Portable NIR spectroscopy: the route to green analytical chemistry.

There is a growing interest for cost-effective and nondestructive analytical techniques in both research and application fields. The growing approach by near-infrared spectroscopy (NIRs) pushes to develop handheld devices devoted to be easily applied for in situ determinations. Consequently, portable NIR spectrometers actually result definitively recognized as powerful instruments, able to perform nondestructive, online, or in situ analyses, and useful tools characterized by increasingly smaller size, lower cost, higher robustness, easy-to-use by operator, portable and with ergonomic profile. Chemometrics play a fundamental role to obtain useful and meaningful results from NIR spectra. In this review, portable NIRs applications, published in the period 2019-2022, have been selected to indicate starting references. These publications have been chosen among the many examples of the most recent applications to demonstrate the potential of this analytical approach which, not having the need for extraction processes or any other pre-treatment of the sample under examination, can be considered the "true green analytical chemistry" which allows the analysis where the sample to be characterized is located. In the case of industrial processes or plant or animal samples, it is even possible to follow the variation or evolution of fundamental parameters over time. Publications of specific applications in this field continuously appear in the literature, often in unfamiliar journal or in dedicated special issues. This review aims to give starting references, sometimes not easy to be found.

Classification-based QSARs for predicting dietary biomagnification in fish.

The assessment of bioaccumulation is an important step to describe the environmental behaviour and the potential risk due to exposure to potentially hazardous chemicals. In the last two decades, several in silico tools have been made available to predict bioconcentration, which is commonly used to assess bioaccumulation in risk assessment frameworks all over the world. However, only a few QSAR studies address the prediction of the biomagnification factor (BMF), which describes the accumulation of chemicals into organisms due to exposure through the diet. No classification models are currently available to this end. In this work, we developed classification QSARs to predict classes based on dietary biomagnification, using three different classifiers (i.e. LDA, ANN and RF). We started from a recently published dataset that includes more than 300 curated dietary BMF values measured in fish. The new models have high-quality performances (accuracy in fitting: from 94 to 96%; accuracy in prediction from 84 to 86%). The good performances of the here proposed QSARs confirm the quality of the original input data and highlight the importance of data curation and data sharing to support the development of new in silico approaches to assist risk assessment and chemicals screening.

Topological QSAR Modelling of Carboxamides Repellent Activity to Aedes Aegypti.

Aedes aegypti vector control is of paramount importance owing to the damages induced by the various severe diseases that these insects may transmit, and the increasing risk of important outbreaks of these pathologies. Search for new chemicals efficient against Aedes aegypti, and devoid of side-effects, which have been associated to the currently most used active substance i. e. N,N-diethyl-m-toluamide (DEET), is therefore an important issue. In this context, we developed various Quantitative Structure Activity Relationship (QSAR) models to predict the repellent activity against Aedes aegypti of 43 carboxamides, by using Multiple Linear Regression (MLR) and various machine learning approaches. The easy computation of the four topological descriptors selected in this study, compared to the CODESSA descriptors used in the literature, and the predictive ability of the here proposed MLR and machine learning models developed using the software QSARINS and R, make the here proposed QSARs attractive. As demonstrated in this study, these models can be applied at the screening level, to guide the design of new alternatives to DEET.

QSAR models for predicting the toxicity of piperidine derivatives against Aedes aegypti.

QSAR models are proposed for predicting the toxicity of 33 piperidine derivatives against Aedes aegypti. From 2D topological descriptors, calculated with the PaDEL software, ordinary least squares multilinear regression (OLS-MLR) treatment from the QSARINS software and machine learning and related approaches including linear and radial support vector machine (SVM), projection pursuit regression (PPR), radial basis function neural network (RBFNN), general regression neural network (GRNN) and k-nearest neighbours (k-NN), led to four-variable models. Their robustness and predictive ability were evaluated through both internal and external validation. Determination coefficients (r2) greater than 0.85 on the training sets and 0.8 on the test sets were obtained with OLS-MLR and linear SVM. They slightly outperform PPR, radial SVM and RBFNN, whereas GRNN and k-NN showed lower performance. The easy availability of the involved structural descriptors and the simplicity of the MLR model make the corresponding model attractive at an exploratory level for proposing, from this limited dataset, guidelines in the design of new potentially active molecules.

Investigation of the influence of protein corona composition on gold nanoparticle bioactivity using machine learning approaches.

