Glioblastoma multiforme (GBM) is the most common and aggressive malignant brain tumor. Conventional treatments for GBM include surgery, chemotherapy, radiotherapy, or a combination of these. However, emerging therapies, such as hyperthermia treatments, are being developed. One of these new therapies is nanoparticle-mediated photothermal therapy (PTT), a non-invasive treatment that converts light into heat using photoagents such as plasmonic nanoparticles. High molecular weight hyaluronic acid (HA) has been described as a potential inhibitor of tumor progression and exhibits a high affinity for the CD44 receptor, which is present in GBM cells. The in vivo efficacy of gold nanorods (GNRs) biofunctionalized with HA-700kDa in PTT has been evaluated in a murine GBM model. Adult male C57/BL-6 mice (N=15), 3–8-month-old, were used for PTT experiments. CT2A cells were injected into the mouse brain to establish a GBM model. Tumor-bearing mice were randomly divided into three groups: Control (untreated, n=5), GNRs (injected with GNRs, n=5) and PTT-treated (injected with GNRs and treated with laser, n=5). After GNR injection, mice were irradiated with a laser at 0.98 A (250mW) for 25 min over three consecutive days. As observed in the analysis of tumor sizes from all MR images, animals treated with a laser following GNR injection exhibited significantly smaller tumor sizes compared to control and GNR-treated animals one week after the treatment. In addition, PTT treatment led to a notable improvement in the exploratory behavior of the treated animals and an increase in their life expectancy compared to untreated control mice. This study demonstrates the efficacy of GNR-based-PTT, applied to an orthotopic tumor model, using GNRs biofunctionalized with HA to target GBM CT2A cells. The treatment resulted in a reduction in tumor mass and an extension of life expectancy in GNR-PTT treated mice.

Metallic nanorods are promising agents for a wide range of biomedical applications. We report an optical hyperthermia method capable of inducing slowdown tumor progression of an experimental in vivo CT-2A glioblastoma tumor. The tumor model used in this research is based on the transplantation of mouse astrocytoma CT-2A cells in the striatum of mice by intracranial stereotaxic surgery. Two weeks after cell implant, the resulting tumor is treated by irradiating intratumoral injected gold nanorods, biofunctionalized with CD133 antibody (B-GNRs), using a continuous wave laser. Nanoparticles convert the absorbed light into localized heat (reaching up to 44 °C) due to the effect of surface plasmon resonance. A significant slowdown in CT-2A tumor progression is evident, by histology and magnetic resonance imaging, at one ( p = 0.03) and two weeks ( p = 0.008) after irradiation treatment. A notable deceleration in tumor size (15%–75%) as compared to the control untreated groups, it is observed. Thus, laser irradiation of B-GNRs is found to be effective for the treatment of CT-2A tumor progression. Similarities between the pre-clinical CT-2A tumor model and the human astrocytoma disease, in terms of anatomy, metastatic behavior and histopathology, suggest that hyperthermic treatment by laser irradiation of B-GNRs administered into high-grade human astrocytoma might constitute a promising alternative treatment to limit the progression of this deadly disease.

A power electronics circuit allows the generation of high-frequency magnetic field that can be used to increase the temperature of cancer cells previously invaded with the magnetic nanoparticles. The circuit designed for this purpose is a high-frequency phase-shift inverter implemented with SiC devices and natural zero voltage switching. The inductive load has been optimized to increase as much as possible the magnetic field at the center of it considering the physical restrictions. Into this inductor, an adiabatic probe filled with nanoparticles is placed being the main objective to increase its temperature. The control of the inverter has been designed in such a way that it is easy to try waveforms different from the classical sine waves to see its effect on the temperature of the sample. Although the research is in one of the early stages, the first conclusions about the optimal frequency and field have been obtained showing that this technique could be a real option in the future.

