Ultrasound-Responsive Nanocarriers for Breast Cancer Chemotherapy

Advances in stimuli-responsive gold nanorods for drug-delivery and targeted therapy systems

In recent years, the use of gold nanorods (AuNRs) has garnered considerable attention in biomedical applications due to their unique optical and physicochemical properties. They have been considered as potential tools for the advanced treatment of diseases by various stimuli such as magnetic fields, pH, temperature and light in the fields of targeted therapy, imaging and drug delivery. Their biocompatibility and tunable plasmonic properties make them a versatile platform for a range of biomedical applications. While endogenous stimuli have limited cargo delivery control at specific sites, exogenous stimuli are a more favored approach despite their circumscribed penetration depth for releasing the cargo at the specific target. Dual/multi-stimuli responsive AuNTs can be triggered by multiple stimuli for enhanced control and specificity in biomedical applications. This review provides to provide a summary of the biomedical applications of stimuli-responsive AuNRs, including their endogenous and exogenous properties, as well as their dual/multi-functionality and potential for clinical delivery. This review provides a comprehensive review on the improvement of therapeutic efficacy and the effective control of drug release with AuNRs, highlights AuNRs design strategies in recent years, discusses the advantages or challenges so far in the field of AuNRs. Finally, we have addressed the clinical translation bio-integrated nanoassemblies (CTBNs) in this field.

[Classification and Application of Ultrasound-Responsive Nanomaterials in Anti-Inflammatory Therapy]

Ultrasound, a high-frequency mechanical wave with excellent tissue penetration, has been widely applied in medical diagnostic imaging. Furthermore, it has been reported that ultrasound has broad prospects for extensive applications in the field of disease treatment in recent years due to its non-invasiveness and high efficiency. Ultrasound-responsive nanomaterials have the unique advantages of a small size and a high reactivity. Such materials have the capability for precision control of drug release under ultrasound stimulation, which provides a new approach to enhancing the efficiency of drug therapy. Therefore, these materials have attracted the attention of a wide range of scholars. Inflammation is a defensive response produced by organisms to deal with injuries. However, excessive inflammatory response may lead to various tissue damages in organisms and even endanger patients' lives. Many studies have demonstrated that limiting the inflammatory response using ultrasound-responsive nanomaterials is a viable way of treating diseases. Currently, there are still challenges in the application of ultrasound-responsive nanomaterials in anti-inflammatory therapy. The design and synthesis process of nanomaterials is complicated, and further verification of the biocompatibility and safety of these materials is needed. Therefore, in this review, we summarized and classified common ultrasound-responsive nanomaterials in the field of anti-inflammation and systematically introduced the properties of different nanomaterials. In addition, the anti-inflammatory applications of ultrasound-responsive nanomaterials in various diseases, such as bone diseases, skin and muscle diseases, autoimmune diseases, and respiratory diseases, are also described in detail. It is expected that this review will provide insights for further research and clinical applications in the realms of precision treatment, targeted drug delivery, and clinical trial validation of ultrasound-responsive nanomaterials used in anti-inflammatory therapies.

Recent Update on Nanocarrier(s) as the Targeted Therapy for Breast Cancer

Despite ongoing advances in cancer therapy, the results for the treatment of breast cancer are not satisfactory. The advent of nanotechnology promises to be an essential tool to improve drug delivery effectiveness in cancer therapy. Nanotechnology provides an opportunity to enhance the treatment modality by preventing degradation, improving tumour targeting, and controlling drug release. Recent advances have revealed several strategies to prevent cancer metastasis using nano-drug delivery systems (NDDS). These strategies include the design of appropriate nanocarriers loaded with anti-cancer drugs that target the optimization of physicochemical properties, modulate the tumour microenvironment, and target biomimetic techniques. Nanocarriers have emerged as a preferential approach in the chemotropic treatment for breast cancer due to their pivotal role in safeguarding the therapeutic agents against degradation. They facilitate efficient drug concentration in targeted cells, surmount the resistance of drugs, and possess a small size. Nevertheless, these nanocarrier(s) have some limitations, such as less permeability across the barrier and low bioavailability of loaded drugs. To overcome these challenges, integrating external stimuli has been employed, encompassing infrared light, thermal stimulation, microwaves, and X-rays. Among these stimuli, ultrasound-triggered nanocarriers have gained significant attention due to their cost-effectiveness, non-invasive nature, specificity, ability to penetrate tissues, and capacity to deliver elevated drug concentrations to intended targets. This article comprehensively reviews recent advancements in different nanocarriers for breast cancer chemotherapy. It also delves into the associated hurdles and offers valuable insights into the prospective directions for this innovative field.

