Search

Extra virgin olive oil (EVOO) is one of the main ingredients of the Mediterranean diet. It is claimed as a functional food for its unique content of health-promoting compounds. Tyrosol (Tyr), Hydroxytyrosol (Htyr), and their phenolic derivatives present in EVOO show beneficial properties, and their identification and quantification, both in their free form and after the hydrolysis of more complex precursors, are important to certify its quality. An alternative method for quantifying free and total Tyr and Htyr in EVOO is presented using an LC–MS interface based on electron ionization (EI), called liquid electron ionization (LEI). This method requires neither sample preparation nor chromatography; the sample is diluted and injected. The selectivity and sensitivity were assessed in multiple reaction monitoring mode (MRM), obtaining confirmation and quantification in actual samples ranging from 5 to 11 mg/Kg for the free forms and from 32 to 80 mg/Kg for their total amount after hydrolysis. Two MS/MS transitions were acquired for both compounds using the Q/q ratios as confirmatory parameters. Standard addition calibration curves demonstrated optimal linearity and negligible matrix effects, allowing a correct quantification even without expensive and difficult to find labeled internal standards. After several weeks of operation, the system’s repeatability was excellent, with an intraday RSD (%) spanning from five to nine and an interday RSD (%) spanning from 9 to 11.

The reduction of chemical inputs due to fertilizer and pesticide applications is a target shared both by farmers and consumers in order to minimize the side effects for human and environmental health. Among the possible strategies, the use of biostimulants has become increasingly important as demonstrated by the fast growth of their global market and by the increased rate of registration of new products. In this work, we assessed the effects of five bacterial strains (Pseu-domonas fluorescens Pf4, P. putida S1Pf1, P. protegens Pf7, P. migulae 8R6, and Pseudomonas sp. 5Vm1K), which were chosen according to their previously reported plant growth promotion traits and their positive effects on fruit/seed nutrient contents, on a local onion cultivar and on zucchini. The possible variations induced by the inoculation with the bacterial strains on the onion nutritional components were also evaluated. Inoculation resulted in significant growth stimulation and improvement of the mineral concentration of the onion bulb, induced particularly by 5Vm1K and S1Pf1, and in different effects on the flowering of the zucchini plants according to the bacterial strain. The present study provides new information regarding the activity of the five plant growth-promoting bacteria (PGPB) strains on onion and zucchini, two plant species rarely considered by the scientific literature despite their economic relevance.

I prodotti per la cura personale sono un gruppo eterogeneo di sostanze chimiche che includono le fragranze sintetiche (PMF) come Galaxolide (HHCB), Tonalide (AHTN), Celestolide (ADBI) e Phantolide (AHDI). I PMF sono molto utilizzati nei profumi, detergenti e nei prodotti per la pulizia della casa e ciò porta al loro rilascio nell'ambiente per lo più attraverso gli scarichi degli impianti di depurazione. Anche se l'Italia è il paese europeo con il più alto utilizzo di PMF, pochi dati sono disponibili sulla loro presenza nelle acque italiane e sul loro destino negli impianti convenzionali. In tale contesto, questa ricerca si è concentrata in primo luogo sullo sviluppo di nuovi protocolli per la determinazione dei PMF nelle acque reflue e nei fanghi attivi. I PMF nei campioni di acqua sono stati estratti mediante SPE e analisi in GC-MS con triplo quadrupolo mentre i fanghi attivi sono stati analizzati usando un bagno ad ultrasuoni e una trappola ionica. I limiti di rilevabilità ottenuti sono stati molto inferiori alle concentrazioni dei campioni. Questi metodi consentono l'analisi di molti campioni in tempi brevi impiegando strumentazione semplice e limitando i volumi di solvente utilizzati. Sono stati poi monitorati i PMF all'interno di un depuratore convenzionale italiano. HHCB e il suo principale sottoprodotto, il Galaxolidone (HHCB-lattone), sono stati trovati in concentrazioni di µg/L, un ordine di grandezza maggiore di AHTN, mentre ADBI è stato rilevato in tracce e AHDI era sempre, Personal care products are a heterogeneous group of chemicals that include Polycyclic Musk Fragrances (PMFs) as Galaxolide (HHCB), Tonalide (AHTN), Celestolide (ADBI) and Phantolide (AHDI). PMFs are widely spread substances employed in perfumes, detergents and house-cleaning products. The massive use leads to PMF release into the environment mainly through Wastewater Treatment Plants (WWTPs) discharges. Even if Italy is the European country with the highest use of PMFs, only a few data are available about these products’ occurrence in Italian water and about PMF fate in conventional WWTPs. In this, PhD was firstly focused on developing protocols for PMF determination in wastewater and in activated sludge. PMFs in water samples were determined by SPE extraction and analysis in GC-MS with a triple-quadrupole while for the analysis of activated sludge an ultrasonic bath and a GC-Ion trap were employed. Detection limits of selected compounds were considerably lower than sample concentrations. These methods allow the analysis of many samples in short times employing simple instrumentation and limiting the solvent volumes used. After that, PMFs inside an Italian conventional WWTPs was monitored. HHCB and its main by-product, Galaxolidone (HHCB-lactone), were found in concentrations of µg/L, one order of magnitude greater than AHTN, AHDI was always lower than LOD while ADBI was measured only at trace levels. No seasonal variability was recorded in PMF input onto WWTP. In the water phase, HHCB and AHTN evidenced a modest reduction during treatments (20% and 50%, respectively) mainly due to adsorption processes during the biological treatment as suggested by the high stable PMF concentrations in activated sludges. HHCB-lactone registered an increase up to 70% during treatments caused by HHCB biotransformation during biological treatment. This study revealed that current technologies are not enough efficient in removing PMFs from wastewaters which are discharged into the envi

