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The aim of the current study was to investigate the anti-lung cancer effects of astragalin. Studies were also undertaken to evaluate its effects on apoptosis induction, ROS production, cellular migration and invasion and JAK/STAT3 signalling pathway. MTT assay was used to evaluate cell viability in NSCLC A549 cells after exposure to astragalin molecule. Apoptosis was investigated using AO/EB staining, comet assay and western blotting assay. Fluorescence microscopy was implemented to estimate ROS production. Cell migration and invasion were measured using transwell chambers assay. Effects of astragalin on JAK/STAT pathway were investigated using western blotting assay. Results showed astragalin molecule induced inhibition of proliferation in A549 cells in a dose-dependent fashion. Further, the antiproliferative effects were found to mediate via apoptosis as suggested by AO/EB staining and western blotting assay. Astragalin modulated the expressions of caspase-3, caspase-9, Bax, Bak, Cyt-c Bcl-2, XIAP and Bcl-xL. Astragalin induced DNA damage in A549 cells which too indicated apoptotic cell death. Astragalin molecule enhanced the production of ROS by A549 cells. It inhibited both cell migration and invasion of A549 cells in a concentration-dependent manner. Finally, astragalin drug was observed with remarkable potential of targeting JAK/STAT pathway in A549 NSCLC cells. These results indicated that astragalin drug could prove helpful in lung cancer treatment and research provided more in-vivo studies are performed.

Specific cellular uptake and sufficient drug release in tumor tissues are important for effective cancer therapy. Hyaluronic acid (HA), a skeleton material, could specifically bind to cluster determinant 44 (CD44) receptors highly expressed on the surface of tumor cells to realize active targeting. Cystamine (cys) is sensitive highly reductive environment inside tumor cells and was used as a connecting arm to connect docosahexaenoic acid (DHA) and chlorin e6 (Ce6) to the HA skeleton to obtain redox-sensitive polymer HA-cys-DHA/Ce6 (CHD). Nanoparticles were fabricated and loaded with chemotherapeutic drug docetaxel (DTX) by physical encapsulation. The prepared nanoparticles had significantly increased uptake by MCF-7 cells that overexpressed CD44 receptors, and DTX was effectively released at high reducing condition. Compared with mono-photodynamic therapy (PDT) or mono-chemotherapy, the prepared nanoparticles exhibited superior anti-tumor effect by inhibiting microtubule depolymerization, blocking cell cycle and generating reactive oxygen species (ROS). In vivo anti-tumor experiments proved that DTX/CHD nanoparticles had the best antitumor response versus DTX and CHD nanoparticles under near-infrared (NIR) irradiation. These studies revealed that redox-responsive DTX-loaded CHD nanoparticles held great potential for the treatment of breast cancer.

Background Mediastinal malignant teratoma, as a germ cell tumor, is rare and lacking of population-based study. This study aims to illustrate the incidence, treatment and prognosis of mediastinal malignant teratoma by using a population-based database. Methods We gathered clinicopathological data (1975–2016) of malignant teratoma from the Surveillance, Epidemiology and End Results (SEER) database. Then we conducted analysis on the incidence, treatment and prognosis of mediastinal malignant teratoma and other malignant teratomas. Results A total of 5,550 cases were in our study cohort, including mediastinal malignant teratoma (n=133) and other malignant teratomas (n=5,417). The incidence of mediastinal malignant teratoma in 2016 was 0.004 per 100,000 persons. Both overall survival (OS) and cancer-specific survival (CSS) of mediastinal malignant teratoma were significantly worse than those of other malignant teratomas (both P

