Development of exosome membrane materials-fused microbubbles for enhanced stability and efficient drug delivery of ultrasound contrast agent.

Lipid-coated microbubbles are widely used as an ultrasound contrast agent, as well as drug delivery carriers. However, the two main limitations in ultrasound diagnosis and drug delivery using microbubbles are the short half-life in the blood system, and the difficulty of surface modification of microbubbles for active targeting. The exosome, a type of extracellular vesicle, has a preferentially targeting ability for its original cell. In this study, exosome-fused microbubbles (Exo-MBs) were developed by embedding the exosome membrane proteins into microbubbles. As a result, the stability of Exo-MBs is improved over the conventional microbubbles. On the same principle that under the exposure of ultrasound, microbubbles are cavitated and self-assembled into nano-sized particles, and Exo-MBs are self-assembled into exosome membrane proteins-embedded nanoparticles (Exo-NPs). The Exo-NPs showed favorable targeting properties to their original cells. A photosensitizer, chlorin e6, was loaded into Exo-MBs to evaluate therapeutic efficacy as a drug carrier. Much higher therapeutic efficacy of photodynamic therapy was confirmed, followed by cancer immunotherapy from immunogenic cell death. We have therefore developed a novel ultrasound image-guided drug delivery platform that overcomes the shortcomings of the conventional ultrasound contrast agent and is capable of simultaneous photodynamic therapy and cancer immunotherapy.

Development of a Polymersome-Based Nanomedicine for Chemotherapeutic and Sonodynamic Combination Therapy.

In anticancer therapy, combination therapy has been suggested as an alternative to the insufficient therapeutic efficacy of single therapy. Among combination therapies, combination chemo- and photodynamic therapy are actively investigated. However, photodynamic therapy shows a limitation in the penetration depth of the laser. Therefore, sonodynamic therapy (SDT), using ultrasound instead of a laser as a trigger, is an upcoming strategy for deep tumors. Additionally, free drugs are easily degraded by enzymes, have difficulty in reaching the target site, and show side effects after systemic administration; therefore, the development of drug delivery systems is desirable for sufficient drug efficacy for combination therapy. However, nanocarriers, such as microbubbles, and albumin nanoparticles, are unstable in the body and show low drug-loading efficiency. Here, we propose polylactide (PLA)-poly (ethylene glycol) (PEG) polymersomes (PLs) with a high drug loading rate of doxorubicin (DOX) and verteporfin (VP) for effective combination therapy in both in vitro and in vivo experiments. The cellular uptake efficiency and cytotoxicity test results of VP-DOX-PLs were higher than that of single therapy. Moreover, in vivo biodistribution showed the accumulation of the VP-DOX-PLs in tumor regions. Therefore, VP-DOX-PLs showed more effective anticancer efficacy than either single therapy in vivo. These results suggest that the combination therapy of SDT and chemotherapy could show novel anticancer effects using VP-DOX-PLs.

Exosome-based photoacoustic imaging guided photodynamic and immunotherapy for the treatment of pancreatic cancer.

Exosomes, which are released from all cells and take part in cell-to-cell communication, have been utilized as drug delivery vehicles in many recent studies. Immunotherapy is an emerging technology which uses patients' innate immune systems. In immunotherapy, immune cells are stimulated through antibodies, the other immune cells and genetic modifications for the purposes of, for instance, cancer therapy. In this study, tumor-derived re-assembled exosome (R-Exo) was simultaneously utilized as both a drug delivery carrier and an immunostimulatory agent. A chlorin e6 photosensitizer was loaded into tumor-derived exosomes during exosomal re-assembly. After this modification, R-Exo retains its original average size and has the same membrane proteins, which allows for targeting of tumor cells. Chlorin e6-loaded R-Exo (Ce6-R-Exo) can be visualized by photoacoustic imaging and can efficiently generate reactive oxygen species inside tumor cells under laser irradiation. In addition, Ce6-R-Exo increased the release of cytokines from immune cells, which indicates that these modified exosomes can be used as an immunotherapeutic agent. In conclusion, we developed a novel strategy that enables photoacoustic imaging-guided photodynamic and immune-combination therapy for the treatment of cancer with tumor-derived Ce6-R-Exo.