Neural networks are rapidly gaining popularity in chemical modeling and Quantitative Structure–Activity Relationship (QSAR) thanks to their ability to handle multitask problems. However, outcomes of neural networks depend on the tuning of several hyperparameters, whose small variations can often strongly affect their performance. Hence, optimization is a fundamental step in training neural networks although, in many cases, it can be very expensive from a computational point of view. In this study, we compared four of the most widely used approaches for tuning hyperparameters, namely, grid search, random search, tree-structured Parzen estimator, and genetic algorithms on three multitask QSAR datasets. We mainly focused on parsimonious optimization and thus not only on the performance of neural networks, but also the computational time that was taken into account. Furthermore, since the optimization approaches do not directly provide information about the influence of hyperparameters, we applied experimental design strategies to determine their effects on the neural network performance. We found that genetic algorithms, tree-structured Parzen estimator, and random search require on average 0.08% of the hours required by grid search; in addition, tree-structured Parzen estimator and genetic algorithms provide better results than random search.

This review article traces the history of the use of liquid chromatography coupled with mass spectrometry (LC-MS) using electron ionization (EI) from the first attempts up to the present day. At the time of the first efforts to couple LC to MS, 70 eV EI was the most common ionization technique, typically used in gas chromatography-mass spectrometry (GC-MS) and providing highly reproducible mass spectra that could be collated in libraries. Therefore, it was obvious to transport this dominant approach to the early LC-MS coupling attempts. The use of LC coupled to EI-MS is challenging mainly due to restrictions related to high-vacuum and high-temperature conditions required for the operation of EI and the need to remove the eluent carrying the analyte before entering the ion source. The authors will take readers through a journey of about 50 years, showing how through the succession of different attempts it has been possible to successfully couple LC with EI-MS, which in principle appear to be incompatible.

The Fenton process activated by Zero Valent Iron (ZVI-Fenton) is shown here to effectively remove antibiotics reserved for hospital settings (specifically used to treat antibiotic-resistant infections) from wastewater, thereby helping in the fight against bacterial resistance. Effective degradation of cefazolin, imipenem and vancomycin in real urban wastewater was achieved at pH 5, which is quite near neutrality when compared with classic Fenton that works effectively at pH 3–4. The possibility to operate successfully at pH 5 has several advantages compared to operation at lower pH values: (i) lower reagent costs for pH adjustment; (ii) insignificant impact on wastewater conductivity, because lesser acid is required to acidify and lesser or no base for neutralization; (iii) undetectable release of dissolved Fe, which could otherwise be an issue for wastewater quality. The cost of reagents for the treatment ranges between 0.04 and 0.07 $ m−3, which looks very suitable for practical applications. The structures of the degradation intermediates of the studied antibiotics and their likely abundance suggest that, once the primary compound is eliminated, most of the potential to trigger antibiotic action has been removed. Application of the ZVI-Fenton technique to wastewater treatment could considerably lower the possibility for antibiotics to trigger the development of resistance in bacteria.

