Hybrid Cell Membrane-Functionalized Biomimetic Nanoparticles for Targeted Therapy of Osteosarcoma.

PURPOSE: In order to prepare a biomimetic nano-carrier which has inflammatory chemotaxis, homologous targeting and reduce immune clearance, for targeted chemotherapy of osteosarcoma, we fabricated the paclitaxel-loaded poly(lactic-co-glycolic) acid (PLGA) nanoparticles coated with 143B-RAW hybrid membrane (PTX-PLGA@[143B-RAW] NPs) and evaluate its anti-cancer efficacy in vitro and vivo. METHODS: PTX-PLGA@[143B-RAW] NPs were prepared by the ultrasonic method and were characterized by size, zeta potential, polymer dispersion index (PDI), Coomassie bright blue staining, transmission electron microscopy (TEM) and high performance liquid chromatography (HPLC). Cellular uptake, cell viability assay, flow cytometry and chemotactic effect of PTX-PLGA@[143B-RAW] NPs were evaluated in vitro. Biodistribution, anti-cancer therapeutic efficacy and safety of PTX-PLGA@[143B-RAW] NPs were evaluated in 143B osteosarcoma xenograft mice. RESULTS: The hybrid membrane successfully coated onto the surface of PLGA nanoparticles. PTX-PLGA@[143B-RAW] NPs had a drug loading capacity of 4.24 ± 0.02% and showed targeting ability to osteosarcoma. PTX-PLGA@[143B-RAW] NPs showed high cellular uptake and improved anti-cancer efficacy against 143B cells. More importantly, PTX-PLGA@[143B-RAW] NPs treatment suppressed tumor growth in tumor-bearing mice with minimal damage to normal tissues. CONCLUSION: PTX-PLGA@[143B-RAW] NPs could be used for targeted drug delivery and osteosarcoma therapy.

Recent progress of macrophage vesicle-based drug delivery systems.

Nanoparticle drug delivery systems (NDDSs) are promising platforms for efficient delivery of drugs. In the past decades, many nanomedicines have received clinical approval and completed translation. With the rapid advance of nanobiotechnology, natural vectors are emerging as novel strategies to carry and delivery nanoparticles and drugs for biomedical applications. Among diverse types of cells, macrophage is of great interest for their essential roles in inflammatory and immune responses. Macrophage-derived vesicles (MVs), including exosomes, microvesicles, and those from reconstructed membranes, may inherit the chemotactic migration ability and high biocompatibility. The unique properties of MVs make them competing candidates as novel drug delivery systems for precision nanomedicine. In this review, the advantages and disadvantages of existing NDDSs and MV-based drug delivery systems (MVDDSs) were compared. Then, we summarized the potential applications of MVDDSs and discuss future perspectives. The development of MVDDS may provide avenues for the treatment of diseases involving an inflammatory process.

Comparative evaluation of methods for isolating small extracellular vesicles derived from pancreatic cancer cells.

BACKGROUND: Small extracellular vesicles (sEVs) are nanosized vesicles involved in cell-to-cell communication. sEVs have been widely studied for clinical applications such as early detection of diseases and as therapeutics. Various methods for sEVs isolation are been using, but different methods may result in different qualities of sEVs and impact downstream analysis and applications. Here, we compared current isolation methods and performed a comparative analysis of sEVs from supernatant of cultured pancreatic cancer cells. METHODS: Ultracentrifugation, ultrafiltration and co-precipitation as concentration methods were firstly evaluated for yield, size, morphology and protein level of pellets. Then, isolate sEVs obtained by four different purification methods: size exclusion chromatography, density gradient ultracentrifugation, ultracentrifugation, and immunoaffinity capturing, were analysed and compared. RESULTS: For the concentration process, ultracentrifugation method obtained high quality and high concentration of pellets. For the purification process, immunoaffinity capturing method obtained the purest sEVs with less contaminants, while density gradient ultracentrifugation-based method obtained sEVs with the smallest size. Proteomic analysis revealed distinct protein contents of purified sEVs from different methods. CONCLUSIONS: For isolating sEVs derived from supernatant of cultured pancreatic cancer cell line, ultracentrifugation-based method is recommended for concentration of sEVs, density gradient ultracentrifugation-based method may be applied for obtaining purified sEVs with controlled size, immunoaffinity capturing may be suitable for studies requiring sEVs with high purity but may loss subtypes of sEVs without specific protein marker.

