In vitro and in vivo combinatorial anticancer effects of oxaliplatin- and resveratrol-loaded N,O-carboxymethyl chitosan nanoparticles against colorectal cancer.

BACKGROUND: Oxaliplatin (OXE) combined with other chemotherapy drugs against colorectal cancer had been reported in the literature before, however, the efficacy of oxaliplatin combined with natural compounds was elusive. In addition, the clinical bioactivity and therapeutic dose of antitumor drugs are severely limited due to poor targeting and side effects. NDDSs offers an excellent strategy to overcome the disadvantages of small molecule anticancer drugs. METHODS: Here, we have prepared N,O-carboxymethyl chitosan Oxaliplatin nanoparticles (CMCS-OXE NPs) and N,O-carboxymethyl chitosan Resveratrol nanoparticles (CMCS-Res NPs) were prepared by ion crosslinking and emulsification crosslinking, respectively. RESULTS: The results revealed that the CMCS-OXE NPs exhibited a high encapsulation efficiency (60%) with a size of approximately 190.0 nm, and the CMCS-Res NPs exhibited a high encapsulation efficiency (65%) with a size of approximately 164.2 nm. The treatment with both types of nanoparticles combined exhibited more significant anti-colon cancer activity than the free drugs or either type of nanoparticle alone. In the in vivo experiments, the inhibition efficiency of the combined nanoparticle treatment was much stronger than the free drugs or either type of nanoparticle alone. CONCLUSIONS: Overall, combination of oxaliplatin and resveratrol into a nanoparticle-drug delivery systems (NDDSs) appears to be a promising strategy for colorectal cancer (CRC) therapy.

Glycyrrhetinic acid remodels the tumor microenvironment and synergizes with doxorubicin for breast cancer treatment in a murine model.

We aimed to combine glycyrrhetinic acid with doxorubicin to prepare, characterize and evaluate a drug delivery nano-system with REDOX sensitivity for the treatment of breast cancer. M-DOX-GA NPs prepared by nano sedimentation were spherical, with a particle size of 181 nm. And the maximum encapsulation efficiency and drug loading in M-DOX-GA NPs were 89.28% and 18.22%, respectively. Cytotoxicity and cellular uptake experiments of nanoparticles to KC cells, Cal-27 cells and 4T1 cells were studied by the CCK-8 method. The result indicated that M-DOX-GA NPs could accurately release the drug into the tumor cells, thus achieving the targeted release of the drug. Comparing the survival rate of the above three cells, it was found that M-DOX-GA NPs had a good tumor selectivity and had a more significant therapeutic effect on breast cancer. A 4T1-bearing mouse model was established, and the tumor inhibition rate was 77.37% after injection of nanoparticle solution for 14 d. Normal tissue H&E stained sections and TUNEL assay were verified M-DOX-GA NPs have excellent tumor suppressive effect, and can efficiently reduce the toxic side effects on normal organisms, and effectively avoided 4T1 cells metastasis. Immunofluorescence detection and Western-blot analysis figured a decline in both CUGBP1 and α-SMA, which verifying the TME remodeling induced by glycyrrhetinic acid. Collectively, the combination of doxorubicin and glycyrrhetinic acid is an effective and safe strategy for remodeling fibrotic TME by improving the therapeutic outcome for breast cancer.

Adoption of Machine Learning in Intelligent Terrain Classification of Hyperspectral Remote Sensing Images.

To overcome the difficulty of automating and intelligently classifying the ground features in remote-sensing hyperspectral images, machine learning methods are gradually introduced into the process of remote-sensing imaging. First, the PaviaU, Botswana, and Cuprite hyperspectral datasets are selected as research subjects in this study, and the objective is to process remote-sensing hyperspectral images via machine learning to realize the automatic and intelligent classification of features. Then, the basic principles of the support vector machine (SVM) and extreme learning machine (ELM) classification algorithms are introduced, and they are applied to the datasets. Next, by adjusting the parameter estimates using a restricted Boltzmann machine (RBM), a new terrain classification model of hyperspectral images that is based on a deep belief network (DBN) is constructed. Next, the SVM, ELM, and DBN classification algorithms for hyperspectral image terrain classification are analysed and compared in terms of accuracy and consistency. The results demonstrate that the average detection accuracies of ELM on the three datasets are 89.54%, 96.14%, and 96.28%, and the Kappa coefficient values are 0.832, 0.963, and 0.924; the average detection accuracies of SVM are 88.90%, 92.11%, and 91.68%, and the Kappa coefficient values are 0.768, 0.913, and 0.944; the average detection accuracies of the DBN classification model are 92.36%, 97.31%, and 98.84%, and the Kappa coefficient values are 0.883, 0.944, and 0.972. The results also demonstrate that the classification accuracy of the DBN algorithm exceeds those of the previous two methods because it fully utilizes the spatial and spectral information of hyperspectral remote-sensing images. In summary, the DBN algorithm that is proposed in this study has high application value in object classification for remote-sensing hyperspectral images.

