A generalized toxicity classification model for 7 different oxide nanomaterials is presented in this study. A data set extracted from multiple literature sources and screened by physicochemical property based quality scores were used for model development. Moreover, a few more preprocessing techniques, such as synthetic minority over-sampling technique, were applied to address the imbalanced class problem in the data set. Then, classification models using four different algorithms, such as generalized linear model, support vector machine, random forest, and neural network, were developed and their performances were compared to find the best performing preprocessing methods as well as algorithms. The neural network model built using the balanced data set was identified as the model with best predictive performance, while applicability domain was defined using k-nearest neighbours algorithm. The analysis of relative attribute importance for the built neural network model identified dose, formation enthalpy, exposure time, and hydrodynamic size as the four most important attributes. As the presented model can predict the toxicity of the nanomaterials in consideration of various experimental conditions, it has the advantage of having a broader and more general applicability domain than the existing quantitative structure-activity relationship model.

Development of nanotoxicity prediction models is becoming increasingly important in the risk assessment of engineered nanomaterials. However, it has significant obstacles caused by the wide heterogeneities of published literature in terms of data completeness and quality. Here, we performed a meta-analysis of 216 published articles on oxide nanoparticles using 14 attributes of physicochemical, toxicological and quantum-mechanical properties. Particularly, to improve completeness and quality of the extracted dataset, we adapted two preprocessing approaches: data gap-filling and physicochemical property based scoring. Performances of nano-SAR classification models revealed that the dataset with the highest score value resulted in the best predictivity with compromise in its applicability domain. The combination of physicochemical and toxicological attributes was proved to be more relevant to toxicity classification than quantum-mechanical attributes. Overall, by adapting these two preprocessing methods, we demonstrated that meta-analysis of nanotoxicity literatures could provide an effective alternative for the risk assessment of engineered nanomaterials.

Workflow for curation of datasets, assessment of their quality and completeness, and nanoSAR model development.

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Quantitative structure-activity relationship (QSAR) models for nanomaterials (nano-QSAR) were developed to predict the cytotoxicity of 20 different types of multiwalled carbon nanotubes (MWCNTs) to human lung cells by using quasi-SMILES. The optimal descriptors, recorded as quasi-SMILES, were encoded to represent the physicochemical properties and experimental conditions for the MWCNTs from 276 data records collected from previously published studies. The quasi-SMILES used to build the optimal descriptors were (i) diameter, (ii) length, (iii) surface area, (iv) in vitro toxicity assay, (v) cell line, (vi) exposure time, and (vii) dose. The model calculations were performed by using the Monte Carlo method and computed with CORAL software ( www.insilico.eu/coral ). The quasi-SMILES-based nano-QSAR model provided satisfactory statistical results ( R for internal validation data sets: 0.60-0.80; R for external validation data sets: 0.81-0.88). The model showed potential for use in the estimation of human lung cell viability after exposure to MWCNTs with the following properties: diameter, 12-74 nm; length, 0.19-20.25 μm; surface area, 11.3-380.0 m/g; and dose, 0-200 ppm.

Wound healing is a recovering process of damaged tissues by replacing dysfunctional injured cellular structures. Natural compounds for wound treatment have been widely used for centuries. Numerous published works provided reviews of natural compounds for wound healing applications, which separated the approaches based on different categories such as characteristics, bioactivities, and modes of action. However, current studies provide reviews of natural compounds that originated from only plants or animals. In this work, we provide a comprehensive review of natural compounds sourced from both plants and animals that target the different bioactivities of healing to promote wound resolution. The compounds were classified into four main groups (i.e., anti-inflammation, anti-oxidant, anti-bacterial, and collagen promotion), mostly studied in current literature from 1992 to 2022. Those compounds are listed in tables for readers to search for their origin, bioactivity, and targeting phases in wound healing. We also reviewed the trend in using natural compounds for wound healing.

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Background/Aim: Silicone implants are widely used biomaterials in surgeries owing to their physiological inertness and low toxicity. However, capsular contracture is a severe complication caused by the insertion of breast implants, which can endanger the health of patients. In this study, twelve different silicone breast implants were tested to determine which could lead to a lower incidence of capsular contracture. Materials and Methods: For in vivo experiments, these silicone implants were implanted into 60 rats (i.e., five implants per rat). The implants were explanted eight weeks after the operation. Samples were analyzed and measured by using hematoxylin and eosin staining, Masson's trichrome staining, and immunofluorescence staining methods. We compared twelve samples for their differences in the thickness of capsular formation, number of inflammatory cells, collagen expression, fibroblast intensity (i.e., Vimentin and α-SMA), and inflammatory cytokines (i.e., IL-8, CD68, MCP-1, and F4/80). Results: Different surface textures of breast implants gave different effects on capsular thickness, collagen formation, fibroblast formation, and potential inflammation. Samples that had smooth textures such as SEBBIN, HANS, and Mentor showed higher collagen formation than other samples. SEBBIN Texture, Motiva Micro, HANS Smooth I, and HANS Micro exhibited higher fibroblast formation (i.e., α-SMA, Vimentin). SEBBIN Smooth and samples in HANS group displayed lower expression of inflammation cytokines (IL-8, CD68, MCP-1, and F4/80). Conclusion: These findings provide preliminary reports on the surface texture effect and support a selection of breast silicone implants in breast reconstruction to avoid the formation of capsular contracture after implantation.