Peucedanum ostruthium (L.) W.D.J.Koch (Apiaceae) is an alpine medicinal plant traditionally used as a panacea to treat various ailments. For the first time, its phytotoxic and nematotoxic properties were investigated. The inhibitory activity toward germination and seedling growth of the weeds Echinochloa oryzoides (Ard.) Fritsch and Lolium multiflorum Lam. was evaluated by two in vitro assays, carried out on filter paper and soil, using different aqueous extract concentrations (1, 10, and 20 %) and 0.25 g of powder of P. ostruthium leaves, inflorescences, and rhizomes. The study showed that all samples were more effective on L. multiflorum than E. oryzoides with p-values = 0.000 on both substrate types. Nevertheless, in all cases, the soil mitigated the P. ostruthium effects. Regarding nematicidal activity, the leaf extract was the most active against larvae and adults of the nematode Panagrolaimus rigidus. According to the motility test, their death was 85.6 ± 2.7 % and 90.5 ± 3.1 % 24 h after treatment. Lastly, NMR and UPLC-HR-MS analyses led to the identification of several compounds in the aqueous extracts, including mono- and di-substituted chlorogenic acids, flavonol glycosides, coumarins, and furanocoumarin glycosides. 5-Caffeoylquinic acid was the most abundant phenolic component in all plant organs.

Background: Humans are exposed to tens of thousands of chemical substances that need to be assessed for their potential toxicity. Acute systemic toxicity testing serves as the basis for regulatory hazard classification, labeling, and risk management. However, it is cost- and time-prohibitive to evaluate all new and existing chemicals using traditional rodent acute toxicity tests. In silica models built using existing data facilitate rapid acute tox- icity predictions without using animals. Objkctivks: The U.S. Interagency Coordinating Committee on the Validation of Alternative Methods (ICCVAM) Acute Toxicity Workgroup organ- ized an international collaboration to develop in silica models for predicting acute oral toxicity based on five different end points: Lethal Dose 50 (LD50 value, U.S. Environmental Protection Agency hazard (four) categories, Globally Harmonized System for Classification and Labeling hazard (five) categories, very toxic chemicals [LD50 (LD50 ≤ 50 mg/kg)], and nontoxic chemicals (LD50 > 2,000 mg/kg). Mkthods: An acute oral toxicity data inventory for 11,992 chemicals was compiled, split into training and evaluation sets, and made available to 35 participating international research groups that submitted a total of 139 predictive models. Predictions that fell within the applicability domains of the submitted models were evaluated using external validation sets. These were then combined into consensus models to leverage strengths of individual approaches. Results: The resulting consensus predictions, which leverage the collective strengths of each individual model, form the Collaborative Acute Toxicity Modeling Suite (CATMoS). CATMoS demonstrated high performance in terms of accuracy and robustness when compared with in viva results. Discussion: CATMoS is being evaluated by regulatory agencies for its utility and applicability as a potential replacement for in viva rat acute oral toxicity studies. CATMoS predictions for more than 800,000 chemica

Multivariate regression is a fundamental supervised chemometric approach that defines the relationship between a set of independent variables and a quantitative response. It enables the subsequent prediction of the response for future samples, thus avoiding its experimental measurement. Regression approaches have been widely applied for data analysis in different scientific fields. In this paper, we describe the regression toolbox for MATLAB, which is a collection of modules for calculating some well-known regression methods: Ordinary Least Squares (OLS), Partial Least Squares (PLS), Principal Component Regression (PCR), Ridge and local regression based on sample similarities, such as Binned Nearest Neighbours (BNN) and k-Nearest Neighbours (kNN) regression methods. Moreover, the toolbox includes modules to couple regression approaches with supervised variable selection based on All Subset models, Forward Selection, Genetic Algorithms and Reshaped Sequential Replacement. The toolbox is freely available at the Milano Chemometrics and QSAR Research Group website and provides a graphical user interface (GUI), which allows the calculation in a user-friendly graphical environment.

Microalgae are a precious source of polyunsaturated fatty acids (PUFA), however extraction is difficult due to the peculiar microalgae cell structure. Here we describe a new method based on the application of chitosan nanoaggregates as CO2 responsive emulsifier, used to promote the swelling of algae cell wall and the formation of a large oil – ethanol interphase area during the ethanolysis. Tests were carried out with Pseudokirchneriella subcapitata and Nannochloropsis sp. at different biomass/ethanol/chitosan ratios. CO2 was added to trigger demulsification to promote an easy recovery of the lipid fraction. The highest yields in PUFA were obtained with Nannochloropsis sp. (207.9 mg/g of oil) using 0.4% wt of chitosan and 1:10 biomass:ethanol ratio; 43.6 mg/g of linolenic acid were obtained from Pseudokirchneriella subcapitata. Overall, because the method employs ethanol, a generally recognized as safe (GRAS) solvent, and food grade additives, it is suited for the preparation of PUFA supplements.

