Classification NanoSAR development for cytotoxicity of metal oxide nanoparticles.

A classification-based cytotoxicity nanostructure-activity relationship (nanoSAR) is presented based on a set of nine metal oxide nanoparticles to which transformed bronchial epithelial cells (BEAS-2B) were exposed over a range of concentrations (0.375-200 mg L(-1) ) and exposure times up to 24 h. The nanoSAR is developed using cytotoxicity data from a high-throughput screening assay that was processed to identify and label toxic (in terms of the propidium iodide uptake of BEAS-2B cells) versus nontoxic events relative to an unexposed control cell population. Starting with a set of fourteen intuitive but fundamental physicochemical nanoSAR input parameters, a number of models were identified which had a classification accuracy above 95%. The best-performing model had a 100% classification accuracy in both internal and external validations. This model is based on three descriptors: atomization energy of the metal oxide, period of the nanoparticle metal, and nanoparticle primary size, in addition to nanoparticle volume fraction (in solution). Notwithstanding the success of the present modeling approach with a relatively small nanoparticle library, it is important to recognize that a significantly larger data set would be needed in order to expand the applicability domain and increase the confidence and reliability of data-driven nanoSARs.

Self-organizing map analysis of toxicity-related cell signaling pathways for metal and metal oxide nanoparticles.

The response of a murine macrophage cell line exposed to a library of seven metal and metal oxide nanoparticles was evaluated via High Throughput Screening (HTS) assay employing luciferase-reporters for ten independent toxicity-related signaling pathways. Similarities of toxicity response among the nanoparticles were identified via Self-Organizing Map (SOM) analysis. This analysis, applied to the HTS data, quantified the significance of the signaling pathway responses (SPRs) of the cell population exposed to nanomaterials relative to a population of untreated cells, using the Strictly Standardized Mean Difference (SSMD). Given the high dimensionality of the data and relatively small data set, the validity of the SOM clusters was established via a consensus clustering technique. Analysis of the SPR signatures revealed two cluster groups corresponding to (i) sublethal pro-inflammatory responses to Al2O3, Au, Ag, SiO2 nanoparticles possibly related to ROS generation, and (ii) lethal genotoxic responses due to exposure to ZnO and Pt nanoparticles at a concentration range of 25-100 µg/mL at 12 h exposure. In addition to identifying and visualizing clusters and quantifying similarity measures, the SOM approach can aid in developing predictive quantitative-structure relations; however, this would require significantly larger data sets generated from combinatorial libraries of engineered nanoparticles.

The application of machine learning algorithms to the analysis of electromyographic patterns from arthritic patients.

The main aim of our study was to investigate the possibility of applying machine learning techniques to the analysis of electromyographic patterns (EMG) collected from arthritic patients during gait. The EMG recordings were collected from the lower limbs of patients with arthritis and compared with those of healthy subjects (CO) with no musculoskeletal disorder. The study involved subjects suffering from two forms of arthritis, viz, rheumatoid arthritis (RA) and hip osteoarthritis (OA). The analysis of the data was plagued by two problems which frequently render the analysis of this type of data extremely difficult. One was the small number of human subjects that could be included in the investigation based on the terms specified in the inclusion and exclusion criteria for the study. The other was the high intra- and inter-subject variability present in EMG data. We identified some of the muscles differently employed by the arthritic patients by using machine learning techniques to classify the two groups and then identified the muscles that were critical for the classification. For the classification we employed least-squares kernel (LSK) algorithms, neural network algorithms like the Kohonen self organizing map, learning vector quantification and the multilayer perceptron. Finally we also tested the more classical technique of linear discriminant analysis (LDA). The performance of the different algorithms was compared. The LSK algorithm showed the highest capacity for classification. Our study demonstrates that the newly developed LSK algorithm is adept for the treatment of biological data. The muscles that were most important for distinguishing the RA from the CO subjects were the soleus and biceps femoris. For separating the OA and CO subjects however, it was the gluteus medialis muscle. Our study demonstrates how classification with EMG data can be used in the clinical setting. While such procedures are unnecessary for the diagnosis of the type of arthritis present, an understanding of the muscles which are responsible for the classification can help to better identify targets for rehabilitative measures.