ionization efficiency-based model, affirming the methodology's robust performance. Notably, our results dismissed the need for a deconjugation step, a typically labor-intensive and time-consuming process.

Comprehensive HRMS Chemical Characterization of Pomegranate-Based Antioxidant Drinks via a Newly Developed Suspect and Target Screening Workflow.

Antioxidants play a significant role in human health, protecting against a variety of diseases. Therefore, the development of products with antioxidant activity is becoming increasingly prominent in the human lifestyle. New antioxidant drinks containing different percentages of pomegranate, blackberries, red grapes, and aronia have been designed, developed, and manufactured by a local industry. The comprehensive characterization of the drinks' constituents has been deemed necessary to evaluate their bioactivity. Thus, LC-qTOFMS has been selected, due to its sensitivity and structure identification capability. Both data-dependent and -independent acquisition modes have been utilized. The data have been treated according to a novel, newly designed workflow based on MS-DIAL and MZmine for suspect, as well as target screening. The classical MS-DIAL workflow has been modified to perform suspect and target screening in an automatic way. Furthermore, a novel methodology based on a compiled bioactivity-driven suspect list was developed and expanded with combinatorial enumeration to include metabolism products of the highlighted metabolites. Compounds belonging to ontologies with possible antioxidant capacity have been identified, such as flavonoids, amino acids, and fatty acids, which could be beneficial to human health, revealing the importance of the produced drinks as well as the efficacy of the new in-house developed workflow.

Longitudinal Plant Health Monitoring via High-Resolution Mass Spectrometry Screening Workflows: Application to a Fertilizer Mediated Tomato Growth Experiment.

Significant efforts have been spent in the modern era towards implementing environmentally friendly procedures like composting to mitigate the negative effects of intensive agricultural practices. In this context, a novel fertilizer was produced via the hydrolysis of an onion-derived compost, and has been previously comprehensively chemically characterized. In order to characterize its efficacy, the product was applied to tomato plants at five time points to monitor plant health and growth. Control samples were also used at each time point to eliminate confounding parameters due to the plant's normal growth process. After harvesting, the plant leaves were extracted using aq. MeOH (70:30, v/v) and analyzed via UPLC-QToF-MS, using a C18 column in both ionization modes (±ESI). The data-independent (DIA/bbCID) acquisition mode was employed, and the data were analyzed by MS-DIAL. Statistical analysis, including multivariate and trend analysis for longitudinal monitoring, were employed to highlight the differentiated features among the controls and treated plants as well as the time-point sequence. Metabolites related to plant growth belonging to several chemical classes were identified, proving the efficacy of the fertilizer product. Furthermore, the efficiency of the analytical and statistical workflows utilized was demonstrated.

Ultrasound-Assisted Extraction of Total Phenolic Compounds and Antioxidant Activity Evaluation from Oregano (Origanum vulgare ssp. hirtum) Using Response Surface Methodology and Identification of Specific Phenolic Compounds with HPLC-PDA and Q-TOF-MS/MS.

Oregano is native to the Mediterranean region and it has been reported to contain several phenolic compounds particularly flavonoids that have been related with multiple bioactivities towards certain diseases. Oregano is cultivated in the island of Lemnos where the climate promotes its growth and thus it could be further used in promoting local economy. The aim of the present study was to establish a methodology for the extraction of total phenolic content along with the antioxidant capacity of oregano by using response surface methodology. A Box-Behnken design was applied to optimize the extraction conditions with regard to the extraction time, temperature, and solvent mixture with the use of ultrasound-assisted extraction. For the optimized extracts, identification of the most abundant flavonoids (luteolin, kaempferol, and apigenin) was performed with an analytical HPLC-PDA and UPLC-Q-TOF MS methodology. The predicted optimal conditions of the statistical model were identified, and the predicted values confirmed. The linear factors evaluated, temperature, time, and ethanol concentration, all showed significant effect (p < 0.05), and the regression coefficient (R2) presented a good correlation between predicted and experimental data. Actual values under optimum conditions were 362.1 ± 1.8 and 108.6 ± 0.9 mg/g dry oregano with regard to total phenolic content and antioxidant activity based on 2,2-Diphenyl-1-picrylhydrazyl (DPPH) assay, respectively. Additionally, further antioxidant activities by 2,2'-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid (ABTS) (115.2 ± 1.2 mg/g dry oregano), Ferric Reducing Antioxidant Power (FRAP) (13.7 ± 0.8 mg/g dry oregano), and Cupric Reducing Antioxidant Capacity (CUPRAC) (1.2 ± 0.2 mg/g dry oregano) assays were performed for the optimized extract. The extract acquired under the optimum conditions contain an adequate quantity of phenolic compounds that could be used in the production of functional foods by food enrichment procedure.