Pathological Insights: Enhanced Vision Transformers for the Early Detection of Colorectal Cancer

Endoscopic pathological findings of the gastrointestinal tract are crucial for the early diagnosis of colorectal cancer (CRC). Previous deep learning works, aimed at improving CRC detection performance and reducing subjective analysis errors, are limited to polyp segmentation. Pathological findings were not considered and only convolutional neural networks (CNNs), which are not able to handle global image feature information, were utilized. This work introduces a novel vision transformer (ViT)-based approach for early CRC detection. The core components of the proposed approach are ViTCol, a boosted vision transformer for classifying endoscopic pathological findings, and PUTS, a vision transformer-based model for polyp segmentation. Results demonstrate the superiority of this vision transformer-based CRC detection method over existing CNN and vision transformer models. ViTCol exhibited an outstanding performance in classifying pathological findings, with an area under the receiver operating curve (AUC) value of 0.9999 ± 0.001 on the Kvasir dataset. PUTS provided outstanding results in segmenting polyp images, with mean intersection over union (mIoU) of 0.8673 and 0.9092 on the Kvasir-SEG and CVC-Clinic datasets, respectively. This work underscores the value of spatial transformers in localizing input images, which can seamlessly integrate into the main vision transformer network, enhancing the automated identification of critical image features for early CRC detection.

Intelligent Systems based on Cyclodextrins for the Treatment of Breast Cancer

The incidence of breast cancer has been increasing over the last four decades, although the mortality rate has decreased. Endocrine therapy and chemotherapy are the most used options for cancer treatment but several obstacles are still attributed to these therapies. Smart materials, such as nanocarriers for targeting, delivery and release of active ingredients, sensitive to intrinsic-stimuli (pH-responsive, redox-responsive, enzyme- responsive, and thermo-responsive) and extrinsic-stimuli (ultrasound-responsive, magnetic-responsive, light-responsive) have been studied as a novel strategy in breast cancer therapy. Cyclodextrins (CDs) are used in the design of these stimuli-responsive drug carrier and delivery systems, either through inclusion complexes with hydrophobic molecules or covalent bonds with large structures to generate new materials. The present work aims to gather and integrate recent data from in vitro and in vivo preclinical studies of CD-based stimuli- responsive systems to contribute to the research in treating breast cancer. All drug carriers showed high in vitro release rates in the presence of a stimulus. The stimuli-responsive nanoplatforms presented biocompatibility and satisfactory results of IC50, inhibition of cell viability and antitumor activity against several breast cancer cell lines. Additionally, these systems led to a significant reduction in drug dosages, which encouraged possible clinical studies for better alternatives to traditional antitumor therapies.

Domain and Histopathology Adaptations-Based Classification for Malignancy Grading System

Accurate proliferation rate quantification can be used to devise an appropriate treatment for breast cancer. Pathologists use breast tissue biopsy glass slides stained with hematoxylin and eosin to obtain grading information. However, this manual evaluation may lead to high costs and be ineffective because diagnosis depends on the facility and the pathologists' insights and experiences. Convolutional neural network acts as a computer-based observer to improve clinicians' capacity in grading breast cancer. Therefore, this study proposes a novel scheme for automatic breast cancer malignancy grading from invasive ductal carcinoma. The proposed classifiers implement multistage transfer learning incorporating domain and histopathologic transformations. Domain adaptation using pretrained models, such as InceptionResNetV2, InceptionV3, NASNet-Large, ResNet50, ResNet101, VGG19, and Xception, was applied to classify the ×40 magnification BreaKHis data set into eight classes. Subsequently, InceptionV3 and Xception, which contain the domain and histopathology pretrained weights, were determined to be the best for this study and used to categorize the Databiox database into grades 1, 2, or 3. To provide a comprehensive report, this study offered a patchless automated grading system for magnification-dependent and magnification-independent classifications. With an overall accuracy (means ± SD) of 90.17% ± 3.08% to 97.67% ± 1.09% and an F1 score of 0.9013 to 0.9760 for magnification-dependent classification, the classifiers in this work achieved outstanding performance. The proposed approach could be used for breast cancer grading systems in clinical settings.