3,5,4'-Trimethoxy-trans-stilbene loaded microemulsion for cutaneous melanoma therapy by transdermal drug delivery.

For therapy of skin cancer, transdermal administration has been a potential way to enhance chemotherapy. However, the drug delivery efficacy remained unsatisfactory because of the physiological barriers from the skin to the tumor, which hindered the effect of 3,5,4'-trimethoxy-trans-stilbene (BTM), a drug that has toxicity to cancer. Herein, we prepared an oil-in-water (O/W) microemulsion to load BTM (BTM-ME) for transdermal therapy of melanoma. BTM-ME was characterized by size, zeta potential, and polymer disperse index (PDI). B16F10 melanoma cell line was used for cell experiments and animal models. And cell uptake, viability assay, and flow cytometry were to test the cell internalization and the ability of BTM-ME to induce cancer cell apoptosis. Skin penetration testing was to detect its penetration efficiency to the skin. And tumor-bearing mice were used to prove the improvement of anti-cancer efficacy of BTM-ME with the combination of Taxol. BTM was successfully loaded in O/W microemulsion, with a drug loading capacity of 24.82 mg/mL. BTM-ME can penetrate the skin and increase the retention of BTM in the epidermis. And the combination of Taxol and BTM-ME effectively suppressed tumor growth and has lower toxicity to normal organs. BTM-ME provides adjuvant therapy to cutaneous melanoma and the combination of Taxol and BTM-ME has the clinical potential for skin cancer therapy. Graphical abstract.

Gemcitabine loaded autologous exosomes for effective and safe chemotherapy of pancreatic cancer.

Pancreatic cancer remains one of the most highly lethal diseases with very poor prognosis. Gemcitabine (GEM) is the first-line chemotherapeutic drug for pancreatic cancer treatment but is associated with significant side effects when administered systemically. Exosomes have emerged as attractive candidates for drug delivery for their high delivery efficiency and biocompatibility. Here, GEM was loaded into autologous exosomes to formulate ExoGEM for targeted chemotherapy of pancreatic cancer. Autologous exosomes facilitate cellular uptake of GEM and contributed to significantly increased cytotoxic effect of GEM, while heterologous cellular uptake showed less efficiency. Autologous exosomes showed targeting ability to pancreatic cancer in biodistribution study, and GEM concentration in tumor site was increased via ExoGEM delivery. ExoGEM treatment, in tumor-bearing mice, significantly suppressed tumor growth, with prolonged survival in a dose-response manner, but caused minimal damage to normal tissues. More importantly, tumors in several mice treated with ExoGEM were disappeared without recurrence. Autologous exosomes are safe and effective vehicles for targeted delivery of GEM against pancreatic cancer. This delivery strategy may have implications for personalized chemotherapy of pancreatic cancer. STATEMENT OF SIGNIFICANCE: Exosomes are efficient delivery vehicles in intracellular communication. Moreover, potential tropism of autologous exosomes to the tumor microenvironment make them competitive delivery vehicles. The use of cancer-derived exosomes for drug delivery and superior targeting efficacy and enhanced anticancer efficacy of therapeutics have been evidenced. Gemcitabine is a mainstay for pancreatic treatment. However, poor cellular uptake and low targeting effects of gemcitabine often lead to severe systemic toxicity. Therefore, to overcome this limitation, we herein loaded gemcitabine into autologous pancreatic cancer-derived exosomes for the targeted chemotherapy of pancreatic cancer.

3,5,4'-trimethoxy-trans-stilbene loaded PEG-PE micelles for the treatment of colon cancer.

