Cell encapsulation in liquified compartments: Protocol optimization and challenges

Cell encapsulation is a widely used technique in the field of Tissue Engineering and Regenerative Medicine (TERM). However, for the particular case of liquefied compartmentalised systems, only a limited number of studies have been reported in the literature. We have been exploring a unique cell encapsulation system composed by liquefied and multilayered capsules. This system transfigured the concept of 3D scaffolds for TERM, and was already successfully applied for bone and cartilage regeneration. Due to a number of appealing features, we envisage that it can be applied in many other fields, including in advanced therapies or as disease models for drug discovery. In this review, we intend to highlight the advantages of this new system, while discussing the methodology, and sharing the protocol optimization and results. The different liquefied systems for cell encapsulation reported in the literature will be also discussed, considering the different encapsulation matrixes as core templates, the types of membranes, and the core liquefaction treatments.publishe

Molecular systematic study in the genus Linum (Linaceae) in Iran

The genus Linum L. is an important plant genus as it contains the species with economic values and particularly Linum usitatissimum L. that is source of fibre and linseed oil. This genus contains 230 species throughout the world and has about 22 species in Iran. Little is known about Linum species relationship and phylogeny. Therefore, the aim of present study was molecular phylogenetic investigation of the Linum species growing in Iran and to present data on their biogeography. We used both ITS and chloroplast DNA sequences (psbA-trnHGUG region) for inferring the species phylogeny and relationship. We also used cpDNA for inferring the species time of divergence and with ISSR markers to identify the path of species distribution in the country. The phylogenetic trees obtained for both ITS and cpDNA sequences were almost congruent. NeighborNet diagram and BEAST tree based on Bayesian method separated the outgroup species Hugonia and Anisadenia from the other species studied. The subspecies studied in Linum macronicum were placed close to each other and along with L. corymbulosum comprised a separate clade. The clades obtained showed divergence time between 5–20 mya. The present study revealed that the species of the sect. Linum are monophyletic, while members of the sections Linastrum and Syllinum are intermixed and seem to be paraphyletic

In vitro antioxidant and anticancer activity of young Zingiber officinale against human breast carcinoma cell lines

<p>Abstract</p> <p>Background</p> <p>Ginger is one of the most important spice crops and traditionally has been used as medicinal plant in Bangladesh. The present work is aimed to find out antioxidant and anticancer activities of two Bangladeshi ginger varieties (Fulbaria and Syedpuri) at young age grown under ambient (400 μmol/mol) and elevated (800 μmol/mol) CO₂concentrations against two human breast cancer cell lines (MCF-7 and MDA-MB-231).</p> <p>Methods</p> <p>The effects of ginger on MCF-7 and MDA-MB-231 cell lines were determined using TBA (thiobarbituric acid) and MTT [3-(4,5-dimethylthiazolyl)-2,5-diphenyl-tetrazolium bromide] assays. Reversed-phase HPLC was used to assay flavonoids composition among Fulbaria and Syedpuri ginger varieties grown under increasing CO₂concentration from 400 to 800 μmol/mol.</p> <p>Results</p> <p>Antioxidant activities in both varieties found increased significantly (P ≤ 0.05) with increasing CO₂concentration from 400 to 800 μmol/mol. High antioxidant activities were observed in the rhizomes of Syedpuri grown under elevated CO₂concentration. The results showed that enriched ginger extract (rhizomes) exhibited the highest anticancer activity on MCF-7 cancer cells with IC₅₀values of 34.8 and 25.7 μg/ml for Fulbaria and Syedpuri respectively. IC₅₀values for MDA-MB-231 exhibition were 32.53 and 30.20 μg/ml for rhizomes extract of Fulbaria and Syedpuri accordingly.</p> <p>Conclusions</p> <p>Fulbaria and Syedpuri possess antioxidant and anticancer properties especially when grown under elevated CO₂concentration. The use of ginger grown under elevated CO₂concentration may have potential in the treatment and prevention of cancer.</p

The Narrative Frame of Daniel: A Literary Assessment

This paper presents a fuzzy multicriteria group decision making approach for evaluating and selecting information systems projects. The inherent subjectiveness and imprecision of the evaluation process is modeled by using linguistic terms characterized by triangular fuzzy numbers. A new algorithm based on the concept of the degree of dominance is developed to avoid the complex and unreliable process of comparing fuzzy numbers usually required in fuzzy multicriteria decision making. A multicriteria decision support system is proposed to facilitate the evaluation and selection process. An information systems project selection problem is presented to demonstrate the effectiveness of the approach

Ensemble-based machine learning algorithms for classifying breast tissue based on electrical impedance spectroscopy

The initial identification of breast cancer and the prediction of its category have become a requirement in cancer research because they can simplify the subsequent clinical management of patients. The application of artificial intelligence techniques (e.g., machine learning and deep learning) in medical science is becoming increasingly important for intelligently transforming all available information into valuable knowledge. Therefore, we aimed to classify six classes of freshly excised tissues from a set of electrical impedance measurement variables using five ensemble-based machine learning (ML) algorithms, namely, the random forest (RF), extremely randomized trees (ERT), decision tree (DT), gradient boosting tree (GBT) and AdaBoost (Adaptive Boosting) (ADB) algorithms, which can be subcategorized as bagging and boosting methods. In addition, the ranked order of the variables based on their importance differed across the ML algorithms. The results demonstrated that the three bagging ensemble ML algorithms, namely, RF ERT and DT, yielded better classification accuracies (78–86%) compared with the two boosting algorithms, GBT and ADB (60–75%). We hope that these our results would help improve the classification of breast tissue to allow the early prediction of cancer susceptibility