Development of an ultrasound triggered nanomedicine-microbubble complex for chemo-photodynamic-gene therapy.

Recently, combination therapy has received much attention because of its highly therapeutic effect in various types of cancers. In particular, chemo-photodynamic combination therapy has been considered as an outstanding strategy. However, an abnormal increase in tumor angiogenesis caused by reactive oxygen species (ROS) generated during photodynamic therapy (PDT) has been reported. In this study, the complex of doxorubicin (DOX)-encapsulating anti-angiogenic small interfering RNA (siRNA) nanoparticle and chlorin e6 (Ce6)-encapsulating microbubble has been developed to suppress tumor angiogenesis. The first compartment, doxorubicin-encapsulating siRNA nanoparticle, was electrostatically coated using two biocompatible polymers to prevent the damage of genetic materials. The other part, Ce6-encapsulating microbubble, serves as an ultrasound-triggered local delivery system as well as a drug carrier. Both the in vitro and in vivo experimental results demonstrate successful inhibition of angiogenesis with a minimized damage of siRNAs caused by ROS as well as improved therapeutic effect by chemo-photodynamic-gene triple combination therapy using ultrasound-triggered local delivery.

Focused ultrasound-triggered chemo-gene therapy with multifunctional nanocomplex for enhancing therapeutic efficacy.

Nanotechnology-based combination therapies, especially chemo-gene therapy, have been spotlighted as promising alternatives for cancer therapy. However, only a small amount of systemically administered nanomedicines reach the tumor site by the enhanced permeability and retention (EPR) effect, resulting in the limited therapeutic efficacy. Furthermore, the design of ideal drug delivery system for chemo-gene therapy has been impeded by the chemical and physical differences between nucleic acids and chemotherapeutics. Herein, we report a precisely designed nanocomplex which exhibits a focused ultrasound (FU)-responsive release and enhanced accumulation of released therapeutics to tumor site. After the nanocomplex composed of siRNA nanoparticles (siRNA-NP) and chemotherapeutics-loaded microbubbles was systemically injected, the nanocomplex was collapsed around the tumor tissue by FU exposure, and both siRNA-NP and chemotherapeutics were penetrated the dense extracellular matrix (ECM) of tumor site, leading to the enhanced chemo-gene therapeutic efficacy. The two-in-one nanocomplex is expected as a promising platform for combination therapy that can enhance the therapeutic efficiency of combination drugs at the cell and/or tissue levels with high drug loading ratio.

Application of deep learning to the diagnosis of cervical lymph node metastasis from thyroid cancer with CT: external validation and clinical utility for resident training.

PURPOSE: This study aimed to validate a deep learning model's diagnostic performance in using computed tomography (CT) to diagnose cervical lymph node metastasis (LNM) from thyroid cancer in a large clinical cohort and to evaluate the model's clinical utility for resident training. METHODS: The performance of eight deep learning models was validated using 3838 axial CT images from 698 consecutive patients with thyroid cancer who underwent preoperative CT imaging between January and August 2018 (3606 and 232 images from benign and malignant lymph nodes, respectively). Six trainees viewed the same patient images (n = 242), and their diagnostic performance and confidence level (5-point scale) were assessed before and after computer-aided diagnosis (CAD) was included. RESULTS: The overall area under the receiver operating characteristics (AUROC) of the eight deep learning algorithms was 0.846 (range 0.784-0.884). The best performing model was Xception, with an AUROC of 0.884. The diagnostic accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of Xception were 82.8%, 80.2%, 83.0%, 83.0%, and 80.2%, respectively. After introducing the CAD system, underperforming trainees received more help from artificial intelligence than the higher performing trainees (p = 0.046), and overall confidence levels significantly increased from 3.90 to 4.30 (p < 0.001). CONCLUSION: The deep learning-based CAD system used in this study for CT diagnosis of cervical LNM from thyroid cancer was clinically validated with an AUROC of 0.884. This approach may serve as a training tool to help resident physicians to gain confidence in diagnosis. KEY POINTS: • A deep learning-based CAD system for CT diagnosis of cervical LNM from thyroid cancer was validated using data from a clinical cohort. The AUROC for the eight tested algorithms ranged from 0.784 to 0.884. • Of the eight models, the Xception algorithm was the best performing model for the external validation dataset with 0.884 AUROC. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were 82.8%, 80.2%, 83.0%, 83.0%, and 80.2%, respectively. • The CAD system exhibited potential to improve diagnostic specificity and accuracy in underperforming trainees (3 of 6 trainees, 50.0%). This approach may have clinical utility as a training tool to help trainees to gain confidence in diagnoses.

