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ABSTRACT Seaweeds are related to anti-inflammatory, anti-bacterial and anti-noceptive effects. This work aimed to verify the potential of seaweed Padina gymnospora (Kützing) Sonder 1871 to improve wound healing in vitro. P. gymnospora was collected at a bethonic area in Espirito Santo. Methanolic extract of P. gymnospora was obtained by percolation. To determine cytotoxicity, colorimetric MTT tests were performed against normal fibroblasts (L929), macrophages (RAW 264.7) and human ovarian carcinoma (OVCAR-3) cell lines using concentration range of 12–110 µg ml-1. To evaluate in vitro wound healing, monolayer of fibroblasts L929 was seeded and artificial wounded. Cell proliferation was blocked by 5 µg ml-1 Mytomycin C. Nitric oxide inhibition was quantified with Raw 264.7 by Griess reaction. Minimal inhibitory concentration (MIC) against Staphylococcus aureus was determined. Eletrospray ionization with Fourier transform ion cyclotron resonance mass spectrometry (ESI-FT-ICR MS) was applied to detail composition of P. gymnospora methanolic extract. No cytotoxic effect in all cell lines was detected until the maximum concentration of 110 µg ml-1. P. gymnospora promoted significantly migration at the concentration of 25 µg ml-1 (p < 0.05). A prominent inhibition of nitric oxide formation was achieved in a concentration of 20 µg ml-1 of methanolic extract of P. gymnospora (62.06 ± 1.20%). Antibacterial activity against S. aureus could be demonstrated with MIC of 500 µg ml-1. ESI-FT-ICR MS analysis indicated eleven molecules between then, linolenic, oleic and linoleic acid. P. gymnospora favored wound repair in vitro what could be related to its fatty acid composition. In addition, its antimicrobial effect, and NO inhibition activity contribute for a new approach of P. gymnospora as a promise natural product for treatment of cutaneous wound.
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OBJECTIVE: The objective of the present study was to employ high throughput image analysis to detect necrosis and apoptosis. Specific markers were replaced by morphological parameters of cells and nuclei. METHOD: Fresh blood was taken from a healthy female and given a treatment to induce cell necrosis and apoptosis. Afterward, the samples were stained with AnnexinV-FITC, DRAQ5 and DAPI. Slides were made and analyzed using the cytometer iCys. Pictures were scanned. The analyzed sample consisted of 73 sets of images of DAPI, DRAQ5 and AnnexinV-FITC, respectively. For image analysis and subsequent statistical processing, the CellProfiler and CellProfilerAnalyst were used. Each sample was analyzed twice. The first analysis was conducted using the markers (DAPI, DRAQ5 and Annexin) for an unequivocal identification and subsequent count of necrotic, apoptotic and live cells (gold standard). Thereafter, a second analysis was performed for the nuclear morphology and texture (morphometric analysis). After the machine learning process was completed, the software calculated the quantity of cells in each of the three groups. A comparison between the result of the gold standard and the morphometric analysis was performed using linear regression and a Bland-Altman test. RESULTS: The linear regression between the two compared analyses was r(2)=0.57 for apoptosis, r(2)=0.84 for necrosis and r(2)=0.79 for living cells. CONCLUSION: It may be concluded that it is possible to replace specific markers against morphology without losing the reproducible high-throughput character of a cytometric analysis.