Multivariate statistical analysis for selecting optimal descriptors in the toxicity modeling of nanomaterials

Predicting the toxicity of nanoparticles using artificial intelligence tools: a systematic review

Nanoparticles have been used extensively in different scientific fields. Due to the possible destructive effects of nanoparticles on the environment or the biological systems, their toxicity evaluation is a crucial phase for studying nanomaterial safety. In the meantime, experimental approaches for toxicity assessment of various nanoparticles are expensive and time-consuming. Thus, an alternative technique, such as artificial intelligence (AI), could be valuable for predicting nanoparticle toxicity. Therefore, in this review, the AI tools were investigated for the toxicity assessment of nanomaterials. To this end, a systematic search was performed on PubMed, Web of Science, and Scopus databases. Articles were included or excluded based on pre-defined inclusion and exclusion criteria, and duplicate studies were excluded. Finally, twenty-six studies were included. The majority of the studies were conducted on metal oxide and metallic nanoparticles. In addition, Random Forest (RF) and Support Vector Machine (SVM) had the most frequency in the included studies. Most of the models demonstrated acceptable performance. Overall, AI could provide a robust, fast, and low-cost tool for the evaluation of nanoparticle toxicity.

A hybrid machine learning approach of fuzzy-rough-k-nearest neighbor, latent semantic analysis, and ranker search for efficient disease diagnosis

Machine learning approaches have a valuable contribution in improving competency in automated decision systems. Several machine learning approaches have been developed in the past studies in individual disease diagnosis prediction. The present study aims to develop a hybrid machine learning approach for diagnosis predictions of multiple diseases based on the combination of efficient feature generation, selection, and classification methods. Specifically, the combination of latent semantic analysis, ranker search, and fuzzy-rough-k-nearest neighbor has been proposed and validated in the diagnosis prediction of the primary tumor, post-operative, breast cancer, lymphography, audiology, fertility, immunotherapy, and COVID-19, etc. The performance of the proposed approach is compared with single and other hybrid machine learning approaches in terms of accuracy, analysis time, precision, recall, F-measure, the area under ROC, and the Kappa coefficient. The proposed hybrid approach performs better than single and other hybrid approaches in the diagnosis prediction of each of the selected diseases. Precisely, the suggested approach achieved the maximum recognition accuracy of 99.12%of the primary tumor, 96.45%of breast cancer Wisconsin, 94.44%of cryotherapy, 93.81%of audiology, and significant improvement in the classification accuracy and other evaluation metrics in the recognition of the rest of the selected diseases. Besides, it handles the missing values in the dataset effectively.

NanoTox: Development of a Parsimonious In Silico Model for Toxicity Assessment of Metal-Oxide Nanoparticles Using Physicochemical Features

Metal-oxide nanoparticles find widespread applications in mundane life today, and cost-effective evaluation of their cytotoxicity and ecotoxicity is essential for sustainable progress. Machine learning models use existing experimental data and learn quantitative feature-toxicity relationships to yield predictive models. In this work, we adopted a principled approach to this problem by formulating a novel feature space based on intrinsic and extrinsic physicochemical properties, including periodic table properties but exclusive of in vitro characteristics such as cell line, cell type, and assay method. An optimal hypothesis space was developed by applying variance inflation analysis to the correlation structure of the features. Consequent to a stratified train-test split, the training dataset was balanced for the toxic outcomes and a mapping was then achieved from the normalized feature space to the toxicity class using various hyperparameter-tuned machine learning models, namely, logistic regression, random forest, support vector machines, and neural networks. Evaluation on an unseen test set yielded >96% balanced accuracy for the random forest, and neural network with one-hidden-layer models. The obtained cytotoxicity models are parsimonious, with intelligible inputs, and an embedded applicability check. Interpretability investigations of the models identified the key predictor variables of metal-oxide nanoparticle cytotoxicity. Our models could be applied on new, untested oxides, using a majority-voting ensemble classifier, NanoTox, that incorporates the best of the above models. NanoTox is the first open-source nanotoxicology pipeline, freely available under the GNU General Public License (https://github.com/NanoTox).