First-Row Transition Metal Complexes Incorporating the 2-(2'-pyridyl)quinoxaline Ligand (pqx), as Potent Inflammatory Mediators: Cytotoxic Properties and Biological Activities against the Platelet-Activating Factor (PAF) and Thrombin.

Inflammatory mediators constitute a recently coined term in the field of metal-based complexes with antiplatelet activities. Our strategy targets Platelet-Activating Factor (PAF) and its receptor, which is the most potent lipid mediator of inflammation. Thus, the antiplatelet (anti-PAF) potency of any substance could be exerted by inhibiting the PAF-induced aggregation in washed rabbit platelets (WRPs), which internationally is a well-accepted methodology. Herein, a series of mononuclear (mer-[Cr(pqx)Cl3(H2O]) (1), [Co(pqx)Cl2(DMF)] (2) (DMF = N,N'-dimethyl formamide), [Cu(pqx)Cl2(DMSO)] (3) (DMSO = dimethyl sulfoxide), [Zn(pqx)Cl2] (4)) and dinuclear complexes ([Mn(pqx)(H2O)2Cl2]2 (5), [Fe(pqx)Cl2]2 (6) and [Ni(pqx)Cl2]2 (7)) incorporating the 2-(2'-pyridyl)quinoxaline ligand (pqx), were biologically evaluated as inhibitors of the PAF- and thrombin-induced aggregation in washed rabbit platelets (WRPs). The molecular structure of the five-co-ordinate analog (3) has been elucidated by single-crystal X-ray diffraction revealing a trigonal bipyramidal geometry. All complexes are potent inhibitors of the PAF-induced aggregation in WRPs in the micromolar range. Complex (6) displayed a remarkable in vitro dual inhibition against PAF and thrombin, with IC50 values of 1.79 µM and 0.46 µM, respectively. Within the series, complex (5) was less effective (IC50 = 39 µM) while complex (1) was almost 12-fold more potent against PAF, as opposed to thrombin-induced aggregation. The biological behavior of complexes 1, 6 and 7 on PAF's basic metabolic enzymatic pathways reveals that they affect key biosynthetic and catabolic enzymes of PAF underlying the anti-inflammatory properties of the relevant complexes. The in vitro cytotoxic activities of all complexes in HEK293T (human embryonic kidney cells) and HeLa cells (cervical cancer cells) are described via the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide (MTT) assay. The results reveal that complex 3 is the most potent within the series.

Development of Liquid Chromatographic Retention Index Based on Cocamide Diethanolamine Homologous Series (C(n)-DEA).

There is a growing need for indexing and harmonizing retention time (tR) data in liquid chromatography derived under different conditions to aid in the identification of compounds in high resolution mass spectrometry (HRMS) based suspect and nontarget screening of environmental samples. In this study, a rigorously tested, inexpensive, and simple system-independent retention index (RI) approach is presented for liquid chromatography (LC), based on the cocamide diethanolamine homologous series (C(n = 0-23)-DEA). The validation of the CDEA based RI system was checked rigorously on eight different instrumentation and LC conditions. The RI values were modeled using molecular descriptor free technique based on structural barcoding and convolutional neural network deep learning. The effect of pH on the elution pattern of more than 402 emerging contaminants were studied under diverse LC settings. The uncertainty associated with the CDEA RI model and the pH effect were addressed and the first RI bank based on CDEA calibrants was developed. The proposed RI system was used to enhance identification confidence in suspect and nontarget screening while facilitating successful comparability of retention index data between various LC settings. The CDEA RI app can be accessed at https://github.com/raalizadeh/RIdea.

First Novel Workflow for Semiquantification of Emerging Contaminants in Environmental Samples Analyzed by Gas Chromatography-Atmospheric Pressure Chemical Ionization-Quadrupole Time of Flight-Mass Spectrometry.