The selective infiltration of cell membranes and tissue barriers often blocks the entry of most active molecules. This natural defense mechanism prevents the invasion of exogenous substances and limits the therapeutic value of most available molecules. Therefore, it is particularly important to find appropriate ways of membrane translocation and therapeutic agent delivery to its target site. Cell penetrating peptides (CPPs) are a group of short peptides harnessed in this condition, possessing a significant capacity for membrane transduction and could be exploited to transfer various biologically active cargoes into the cells. Since their discovery, CPPs have been employed for delivery of a wide variety of therapeutic molecules to treat various disorders including cranial nerve involvement, ocular inflammation, myocardial ischemia, dermatosis and cancer. The promising results of CPPs-derived therapeutics in various tumor models demonstrated a potential and worthwhile scope of CPPs in chemotherapy. This review describes the detailed description of CPPs and CPPs-assisted molecular delivery against various tissues and organs disorders. An emphasis is focused on summarizing the novel insights and achievements of CPPs in surmounting the natural membrane barriers during the last 5 years.

In the field of modern information technology, how to find information quickly, accurately and comprehensively that users really needed has become the focus of research in this field. In this article, a feature selection method based on a complex network is proposed for the structure and content characteristics of large-scale web text information. The preprocessed web text is converted into a complex network. The nodes in the network correspond to the entries in the text. The edges of the network correspond to the links between the entries in the text, and the degree of nodes and the aggregation system are used. Second, the text classification method is studied from the point of view of data sampling, and a text classification method based on density statistics is proposed. This method uses not only the density information of the text feature set in the classification process, but also the use of statistical merging criteria to get the text. The difference information of each feature has a better classification effect for large text collections.

The present study aimed to investigate the correlation of long non-coding RNA nuclear-enriched abundant transcript 1 (lncRNA NEAT1) with microRNA (miR)-21, miR-124, and miR-125a, and their associations with disease risk, severity, and inflammatory cytokines of allergic rhinitis (AR). Totally 70 AR patients and 70 non-atopic obstructive snoring patients (as controls) were recruited. Inferior turbinate mucosa samples were collected from all participants for lncRNA NEAT1, its targets (miR-21, miR-124, and miR-125a), interleukin (IL)-4, IL-6, IL-10, and IL-17 detection via reverse transcription quantitative polymerase chain reaction. Disease severity of AR patients was assessed using individual nasal symptom score (INSS) and total nasal symptom score (TNSS). LncRNA NEAT1 was upregulated, while miR-21, miR-124, and miR-125a were downregulated in AR patients compared with controls. Additionally, lncRNA NEAT1, miR-21, and miR-125a displayed good values in differentiating AR patients from controls, while miR-124 could only slightly differentiate AR patients from controls. In AR patients, lncRNA NEAT1 was negatively associated with miR-21 and miR-125a, but not miR-124. However, in controls, no correlation of lncRNA NEAT1 with miR-21, miR-124, or miR-125a was observed. Furthermore, in AR patients, lncRNA NEAT1 was positively, while miR-21 and miR-125a was negatively associated with INSS (rhinorrhea, itching, congestion scores), TNSS and inflammatory cytokines; however, correlation of miR-124 with INSS, TNSS, and inflammatory cytokines was slight. LncRNA NEAT1 and its targets (miR-21 and miR-125a) present close correlations with disease risk, severity, and inflammation of AR, suggesting their potential as biomarkers for AR assessment.

A new denoising method is proposed to solve the problem that the noise of three-dimensional target images collected by photoelectric theodolites is varied and randomly distributed. Firstly, based on the universal law of gravity, the cross-correlation degree is calculated by taking the image pixels as the particles. Secondly, the Otsu algorithm is used to calculate the threshold to determine whether the pixel is noise. Finally, the results of the denoising algorithm are compared with those of Wiener filter, mean filter, and median filter, and the four algorithms are compared and evaluated from both subjective and objective data [1, 2, 3]. The results show that when the salt and pepper noise density is less than 20% [4], the visual effect of the algorithm is better than the other four algorithms, and the peak signal-to-noise ratio (psnr) is between 30 and 45 dB. The PSNR is increased to more than 10 dB by using this algorithm instead of the optimal algorithm of the other three algorithms. The visual effect is consistent with the data. The pixel correlation filtering algorithm is feasible and meets the needs of practical engineering.