International landscape of limits and recommendations for occupational exposure to engineered nanomaterials

The increasing concern of possible adverse effects on human health derived from occupational engineered nanomaterials (ENMs) exposure is an issue addressed by entities related to provide guidelines and/or protocols for ENMs regulation. Here we analysed 17 entities from America, Europe and Asia, and some of these entities provide limits of exposure extrapolated from the non-nanosized counterparts of ENMs. The international landscape shows that recommendations are mostly made for metal oxide based ENMs and tonnage is one of the main criteria for ENMs registration, however, sub-nanometric ENMs are emerging and perhaps a novel category of ENMs will appear soon. We identify that besides the lack of epidemiological evidence of ENMs toxicity in humans and difficulties in analysing the toxicological data derived from experimental models, the lack of information on airborne concentrations of ENMs in occupational settings is an important limitation to improve the experimental designs. The development of regulations related to ENMs exposure would lead to provide safer work places for ENMs production without delaying the nanotechnology progress but will also help to protect the environment by taking opportune and correct measures for nanowaste, considering that this could be a great environmental problem in the coming future.

Practices and Trends of Machine Learning Application in Nanotoxicology

Machine Learning (ML) techniques have been applied in the field of nanotoxicology with very encouraging results. Adverse effects of nanoforms are affected by multiple features described by theoretical descriptors, nano-specific measured properties, and experimental conditions. ML has been proven very helpful in this field in order to gain an insight into features effecting toxicity, predicting possible adverse effects as part of proactive risk analysis, and informing safe design. At this juncture, it is important to document and categorize the work that has been carried out. This study investigates and bookmarks ML methodologies used to predict nano (eco)-toxicological outcomes in nanotoxicology during the last decade. It provides a review of the sequenced steps involved in implementing an ML model, from data pre-processing, to model implementation, model validation, and applicability domain. The review gathers and presents the step-wise information on techniques and procedures of existing models that can be used readily to assemble new nanotoxicological in silico studies and accelerates the regulation of in silico tools in nanotoxicology. ML applications in nanotoxicology comprise an active and diverse collection of ongoing efforts, although it is still in their early steps toward a scientific accord, subsequent guidelines, and regulation adoption. This study is an important bookend to a decade of ML applications to nanotoxicology and serves as a useful guide to further in silico applications.

Recent Technological Advances in the Mass Spectrometry-based Nanomedicine Studies: An Insight from Nanoproteomics

Nanoscience becomes one of the most cutting-edge research directions in recent years since it is gradually matured from basic to applied science. Nanoparticles (NPs) and nanomaterials (NMs) play important roles in various aspects of biomedicine science, and their influences on the environment have caused a whole range of uncertainties which require extensive attention. Due to the quantitative and dynamic information provided for human proteome, mass spectrometry (MS)-based quantitative proteomic technique has been a powerful tool for nanomedicine study. In this article, recent trends of progress and development in the nanomedicine of proteomics were discussed from quantification techniques and publicly available resources or tools. First, a variety of popular protein quantification techniques including labeling and label-free strategies applied to nanomedicine studies are overviewed and systematically discussed. Then, numerous protein profiling tools for data processing and postbiological statistical analysis and publicly available data repositories for providing enrichment MS raw data information sources are also discussed.

Facile synthesis of carbon-coated layered double hydroxide and its comparative characterisation with Zn-Al LDH: application on crystal violet and malachite green dye adsorption-isotherm, kinetics and Box-Behnken design

The adsorption of crystal violet (CV) and malachite green (MG) dyes using carbon-coated Zn-Al-layered double hydroxide (C-Zn-Al LDH) was investigated in this work. The characterisation of both Zn-Al LDH and C-Zn-Al LDH was performed using XRD, SEM, TEM, EDX, XPS, FTIR, BET and TGA. The results indicated that carbon particles were effectively coated on Zn-Al LDH surface. The average total pore volume and pore diameter of C-Zn-Al LDH were observed as 0.007 cc/g and 3.115 nm. The impact of parameters like initial dye concentration, pH and adsorbent dosage on the dye removal efficiency was confirmed by carrying out Box-Behnken design experiments. Langmuir isotherm was well suited for both CV and MG adsorption among other isotherm models. The adsorption capacity was maximally obtained as 129.87 and 126.58 mg/g for CV and MG respectively. Pseudo-second order fits the adsorption kinetics than any other kinetic models for both the dyes. The thermodynamic study indicates that the adsorption process of CV was exothermic, whereas for MG was endothermic. Electrostatic attraction, H-bonding, n-π and π- π interactions were mainly influenced in the adsorption process. This study concludes that C-Zn-Al LDH is an efficient adsorbent for the CV and MG dye removal from aqueous solutions. Graphical abstract ᅟ Graphical abstract contains text below the minimum required font size of 6pts inside the artwork, and there is no sufficient space available for the text to be enlarged. Please provide replacement figure file.Graphical abstract contains text is rewritten with the maximum required font size inside the artwork and provided sufficient space between the text which is enlarged.The new Graphical abstract is attached as an image in the attachment file for your further usage.