Preparation of Ultrafine Fly Ash-Based Superhydrophobic Composite Coating and Its Application to Foam Concrete.

The waterproof and thermal insulation property of foamed concrete is very important. In this study, the ultrafine fly ash (UFA)-based superhydrophobic composite coating was applied onto foam concrete. The UFA-based base coating that closely adhered to the concrete initially improved the waterproofness of the test block, and the silane coupling agent-modified UFA-based surface coating further achieved superhydrophobicity. The UFA on the coating surface and the asperities on the surface jointly formed a lotus leaf-like rough micro-nanostructure. The 154.34° water drop contact angle and 2.41° sliding angle on No. 5 coating were reached, indicating that it was a superhydrophobic surface. The water absorption ratios of the composite coating block were 1.87% and 16.6% at 4 h and 7 days, which were reduced by 97% and 75% in comparison with the original foam concrete. The compressive strength and heat conductivity coefficient after soaking for 4 h of the composite coating block were higher than 4.0 MPa and 0.225 W·m-1·K-1, respectively. The UFA-based superhydrophobic composite coating proposed in this study and applied onto foam concrete is simple and cheap, requires no precise instrument, and can be applied in a large area.

Anti-PD-L1-modified and ATRA-loaded nanoparticles for immuno-treatment of oral dysplasia and oral squamous cell carcinoma.

Aim: To develop nanomedicines for immuno-therapy of oral dysplasia and oral squamous cell carcinoma. Materials & methods: All-trans retinoic acid (ATRA)-poly(lactide-co-glycolide acid) (PLGA)-poly(ethylene glycol) (PEG)-programmed death-ligand 1 (PD-L1) nanomedicines were fabricated by loading ATRA into PLGA-PEG nanocarriers and modification using an anti-PD-L1 antibody. Results: ATRA-PLGA-PEG-PD-L1 nanoparticles showed fast cellular uptake, significantly inhibited proliferation and induced apoptosis in DOK and CAL27 cells. Moreover, in C3H tumor-bearing mice, ATRA-PLGA-PEG-PD-L1 nanoparticles more specifically targeted tumor cells, enhanced anticancer activity and reduced side effects when compared with free ATRA. Furthermore, CD8+ T cells were activated around PD-L1 positive cells in the tumor microenvironment after treatment. Conclusion: ATRA-PLGA-PEG-PD-L1 nanoparticles had low toxicity, high biocompatibility and specifically targeted oral dysplasia and squamous carcinoma cells both in vitro and in vivo.

Gold Nanoclusters for Bacterial Detection and Infection Therapy.

Infections caused by antibiotic-resistant bacteria have become one of the most serious global public health crises. Early detection and effective treatment can effectively prevent deterioration and further spreading of the bacterial infections. Therefore, there is an urgent need for time-saving diagnosis as well as therapeutically potent therapy approaches. Development of nanomedicine has provided more choices for detection and therapy of bacterial infections. Ultrasmall gold nanoclusters (Au NCs) are emerging as potential antibacterial agents and have drawn intense attention in the biomedical fields owing to their excellent biocompatibility and unusual physicochemical properties. Recent significant efforts have shown that these versatile Au NCs also have great application potential in the selective detection of bacteria and infection treatment. In this review, we will provide an overview of research progress on the development of versatile Au NCs for bacterial detection and infection treatment, and the mechanisms of action of designed diagnostic and therapeutic agents will be highlighted. Based on these cases, we have briefly discussed the current issues and perspective of Au NCs for bacterial detection and infection treatment applications.