Adhesion molecule-targeted magnetic particle imaging nanoprobe for visualization of inflammation in acute lung injury.

PURPOSE: Uncontrolled intra-alveolar inflammation is a central pathogenic feature, and its severity translates into a valid prognostic indicator of acute lung injury (ALI). Unfortunately, current clinical imaging approaches are unsuitable for visualizing and quantifying intra-alveolar inflammation. This study aimed to construct a small-sized vascular cell adhesion molecule-1 (VCAM-1)-targeted magnetic particle imaging (MPI) nanoprobe (ESPVPN) to visualize and accurately quantify intra-alveolar inflammation at the molecular level. METHODS: ESPVPN was engineered by conjugating a peptide (VHPKQHRGGSK(Cy7)GC) onto a polydopamine-functionalized superparamagnetic iron oxide core. The MPI performance, targeting, and biosafety of the ESPVPN were characterized. VCAM-1 expression in HUVECs and mouse models was evaluated by western blot. The degree of inflammation and distribution of VCAM-1 in the lungs were assessed using histopathology. The expression of pro-inflammatory markers and VCAM-1 in lung tissue lysates was measured using ELISA. After intravenous administration of ESPVPN, MPI and CT imaging were used to analyze the distribution of ESPVPN in the lungs of the LPS-induced ALI models. RESULTS: The small-sized (~10 nm) ESPVPN exhibited superior MPI performance compared to commercial MagImaging® and Vivotrax, and ESPVPN had effective targeting and biosafety. VCAM-1 was highly expressed in LPS-induced ALI mice. VCAM-1 expression was positively correlated with the LPS-induced dose (R = 0.9381). The in vivo MPI signal showed positive correlations with both VCAM-1 expression (R = 0.9186) and representative pro-inflammatory markers (MPO, TNF-α, IL-6, IL-8, and IL-1ß, R > 0.7). CONCLUSION: ESPVPN effectively targeted inflammatory lungs and combined the advantages of MPI quantitative imaging to visualize and evaluate the degree of ALI inflammation.

Integration of photomagnetic bimodal imaging to monitor an autogenous exosome loaded platform: unveiling strong targeted retention effects for guiding the photothermal and magnetothermal therapy in a mouse prostate cancer model.

BACKGROUND: Prostate cancer (PCa) is the most prevalent cancer among males, emphasizing the critical need for precise diagnosis and treatment to enhance patient prognosis. Recent studies have extensively utilized urine exosomes from patients with cancer for targeted delivery. This study aimed to employ highly sensitive magnetic particle imaging (MPI) and fluorescence molecular imaging (FMI) to monitor the targeted delivery of an exosome-loaded platform at the tumour site, offering insights into a potential combined photothermal and magnetic thermal therapy regime for PCa. RESULTS: MPI and FMI were utilized to monitor the in vivo retention performance of exosomes in a prostate tumour mouse model. The exosome-loaded platform exhibited robust homologous targeting ability during imaging (SPIONs@EXO-Dye:66·48%±3·85%; Dye-SPIONs: 34·57%±7·55%, **P<0·01), as verified by in vitro imaging and in vitro tissue Prussian blue staining. CONCLUSIONS: The experimental data underscore the feasibility of using MPI for in vivo PCa imaging. Furthermore, the exosome-loaded platform may contribute to the precise diagnosis and treatment of PCa.

Non-invasively identifying candidates of active surveillance for prostate cancer using magnetic resonance imaging radiomics.

Active surveillance (AS) is the primary strategy for managing patients with low or favorable-intermediate risk prostate cancer (PCa). Identifying patients who may benefit from AS relies on unpleasant prostate biopsies, which entail the risk of bleeding and infection. In the current study, we aimed to develop a radiomics model based on prostate magnetic resonance images to identify AS candidates non-invasively. A total of 956 PCa patients with complete biopsy reports from six hospitals were included in the current multicenter retrospective study. The National Comprehensive Cancer Network (NCCN) guidelines were used as reference standards to determine the AS candidacy. To discriminate between AS and non-AS candidates, five radiomics models (i.e., eXtreme Gradient Boosting (XGBoost) AS classifier (XGB-AS), logistic regression (LR) AS classifier, random forest (RF) AS classifier, adaptive boosting (AdaBoost) AS classifier, and decision tree (DT) AS classifier) were developed and externally validated using a three-fold cross-center validation based on five classifiers: XGBoost, LR, RF, AdaBoost, and DT. Area under the receiver operating characteristic curve (AUC), accuracy (ACC), sensitivity (SEN), and specificity (SPE) were calculated to evaluate the performance of these models. XGB-AS exhibited an average of AUC of 0.803, ACC of 0.693, SEN of 0.668, and SPE of 0.841, showing a better comprehensive performance than those of the other included radiomic models. Additionally, the XGB-AS model also presented a promising performance for identifying AS candidates from the intermediate-risk cases and the ambiguous cases with diagnostic discordance between the NCCN guidelines and the Prostate Imaging-Reporting and Data System assessment. These results suggest that the XGB-AS model has the potential to help identify patients who are suitable for AS and allow non-invasive monitoring of patients on AS, thereby reducing the number of annual biopsies and the associated risks of bleeding and infection.