Multivariate analyses of MRI findings for predicting osteomyelitis of the foot in diabetic patients.

BACKGROUND: There have been no previous magnetic resonance imaging (MRI) studies using multivariable analysis to diagnose osteomyelitis in patients with diabetic foot. PURPOSE: To retrospectively investigate the MRI findings of osteomyelitis in patients with diabetic foot using multivariate analyses. MATERIAL AND METHODS: From November 2015 to March 2018, 118 patients who underwent MRI of the foot to evaluate suspected osteomyelitis were included in this study. The patients were categorized into the presence or absence of osteomyelitis. The primary and secondary MRI findings were retrospectively reviewed. To identify independent predictive MRI findings, multivariate analyses with binary logistic regression and receiver operating characteristic curve analyses were performed including all 118 patients and 93 patients presenting decreased T1 signal intensity, respectively. RESULTS: T1 signal intensity, T1 marrow pattern, T1 marrow distribution, T2 signal intensity, concordance of marrow signal intensity, cortical interruption, ulcer depth, abscess, and wet gangrene were significantly different between the two groups (P < 0.05). Multivariate analyses indicated that fluid equivalent T2 signal intensity, deep ulcer, and confluent T1 marrow pattern were major factors associated with osteomyelitis. The area under the curve of predicted probabilities for the combination of these factors was 0.799 across all 118 patients and 0.761 across 93 patients with decreased T1 signal intensity. CONCLUSION: Confluent T1 marrow pattern is a reliable finding to suggest osteomyelitis in patients with diabetic foot. In addition, fluid equivalent T2 signal intensity and deep ulcer are important findings that may suggest osteomyelitis, irrespective of T1 signal intensity change.

Development and evaluation of a CEACAM6-targeting theranostic nanomedicine for photoacoustic-based diagnosis and chemotherapy of metastatic cancer.

Metastasis is the leading cause of cancer-related deaths. A number of chemotherapeutic and early diagnosis strategies, including nanomedicine, have been developed to target metastatic tumor cells. However, simultaneous inhibition and imaging of metastasis is yet to be fully achieved. Methods: To overcome this limitation, we have developed human serum albumin-based nanoparticles (tHSA-NPs) with photoacoustic imaging capability, which target carcinoembryonic antigen-related cell adhesion molecule 6 (CEACAM6). CEACAM6 is highly expressed in metastatic anoikis-resistant tumor cells. Results:In vitro, the CEACAM6-targeting tHSA-NPs efficiently targeted CEACAM6-overexpressing metastatic anoikis-resistant tumor cells. In vivo, CEACAM6-targeting tHSA-NPs administered intravenously to BALB/c nude mice efficiently inhibited lung metastasis in circulating anoikis-resistant tumor cells compared to the controls. In addition, anoikis-resistant tumor cells can be successfully detected by photoacoustic imaging, both in vitro and in vivo, using the intrinsic indocyanine green-binding affinity of albumin. Conclusion: In summary, the CEACAM6-targeting albumin-based nanoparticles allowed the delivery of drugs and photoacoustic imaging to metastatic anoikis-resistant tumor cells in vitro and in vivo. Based on the expression of CEACAM6 in a variety of tumors, CEACAM6-targeting nanomedicine might be used to target various types of metastatic tumor cells.