Influence of lanthanum on the uptake of trace elements in cucumber plant

The effects of La3+ on the uptake of trace elements (Se, Co, V, and Tc) in cucumber plants were studied by a radioactive multitracer technique. It was observed that the uptake and distribution of these trace elements in roots, stems, and leaves are different under different La3+, treatments. Furthermore, in the control, the plant accumulates Se-75, Co-56, and V-48 all in the order roots>leaves>stems, whereas Tc-95m was in the order leaves>stems>roots. The accumulations of Se-75 and Tc-95m in plants treated with different La3+ concentration were in the same order as those in the control, but the uptakes percentages of other kinds of element changed differently. The results indicate that lanthanum treatments to a growing cucumber lead to the change of uptake of trace elements, which suggest that a rare earth element is directly or indirectly involved in the ion transport of the plant and affects plant growth by regulating the uptake and distribution of elements that influence the plant cell physiology and biochemistry.

Uptake and distribution of trace elements in growing cucumber

Multitracer technique was used to study the uptake and distribution of some relatively long half-life radionuclides Be, Na, Mn, Co, Sc to growing cucumber (Cucumis Sativus L.) with two different treatments. In Hoagland solution, only Mn-54 and Co-60 accumulated in the every part of plants. Mn-54, Co-60 and other radionuclides were absorbed in distilled water. The results indicate that there were major differences in the accumulation of trace elements between the two different treatments.

Docking and molecular dynamics studies toward the binding of new natural phenolic marine inhibitors and aldose reductase

Phenolic marine natural product is a kind of new potential aldose reductase inhibitors (ARIs). In order to investigate the binding mode and inhibition mechanism, molecular docking and dynamics studies were performed to explore the interactions of six phenolic inhibitors with human aldose reductase (hALR2). Considering physiological environment, all the neutral and other two ionized states of each phenolic inhibitor were adopted in the simulation. The calculations indicate that all the inhibitors are able to form stable hydrogen bonds with the hALR2 active pocket which is mainly constructed by residues TYR48, HIS110 and TRP111, and they impose the inhibition effect by occupying the active space. In all inhibitors, only La and its two ionized derivatives La_ion1 and La_ion2, in which neither of the ortho-hydrogens of 3-hydroxyl is substituted by Br, bind with hALR2 active residues using the terminal 3-hydroxyl. While, all the other inhibitors, at least one of whose ortho-sites of 3- and 6-hydroxyls are substituted by Br substituent which take much electron-withdrawing effect and steric hindrance, bind with hALR2 through the lactone group. This means that the Br substituent can effectively regulate the binding modes of phenolic inhibitors. Although the lactone bound inhibitors have relatively high RMSD values, our dynamics study shows that both binding modes are of high stability. For each inhibitor molecule, the ionization does not change its original binding mode, but it does gradually increase the binding free energy, which reveals that besides hydrogen bonds, the electrostatic effect is also important to the inhibitor–hALR2 interaction.

An in silico approach for screening flavonoids as P-glycoprotein inhibitors based on a Bayesian-regularized neural network

P-glycoprotein (P-gp), an ATP-binding cassette (ABC) transporter, functions as a biological barrier by extruding cytotoxic agents out of cells, resulting in an obstacle in chemotherapeutic treatment of cancer. In order to aid in the development of potential P-gp inhibitors, we constructed a quantitative structure-activity relationship (QSAR) model of flavonoids as P-gp inhibitors based on Bayesian-regularized neural network (BRNN). A dataset of 57 flavonoids collected from a literature binding to the C-terminal nucleotide-binding domain of mouse P-gp was compiled. The predictive ability of the model was assessed using a test set that was independent of the training set, which showed a standard error of prediction of 0.146 +/- 0.006 (data scaled from 0 to 1). Meanwhile, two other mathematical tools, back-propagation neural network (BPNN) and partial least squares (PLS) were also attempted to build QSAR models. The BRNN provided slightly better results for the test set compared to BPNN, but the difference was not significant according to F-statistic at p = 0.05. The PLS failed to build a reliable model in the present study. Our study indicates that the BRNN-based in silico model has good potential in facilitating the prediction of P-gp flavonoid inhibitors and might be applied in further drug design.