Background: Iron oxide nanoparticles (IONPs) have increasing applications in biomedicine, however fears over long term stability of polymer coated particles have arisen. Gold coating IONPs results in particles of increased stability and robustness. The unique properties of both the iron oxide (magnetic) and gold (surface plasmon resonance) result in a multimodal platform for use as MRI contrast agents and as a nano-heater. Results: Here we synthesize IONPs of core diameter 30 nm and gold coat using the seeding method with a poly(ethylenimine) intermediate layer. The final particles were coated in poly(ethylene glycol) to ensure biocompatibility and increase retention times in vivo. The particle coating was monitored using FTIR, PCS, UV–vis absorption, TEM, and EDX. The particles appeared to have little cytotoxic effect when incubated with A375M cells. The resultant hybrid nanoparticles (HNPs) possessed a maximal absorbance at 600 nm. After laser irradiation in agar phantom a ΔT of 32°C was achieved after only 90 s exposure (50 μgmL^-1). The HNPs appeared to decrease T₂ values in line with previously clinically used MRI contrast agent Feridex®. Conclusions: The data highlights the potential of these HNPs as dual function MRI contrast agents and nano-heaters for therapies such as cellular hyperthermia or thermo-responsive drug delivery.

Heartbeat classification is a crucial step for arrhythmia diagnosis during electrocardiographic (ECG) analysis. The new scenario of wireless body sensor network- (WBSN-) enabled ECG monitoring puts forward a higher-level demand for this traditional ECG analysis task. Previously reported methods mainly addressed this requirement with the applications of a shallow structured classifier and expert-designed features. In this study, modified frequency slice wavelet transform (MFSWT) was firstly employed to produce the time-frequency image for heartbeat signal. Then the deep learning (DL) method was performed for the heartbeat classification. Here, we proposed a novel model incorporating automatic feature abstraction and a deep neural network (DNN) classifier. Features were automatically abstracted by the stacked denoising auto-encoder (SDA) from the transferred time-frequency image. DNN classifier was constructed by an encoder layer of SDA and a softmax layer. In addition, a deterministic patient-specific heartbeat classifier was achieved by fine-tuning on heartbeat samples, which included a small subset of individual samples. The performance of the proposed model was evaluated on the MIT-BIH arrhythmia database. Results showed that an overall accuracy of 97.5% was achieved using the proposed model, confirming that the proposed DNN model is a powerful tool for heartbeat pattern recognition.

To date, there has not been an agreement on the best methods for the characterisation of multi-walled carbon nanotube (MWCNT) toxicity. The length of MWCNTs has been identified as a factor in in vitro and in vivo studies, in addition to their purity and biocompatible coating. Another unresolved issue relates to the variable toxicity of MWCNTs on different cell types. The present study addressed the effects of MWCNTs' length on mammalian immune and epithelial cancer cells RAW264.7 and MCF-7, respectively. Our data confirm that MWCNTs induce cytotoxicity in a length- and cell type-dependent manner. Whereas, longer (3 to 14 μm) MWCNTs exert high toxicity, especially to RAW264.7 cells, shorter (1.5 μm) MWCNTs are significantly less cytotoxic. These findings confirm that the degree of biocompatibility of MWCNTs is closely related to their length and that immune cells appear to be more susceptible to damage by MWCNTs. Our study also indicates that MWCNT nanotoxicity should be analysed for various components of cellular response, and cytotoxicity data should be validated by the use of more than one assay system. Results from chromogenic-based assays should be confirmed by trypan blue exclusion.

Magnetically labelled cells are used for in vivo cell tracking by MRI, used for the clinical translation of cell-base therapies. Studies involving magnetic labelled cells may include separation of labelled cells, targeted delivery and controlled release of drugs, contrast enhanced MRI and magnetic hyperthermia for the in situ ablation of tumours. Dextran-coated super-paramagnetic iron oxide (SPIO) ferumoxides are used clinically as an MR contrast agents primarily for hepatic imaging. The material is also widely used for in vitro cell labelling, as are other SPIO-based particles. Our results on the uptake by human cancer cell lines of ferumoxides indicate that electroporation in the presence of protamine sulphate (PS) results in rapid high uptake of SPIO nanoparticles (SPIONs) by parenchymal tumour cells without significant impairment of cell viability. Quantitative determination of cellular iron uptake performed by colorimetric assay is in agreement with data from the literature. These results on intracellular iron content together with the intracellular distribution of SPIONs by magnetic force microscopy (MFM) following in vitro uptake by parenchymal tumour cells confirm the potential of this technique for clinical tumour cell detection and destruction.

Purpose: Minimally invasive treatment of solid cancers, especially in the breast and liver, remains clinically challenging, despite a variety of treatment modalities, including radiofrequency ablation (RFA), microwave ablation or high-intensity focused ultrasound. Each treatment modality has advantages and disadvantages, but all are limited by placement of a probe or US beam in the target tissue for tumor ablation and monitoring. The placement is difficult when the tumor is surrounded by large blood vessels or organs. Patient-specific image-based 3D modeling for thermal ablation simulation was developed to optimize treatment protocols that improve treatment efficacy. Methods: A tissue-mimicking breast gel phantom was used to develop an image-based 3D computer-aided design (CAD) model for the evaluation of a planned RF ablation. First, the tissue-mimicking gel was cast in a breast mold to create a 3D breast phantom, which contained a simulated solid tumor. Second, the phantom was imaged in a medical MRI scanner using a standard breast imaging MR sequence. Third, the MR images were converted into a 3D CAD model using commercial software (ScanIP, Simpleware), which was input into another commercial package (COMSOL Multiphysics) for RFA simulation and treatment planning using a finite element method (FEM). For validation of the model, the breast phantom was experimentally ablated using a commercial (RITA) RFA electrode and a bipolar needle with an electrosurgical generator (DRE ASG-300). The RFA results obtained by pre-treatment simulation were compared with actual experimental ablation. Results: A 3D CAD model, created from MR images of the complex breast phantom, was successfully integrated with an RFA electrode to perform FEM ablation simulation. The ablation volumes achieved both in the FEM simulation and the experimental test were equivalent, indicating that patient-specific models can be implemented for pre-treatment planning of solid tumor ablation. Conclusion: A tissue-mimicking breast gel phantom and its MR images were used to perform FEM 3D modeling and validation by experimental thermal ablation of the tumor. Similar patient-specific models can be created from preoperative images and used to perform finite element analysis to plan radiofrequency ablation. Clinically, the method can be implemented for pre-treatment planning to predict the effect of an individual’s tissue environment on the ablation process, and this may improve the therapeutic efficacy.