Irinotecan (CPT-11) is a water-soluble anticancer drug widely used to treat several types of cancer. Even if the metabolites of CPT-11 are well-known and investigated, only limited information is available in the literature about the formation of photo-degradation products that can naturally originate from sunlight irradiation when the drug is released in aqueous systems. CTP-11 solutions at 10.0 mg L-1 were irradiated by simulated sunlight. The intensity of the drug decreased by 90% after 7.5 days of irradiation and no significant reduction of absorbance values was observed after 13 days. A sensitive UHPLC-MS/MS method was developed employing a hybrid triple quadrupole/linear ion trap mass spectrometer, that is able to work in data-dependent acquisition mode and to obtain information about the compounds formed during the photoirradiation. Moreover, a selected reaction monitoring method was built using the MS/MS fragmentation pattern of the compounds previously investigated. The method was validated considering LOD, LOQ, linearity, precision, selectivity, recovery and matrix effect. LOD and LOQ values were 0.02 and 0.05 ng mL-1, respectively, whereas MDL and MQL values in real water samples (river water, groundwater, well water, and wastewater) were lower than 0.05 and 0.2 ng mL-1, respectively. Eight photodegradation products were identified, among which five for the first time. Based on the MS and MS/MS fragmentation, the chemical structures of the degradation products were proposed. Hydrolysis experiments were carried out on the same solutions preserved in the dark, but no formation of other species was highlighted. The method was applied to several real samples: CPT-11 was detected and quantified only in a hospital effluent sample at the concentration of 0.41 ± 0.2 ng mL-1 together with the occurrence of PDP3. The outcomes of this study may be useful for updating the pollutant screening in water samples

Oleander is a spontaneous shrub widely occurring in Mediterranean regions. Poisoning is sporadically reported in livestock, mainly due to the ingestion of leaves containing toxic cardiac glycosides (primarily oleandrin). In this study, 50 lactating Fleckvieh cows were affected after being offered a diet containing dry oleander pruning wastes accidentally mixed with fodder. Clinical examination, electrocardiogram, and blood sampling were conducted. Dead animals were necropsied, and heart, liver, kidney, spleen, and intestine were submitted to histological investigation. Oleandrin detection was performed through ultra-high performance liquid chromatography-tandem mass spectrometry in blood, serum, liver, heart, milk, and cheese samples. Severe depression, anorexia, ruminal atony, diarrhea, serous nasal discharge, tachycardia, and irregular heartbeat were the most common clinical signs. The first animal died within 48 h, and a total of 13 cows died in 4 days. Disseminated hyperemia and hemorrhages, multifocal coagulative necrosis of the cardiac muscle fibers, and severe and diffuse enteritis were suggestive of oleander poisoning. The diagnosis was confirmed by the presence of oleandrin in serum, liver, heart, milk, and cheese. Our results confirm the high toxicity of oleander in cattle and report for the first time the transfer into milk and dairy products, suggesting a potential risk for the consumers.

Consensus strategies have been widely applied in many different scientific fields, based on the assumption that the fusion of several sources of information increases the outcome reliability. Despite the widespread application of consensus approaches, their advantages in quantitative structure-activity relationship (QSAR) modeling have not been thoroughly evaluated, mainly due to the lack of appropriate large-scale data sets. In this study, we evaluated the advantages and drawbacks of consensus approaches compared to single classification QSAR models. To this end, we used a data set of three properties (androgen receptor binding, agonism, and antagonism) for approximately 4000 molecules with predictions performed by more than 20 QSAR models, made available in a large-scale collaborative project. The individual QSAR models were compared with two consensus approaches, majority voting and the Bayes consensus with discrete probability distributions, in both protective and nonprotective forms. Consensus strategies proved to be more accurate and to better cover the analyzed chemical space than individual QSARs on average, thus motivating their widespread application for property prediction. Scripts and data to reproduce the results of this study are available for download.