Aiming at the problem of conceptual ambiguity and underlying semantic structure of multi label text categorization, an ensemble classification method is proposed, which combines random forest (RF) algorithm and semantic core co-occurrence latent semantic vector space (CLSVSM). Through the random segmentation of words, the diversity of integration is increased, and the different orthogonal projection of low dimensional implicit semantic space is obtained. Random forest can effectively solve binary classification problem, and implicit semantics reveals the underlying semantic structure of text. The combination of them can represent the diversity and accuracy of individuals. The experimental results on Yahoo dataset demonstrate the effectiveness of the proposed method, which is superior to other methods in Hamming loss, coverage, first error and average accuracy.

Currently, multidrug resistance (MDR) is one of the major reasons for failure in clinical cancer chemotherapy. Overexpression of the ATP binding cassette (ABC) transporter P-glycoprotein (P-gp), which significantly increases the efflux of anticancer drugs from tumor cells, enhances MDR. In the past few decades, four generations of P-gp inhibitors have appeared. However, they are limited in clinical application due to their severe toxic side effects. As a P-gp inhibitor and carrier for loading chemotherapy agents, TPGS has received increasing attention due to its advantages and unique properties of reversing MDR. TPGS is an amphipathic agent that increases the solubility of most chemotherapy drugs and decreases severe side effects. In addition, TPGS is an excellent carrier with P-gp-inhibiting ability. In this review, we summarize the latest articles on TPGS-based nanodelivery systems to prevent MDR.

In view of the low recall rate of archives retrieval at this stage, this paper proposes a cross-modal multimedia archives information retrieval model. According to the characteristics of archives, this method carries out feature processing and semantic mapping of archives information, applies the integrated learning method to cross-media archives retrieval, proposes Bagging-SM method for semantic matching of different modes of multimedia archives information, and finally proves that the cross-modal multimedia archives information retrieval model can effectively improve archives information retrieval and retrieval. Full rate is helpful to the development of intelligent retrieval in archives system.

In view of the problems faced by the effective classification and management of massive text, a new classification method for mass Web text information is proposed. The core idea is based on the characteristics of the low quantity, high value rate of the long text and the high quantity and low price rate of the short text in the current network environment. The feature selection method based on complex network is proposed. The number of features obtained by this method is more stable, and the accuracy of the selection of features in large text centralization is improved. Secondly, a text classification method based on density statistical merging is proposed, and the classification method is studied from the point of view of data sampling. The method is classified. In the process, we not only use the density information of the text feature set, but also use the difference information of each feature of the text obtained by the statistical merging criteria. Therefore, the algorithm has better robustness to noise and has a better classification effect to the large text set.

Advanced strategies have been developed to overcome limitations of traditional pharmacological treatments like low specificity and severe side effects. Remarkably, the application of nano drug delivery systems (NDDS) to improve intracellular uptake has promising prospects. Among other proposed materials, metal-organic frameworks (MOFs) formed by the assembly of metal ions and organic ligands exhibit excellent advantages as a nanocarrier, such as high drug loading, excellent biocompatibility and biodegradability, controllable drug release as well as easy modification. Thus, MOFs are widely used in NDDS. Based on this, scientists devoted much effort to designing multi-functional and versatile MOFs to achieve desirable therapeutic effects. This review introduced the latest MOFs for tumor treatment. Here, the classification of MOFs was introduced and, some responsive MOFs to control the release behavior of drugs were reviewed. Besides, some advanced active targeting MOFs to improve drug accumulation in tumor were summarized. Finally, the remaining challenges encountered by MOFs were discussed and proposed, which could provide guidance for developing more intelligent NDDS based on the promising materials of MOFs.

