Perspectives of the Apiaceae Hepatoprotective Effects - A Review

The liver has the crucial role in the regulation of various physiological processes and in the excretion of endogenous waste metabolites and xenobiotics. Liver structure impairment can be caused by various factors including microorganisms, autoimmune diseases, chemicals, alcohol and drugs. The plant kingdom is full of liver protective chemicals such as phenols, coumarins, lignans, essential oils, monoterpenes, carotenoids, glycosides, flavonoids, organic acids, lipids, alkaloids and xanthenes. Apiaceae plants are usually used as a vegetable or as a spice, but their other functional properties are also very important. This review highlights the significance of caraway, dill, cumin, aniseed, fennel, coriander, celery, lovage, angelica, parsley and carrot, which are popular vegetables and spices, but possess hepatoprotective potential. These plants can be used for medicinal applications to patients who suffer from liver damage

A three-drug nanoscale drug delivery system designed for preferential lymphatic uptake for the treatment of metastatic melanoma

Metastatic melanoma has a high mortality rate due to lymphatic progression of the disease. Current treatment is surgery followed by radiation and intravenous chemotherapy. However, drawbacks for current chemotherapeutics lie in the fact that they develop resistance and do not achieve therapeutic concentrations in the lymphatic system. We hypothesize that a three-drug nanoscale drug delivery system, tailored for lymphatic uptake, administered subcutaneously, will have decreased drug resistance and therefore offer better therapeutic outcomes. We prepared and characterized nanoparticles (NPs) with docetaxel, everolimus, and LY294002 in polyethyleneglycol-block-poly(ε-caprolactone) (PEG-PCL) polymer with different charge distributions by modifying the ratio of anionic and neutral end groups on the PEG block. These NPs are similarly sized (~48nm), with neutral, partially charged, or fully charged surface. The NPs are able to load ~2mg/mL of each drug and are stable for 24h. The NPs are assessed for safety and efficacy in two transgenic metastatic melanoma mouse models. All the NPs were safe in both models based on general appearance, weight changes, death, and blood biochemical analyses. The partially charged NPs are most effective in decreasing the number of melanocytes at both the proximal (sentinel) lymph node (LN) and the distal LN from the injection site. The neutral NPs are efficacious at the proximal LN, while the fully charged NPs have no effect on either LNs. Thus, our data indicates that the NP surface charge and lymphatic efficacy are closely tied to each other and the partially charged NPs have the highest potential in treating metastatic melanoma

Pseudo-Derivative and Pseudo-Integral of Fractional Order

Integral and differential of fractional order are important notion that is often used in dealing with Frechet geometry. Based on pseudo-operations given by monotone and continuous function g, we study pseudo-derivative and pseudo-integral of fractional order through this pape

AN EFFICIENT INITIALIZATION METHOD FOR K-MEANS CLUSTERING OF HYPERSPECTRAL DATA

K-means is definitely the most frequently used partitional clustering algorithm in the remote sensing community. Unfortunately due to its gradient decent nature, this algorithm is highly sensitive to the initial placement of cluster centers. This problem deteriorates for the high-dimensional data such as hyperspectral remotely sensed imagery. To tackle this problem, in this paper, the spectral signatures of the endmembers in the image scene are extracted and used as the initial positions of the cluster centers. For this purpose, in the first step, A Neyman–Pearson detection theory based eigen-thresholding method (i.e., the HFC method) has been employed to estimate the number of endmembers in the image. Afterwards, the spectral signatures of the endmembers are obtained using the Minimum Volume Enclosing Simplex (MVES) algorithm. Eventually, these spectral signatures are used to initialize the k-means clustering algorithm. The proposed method is implemented on a hyperspectral dataset acquired by ROSIS sensor with 103 spectral bands over the Pavia University campus, Italy. For comparative evaluation, two other commonly used initialization methods (i.e., Bradley & Fayyad (BF) and Random methods) are implemented and compared. The confusion matrix, overall accuracy and Kappa coefficient are employed to assess the methods’ performance. The evaluations demonstrate that the proposed solution outperforms the other initialization methods and can be applied for unsupervised classification of hyperspectral imagery for landcover mapping