Background and Objectives: Breast cancer remains a leading cause of cancer-related morbidity and mortality worldwide, and robust, interpretable artificial intelligence (AI) pipelines are increasingly being explored to support mammography-based detection. This study combines a PRISMA 2020-compliant systematic review with an original experimental validation to characterize current evidence and address identified gaps in reproducibility and interpretability. Materials and Methods: A PRISMA 2020-guided systematic review and an original experimental study were conducted. The review searched PubMed and Scopus/ScienceDirect for studies using convolutional neural networks (CNNs), support vector machines (SVMs) or eXtreme Gradient Boosting (XGBoost) for breast cancer detection in mammography and related imaging modalities, and identified 45 eligible articles. In parallel, we implemented and evaluated representative CNN (ResNet-50, EfficientNetB0 and MobileNetV3-Small) and classical machine learning (SVM and XGBoost) pipelines on the CBIS-DDSM dataset, following a CRISP-DM-inspired workflow and using Grad-CAM and SHAP to provide image- and feature-level explanations within a reproducible machine-learning-operations (MLOps)-oriented framework. Results: The systematic review revealed substantial heterogeneity in datasets, preprocessing pipelines, and validation strategies, with a predominant reliance on internal validation and limited use of explainable AI methods. In our experimental evaluation, ResNet-50 achieved the best performance (AUC-ROC 0.95; sensitivity 89%), followed by XGBoost (AUC-ROC 0.90; sensitivity 74%) and SVM (AUC-ROC 0.84; sensitivity 66%), while EfficientNetB0 and MobileNetV3-Small showed lower discrimination. Grad-CAM produced qualitatively plausible heatmaps centered on annotated lesions, and SHAP analyses indicated that simple global image-intensity and size descriptors dominated the predictions of the classical models. Conclusions: By integrating systematic evidence and large-scale experiments on CBIS-DDSM, this study highlights both the potential and the limitations of current AI pipelines for mammography-based breast cancer detection, underscoring the need for more standardized preprocessing, rigorous external validation, and routine use of explainable AI before clinical deployment.

Glioblastoma, IDH-wildtype (GBM), is the most aggressive and complex brain tumour classified by the World Health Organization (WHO), characterised by high mortality rates and diagnostic limitations inherent to invasive conventional procedures. Early detection is essential for improving patient outcomes, underscoring the need for non-invasive diagnostic tools. This study presents a convolutional neural network (CNN) specifically optimised for GBM detection from T1-weighted magnetic resonance imaging (MRI), with systematic evaluations of layer depth, activation functions, and hyperparameters. The model was trained on the RSNA-MICCAI data set and externally validated on the Erasmus Glioma Database (EGD), which includes gliomas of various grades and preserves cranial structures, unlike the skull-stripped RSNA-MICCAI images. This morphological discrepancy demonstrates the generalisation capacity of the model across anatomical and acquisition differences, achieving an F1-score of 0.88. Furthermore, statistical tests, such as Shapiro–Wilk, Mann–Whitney U, and Chi-square, confirmed the absence of demographic bias in model predictions, based on p-values, confidence intervals, and statistical power analyses supporting its demographic fairness. The proposed model achieved an area under the curve–receiver operating characteristic (AUC-ROC) of 0.63 on the RSNA-MICCAI test set, surpassing all prior results submitted to the BraTS 2021 challenge, and establishing a reliable and generalisable approach for non-invasive GBM detection.

Biomedical applications based on the magnetic properties of superparamagnetic iron oxide nanoparticles (SPIONs) may be altered by the mechanical attachment or cellular uptake of these nanoparticles. When nanoparticles interact with living cells, they are captured and internalized into intracellular compartments. Consequently, the magnetic behavior of the nanoparticles is modified. In this paper, we investigated the change in the magnetic response of 14 nm magnetic nanoparticles (Fe ₃ O ₄ ) in different solutions, both as a stable liquid suspension (one of them mimicking the cellular cytoplasm) and when associated with cells. The field-dependent magnetization curves from inert fluids and cell cultures were determined by using an alternating gradient magnetometer, MicroMagTM 2900. The equipment was adapted to measure liquid samples because it was originally designed only for solids. In order to achieve this goal, custom sample holders were manufactured. Likewise, the nuclear magnetic relaxation dispersion profiles for the inert fluid were also measured by fast field cycling nuclear magnetic relaxation relaxometry. The results show that SPION magnetization in inert fluids was affected by the carrier liquid viscosity and the concentration. In cell cultures, the mechanical attachment or confinement of the SPIONs inside the cells accounted for the change in the dynamic magnetic behavior of the nanoparticles. Nevertheless, the magnetization value in the cell cultures was slightly lower than that of the fluid simulating the viscosity of cytoplasm, suggesting that magnetization loss was not only due to medium viscosity but also to a reduction in the mechanical degrees of freedom of SPIONs rotation and translation inside cells. The findings presented here provide information on the loss of magnetic properties when nanoparticles are suspended in viscous fluids or internalized in cells. This information could be exploited to improve biomedical applications based on magnetic properties such as magnetic hyperthermia, contrast agents and drug delivery.