The understanding of the mechanisms and interactions that occur when nanomaterials enter biological systems is important to improve their future use. The adsorption of proteins from biological fluids in a physiological environment to form a corona on the surface of nanoparticles represents a key step that influences nanoparticle behaviour. In this study, the quantitative description of the composition of the protein corona was used to study the effect on cell association induced by 84 surface-modified gold nanoparticles of different sizes. Quantitative relationships between the protein corona and the activity of the gold nanoparticles were modelled by using several machine learning-based linear and non-linear approaches. Models based on a selection of only six serum proteins had robust and predictive results. The Projection Pursuit Regression method had the best performances (r(2) = 0.91; Q(2)loo = 0.81; r(2)ext = 0.79). The present study confirmed the utility of protein corona composition to predict the bioactivity of gold nanoparticles and identified the main proteins that act as promoters or inhibitors of cell association. In addition, the comparison of several techniques showed which strategies offer the best results in prediction and could be used to support new toxicological studies on gold-based nanomaterials.

Mesenchymal stromal cell-derived extracellular vesicles rescue radiation damage to murine marrow hematopoietic cells.

Mesenchymal stromal cells (MSCs) have been shown to reverse radiation damage to marrow stem cells. We have evaluated the capacity of MSC-derived extracellular vesicles (MSC-EVs) to mitigate radiation injury to marrow stem cells at 4 h to 7 days after irradiation. Significant restoration of marrow stem cell engraftment at 4, 24 and 168 h post irradiation by exposure to MSC-EVs was observed at 3 weeks to 9 months after transplant and further confirmed by secondary engraftment. Intravenous injection of MSC-EVs to 500cGy exposed mice led to partial recovery of peripheral blood counts and restoration of the engraftment of marrow. The murine hematopoietic cell line, FDC-P1 exposed to 500cGy, showed reversal of growth inhibition, DNA damage and apoptosis on exposure to murine or human MSC-EVs. Both murine and human MSC-EVs reverse radiation damage to murine marrow cells and stimulate normal murine marrow stem cell/progenitors to proliferate. A preparation with both exosomes and microvesicles was found to be superior to either microvesicles or exosomes alone. Biologic activity was seen in freshly isolated vesicles and in vesicles stored for up to 6 months in 10% dimethyl sulfoxide at -80 °C. These studies indicate that MSC-EVs can reverse radiation damage to bone marrow stem cells.

Linear and non-linear modelling of the cytotoxicity of TiO2 and ZnO nanoparticles by empirical descriptors.

Titanium oxide (TiO2) and zinc oxide (ZnO) nanoparticles are among the most widely used in different applications in daily life. In this study, local regression and classification models were developed for a set of ZnO and TiO2 nanoparticles tested at different concentrations for their ability to disrupt the lipid membrane in cells. Different regression techniques were applied and compared by checking the robustness of the models and their external predictive ability. Additionally, a simple classification model was developed, which predicts the potential for disruption of the studied nanoparticles with good accuracy (overall accuracy, specificity, and sensitivity >80%) on the basis of two empirical descriptors. The present study demonstrates that empirical descriptors, such as experimentally determined size and tested concentrations, are relevant to modelling the activity of nanoparticles. This information may be useful to screen the potential for harmful effect of nanoparticles in different experimental conditions and to optimize the design of toxicological tests. Results from the present study are useful to support and refine the future application of in silico tools to nanoparticles, for research and regulatory purposes.

Pneumatization of the temporal portion of the zygomatic arch: The contribution of computed tomography to the reconstruction in volumetric two-dimensional and three-dimensional, with the aid of image rendering protocols.

Pneumatization refers to the asymptomatic development of cavities containing air within them. There is great variability in the extent of temporal bone pneumatization. Nevertheless, in a few cases it extends to the zygomatic process. Images are presented in which the panoramic radiograph and hypocycloidal tomography reveal this variation from the norm, to which professionals must be alert, since the images may simulate the presence of pathology. In this case report we describe the presence of pneumatization of the petrous and zygomatic portions of the temporal bone, demonstrating the contribution of CT to reconstruction in volumetric 2D and 3D, with the aid of image rendering protocols.

Semivolatile PAH and n-alkane gas/particle partitioning using the dual model: up-to-date coefficients and comparison with experimental data.

The gas/particle partitioning coefficient K p, of a semivolatile compound is a key parameter for its atmospheric fate. The most complete method of predicting K p for polycyclic aromatic hydrocarbons (PAHs) is offered by the dual model, as it describes both the adsorption on soot and absorption into organic matter processes. However, experimental and model data exist almost exclusively for PAHs. In order to bridge this gap, experimental data on the phase partitioning of both PAHs and n-alkanes were collected at an urban and a remote site. Moreover, all the necessary parameters (e.g., octanol-air and soot-air partitioning coefficients) for the dual model have been collected and updated or (if missing) estimated for the first time. The results point out that both absorption and adsorption seem to contribute to the partitioning of PAHs and n-alkanes. However, it seems that the dual model always underestimates the particle sorption not only for PAHs but also for n-alkanes.

