Asahi Tomitaka

Magnetic hyperthermia using heat generation of magnetic nanoparticles (MNPs) has great potential as a noninvasive cancer treatment with minimal side effects. Despite its promising results from basic research, the limited success in clinical translation stems from a lack of systematic nanoparticle optimization. To bridge this gap, machine learning (ML) models that predict the heating efficiency of MNPs composed of different compositions from the physical and magnetic properties of nanoparticles and external magnetic field conditions were developed in this study. The comparative analysis showed that the artificial neural network (ANN), gradient boosting regressor, and random forest regressor outperformed compared to support vector machine (SVM) and k-nearest neighbor (KNN). The ANN models with two hidden layers showed good prediction performances comparative to deeper ANN models. Furthermore, the feature importance analysis revealed the contributions of each input features toward the heating efficiency of MNPs based on the real experimental data.

Lateral flow assay has been extensively used for at-home testing and point-of-care diagnostics in rural areas. Despite its advantages as convenient and low-cost testing, it suffers from poor quantification capacity where only yes/no or positive/negative diagnostics are achieved. In this study, machine learning and deep learning models were developed to quantify the analyte load from smartphone-captured images of the lateral flow assay test. The comparative analysis identified that random forest and convolutional neural network (CNN) models performed well in classifying the lateral flow assay results compared to other well-established machine learning models. When trained on small-size images, random forest models excelled CNN models in image classification. Contrarily, CNN models outperformed random forest models in classifying noisy images.

Machine learning allows the prediction of nanoparticles properties for various biomedical applications and disease detection using image classification. It has the potential to bridge the gap between basic research and clinical research.

Breast cancer is one of the leading causes of death in the female population worldwide. Standard treatments such as chemotherapy show noticeable results. However, along with killing cancer cells, it causes systemic toxicity and apoptosis of the nearby healthy cells, therefore patients must endure side effects during the treatment process. Implantable drug delivery devices that enhance therapeutic efficacy by allowing localized therapy with programmed or controlled drug release can overcome the shortcomings of conventional treatments. An implantable device can be composed of biopolymer materials, nanocomposite materials, or a combination of both. This review summarizes the recent research and current state-of-the art in these types of implantable devices and gives perspective for future directions.

Smart multifunctional nanoparticles with magnetic and plasmonic properties assembled on a single nanoplatform are promising for various biomedical applications. Owing to their expanding imaging and therapeutic capabilities in response to external stimuli, they have been explored for on-demand drug delivery, image-guided drug delivery, and simultaneous diagnostic and therapeutic (i.e. theranostic) applications. In this study, we engineered nanoparticles with unique morphology consisting of a superparamagnetic iron oxide core and star-shaped plasmonic shell with high-aspect-ratio gold branches. Strong magnetic and near-infrared (NIR)-responsive plasmonic properties of the engineered nanostars enabled multimodal quantitative imaging combining advantageous functions of magnetic resonance imaging (MRI), magnetic particle imaging (MPI), photoacoustic imaging (PAI), and image-guided drug delivery with a tunable drug release capacity. The model drug molecules bound to the core-shell nanostars were released upon NIR illumination due to the heat generation from the core-shell nanostars. Moreover, our simulation analysis showed that the specific design of the core-shell nanostars demonstrated a pronounced multipolar plasmon resonance, which has not been observed in previous reports. The multimodal imaging and NIR-triggered drug release capabilities of the proposed nanoplatform verify their potential for precise and controllable drug release with different applications in personalized medicine.

The gold coating on magnetic core nanoparticles enhanced magnetic particle imaging performance due to an alteration in dynamic magnetic responses.

HIV and substance abuse plays an important role in infection and disease progression. Further, the presence of persistent viral CNS reservoirs makes the complete eradication difficult. Thus, neutralizing the drug of abuse effect on HIV-1 infectivity and elimination of latently infected cells is a priority. The development of a multi-component [antiretroviral drugs (ARV), latency reactivating agents (LRA) and drug abuse antagonist (AT)] sustained release nanoformulation targeting the CNS can overcome the issues of HIV-1 cure and will help in improving the drug adherence. The novel magneto-liposomal nanoformulation (NF) was developed to load different types of drugs (LRAs, ARVs, and Meth AT) and evaluated forin-vitro and in-vivoBBB transmigration and antiviral efficacy in primary CNS cells. We established the HIV-1 latency model using human astrocyte cells (HA) and optimized the dose of LRA for latency reversal, Meth AT inin-vitrocell culture system. Further, PEGylated magneto-liposomal NF was developed, characterized for size, shape, drug loading and BBB transportin-vitro. Results showed that drug released in a sustained manner up to 10 days and able to reduce the HIV-1 infectivity up to ~40–50% (>200 pg/mL to in-vitro. The magnetic treatment (0.8 T) was able to transport (15.8% ± 5.5%) NF effectively without inducing any toxic effects due to NF presence in the brain. Thus, our approach and result showed a way to eradicate HIV-1 reservoirs from the CNS and possibility to improve the therapeutic adherence to drugs in drug abusing (Meth) population. In conclusion, the developed NF can provide a better approach for the HIV-1 cure and a foundation for future HIV-1 purging strategies from the CNS using nanotechnology platform.

Engineered terahertz (THz) plasmonic metamaterials have emerged as promising platforms for quick infection diagnosis, cost-effective and real-time pharmacology applications owing to their non-destructive and harmless interaction with biological tissues in both in vivo and in vitro assays. As a recent member of THz metamaterials family, toroidal metamaterials have been demonstrated to be supporting high-quality sharp resonance modes. Here we introduce a THz metasensor based on a plasmonic surface consisting of metamolecules that support ultra-narrow toroidal resonances excited by the incident radiation and demonstrate detection of an ultralow concertation targeted biomarker. The toroidal plasmonic metasurface was designed and optimized through extensive numerical studies and fabricated by standard microfabrication techniques. The surface then functionalized by immobilizing the antibody for virus-envelope proteins (ZIKV-EPs) for selective sensing. We sensed and quantified the ZIKV-EP in the assays by measuring the spectral shifts of the toroidal resonances while varying the concentration. In an improved protocol, we introduced gold nanoparticles (GNPs) decorated with the same antibodies onto the metamolecules and monitored the resonance shifts for the same concentrations. Our studies verified that the presence of GNPs enhances capturing of biomarker molecules in the surrounding medium of the metamaterial. By measuring the shift of the toroidal dipolar momentum (up to Δω~0.35 cm⁻¹) for different concentrations of the biomarker proteins, we analyzed the sensitivity, repeatability, and limit of detection (LoD) of the proposed toroidal THz metasensor. The results show that up to 100-fold sensitivity enhancement can be obtained by utilizing plasmonic nanoparticles-integrated toroidal metamolecules in comparison to analogous devices. This approach allows for detection of low molecular-weight biomolecules (≈13 kDa) in diluted solutions using toroidal THz plasmonic unit cells.