Health promoting potential of herbal teas and tinctures from Artemisia campestris subsp maritima: from traditional remedies to prospective products

This work explored the biotechnological potential of the medicinal halophyte Artemisia campestris subsp. maritima (dune wormwood) as a source of health promoting commodities. For that purpose, infusions, decoctions and tinctures were prepared from roots and aerial-organs and evaluated for in vitro antioxidant, anti-diabetic and tyrosinase-inhibitory potential, and also for polyphenolic and mineral contents and toxicity. The dune wormwood extracts had high polyphenolic content and several phenolics were identified by ultra-high performance liquid chromatography-photodiode array-mass-spectrometry (UHPLC-PDA-MS). The main compounds were quinic, chlorogenic and caffeic acids, coumarin sulfates and dicaffeoylquinic acids; several of the identified phytoconstituents are here firstly reported in this A. campestris subspecies. Results obtained with this plant's extracts point to nutritional applications as mineral supplementary source, safe for human consumption, as suggested by the moderate to low toxicity of the extracts towards mammalian cell lines. The dune wormwood extracts had in general high antioxidant activity and also the capacity to inhibit a-glucosidase and tyrosinase. In summary, dune wormwood extracts are a significant source of polyphenolic and mineral constituents, antioxidants and a-glucosidase and tyrosinase inhibitors, and thus, relevant for different commercial segments like the pharmaceutical, cosmetic and/or food industries.FCT - Foundation for Science and Technology [CCMAR/Multi/04326/2013]; Portuguese National Budget; FCT [IF/00049/2012, SFRH/BD/94407/2013]; Research Foundation - Flanders (FWO) [12M8315N]info:eu-repo/semantics/publishedVersio

Development of polymer-assisted nanoparticles and nanogels for cancer therapy: An update

With cancer remaining as one of the main causes of deaths worldwide, many studies are undergoing the effort to look for a novel and potent anticancer drug. Nanoparticles (NPs) are one of the rising fields in research for anticancer drug development. One of the key advantages of using NPs for cancer therapy is its high flexibility for modification, hence additional properties can be added to the NPs in order to improve its anticancer action. Polymer has attracted considerable attention to be used as a material to enhance the bioactivity of the NPs. Nanogels, which are NPs cross-linked with hydrophilic polymer network have also exhibited benefits in anticancer application. The characteristics of these nanomaterials include non-toxic, environment-friendly, and variable physiochemical properties. Some other unique properties of polymers are also attributed by diverse methods of polymer synthesis. This then contributes to the unique properties of the nanodrugs. This review article provides an in-depth update on the development of polymer-assisted NPs and nanogels for cancer therapy. Topics such as the synthesis, usage, and properties of the nanomaterials are discussed along with their mechanisms and functions in anticancer application. The advantages and limitations are also discussed in this article

Interactive Effect of UVR and Phosphorus on the Coastal Phytoplankton Community of the Western Mediterranean Sea: Unravelling Eco- Physiological Mechanisms

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Nuclei Classification in ER-IHC Stained Histopathology Images using Deep Learning Models

Breast cancer treatment is highly dependent on the carcinoma stage, which was obtained by evaluating the pathological slides and the estrogen receptor status. The Allred score has been manually calculated by the pathologists to represent the percentage and intensity of tumor nuclei. The task can be automated by enabling digital pathology, by classifying the nuclei using learning-based method. We present here a comprehensive analysis of 32 pretrained deep learning models from DenseN et, EfficientN et, InceptionResN et, Inception, ResN et, MobileNet, NasNet, VGG and Xception. The aim of this exper-iment is to identify the best pre-trained model for classifying the negative, weak, moderate and strong nuclei taken from 44 whole slide images of estrogen receptor immunohistochemistry stained histopathology. The highest test accuracy is achieved by DenseNet169 with the measure of 94.91 %. This study will be a basis for the future development of more complex deep learning models with cascading or any combination of the tested models