Smart Magnetotactic Bacteria Enable the Inhibition of Neuroblastoma under an Alternating Magnetic Field.

Magnetotactic bacteria are ubiquitous microorganisms in nature that synthesize intracellular magnetic nanoparticles called magnetosomes in a gene-controlled way and arrange them in chains. From in vitro to in vivo, we demonstrate that the intact body of Magnetospirillum magneticum AMB-1 has potential as a natural magnetic hyperthermia material for cancer therapy. Compared to chains of magnetosomes and individual magnetosomes, the entire AMB-1 cell exhibits superior heating capability under an alternating magnetic field. When incubating with tumor cells, the intact AMB-1 cells disperse better than the other two types of magnetosomes, decreasing cellular viability under the control of an alternating magnetic field. Furthermore, in vivo experiments in nude mice with neuroblastoma found that intact AMB-1 cells had the best antitumor activity with magnetic hyperthermia therapy compared to other treatment groups. These findings suggest that the intact body of magnetotactic bacteria has enormous promise as a natural material for tumor magnetic hyperthermia. In biomedical applications, intact and living magnetotactic bacteria play an increasingly essential function as a targeting robot due to their magnetotaxis.

Supervised Machine Learning Algorithms for Bioelectromagnetics: Prediction Models and Feature Selection Techniques Using Data from Weak Radiofrequency Radiation Effect on Human and Animals Cells.

The emergence of new technologies to incorporate and analyze data with high-performance computing has expanded our capability to accurately predict any incident. Supervised Machine learning (ML) can be utilized for a fast and consistent prediction, and to obtain the underlying pattern of the data better. We develop a prediction strategy, for the first time, using supervised ML to observe the possible impact of weak radiofrequency electromagnetic field (RF-EMF) on human and animal cells without performing in-vitro laboratory experiments. We extracted laboratory experimental data from 300 peer-reviewed scientific publications (1990-2015) describing 1127 experimental case studies of human and animal cells response to RF-EMF. We used domain knowledge, Principal Component Analysis (PCA), and the Chi-squared feature selection techniques to select six optimal features for computation and cost-efficiency. We then develop grouping or clustering strategies to allocate these selected features into five different laboratory experiment scenarios. The dataset has been tested with ten different classifiers, and the outputs are estimated using the k-fold cross-validation method. The assessment of a classifier's prediction performance is critical for assessing its suitability. Hence, a detailed comparison of the percentage of the model accuracy (PCC), Root Mean Squared Error (RMSE), precision, sensitivity (recall), 1 - specificity, Area under the ROC Curve (AUC), and precision-recall (PRC Area) for each classification method were observed. Our findings suggest that the Random Forest algorithm exceeds in all groups in terms of all performance measures and shows AUC = 0.903 where k-fold = 60. A robust correlation was observed in the specific absorption rate (SAR) with frequency and cumulative effect or exposure time with SAR×time (impact of accumulated SAR within the exposure time) of RF-EMF. In contrast, the relationship between frequency and exposure time was not significant. In future, with more experimental data, the sample size can be increased, leading to more accurate work.

Lessons learned from the application of machine learning to studies on plant response to radio-frequency.

This paper applies Machine Learning (ML) algorithms to peer-reviewed publications in order to discern whether there are consistent biological impacts of exposure to non-thermal low power radio-frequency electromagnetic fields (RF-EMF). Expanding on previous analysis that identified sensitive plant species, we extracted data from 45 articles published between 1996 and 2016 that included 169 experimental case studies of plant response to RF-EMF. Raw-data from these case studies included six different attributes: frequency, specific absorption rate (SAR), power flux density, electric field strength, exposure time and plant type (species). This dataset has been tested with two different classification algorithms: k-Nearest Neighbor (kNN) and Random Forest (RF). The outputs are estimated using k-fold cross-validation method to identify and compare classifier mean accuracy and computation time. We also developed an optimization technique to distinguish the trade-off between prediction accuracy and computation time based on the classification algorithm. Our analysis illustrates kNN (91.17%) and RF (89.41%) perform similarly in terms of mean accuracy, nonetheless, kNN takes less computation time (3.38 s) to train a model compared to RF (248.12 s). Very strong correlations were observed between SAR and frequency, and SAR with power flux density and electric field strength. Despite the low sample size (169 reported experimental case studies), that limits statistical power, nevertheless, this analysis indicates that ML algorithms applied to bioelectromagnetics literature predict impacts of key plant health parameters from specific RF-EMF exposures. This paper addresses both questions of the methodological importance and relative value of different methods of ML and the specific finding of impacts of RF-EMF on specific measures of plant growth and health. Recognizing the importance of standardizing nomenclature for EMF-RF, we conclude that Machine Learning provides innovative and efficient RF-EMF exposure prediction tools, and we propose future applications in occupational and environmental epidemiology and public health.