BACKGROUND: Endocrine disrupting chemicals (EDCs) are xenobiotics that mimic the interaction of natural hormones and alter synthesis, transport, or metabolic pathways. The prospect of EDCs causing adverse health effects in humans and wildlife has led to the development of scientific and regulatory approaches for evaluating bioactivity. This need is being addressed using high-throughput screening (HTS) in vitro approaches and computational modeling. OBJECTIVES: In support of the Endocrine Disruptor Screening Program, the U.S. Environmental Protection Agency (EPA) led two worldwide consortiums to virtually screen chemicals for their potential estrogenic and androgenic activities. Here, we describe the Collaborative Modeling Project for Androgen Receptor Activity (CoMPARA) efforts, which follows the steps of the Collaborative Estrogen Receptor Activity Prediction Project (CERAPP). METHODS: The CoMPARA list of screened chemicals built on CERAPP’s list of 32,464 chemicals to include additional chemicals of interest, as well as simulated ToxCastTM metabolites, totaling 55,450 chemical structures. Computational toxicology scientists from 25 international groups contributed 91 predictive models for binding, agonist, and antagonist activity predictions. Models were underpinned by a common training set of 1,746 chemicals compiled from a combined data set of 11 ToxCastTM/Tox21 HTS in vitro assays. RESULTS: The resulting models were evaluated using curated literature data extracted from different sources. To overcome the limitations of single-model approaches, CoMPARA predictions were combined into consensus models that provided averaged predictive accuracy of approximately 80% for the evaluation set. DISCUSSION: The strengths and limitations of the consensus predictions were discussed with example chemicals; then, the models were implemented into the free and open-source OPERA application to enable screening of new chemicals with a defined applicability domain and accuracy ass

Chemometrics plays a fundamental role in quantitative structure-activity relationships (QSARs) and quantitative structure-property relationships (QSPRs) methods, which aim at empirically linking the molecular structure of chemicals to experimentally-measurable properties. In fact, chemometrics statistics and chemoinformatics are the basic tools for finding meaningful mathematical relationships between the molecular structure and biological, physico-chemical, toxicological and environmental properties of chemicals. The key elements of QSAR/QSPR are molecular descriptors, which are numerical indices encoding information related to the structure of chemicals and are used as independent variables in the subsequent modeling, thus connecting the QSAR approaches to the multivariate and chemometric world. In this article, historical and novel QSAR modeling approaches are presented. After describing the historical background of QSAR and the classical approaches, molecular descriptors are illustrated, with state-of-the-art as well as novel description methodologies. Additionally, the key principles of QSAR are introduced, along with specific elements of the QSAR modeling workflow, e.g., variable reduction and selection, similarity-based approaches, validation, the definition of applicability domain and consensus modeling.

Several similarity/diversity measures for data mining, chemometrics, and chemoinformatics are presented and discussed toward the different data they are applied to. After a short presentation of the axioms for dissimilarity and similarity functions, their relationships and the required data pretreatment, the theoretical definitions and formulas of distance and similarity measures for real‐valued, binary, ranked, frequency, and mixed‐type data are provided along with the main concepts on distances between sets and meta‐distances. Simple examples of calculation are given and extended comparisons are performed on the distances defined for real valued and binary data.

Recombinant bacterial cocaine esterase (CocE) represents a potential protein therapeutic for cocaine use disorder treatment. Unfortunately, the native enzyme was highly unstable and the corresponding mutagenized derivatives, RBP-8000 and E196-301, although improving in vitro thermo-stability and in vivo half-life, were a partial solution to the problem. For cocaine use disorder treatment, an efficient cocaine-metabolizing enzyme with a longer residence time in circulation would be needed. We investigated in vitro the possibility of developing red blood cells (RBCs) loaded with RBP-8000 and E196-301 as a biocompatible system to metabolize cocaine for a longer period of time. RBP 8000 stability within human RBCs is limited (approximately 50% residual activity after 1 h at 37°C) and not different as for the free enzyme, while both free and encapsulated E196-301 showed a greater thermo-stability. By reducing cellular glutathione content during the loading procedure, in order to preserve the disulfide bonds opportunely created to stabilize the enzyme dimer structure, it was possible to produce an encapsulated protein maintaining 100% stability at least after 4 h at 37°C. Moreover, E196-301-loaded RBCs were efficiently able to degrade cocaine in a time- and concentration-dependent manner. The same stability results were obtained when murine RBCs were used paving the way to preclinical investigations. Thus, our in vitro data show that E196-301-loaded RBCs could act as efficient bioreactors in degrading cocaine to non-toxic metabolites to be possibly considered in substance-use disorder treatments. This approach should now be investigated in a preclinical model of cocaine use disorder to evaluate if further protein modifications are needed to further improve long term enzyme stability.

