Luteolin and thiosalicylate inhibit HgCl(2) and thimerosal-induced VEGF release from human mast cells.

HgCl2 is a known environemental neurotoxin, but is also used as preservative in vaccines as thimerosal containing ethyl mercury covalently linked to thiosalicylate. We recently reported that mercury choloride (HgCl(2)) can stimulate human mast cells to release vascular endothelial growth factor (VEGF), which is also vasoactive and pro-inflammatory. Here we show that thimerosal induces significant VEGF release from human leukemic cultured LAD2 mast cells (at 1 microM 326 ± 12 pg/106 cells and 335.5 ± 12 pg/106 cells at 10 microM) compared to control cells (242 ± 21 pg/106 cells, n=5, p less than 0.05); this effect is weaker than that induced by HgCl2 at 10 microM (448 ± 14 pg/106 cells) (n=3, p less than 0.05). In view of this finding, we hypothesize that the thiosalicylate component of thimerosal may have an inhibitory effect on VEGF release. Thimerosal (10 microM) added together with the peptide Substance P (SP) at 2 microM, used as a positive control, reduced VEGF release by 90 percent. Methyl thiosalicylate (1 or 10 microM) added with either SP or HgCl2 (10 microM) inhibited VEGF release by 100 percent, while sodium salicylate or ibuprofen had no effect. Pretreatment for 10 min with the flavonoid luteolin (0.1 mM) before HgCl2 or thimerosal compeletly blocked their effect. Luteolin and methyl thiosalicylate may be useful in preventing mercury-induced toxicity.

On AR modelling for MEG spectral estimation, data compression and classification.

The use of the autoregressive (AR) model for magnetoencephalogram (MEG) processing is examined and compared to other methods. Spectral estimation, classification and data compression of MEG signals are studied. In application to spectral estimation the AR model is compared to the classical modified periodogram method. Also, AR modelling appears to perform very successfully when used for the classification of normal and epileptic MEG signals. Finally, the 17:1 to 23:1 data compression achieved by AR modelling, along with the above-mentioned advantages, render it suitable for storage applications. For comparison, the method of feature selection via orthogonal expansion is used as a tool to achieve data reduction. It is seen that while effective, this is less drastic than the compression of data volume achieved by AR modelling.