The murine long-term multi-lineage renewal marrow stem cell is a cycling cell.

Prevailing wisdom holds that hematopoietic stem cells (HSCs) are predominantly quiescent. Although HSC cycle status has long been the subject of scrutiny, virtually all marrow stem cell research has been based on studies of highly purified HSCs. Here we explored the cell cycle status of marrow stem cells in un-separated whole bone marrow (WBM). We show that a large number of long-term multi-lineage engraftable stem cells within WBM are in S/G2/M phase. Using bromodeoxyuridine, we show rapid transit through the cell cycle of a previously defined relatively dormant purified stem cell, the long-term HSC (LT-HSC; Lineage(-)/c-kit(+)/Sca-1(+)/Flk-2(-)). Actively cycling marrow stem cells have continually changing phenotype with cell cycle transit, likely rendering them difficult to purify to homogeneity. Indeed, as WBM contains actively cycling stem cells, and highly purified stem cells engraft predominantly while quiescent, it follows that the population of cycling marrow stem cells within WBM are lost during purification. Our studies indicate that both the discarded lineage-positive and lineage-negative marrow cells in a stem cell separation contain cycling stem cells. We propose that future work should encompass this larger population of cycling stem cells that is poorly represented in current studies solely focused on purified stem cell populations.

QSAR prediction of the competitive interaction of emerging halogenated pollutants with human transthyretin.

The determination of the potential endocrine disruption (ED) activity of chemicals such as poly/perfluorinated compounds (PFCs) and brominated flame retardants (BFRs) is still hindered by a limited availability of experimental data. Quantitative structure-activity relationship (QSAR) strategies can be applied to fill this data gap, help in the characterization of the ED potential, and screen PFCs and BFRs with a hazardous toxicological profile. This paper proposes the modelling of T4-TTR (thyroxin-transthyretin) competing potency and relative binding potency toward T4 (logT4-REP) of PFCs and BFRs by regression and classification QSAR models. This study is a follow up of a former work, which analysed separately the interaction of BFRs and PFCs with the carrier TTR. The new results demonstrate the possibility of developing robust and predictive QSARs, which include both BFRs and PFCs in the training set, obtaining larger applicability domains than the existing models developed separately for BFRs and PFCs. The selection of modelling molecular descriptors confirms the importance of structural features, such as the aromatic OH or the molecular length, to increase the binding of the studied chemicals to TTR. Additionally, the need of experimental tests for some chemicals, and in particular for some of the BFRs, is highlighted.

QSAR classification models for the screening of the endocrine-disrupting activity of perfluorinated compounds.

Perfluorinated compounds (PFCs) are a class of emerging pollutants still widely used in different materials as non-adhesives, waterproof fabrics, fire-fighting foams, etc. Their toxic effects include potential for endocrine-disrupting activity, but the amount of experimental data available for these pollutants is limited. The use of predictive strategies such as quantitative structure-activity relationships (QSARs) is recommended under the REACH regulation, to fill data gaps and to screen and prioritize chemicals for further experimentation, with a consequent reduction of costs and number of tested animals. In this study, local classification models for PFCs were developed to predict their T4-TTR (thyroxin-transthyretin) competing potency. The best models were selected by maximizing the sensitivity and external predictive ability. These models, characterized by robustness, good predictive power and a defined applicability domain, were applied to predict the activity of 33 other PFCs of environmental concern. Finally, classification models recently published by our research group for T4-TTR binding of brominated flame retardants and for estrogenic and anti-androgenic activity were applied to the studied perfluorinated chemicals to compare results and to further evaluate the potential for these PFCs to cause endocrine disruption.

Quantitative structure-activity relationship modelling of oral acute toxicity and cytotoxic activity of fragrance materials in rodents.

