Antibody-Targeted Immunocarriers for Cancer Treatment

Nanocarrier’s engineering based on fine chemical design and novel structural tailoring can provide practical solution to solve the problems in traditional cancer immunotherapy. Nanoimmunotherapy is thus defined as the application and further development of novel nanocarriers for enhancing immunotherapy. It has become one of the most intriguing fields due to its unique power in treatment and even cure of cancer since reported in last year. Herein, this chapter illustrates the state-of-the-art development in antibody engineering and cancer immunotherapy and gives an explanation why functional nanocarries including micelles and liposomes can be efficient for nanoimmunotherapy. We further illustrate how to promote the nanoimmunotherapy by the chemical design and carrier’s engineering for the first time

Deep learning–based radiomic nomograms for predicting Ki67 expression in prostate cancer

Abstract Background To explore the value of a multiparametric magnetic resonance imaging (MRI)-based deep learning model for the preoperative prediction of Ki67 expression in prostate cancer (PCa). Materials The data of 229 patients with PCa from two centers were retrospectively analyzed and divided into training, internal validation, and external validation sets. Deep learning features were extracted and selected from each patient’s prostate multiparametric MRI (diffusion-weighted imaging, T2-weighted imaging, and contrast-enhanced T1-weighted imaging sequences) data to establish a deep radiomic signature and construct models for the preoperative prediction of Ki67 expression. Independent predictive risk factors were identified and incorporated into a clinical model, and the clinical and deep learning models were combined to obtain a joint model. The predictive performance of multiple deep-learning models was then evaluated. Results Seven prediction models were constructed: one clinical model, three deep learning models (the DLRS-Resnet, DLRS-Inception, and DLRS-Densenet models), and three joint models (the Nomogram-Resnet, Nomogram-Inception, and Nomogram-Densenet models). The areas under the curve (AUCs) of the clinical model in the testing, internal validation, and external validation sets were 0.794, 0.711, and 0.75, respectively. The AUCs of the deep models and joint models ranged from 0.939 to 0.993. The DeLong test revealed that the predictive performance of the deep learning models and the joint models was superior to that of the clinical model (p < 0.01). The predictive performance of the DLRS-Resnet model was inferior to that of the Nomogram-Resnet model (p < 0.01), whereas the predictive performance of the remaining deep learning models and joint models did not differ significantly. Conclusion The multiple easy-to-use deep learning–based models for predicting Ki67 expression in PCa developed in this study can help physicians obtain more detailed prognostic data before a patient undergoes surgery

A novel radiomics based on multi-parametric magnetic resonance imaging for predicting Ki-67 expression in rectal cancer: a multicenter study

Abstract Background To explore the value of multiparametric MRI markers for preoperative prediction of Ki-67 expression among patients with rectal cancer. Methods Data from 259 patients with postoperative pathological confirmation of rectal adenocarcinoma who had received enhanced MRI and Ki-67 detection was divided into 4 cohorts: training (139 cases), internal validation (in-valid, 60 cases), and external validation (ex-valid, 60 cases) cohorts. The patients were divided into low and high Ki-67 expression groups. In the training cohort, DWI, T2WI, and contrast enhancement T1WI (CE-T1) sequence radiomics features were extracted from MRI images. Radiomics marker scores and regression coefficient were then calculated for data fitting to construct a radscore model. Subsequently, clinical features with statistical significance were selected to construct a combined model for preoperative individualized prediction of rectal cancer Ki-67 expression. The models were internally and externally validated, and the AUC of each model was calculated. Calibration and decision curves were used to evaluate the clinical practicality of nomograms. Results Three models for predicting rectal cancer Ki-67 expression were constructed. The AUC and Delong test results revealed that the combined model had better prediction performance than other models in three chohrts. A decision curve analysis revealed that the nomogram based on the combined model had relatively good clinical performance, which can be an intuitive prediction tool for clinicians. Conclusion The multiparametric MRI radiomics model can provide a noninvasive and accurate auxiliary tool for preoperative evaluation of Ki-67 expression in patients with rectal cancer and can support clinical decision-making

Mastocarcinoma therapy synergistically promoted by lysosome dependent apoptosis specifically evoked by 5-Fu@nanogel system with passive targeting and pH activatable dual function

This manuscript describes a synergistic therapy for mastocarcinoma by pH and temperature dual-sensitive nanogel, and effects of microstructure, composition and properties of nanogel on the cellular response mechanism. The extracellular internalization of nanogels was obviously enhanced, due to the passive targeting function at T > VPTT. Interestingly, the increased cytotoxicity was further synergistically enhanced by an unexpected apoptosis as evoked by the 5-fluorouracil loaded nanogel (FLNG). The systemically evaluation of the effectors generated from different sub-cellular organelles including endosome, lysosome, autophagosome confirmed that it was a lysomal dependent apoptosis. Such specific apoptosis was mainly attributed to its activatable protonated PEI at low pH, which caused lysosomal membrane destruction and lysosomal enzyme cathepsin B (Cat B) leakage. This Cat B was then translocated to the mitochondria resulting in mitochondrial membrane permeability increase and mitochondrial membrane potential (MMP) decrease, followed by cytochrome c (Cyt C) release. Cyt C was the main molecule that evoked apoptosis as reflected by overexpression of caspase 9. Additionally, such lysosome dependent, apoptosis was further enhanced by the passive cellular targeting at T > VPTT. Thus, the tumor growth inhibition was synergistically enhanced by the extracellular temperature dependent passive targeting and intracellular pH activatable lysosomal dependent apoptosis.This work was financially supported by the National Natural Science Foundation of China including the project (31470964, 81171450, 81302363). This work was financially supported by Ministry of Science and Technology of China (2012CB934002,2012AA02A304). Prof. Teruo Okano should be appreciated to his professional advice on the thermosensitive materialsinfo:eu-repo/semantics/publishedVersio

Flexible Light Emission Diode Arrays Made of Transferred Si Microwires-ZnO Nanofilm with Piezo-Phototronic Effect Enhanced Lighting

Due to the fragility and the poor optoelectronic performances of Si, it is challenging and exciting to fabricate the Si-based flexible light-emitting diode (LED) array devices. Here, a flexible LED array device made of Si microwires-ZnO nanofilm, with the advantages of flexibility, stability, lightweight, and energy savings, is fabricated and can be used as a strain sensor to demonstrate the two-dimensional pressure distribution. Based on piezo-phototronic effect, the intensity of the flexible LED array can be increased more than 3 times (under 60 MPa compressive strains). Additionally, the device is stable and energy saving. The flexible device can still work well after 1000 bending cycles or 6 months placed in the atmosphere, and the power supplied to the flexible LED array is only 8% of the power of the surface-contact LED. The promising Si-based flexible device has wide range application and may revolutionize the technologies of flexible screens, touchpad technology, and smart skin