Biomaterials releasing drug responsively to promote wound healing via regulation of pathological microenvironment

Wound healing is characterized by complex, orchestrated, spatiotemporal dynamic processes. Recent findings demonstrated suitable local microenvironments were necessities for wound healing. Wound microenvironments include various biological, biochemical and physical factors, which are produced and regulated by endogenous biomediators, exogenous drugs, and external environment. Successful drug delivery to wound is complicated, and need to overcome the destroyed blood supply, persistent inflammation and enzymes, spatiotemporal requirements of special supplements, and easy deactivation of drugs. Triggered by various factors from wound microenvironment itself or external elements, stimuli-responsive biomaterials have tremendous advantages of precise drug delivery and release. Here, we discuss recent advances of stimuli-responsive biomaterials to regulate local microenvironments during wound healing, emphasizing on the design and application of different biomaterials which respond to wound biological/biochemical microenvironments (ROS, pH, enzymes, glucose and glutathione), physical microenvironments (mechanical force, temperature, light, ultrasound, magnetic and electric field), and the combination modes. Moreover, several novel promising drug carriers (microbiota, metal-organic frameworks and microneedles) are also discussed.

Vision-Transformer-Based Transfer Learning for Mammogram Classification

Breast mass identification is a crucial procedure during mammogram-based early breast cancer diagnosis. However, it is difficult to determine whether a breast lump is benign or cancerous at early stages. Convolutional neural networks (CNNs) have been used to solve this problem and have provided useful advancements. However, CNNs focus only on a certain portion of the mammogram while ignoring the remaining and present computational complexity because of multiple convolutions. Recently, vision transformers have been developed as a technique to overcome such limitations of CNNs, ensuring better or comparable performance in natural image classification. However, the utility of this technique has not been thoroughly investigated in the medical image domain. In this study, we developed a transfer learning technique based on vision transformers to classify breast mass mammograms. The area under the receiver operating curve of the new model was estimated as 1 ± 0, thus outperforming the CNN-based transfer-learning models and vision transformer models trained from scratch. The technique can, hence, be applied in a clinical setting, to improve the early diagnosis of breast cancer.

Imaging-Guided Micromachines: Towards Intelligent Systems

Micromachines with controllable motion, deformation, and collective behaviors provide advanced methods for performing tasks that traditional machines have difficulty completing thanks to the development of small-scale robotics, nanotechnology, biocompatible materials, and imaging techniques [...].

BUViTNet: Breast Ultrasound Detection via Vision Transformers

Convolutional neural networks (CNNs) have enhanced ultrasound image-based early breast cancer detection. Vision transformers (ViTs) have recently surpassed CNNs as the most effective method for natural image analysis. ViTs have proven their capability of incorporating more global information than CNNs at lower layers, and their skip connections are more powerful than those of CNNs, which endows ViTs with superior performance. However, the effectiveness of ViTs in breast ultrasound imaging has not yet been investigated. Here, we present BUViTNet breast ultrasound detection via ViTs, where ViT-based multistage transfer learning is performed using ImageNet and cancer cell image datasets prior to transfer learning for classifying breast ultrasound images. We utilized two publicly available ultrasound breast image datasets, Mendeley and breast ultrasound images (BUSI), to train and evaluate our algorithm. The proposed method achieved the highest area under the receiver operating characteristics curve (AUC) of 1 ± 0, Matthew’s correlation coefficient (MCC) of 1 ± 0, and kappa score of 1 ± 0 on the Mendeley dataset. Furthermore, BUViTNet achieved the highest AUC of 0.968 ± 0.02, MCC of 0.961 ± 0.01, and kappa score of 0.959 ± 0.02 on the BUSI dataset. BUViTNet outperformed ViT trained from scratch, ViT-based conventional transfer learning, and CNN-based transfer learning in classifying breast ultrasound images (p < 0.01 in all cases). Our findings indicate that improved transformers are effective in analyzing breast images and can provide an improved diagnosis if used in clinical settings. Future work will consider the use of a wide range of datasets and parameters for optimized performance.