The ionization efficiency of emerging contaminants was modeled for the first time in gas chromatography-high-resolution mass spectrometry (GC-HRMS) which is coupled to an atmospheric pressure chemical ionization source (APCI). The recent chemical space has been expanded in environmental samples such as soil, indoor dust, and sediments thanks to recent use of high-resolution mass spectrometric techniques; however, many of these chemicals have remained unquantified. Chemical exposure in dust can pose potential risk to human health, and semiquantitative analysis is potentially of need to semiquantify these newly identified substances and assist with their risk assessment and environmental fate. In this study, a rigorously tested semiquantification workflow was proposed based on GC-APCI-HRMS ionization efficiency measurements of 78 emerging contaminants. The mechanism of ionization of compounds in the APCI source was discussed via a simple connectivity index and topological structure. The quantitative structure-property relationship (QSPR)-based model was also built to predict the APCI ionization efficiencies of unknowns and later use it for their quantification analyses. The proposed semiquantification method could be transferred into the household indoor dust sample matrix, and it could include the effect of recovery and matrix in the predictions of actual concentrations of analytes. A suspect compound, which falls inside the application domain of the tool, can be semiquantified by an online web application, free of access at http://trams.chem.uoa.gr/semiquantification/.

Development and Analytical Validation of a One-Step Five-Plex RT-ddPCR Assay for the Quantification of SARS-CoV-2 Transcripts in Clinical Samples.

Highly sensitive methodologies for SARS-CoV-2 detection are essential for the control of COVID-19 pandemic. We developed and analytically validated a highly sensitive and specific five-plex one-step RT-ddPCR assay for SARS-CoV-2. We first designed in-silico novel primers and probes for the simultaneous absolute quantification of three different regions of the nucleoprotein (N) gene of SARS-CoV-2 (N1, N2, N3), a synthetic RNA as an external control (RNA-EC), and Beta-2-Microglobulin (B2M) as an endogenous RNA internal control (RNA-IC). The developed assay was analytically validated using synthetic DNA and RNA calibrator standards and then was applied to 100 clinical specimens previously analyzed with a commercially available CE-IVD RT-qPCR assay. The analytical validation of the developed assay resulted in very good performance characteristics in terms of analytical sensitivity, linearity, analytical specificity, and reproducibility and recovery rates even at very low viral concentrations. The simultaneous absolute quantification of the RNA-EC and RNA-IC provides the necessary metrics for quality control assessment. Direct comparison of the developed one-step five-plex RT-ddPCR assay with a CE-IVD RT-qPCR kit revealed a very high concordance and a higher sensitivity [concordance: 99/100 (99.0%, Spearman's correlation coefficient: -0.850, p < 0.001)]. The developed assay is highly sensitive, specific, and reproducible and has a broad linear dynamic range, providing absolute quantification of SARS-COV-2 transcripts. The inclusion of two RNA quality controls, an external and an internal, is highly important for standardization of SARS-COV-2 molecular testing in clinical and wastewater samples.

A novel workflow for semi-quantification of emerging contaminants in environmental samples analyzed by LC-HRMS.

There is an increasing need for developing a strategy to quantify the newly identified substances in environmental samples, where there are not always reference standards available. The semi-quantitative analysis can assist risk assessment of chemicals and their environmental fate. In this study, a rigorously tested and system-independent semi-quantification workflow is proposed based on ionization efficiency measurement of emerging contaminants analyzed in liquid chromatography-high-resolution mass spectrometry. The quantitative structure-property relationship (QSPR)-based model was built to predict the ionization efficiency of unknown compounds which can be later used for their semi-quantification. The proposed semi-quantification method was applied and tested in real environmental seawater samples. All semi-quantification-related calculations can be performed online and free of access at http://trams.chem.uoa.gr/semiquantification/ .

Chemical characterisation of Pelargonium sidoides root based on LC-QToF-MS non-target screening strategies.

INTRODUCTION: Pelargonium sidoides is a member of the Geraniaceae family and it originates from the coastal regions of South Africa. In the last decades, Pelargonium sidoides root has been subjected to several surveys due to the assertion of its health benefits, such as the relief of symptoms of acute bronchitis, common cold and acute rhinosinusitis. Many studies have been conducted to reveal its naturally occurring bioactive chemicals, yet no wide-scope chemical characterisation strategies have been done using mass spectrometry. OBJECTIVE: This research aimed to comprehensively characterise the chemical profile of Pelargonium sidoides root via high-resolution mass spectrometry. METHODOLOGY: The Pelargonium sidoides root was extracted by a mixture of methanol: water in the proportion of 80:20. The extraction procedure included vortexing, shaking as well as the use of an ultrasound sonication bath under 40°C. After centrifugation, the supernatant was evaporated to dryness. The dry residue was reconstituted with a mixture of methanol/water (50:50, v/v), filtered and injected into an ultra-high-pressure liquid chromatography-quadruple time-of-flight mass spectrometer. RESULTS: Overall, 33 compounds were identified in the root using suspect and non-target screening. These compounds were originated from different classes of compounds such as amino acids, phenolic acids, α-hydroxy-acids, vitamins, polyphenols, flavonoids, coumarins, coumarins glucosides, coumarin sulphates and nucleotides. Quantitative results were provided for the identified compounds, where their reference standards were available. CONCLUSION: Some important compounds were elucidated, belonging to different classes of compounds such as antioxidants (coumarins and phenolic compounds), amino acids, nucleotides and vitamins revealing the importance of the bioactive content of this root.

