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Purpose: The object of this literature review was to highlight the value of CBCT as a key resource for the oral surgeon, both in the diagnostic phase and in the therapeutic planning phase, in order to improve the quality and safety of clinical practice. Complementary aspects were also considered such as: basic operation of the machine, radiation dose and comparison with other diagnostic tools when needed. Materials & Methods: A literature research was carried out mainly using online sources, PubMed database, and paper sources (specialized books). 100 articles and 10 authoritative books published over the last 10 years have been firstly examined. Systematic reviews, narrative reviews, mini reviews and meta-analysis were primarily selected. If a subject matter appeared to have few or no such sources, articles based on case reports were also considered. Results: Finally, 62 sources considered eligible for the purpose of the review, 58 scientific articles and 4 specialized books (mainly comprising topics of advanced oral surgery, pathology and oral radiology) were selected. Conclusions: CBCT proves to be a fundamental tool for almost all procedures, whether or not routine, pertaining to the contemporary oral surgeon. It appears to be sufficiently accurate, safe and reliable for the therapeutic planning of all procedures related to impacted elements (extraction or exposure), implant planning (particularly for guided implant surgery) and most of the diagnostic questions (with the exception of some pathologies and/or neoformations that need to be investigated by other specific tools). [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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-mtoluamide (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. [ABSTRACT FROM AUTHOR]

QSAR models are proposed for predicting the toxicity of 33 piperidine derivatives againstAedes 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) andk-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 andk-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. [ABSTRACT FROM PUBLISHER]

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 (r2= 0.91;Q2loo = 0.81;r2ext = 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. [ABSTRACT FROM PUBLISHER]

The production and the properties of lightweight composite panels, with expanded vermiculite as lightweight aggregate and geopolymer as binder, were investigated. Different compositions of the geopolymer binders (metakaolin or alumina-based) and two sizes of expanded vermiculite were tested. The produced composites were subjected tomicrostructural analyses, aswell as to thermal andmechanical tests. Densities ranged between 700 and 900 kg/m³, while the average strength and thermal conductivity were about 2 MPa and 0.2 W/mK, respectively. Results showthat lightweight composites can be produced with satisfactory density andmechanical and thermal properties compared with other materials used in building sector, such as plasterboard or cellular concrete. [ABSTRACT FROM AUTHOR]

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 TiO2nanoparticles 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 ofin silicotools to nanoparticles, for research and regulatory purposes. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM AUTHOR]

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Disposal of wool waste still encounters significant problems due to the need to satisfy environmental legislation and to the increasing cost of landfilling. Conversely, given the high content of useful nutrients it contains, sheep wool waste could still have a great potential for many applications, such as crop plant fertilizer and growth medium. The aim of this work was to highlight the possibility of reusing wool scraps as a constituent material for plant pots, producing innovative wool-geopolymer composite artifacts. Environmental-friendly pots for plant growing have been designed by impregnating chopped or hank wool scraps with a geopolymer resin. Composite pots were successfully realized. Preliminary comparative evaluations conduced on plant crops grown in peat or wool-geopolymer pots demonstrated that the presence of the organic material positively influenced the plant growth ratio. The composite pots were characterized both in term of macro- and micro-structure, before and after the plant growth testing. Wool fibers and geopolymer matrix acted together in providing a controlled release of nutrients to the plants and maintaining a higher and longer lasting level of soil moisture. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 LD50, inhibition of NADH-oxidase (EC50 NADH-Ox) and the effect on mitochondrial membrane potential (EC50 ΔΨm). 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. [ABSTRACT FROM AUTHOR]

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 (NO3), 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 [image omitted]: 89-92%) as well as external predictivity (range of Qext²: 75-89%). The AD of the models was analysed by the leverage approach, and was represented graphically in the Williams graph. [ABSTRACT FROM AUTHOR]