Mass spectrometry has been the “gold standard” for drugs of abuse (DoA) analysis for many decades because of the selectivity and sensitivity it affords. Recent progress in all aspects of mass spectrometry has seen significant developments in the field of DoA analysis. Mass spectrometry is particularly well suited to address the rapidly proliferating number of very high potency, novel psychoactive substances that are causing an alarming number of fatalities worldwide. This review surveys advancements in the areas of sample preparation, gas and liquid chromatography‐mass spectrometry, as well as the rapidly emerging field of ambient ionization mass spectrometry. We have predominantly targeted literature progress over the past ten years and present our outlook for the future.

Background: Based on the medical history and laboratory analytical tests, a patient presenting symptoms compatible with Chronic Fatigue Syndrome was suspected of metal intoxication; therefore, a chelating therapy was attempted. In parallel, the profile of elemental excretion in urine was determined. Methods: Chelation therapy by CaNa2EDTA was administered every two weeks and urine samples were routinely collected for 17 months. The samples were mineralized with HNO3 69 % and analyzed by Inductively-Coupled Plasma – Mass Spectrometry. Data were processed by multivariate statistical methods. Results: Most of the toxic elements showed a peak of excretion in 12−24 h after EDTA administration, which returned to basal level by 24−36 h after the treatment. Yet, the excretion of some trace elements persisted in the urine collected 26 h after the treatment. Conclusions: The analysis of excreted metals following the CaNa2EDTA infusion allowed to monitor dynamically the chelation therapy. The chelation therapy was effective in mobilizing and eliminating the principal heavy metals present from the body. However, since such clearance almost vanished 24 h after the treatment, a protocol with more frequent and low-dose administrations is advisable to improve the metal excretion.

Soybean (Glycine max (L.) Merril) is a popular foodstuff and crop plant, used in human and animal food. In this work, multielement analysis of soybean grains samples in combination with chemometric tools was used to classify the geographical origins. For this purpose, 120 samples from three provinces of Argentina were analyzed for a panel of 20 trace elements by inductively coupled plasma mass spectrometry. First, we used principal component analysis for exploratory analysis. Then, supervised classification techniques such as support vector machine (SMV) discriminant analysis (SVM-DA), random forest, k-nearest neighbors, and class-modeling techniques such as soft independent modeling of class analogy (SIMCA), potential functions, and one-class SVM were applied as tools to establish a model of origin of samples. The performance of the techniques was compared using global indexes. Among all the models tested, SVM and SIMCA showed the highest percentages in terms of prediction ability in cross-validation with average values of 99.3% for SVM-DA and a median value of balanced accuracy of 96.0%, 91.7%, and 88.3% for the three origins using SIMCA. Results suggested that the developed methodology by chemometric techniques is robust and reliable for the geographical classification of soybean samples from Argentina.

Nuclear receptors (NRs) are key regulators of human health and constitute a relevant target for medicinal chemistry applications as well as for toxicological risk assessment. Several open databases dedicated to small molecules that modulate NRs exist; however, depending on their final aim (i.e., adverse effect assessment or drug design), these databases contain a different amount and type of annotated molecules, along with a different distribution of experimental bioactivity values. Stemming from these considerations, in this work we aim to provide a unified dataset, NURA (NUclear Receptor Activity) dataset, collecting curated information on small molecules that modulate NRs, to be intended for both pharmacological and toxicological applications. NURA contains bioactivity annotations for 15,247 molecules and 11 selected NRs, and it was obtained by integrating and curating data from toxicological and pharmacological databases (i.e., Tox21, ChEMBL, NR-DBIND and BindingDB). Our results show that NURA dataset is a useful tool to bridge the gap between toxicology- and medicinal-chemistry-related databases, as it is enriched in terms of number of molecules, structural diversity and covered atomic scaffolds compared to the single sources. To the best of our knowledge, NURA dataset is the most exhaustive collection of small molecules annotated for their modulation of the chosen nuclear receptors. NURA dataset is intended to support decision-making in pharmacology and toxicology, as well as to contribute to data-driven applications, such as machine learning. The dataset and the data curation pipeline can be downloaded free of charge on Zenodo at the following DOI: https://doi.org/10.5281/zenodo.3991561.

