Monomeric and oligomeric flavan-3-ols and antioxidant activity of leaves from different Laurus sp.

The phenolic profile and antioxidant activity of three endemic Laurus sp. from Portugal were analysed. Dried leaves of L. nobilis L., L. azorica (Seub.) Franco, and L. novocanariensis Rivas Mart., Lousã, Fern. Prieto, E. Días, J. C. Costa & C. Aguiar, collected in the mainland and in the Azores and Madeira archipelagos, respectively, were used to prepare different extracts (aqueous, ethanolic and hydroalcoholic). They were studied regarding their DPPH˙ scavenging activity, total phenolic and flavonoid contents, and the main phenolic compounds were identified by HPLC-DAD-ESI-MS/MS. Total flavonoid contents were 30.1, 46.3, and 36.7 mg of epicatechin equivalents per g of sample (dry weight) for L. nobilis, L. azorica and L. novocanariensis, respectively. Epicatechin was the major compound, representing ∼12.1% of total flavan-3-ols in L. nobilis, ∼25.6% in L. azorica, and ∼19.9% in L. novocanariensis. Although all samples presented a similar phenolic profile, significant differences were observed in their total contents and antioxidant activity.

Eruca sativa: Benefits as antioxidants source versus risks of already banned pesticides

Eruca sativa (rocket salad) has been intensely consumed all over the world, insomuch as, this work was undertaken to evaluate the antioxidant status and the environmental contamination (positive and negative nutritional contribution) of leaves and stems from this vegetable. Antioxidant capacity of rocket salad was assessed by mean of optical methods, such as the total phenolic content (TPC), reducing power assay and DPPH radical scavenging activity. The extent of the environmental contamination was reached through the quantification of thirteen organochlorine pesticides (OCP) by using gas chromatography coupled with electron-capture detector (GC-ECD) and compound confirmations employing gas chromatography tandem mass-spectrometry (GC-MS/MS). The OCP residues were extracted by using Quick, Easy, Cheap, Effective, Rugged and Safe (QuEChERS) methodology.The extent of the environmental contamination was reached through the quantification of thirteen OCP by using gas chromatography coupled with electron-capture detector (GC-ECD) and compound confirmations employing GC-MS/MS. The OCP residues were extracted by using Quick, Easy, Cheap, Effective, Rugged and Safe (QuEChERS) methodology. This demonstrated that leaves presented more antioxidant activity than stems, emphasizing that leaves contained six times more polyphenolic compounds than stems. In what concerns the OCP occurrence, the average recoveries obtained at the three levels tested (40, 60 and 80 µg kg−1) ranged from 55% to 149% with a relative standard deviation of 11%, (except hexachrorobenzene). Three vegetables samples were collected from supermarkets and analysed following this study. According to data, only one sample achieved 16.21 of β-hexachlorocyclohexane, confirmed by GC-MS/MS. About OCP quantification, the data indicated that only one sample achieved 16.21 µg kg−1 of β-hexachlorocyclohexane, confirmed by GC-MS/MS, being the QuEChERS a good choice for the of OCPs extraction. Furthermore, the leaves consumption guaranty higher levels of antioxidants than stems.

Exploring cinnamic acid scaffold: development of promising neuroprotective lipophilic antioxidants

New lipophilic hydroxycinnamic acid based derivatives were designed and synthesized and their antioxidant and neuroprotective activities evaluated. The chemical modification introduced in the cinnamic acid scaffold leads to compounds with amplified lipophilicity and in general with increased antioxidant activity when compared to natural models (caffeic and ferulic acids). The compounds did not display cytotoxicity and present a significant neuroprotective effect against 6-OH-DA induced damage to SH-SY5Y cells. Compound 6 stands out as an efficient radical scavenger and iron(II) chelator that ensures drug-like properties. Moreover, neuroprotection against oxidative damage was observed even at low concentration (1 μM). Therefore, compound 6 developed by a biology-oriented approach displays a combination of important features for a further optimization process that will generate a new effective antioxidant with therapeutic application for oxidative-stress-related events, namely neurodegenerative diseases.

High-Content Analysis of Breast Cancer Using Single-Cell Deep Transfer Learning

High-content analysis has revolutionized cancer drug discovery by identifying substances that alter the phenotype of a cell, which prevents tumor growth and metastasis. The high-resolution biofluorescence images from assays allow precise quantitative measures enabling the distinction of small molecules of a host cell from a tumor. In this work, we are particularly interested in the application of deep neural networks (DNNs), a cutting-edge machine learning method, to the classification of compounds in chemical mechanisms of action (MOAs). Compound classification has been performed using image-based profiling methods sometimes combined with feature reduction methods such as principal component analysis or factor analysis. In this article, we map the input features of each cell to a particular MOA class without using any treatment-level profiles or feature reduction methods. To the best of our knowledge, this is the first application of DNN in this domain, leveraging single-cell information. Furthermore, we use deep transfer learning (DTL) to alleviate the intensive and computational demanding effort of searching the huge parameter's space of a DNN. Results show that using this approach, we obtain a 30% speedup and a 2% accuracy improvement.