From By-Products to Fertilizer: Chemical Characterization Using UPLC-QToF-MS via Suspect and Non-Target Screening Strategies.

The increasing demands of agriculture and the food market have resulted in intensive agricultural practices using synthetic fertilizers to maximize production. However, significant efforts have been made to implement more environmentally friendly procedures, such as composting, to overcome the adverse impact of these invasive practices. In the terms of this research, composting was applied to the production of two biofertilizers, using onion and mushroom by-products as raw materials respectively. The main purposes of this work were to identify the compounds that pass from the raw materials to the final products (onion-based and mushroom-based), as well as the characterization of the chemical profile of these final products following suspect and non-target screening workflows via UPLC-qToF-MS. Overall, 14 common compounds were identified in the onion and its final product, while 12 compounds were found in the mushroom and its corresponding product. These compounds belong to fatty acids, organic acids, and flavonoids, which could be beneficial to plant health. The determination of parameters, such as the pH, conductivity, organic matter, nitrogen content, and elemental analysis, were conducted for the overall characterization of the aforementioned products.

Comparative Evaluation of Different Targeted and Untargeted Analytical Approaches to Assess Greek Extra Virgin Olive Oil Quality and Authentication.

Extra virgin olive oil (EVOO) is a key component of the Mediterranean diet, with several health benefits derived from its consumption. Moreover, due to its eminent market position, EVOO has been thoroughly studied over the last several years, aiming at its authentication, but also to reveal the chemical profile inherent to its beneficial properties. In the present work, a comparative study was conducted to assess Greek EVOOs' quality and authentication utilizing different analytical approaches, both targeted and untargeted. 173 monovarietal EVOOs from three emblematic Greek cultivars (Koroneiki, Kolovi and Adramytiani), obtained during the harvesting years of 2018-2020, were analyzed and quantified as per their fatty acids methyl esters (FAMEs) composition via the official method (EEC) No 2568/91, as well as their bioactive content through liquid chromatography coupled to high resolution mass spectrometry (LC-HRMS) methodology. In addition to FAMEs analysis, EVOO samples were also analyzed via HRMS-untargeted metabolomics and optical spectroscopy techniques (visible absorption, fluorescence and Raman). The data retrieved from all applied techniques were analyzed with Machine Learning methods for the authentication of the EVOOs' variety. The models' predictive performance was calculated through test samples, while for further evaluation 30 commercially available EVOO samples were also examined in terms of variety. To the best of our knowledge, this is the first study where different techniques from the fields of standard analysis, spectrometry and optical spectroscopy are applied to the same EVOO samples, providing strong insight into EVOOs chemical profile and a comparative evaluation through the different platforms.

Thorough Investigation of the Phenolic Profile of Reputable Greek Honey Varieties: Varietal Discrimination and Floral Markers Identification Using Liquid Chromatography-High-Resolution Mass Spectrometry.

Honey is a highly consumed commodity due to its potential health benefits upon certain consumption, resulting in a high market price. This fact indicates the need to protect honey from fraudulent acts by delivering comprehensive analytical methodologies. In this study, targeted, suspect and non-targeted metabolomic workflows were applied to identify botanical origin markers of Greek honey. Blossom honey samples (n = 62) and the unifloral fir (n = 10), oak (n = 24), pine (n = 39) and thyme (n = 34) honeys were analyzed using an ultra-high-performance liquid chromatography hybrid quadrupole time-of-flight mass spectrometry (UHPLC-q-TOF-MS) system. Several potential authenticity markers were revealed from the application of different metabolomic workflows. In detail, based on quantitative targeted analysis, three blossom honey markers were found, namely, galangin, pinocembrin and chrysin, while gallic acid concentration was found to be significantly higher in oak honey. Using suspect screening workflow, 12 additional bioactive compounds were identified and semi-quantified, achieving comprehensive metabolomic honey characterization. Lastly, by combining non-targeted screening with advanced chemometrics, it was possible to discriminate thyme from blossom honey and develop binary discriminatory models with high predictive power. In conclusion, a holistic approach to assessing the botanical origin of Greek honey is presented, highlighting the complementarity of the three applied metabolomic approaches.