BACKGROUND: 3,5,4'-trimethoxy-trans-stilbene (BTM) is a methylated derivative of resveratrol. To improve the pharmaceutical properties of BTM, BTM loaded PEG-PE micelles (BTM@PEG-PE) were fabricated and its anti-cancer efficacy against colon cancer was evaluated. METHODS: BTM@PEG-PE micelles were prepared by the solvent evaporation method and were characterized by nuclear magnetic resonance (NMR), size, zeta potential, polymer disperse index (PDI) and transmission electron microscopy (TEM). Cellular uptake, cell viability assay, caspase-3 activity assay and flow cytometry were performed to evaluate the cell internalization and anti-cancer efficacy of BTM@PEG-PE micelles in vitro. Pharmacokinetic profiles of BTM and BTM@PEG-PE micelles were compared and in vivo anti-cancer therapeutic efficacy and safety of BTM@PEG-PE micelles on CT26 xenograft mice were evaluated. RESULTS: BTM was successfully embedded in the core of PEG-PE micelles, with a drug loading capacity of 5.62±0.80%. PEG-PE micelles facilitated BTM entering to the CT26 cells and BTM@PEG-PE micelles exerted enhanced anti-cancer efficacy against CT26 cells. BTM@PEG-PE micelles showed prolonged half-life and increased bioavailability. More importantly, BTM@PEG-PE micelles treatment suppressed tumor growth in tumor-bearing mice and prolonged survival with minimal damage to normal tissues. CONCLUSION: Altogether, the BTM@PEG-PE micelles might be a promising strategy to enhance the pharmacokinetic and pharmacodynamic potentials of BTM for colon cancer therapy.

Antenna selection for multiple-input multiple-output systems based on deep convolutional neural networks.

Antenna selection in Multiple-Input Multiple-Output (MIMO) systems has attracted increasing attention due to the challenge of keeping a balance between communication performance and computational complexity. Recently, deep learning based methods have achieved promising performance in many application fields. This paper proposed a deep learning (DL) based antenna selection technique. First, we generated the label of training antenna systems by maximizing the channel capacity. Then, we adopted the deep convolutional neural network (CNN) on the channel matrices to explicitly exploit the massive latent cues of attenuation coefficients. Finally, we used the adopted CNN to assign the class label and then select the optimal antenna subset. Experimental results demonstrate that our method can achieve better performance than the state-of-the-art baselines for data-driven based antenna selection.

Learning Low-Rank Class-Specific Dictionary and Sparse Intra-Class Variant Dictionary for Face Recognition.

Face recognition is challenging especially when the images from different persons are similar to each other due to variations in illumination, expression, and occlusion. If we have sufficient training images of each person which can span the facial variations of that person under testing conditions, sparse representation based classification (SRC) achieves very promising results. However, in many applications, face recognition often encounters the small sample size problem arising from the small number of available training images for each person. In this paper, we present a novel face recognition framework by utilizing low-rank and sparse error matrix decomposition, and sparse coding techniques (LRSE+SC). Firstly, the low-rank matrix recovery technique is applied to decompose the face images per class into a low-rank matrix and a sparse error matrix. The low-rank matrix of each individual is a class-specific dictionary and it captures the discriminative feature of this individual. The sparse error matrix represents the intra-class variations, such as illumination, expression changes. Secondly, we combine the low-rank part (representative basis) of each person into a supervised dictionary and integrate all the sparse error matrix of each individual into a within-individual variant dictionary which can be applied to represent the possible variations between the testing and training images. Then these two dictionaries are used to code the query image. The within-individual variant dictionary can be shared by all the subjects and only contribute to explain the lighting conditions, expressions, and occlusions of the query image rather than discrimination. At last, a reconstruction-based scheme is adopted for face recognition. Since the within-individual dictionary is introduced, LRSE+SC can handle the problem of the corrupted training data and the situation that not all subjects have enough samples for training. Experimental results show that our method achieves the state-of-the-art results on AR, FERET, FRGC and LFW databases.