Diabetes mellitus type 2 (DMT2) is a severe and complex health problem. It is the most common type of diabetes. DMT2 is a chronic metabolic disorder that affects the way your body metabolizes sugar. With DMT2, your body either resists the effects of insulin or does not produce sufficient insulin to continue normal glucose levels. DMT2 is a disease that requires a multifactorial approach of controlling that includes lifestyle change and pharmacotherapy. Less than ideal management increases the risk of developing complications and comorbidities such as cardiovascular disease and numerous social and economic penalties. That is why the studies dedicated to the pathophysiological mechanisms and the treatment of DMT2 are extremely numerous and diverse. In this study, exploratory data analysis approaches are applied for the treatment of clinical and anthropometric readings of patients with DMT2. Since multivariate statistics is a well-known method for classification, modeling and interpretation of large collections of data, the major aim of the present study was to reveal latent relations between the objects of the investigation (group of patients and control group) and the variables describing the objects (clinical and anthropometric parameters). In the proposed method by the application of hierarchical cluster analysis and principal component analysis it is possible to identify reduced number of parameters which appear to be the most significant discriminant parameters to distinguish between four patterns of patients with DMT2. However, there is still lack of multivariate statistical studies using DMT2 data sets to assess different aspects of the problem like optimal rapid monitoring of the patients or specific separation of patients into patterns of similarity related to their health status which could be of help in preparation of data bases for DMT2 patients. The outcome from the study could be of custom for the selection of significant tests for rapid monitoring of pa

We present an optimization of the toroidal self-organizing map (SOM) algorithm for the accurate visualization of hyperspectral data. This represents a significant advancement on our previous work, in which we demonstrated the use of toroidal SOMs for the visualization of time-of-flight secondary ion mass spectrometry (ToF-SIMS) imaging data. We have previously shown that the toroidal SOM can be used, unsupervised, to produce a multicolor similarity map of the analysis area, in which pixels with similar mass spectra are assigned a similar color. Here, we use an additional algorithm, relational perspective mapping (RPM), to produce more accurate visualizations of hyperspectral data. The SOM output is used as an input for the RPM algorithm, which is a nonlinear dimensionality reduction technique designed to produce a two-dimensional map of high-dimensional data. Using the topological information provided by the SOM, RPM provides complementary distance information. The result is a color scheme that more accurately reflects the local spectral distances between pixels in the data. We exemplify SOM-RPM using ToF-SIMS imaging data from a mouse tumor tissue section. The similarity maps produced are compared with those produced by two leading hyperspectral visualization techniques in the field of mass spectrometry imaging: t-distributed stochastic neighborhood embedding (t-SNE) and uniform manifold approximation and projection (UMAP). We evaluate the performance of each technique both qualitatively and quantitatively, investigating the correlations between distances in the models and distances in the data. SOM-RPM is demonstrably highly competitive with t-SNE and UMAP, according to our evaluations. Furthermore, the use of a neural network offers distinct advantages in data characterization, which we discuss. We also show how spectra extracted from regions of interest identified by SOM-RPM can be further analyzed using linear discriminant analysis for the validation and charac

Multi-criteria decision making processes comprehend several ranking methods able to handle multiple and often conflicting criteria and sorting objects according to a definition of the optimality direction for each criterion. In the field of polypharmacology it can be useful for virtual screening or prioritization purposes to rank molecules distinguishing the most multi-target ones, i.e. molecules able to interact with different targets, from the selective or inactive ones. In this work, the Deep Ranking Analysis by Power Eigenvectors approach is applied to a small set of molecules characterized by their half maximal binding concentrations for seven different nuclear receptors. The existence of a correspondence between the DRAPE ranking and a manually grouping of molecules based on their multi-target behaviour was verified. Moreover, a comparison between DRAPE rankings and those obtained from five traditional methods was carried out in order to highlight the main similarities and the differences of the approaches.

The authentication and traceability of craft beers is an important issue for both beer consumers and producers. Reliable analytical methods able to identify and discriminate products are needed to protect the craft brew market against fraud and counterfeit. Here, 1H NMR analysis of 31 beer samples, differing for beer style and brewing method (craft or industrial) was combined with multivariate statistical analysis, following both an untargeted and a targeted approach. NMR-based analysis of beer samples was sped developing a specific protocol enabling the automatic identification and quantification of metabolites in approximately thirty seconds per spectrum. A clear discrimination was achieved by exploiting 1H NMR analysis and multivariate chemometric methods and the targeted approach identified the metabolites responsible for the segregation. Overall, this study reports an analytical approach addressing beer traceability and is the starting point for the development of a standardized protocol for the discrimination of industrial and craft beers.