As a major therapeutic approach for cancer treatment, the effectiveness of chemotherapy is challenged by multidrug resistance (MDR). Herein, we fabricated novel redox-responsive, chondroitin sulfate-based nanoparticles that could simultaneously deliver quercetin (chemosensitizer), chlorin e6 (photosensitizer) and paclitaxel (chemotherapeutic agent) to exert enhanced chemo-photodynamic therapy for overcoming MDR and lung metastasis of breast cancer. In vitro cell study showed that nanoparticles down-regulated the expression of P-glycolprotein (P-gp) on MCF-7/ADR cells and thereby improved the anticancer efficacy of PTX against MCF-7/ADR cells. Moreover, NIR laser irradiation could induce nanoparticles to generate cellular reactive oxygen species (ROS), leading to mitochondrial membrane potential loss, and meanwhile facilitating lysosomal escape of drugs. Importantly, the novel nanoplatform exhibited effective in vivo MDR inhibition and anti-metastasis efficacy through enhanced chemo-photodynamic therapy. Thus, the study suggested that the multifunctional nanoplatform had good application prospect for effective breast cancer therapy.

List Comprehension representing comes from comprehension of mathematics, which provides a strong and concise representing method. Monad representing is concise and readability mainly because Monad extends List Comprehension to any Monad. This paper is concerned with the transforming from List Comprehension of functional programming language Haskell to C programs. Using Haskell itself we have also realized this transformation. Haskell is the only language that supports Monad, so our research is helpful to the building of Monad researching environments.

Most of current ontology mapping methods can not treat different mapping tasks in different ways referred to the features of the input ontology. And they combine different mapping strategies without full consideration of the influences on mapping results caused mapping strategies. In view of the above questions, this paper proposes a dynamic mapping policy selection method which can pre-select mapping strategy and use entropy decision-making method to determine the combined weight of the selected strategy. Experiments show that this method can maintain the stability and the commonality, and improve the recall ratio and the precision ratio at the same time.

Most of current ontology mapping methods can not treat different mapping tasks in different ways referred to the features of the input ontology. And they combine different mapping strategies without full consideration of the influences on mapping results caused by mapping strategies. In view of the above questions, this paper proposes a dynamic mapping policy selection method which can pre-select mapping strategy and use entropy decision-making method to determine the combined weight of the selected strategy. Experiments show that this method can maintain the stability and the commonality, and improve the recall ratio and the precision ratio at the same time.

Hand-based personal identification is a stable and reliable biometrically technique in the field of personal identity recognition. In this paper, both hand shape and palmprint texture features are extracted to facilitate a coarse-to-fine dynamic identification task. The wavelet zero-crossing method is first used to extract hand shape features to guide the fast selection of a small set of similar candidates from the database. Then, a circular Gabor filter, which is robust against brightness, and modified Zernike moments methods are used to extract the features of palmprint. And one-class-one-network (Back-Propagation Neural Network (BPNN) classification structure is employed for final classification. The experimental results show the effectiveness and accuracy of the proposed approach.

A method based on genetic algorithms (GA) for fusing multiple images of a static scene into an image with maximum information content is introduced. It partitions the image domain into uniform blocks and for each block selects the image that contains the most information within that block. The selected images are then blended together using rational Gaussian blending functions that are centered at the blocks. In this paper, we employ GA for optimizing both the block size and width of the blending functions. We also examine the effectiveness of our scheme by checking the fitness function in GA, which includes both factors related to information and human vision.

For most image fusion algorithms split relationship among pixels and treat them more or less independently, this paper proposes a region based multi-focus image fusion scheme using wavelet transform, morphological and genetic algorithm, which combines aspects of feature and pixel-level fusion. The basic idea is to select sharply focused regions from source images and to combine them together to reconstruct the resultant image in which all the regions are properly focused. Experimental results demonstrate that the proposed scheme outperforms Haar wavelet approach and morphological wavelet approach, both in visual effect and objective evaluation criteria, particularly when there is movement in the objects or mis-registration of the source images.

Most of current ontology mapping methods can not treat different mapping tasks in different ways referred to the features of the input ontology. And they combine different features of ontology without full consideration of the influences on mapping results caused mapping features. In view of the above questions, this paper proposes mapping method which can use entropy decision-making method to determine the combined weight of the different features of ontology. Experiments show that this method can maintain the stability and the commonality, and improve the recall ratio and the precision ratio at the same time.