Development of innovative nanomedicine formulations to traverse the blood–brain barrier (BBB) for effective theranostics of glioma remains a great challenge. Herein, we report the creation of macrophage membrane-camouflaged multifunctional polymer nanogels coloaded with manganese dioxide (MnO2) and cisplatin for magnetic resonance (MR) imaging-guided chemotherapy/chemodynamic therapy (CDT) of orthotopic glioma. Redox-responsive poly­(N-vinylcaprolactam) (PVCL) nanogels (NGs) formed via precipitation polymerization were in situ loaded with MnO2 and physically encapsulated with cisplatin to have a mean size of 106.3 nm and coated with macrophage membranes to have a good colloidal stability. The generated hybrid NGs display dual pH- and redox-responsive cisplatin and Mn­(II) release profiles and can deplete glutathione (GSH) rich in tumor microenvironment through reaction with disulfide-containing cross-linkers within the NGs and MnO2. The thus created Mn­(II) enables enhanced CDT through a Fenton-like reaction and T 1-weighted MR imaging, while the loaded cisplatin not only exerts its chemotherapy effect but also promotes the reactive oxygen species generation to enhance the CDT efficacy. Importantly, the macrophage membrane coating rendered the hybrid NGs with prolonged blood circulation time and ability to traverse BBB for specific targeted chemotherapy/CDT of orthotopic glioma. Our study demonstrates a promising self-adaptive and cooperative NG-based nanomedicine platform for highly efficient theranostics of glioma, which may be extended to tackle other difficult cancer types.

BackgroundEarly diagnosis of renal cell carcinoma is extremely significant for the effective treatment of kidney cancer. The development of AS1411 aptamer modified Mn-MoS2 QDs provides a promising fluorescence/magnetic resonance (MR) dual-modal imaging probe for the precise diagnosis of renal clear cell carcinoma.MethodsIn this work, Mn-MoS2 QDs were synthesized through a simple “bottom-up” one-step hydrothermal method. AS1411 aptamer was modified on the Mn-MoS2 QDs to improve the specificity to renal cell carcinoma. The characteristics of Mn-MoS2 QDs were confirmed by transmission electronic microscopy (TEM), atomic force microscope (AFM), X-ray photoelectron spectrometer (XPS), photoluminescence (PL) emission spectra, etc. Cellular fluorescence labelling was investigated using the Mn-MoS2 QDs and AS1411-Mn-MoS2 QDs. The T1-weighted MR imaging was assessed by the in vitro MR cell imaging and in vivo MR imaging. Finally, the long-term toxicity of Mn-MoS2 QDs was investigated by the hematology and histological analysis.ResultsThe prepared Mn-MoS2 QDs exhibited excellent aqueous property, intense fluorescence, low toxicity, high quantum yield of 41.45% and high T1 relaxivity of 16.95 mM−1s−1. After conjugated with AS1411 aptamer, the AS1411-Mn-MoS2 QDs could specifically fluorescently label the renal carcinoma cells and present a specific MRI signal enhancement in the tumor region of mice bearing renal carcinoma tumors. Furthermore, Mn-MoS2 QDs revealed low toxicity to the mice via hematology and histological analysis.ConclusionThese results demonstrated the potential of AS1411-Mn-MoS2 QD as a novel nanoprobe for targeted MR imaging and fluorescence labelling of renal cell carcinoma.

ObjectivesThis study aims to develop and evaluate multiparametric MRI (MP-MRI)-based radiomic models as a noninvasive diagnostic method to predict several biological characteristics of prostate cancer.MethodsA total of 252 patients were retrospectively included who underwent radical prostatectomy and MP-MRI examinations. The prediction characteristics of this study were as follows: Ki67, S100, extracapsular extension (ECE), perineural invasion (PNI), and surgical margin (SM). Patients were divided into training cohorts and validation cohorts in the ratio of 4:1 for each group. After lesion segmentation manually, radiomic features were extracted from MP-MRI images and some clinical factors were also included. Max relevance min redundancy (mRMR) and recursive feature elimination (RFE) based on random forest (RF) were adopted to select features. Six classifiers were included (SVM, KNN, RF, decision tree, logistic regression, XGBOOST) to find the best diagnostic performance among them. The diagnostic efficiency of the construction models was evaluated by ROC curves and quantified by AUC.ResultsRF performed best among the six classifiers for the four groups according to AUC values (Ki67 = 0.87, S100 = 0.80, ECE = 0.85, PNI = 0.82). The performance of SVM was relatively the best for SM (AUC = 0.77). The number and importance of DCE features ranked first in the models of each group. The combined models of MP-MRI and clinical characteristics showed no significant difference compared with MP-MRI models according to Delong’s tests.ConclusionsRadiomics models based on MP-MRI have the potential to predict biological characteristics and are expected to be a noninvasive method to evaluate the risk stratification of prostate cancer.

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