Combinatorial approaches to materials discovery offer promising potential for the rapid development of novel polymer systems. Polymer microarrays enable the high-throughput comparison of material physical and chemical properties-such as surface chemistry and properties like cell attachment or protein adsorption-in order to identify correlations that can progress materials development. A challenge for this approach is to accurately discriminate between highly similar polymer chemistries or identify heterogeneities within individual polymer spots. Time-of-flight secondary ion mass spectrometry (ToF-SIMS) offers unique potential in this regard, capable of describing the chemistry associated with the outermost layer of a sample with high spatial resolution and chemical sensitivity. However, this comes at the cost of generating large scale, complex hyperspectral imaging data sets. We have demonstrated previously that machine learning is a powerful tool for interpreting ToF-SIMS images, describing a method for color-tagging the output of a self-organizing map (SOM). This reduces the entire hyperspectral data set to a single reconstructed color similarity map, in which the spectral similarity between pixels is represented by color similarity in the map. Here, we apply the same methodology to a ToF-SIMS image of a printed polymer microarray for the first time. We report complete, single-pixel molecular discrimination of the 70 unique homopolymer spots on the array while also identifying intraspot heterogeneities thought to be related to intermixing of the polymer and the pHEMA coating. In this way, we show that the SOM can identify layers of similarity and clusters in the data, both with respect to polymer backbone structures and their individual side groups. Finally, we relate the output of the SOM analysis with fluorescence data from polymer-protein adsorption studies, highlighting how polymer performance can be visualized within the context of the global topology of the

Due to the increasing presence of plastic and plastic associated contaminants in the aquatic environments, the monitoring of this contamination in fish products and the understanding of possible human health implications is considered urgent. However, data are still relatively scarce, mostly due to the methodological challenges in the chemical analysis: these contaminants are ubiquitous and procedural contamination from the laboratory is frequent. In this work, we compared solid-phase microextraction (SPME) to ultrasonic assisted solvent extraction (UASE) as sample preparation methods for the liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) determination of phthalates in fish fillets. UASE was carried out with an acetone-hexane (1:1) solution and according to a reference procedure aimed to obtain the exhaustive extraction of the target analytes. SPME was carried out by applying C18 fibers in direct immersion mode and by using water/methanol 20:80 mixture to desorb the aliquot required for the analysis. Overall, SPME displayed an improved control of the background contamination and enabled lower LOQs. Precision, calculated as relative standard deviation (RSD) on replicates of a reference sample, was below 24% for both the method. Analysis of real samples purchased from Italian supermarkets showed that SPME might be an efficient tool for estimating the risk associated with fish consumption.

Chianti is a precious red wine and enjoys a high reputation for its high quality in the world wine market. Despite this, the production region is small and product needs efficient tools to protect its brands and prevent adulterations. In this sense, ICP-MS combined with chemometrics has demonstrated its usefulness in food authentication. In this study, Chianti/Chianti Classico, authentic wines from vineyard of Toscana region (Italy), together samples from 18 different geographical regions, were analyzed with the objective of differentiate them from other Italian wines. Partial Least Squares-Discriminant Analysis (PLS-DA) identified variables to discriminate wine geographical origin. Rare Earth Elements (REE), major and trace elements all contributed to the discrimination of Chianti samples. General model was not suited to distinguish PDO red wines from samples, with similar chemical fingerprints, collected in some regions. Specific classification models enhanced the capability of discrimination, emphasizing the discriminant role of some elements.

The thienopyrimidine TP053 is an antitubercular prodrug active against both replicating and nonreplicating Mycobacterium tuberculosis (M. tuberculosis) cells, which requires activation by the mycothiol-dependent nitroreductase Mrx2. The investigation of the mechanism of action of TP053 revealed that Mrx2 releases nitric oxide from this drug both in the enzyme assays with purified Mrx2 and in mycobacterial cultures, which can explain its activity against nonreplicating bacilli, similar to pretomanid activated by the nitroreductase Ddn. In addition, we identified a highly reactive metabolite, 2-(4-mercapto-6-(methylamino)-2-phenylpyrimidin-5-yl)ethan-1-ol, which can contribute to the antimycobacterial effects on replicating cells as well as on nonreplicating cells. In summary, we explain the mechanism of action of TP053 on both replicating and nonreplicating M. tuberculosis and report a novel activity for Mrx2, which in addition to Ddn, represents another example of nitroreductase releasing nitric oxide from its substrate. These findings are particularly relevant in the context of drugs targeting nonreplicating M. tuberculosis, which is shown to be killed by increased levels of nitric oxide.