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16 pages, 3191 KiB

Open AccessArticle

Efficacy of 15 nm Gold Nanoparticles for Image-Guided Gliosarcoma Radiotherapy

by Elette Engels, Michael Lerch, Stéphanie Corde and Moeava Tehei

J. Nanotheranostics 2023, 4(4), 480-495; https://doi.org/10.3390/jnt4040021 - 26 Oct 2023

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Targeted brain cancer treatments are sorely needed to improve long-term prognosis, particularly for gliosarcoma and glioblastoma patients. Gold nanoparticles (GNPs) have unique properties including high atomic number, biocompatibility, and small size for cancer cell internalization. GNPs are consequently an ideal candidate for improved [...] Read more.

Targeted brain cancer treatments are sorely needed to improve long-term prognosis, particularly for gliosarcoma and glioblastoma patients. Gold nanoparticles (GNPs) have unique properties including high atomic number, biocompatibility, and small size for cancer cell internalization. GNPs are consequently an ideal candidate for improved cancer targeting using image-guided radiotherapy. This work investigated 15 nm AuroVist^TM GNPs for image-guided gliosarcoma radiotherapy and identified optimum GNP concentrations. The GNPs were found to be 15–20 nm using optical surface plasmon resonance absorption, with a (41.3 ± 0.3) nm hydrodynamic diameter. Confocal imaging showed that 50–500 µg/mL of the GNPs was well-internalized into the 9L cells within 24–48 h. γ-H2AX assays showed that 50–500 µg/mL of the GNPs radiosensitized the 9L cells irradiated with 125 and 150 kVp X-rays. However, only 500 µg/mL of the GNPs produced significant long-term dose enhancement with 150 kVp X-rays (with a sensitization enhancement ratio at 10% survival of 1.43, and 1.13 with 50 µg/mL) using clonogenic assay. CT imaging of the GNPs in the 9L tumors in Fischer rats further showed that GNP concentrations above 500 µg/mL were required to distinguish the tumor from the brain, and the GNPs were detected 48 h after injection. These promising results indicate that the GNPs can be used for selective gliosarcoma treatment with image-guided X-ray radiotherapy at concentrations above 500 µg/mL. Full article

(This article belongs to the Special Issue Emerging Strategies in Nanomedicine)

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20 pages, 5542 KiB

Open AccessFeature PaperArticle

Convolutional Neural Network Classification of Exhaled Aerosol Images for Diagnosis of Obstructive Respiratory Diseases

by Mohamed Talaat, Jensen Xi, Kaiyuan Tan, Xiuhua April Si and Jinxiang Xi

J. Nanotheranostics 2023, 4(3), 228-247; https://doi.org/10.3390/jnt4030011 - 26 Jun 2023

Cited by 4 | Viewed by 1108

Abstract

Aerosols exhaled from the lungs have distinctive patterns that can be linked to the abnormalities of the lungs. Yet, due to their intricate nature, it is highly challenging to analyze and distinguish these aerosol patterns. Small airway diseases pose an even greater challenge, [...] Read more.

Aerosols exhaled from the lungs have distinctive patterns that can be linked to the abnormalities of the lungs. Yet, due to their intricate nature, it is highly challenging to analyze and distinguish these aerosol patterns. Small airway diseases pose an even greater challenge, as the disturbance signals tend to be weak. The objective of this study was to evaluate the performance of four convolutional neural network (CNN) models (AlexNet, ResNet-50, MobileNet, and EfficientNet) in detecting and staging airway abnormalities in small airways using exhaled aerosol images. Specifically, the model’s capacity to classify images inside and outside the original design space was assessed. In doing so, multi-level testing on images with decreasing similarities was conducted for each model. A total of 2745 images were generated using physiology-based simulations from normal and obstructed lungs of varying stages. Multiple-round training on datasets with increasing images (and new features) was also conducted to evaluate the benefits of continuous learning. Results show reasonably high classification accuracy on inbox images for models but significantly lower accuracy on outbox images (i.e., outside design space). ResNet-50 was the most robust among the four models for both diagnostic (2-class: normal vs. disease) and staging (3-class) purposes, as well as on both inbox and outbox test datasets. Variation in flow rate was observed to play a more important role in classification decisions than particle size and throat variation. Continuous learning/training with appropriate images could substantially enhance classification accuracy, even with a small number (~100) of new images. This study shows that CNN transfer-learning models could detect small airway remodeling (<1 mm) amidst a variety of variants and that ResNet-50 can be a promising model for the future development of obstructive lung diagnostic systems. Full article

(This article belongs to the Special Issue Emerging Strategies in Nanomedicine)

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Review

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23 pages, 832 KiB

Open AccessReview

An Overview of Nanotherapeutic Drug Delivery Options for the Management of Glioblastoma

by William H. Pentz, Vincenzo J. Pizzuti, Matthew E. Halbert, Tritan J. Plute, Paul R. Lockman and Samuel A. Sprowls

J. Nanotheranostics 2023, 4(3), 323-345; https://doi.org/10.3390/jnt4030015 - 01 Aug 2023

Cited by 1 | Viewed by 2181

Abstract

Glioblastoma is the most common primary, malignant brain tumor that remains uniformly lethal in nearly all cases as a result of extreme cellular heterogeneity, treatment resistance, and recurrence. A major hurdle in therapeutic delivery to brain tumors is the blood–brain barrier (BBB), which [...] Read more.

Glioblastoma is the most common primary, malignant brain tumor that remains uniformly lethal in nearly all cases as a result of extreme cellular heterogeneity, treatment resistance, and recurrence. A major hurdle in therapeutic delivery to brain tumors is the blood–brain barrier (BBB), which is the tightly regulated vascular barrier between the brain parenchyma and systemic circulation that prevents distribution of otherwise beneficial chemotherapeutics to central nervous system tumors. To overcome the obstacle of drug delivery beyond the BBB, nanoparticle formulations have come to the forefront, having demonstrated success in preclinical observations, but have not translated well into the clinical setting. In summary, this review article discusses brain tumors and challenges for drug delivery caused by the BBB, explores the benefits of nanoparticle formulations for brain tumor delivery, describes the characteristics these formulations possess that make them attractive therapeutic strategies, and provides preclinical examples that implement nanoparticles within glioma treatment regimens. Additionally, we explore the pitfalls associated with clinical translation and conclude with remarks geared toward overcoming these issues. Full article

(This article belongs to the Special Issue Emerging Strategies in Nanomedicine)

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