Fragrance materials are used as ingredients in many consumer and personal care products. The wide and daily use of these substances, as well as their mainly uncontrolled discharge through domestic sewage, make fragrance materials both potential indoor and outdoor air pollutants which are also connected to possible toxic effects on humans (asthma, allergies, headaches). Unfortunately, little is known about the environmental fate and toxicity of these substances. However, the use of alternative, predictive approaches, such as quantitative structure-activity relationships (QSARs), can help in filling the data gap and in the characterization of the environmental and toxicological profile of these substances. In the proposed study, ordinary least squares regression-based QSAR models were developed for three toxicological endpoints: mouse oral LD(50), inhibition of NADH-oxidase (EC(50) NADH-Ox) and the effect on mitochondrial membrane potential (EC(50) DeltaPsim). Theoretical molecular descriptors were calculated by using DRAGON software, and the best QSAR models were developed according to the principles defined by the Organization for Economic Co-operation and Development.

Externally validated QSPR modelling of VOC tropospheric oxidation by NO3 radicals.

The troposphere is the principal recipient of volatile organic chemicals (VOCs) of both anthropogenic and biogenic origin. The persistence of these compounds in the troposphere is an important factor for the evaluation of their fate, and the possible negative effects to the environment and human health. In this study, the tropospheric lifetime of 166 VOCs, in terms of night-time degradation rates with nitrate radical (NO(3)), was modelled by the quantitative structure-property relationships (QSPR) approach. The multiple linear regression method was applied, in combination with the genetic algorithm-variable subset selection procedure, to a variety of theoretical molecular descriptors, calculated by the DRAGON software. The models were developed according to the OECD principles for regulatory acceptance of QSARs, with particular attention to external validation and applicability domain (AD). The external validation was performed on an unbiased external test set or by splitting the available data using self-organized maps or the random by response approach. The best QSPR models presented in this study showed good internal (range of Q(loo)(2): 89-92%) as well as external predictivity (range of Q(ext)(2): 75-89%). The AD of the models was analysed by the leverage approach, and was represented graphically in the Williams graph.

Prediction of PAH mutagenicity in human cells by QSAR classification.

Polycyclic aromatic hydrocarbons (PAHs) are ubiquitous pollutants of high environmental concern. The experimental data of a mutagenicity test on human B-lymphoblastoid cells (alternative to the Ames bacterial test) for a set of 70 oxo-, nitro- and unsubstituted PAHs, detected in particulate matter (PM), were modelled by Quantitative Structure-Activity Relationships (QSAR) classification methods (k-NN, k-Nearest Neighbour, and CART, Classification and Regression Tree) based on different theoretical molecular descriptors selected by Genetic Algorithms. The best models were validated for predictivity both externally and internally. For external validation, Self Organizing Maps (SOM) were applied to split the original data set. The best models, developed on the training set alone, show good predictive performance also on the prediction set chemicals (sensitivity 69.2-87.1%, specificity 62.5-87.5%). The classification of PAHs according to their mutagenicity, based only on a few theoretical molecular descriptors, allows a preliminary assessment of the human health risk, and the prioritisation of these compounds.

Approaches for externally validated QSAR modelling of Nitrated Polycyclic Aromatic Hydrocarbon mutagenicity.

Nitrated Polycyclic Aromatic Hydrocarbons (nitro-PAHs), ubiquitous environmental pollutants, are recognized mutagens and carcinogens. A set of mutagenicity data (TA100) for 48 nitro-PAHs was modeled by the Quantitative Structure-Activity Relationships (QSAR) regression method, and OECD principles for QSAR model validation were applied. The proposed Multiple Linear Regression (MLR) models are based on two topological molecular descriptors. The models were validated for predictivity by both internal and external validation. For the external validation, three different splitting approaches, D-optimal Experimental Design, Self Organizing Maps (SOM) and Random Selection by activity sampling, were applied to the original data set in order to compare these methodologies and to select the best descriptors able to model each prediction set chemicals independently of the splitting method applied. The applicability domain was verified by the leverage approach.

Neutrophils invade lumbar dorsal root ganglia after chronic constriction injury of the sciatic nerve.

To test whether neutrophils (PMN) target lumbar dorsal root ganglia (DRG) following axonal injury leading to neuropathic pain, we visualized PMN infiltration in DRG tissue sections and estimated PMN count by flow cytometry following sciatic chronic constriction injury (CCI). Seven days after CCI, results show PMN within DRG where their count increased by three fold ipsilateral to injury compared to contralateral or sham, concomitant with peak neuropathic pain behavior. Superoxide burst in PMN isolated from rats d7 after CCI was elevated by 170% +/-18 compared to naïve and MCP-1 mRNA expression in DRG increased by 8.9+/-2.9 fold, but that of MIP-2, CINC-1, and RANTES did not change. We conclude that CCI causes PMN invasion of the DRG whereby the functional implication of their close proximity to neuronal axon and soma remains unknown.