Study on the therapeutic mechanism of the active principle of the Chinese drug Paeoniae Radix 801 through affinity biosensors IAsys Plus quartz crystal microbalance

Effects of propyl gallate on adhesion of polymorphonuclear leukocytes to human endothelial cells induced by tumor necrosis factor alpha

OBJECTIVE

To investigate the effects of Propyl Gallate (PrG) on cellular adhesion between human To investigate the effects of Propyl Gallate (PrG) on cellular adhesion between human umbilical vein endothelial cells (HUVEC) and polymorphonuclear leukocytes (PMN) as well as the expression umbilical vein endothelial cells (HUVEC) and polymorphonuclear leukocytes (PMN) as well as the expression of intercellular adhesion molecule-1 (ICAM-1, CD54) and E-selectin (CD62E) on the VEC surface. of intercellular adhesion molecule-1 (ICAM-1, CD54) and E-selectin (CD62E) on the VEC surface.

METHODS

A human VEC inflammation model was induced by tumor necrosis factor alpha (TNF-alpha). VECs were pre- A human VEC inflammation model was induced by tumor necrosis factor alpha (TNF-alpha). VECs were preincubated with varying concentrations of PrG (0.001-5 mmol/L) or 1 per thousand DMSO (v:v) or 10 mmol/L acetylsalicylic incubated with varying concentrations of PrG (0.001-5 mmol/L) or 1 per thousand DMSO (v:v) or 10 mmol/L acetylsalicylic acid (ASA) for 1 h, and then were stimulated with 10 ng/mL TNF-alpha for 6 h. Rose bengal vital staining method acid (ASA) for 1 h, and then were stimulated with 10 ng/mL TNF-alpha for 6 h. Rose bengal vital staining method was used to measure the adherence rate of PMN to VEC, while flow cytometry was used to determine the was used to measure the adherence rate of PMN to VEC, while flow cytometry was used to determine the expression of CD54 and CD62E on the VEC surface. expression of CD54 and CD62E on the VEC surface.

RESULTS

After 6 h of incubation with TNF-alpha, the adherence After 6 h of incubation with TNF-alpha, the adherence of PMN to HUVECs as well as the percentage of fluorescence-positive cells and mean fluorescence intensity of PMN to HUVECs as well as the percentage of fluorescence-positive cells and mean fluorescence intensity (MFI) of surface CD54 and CD62E in HUVECs increased significantly ( (MFI) of surface CD54 and CD62E in HUVECs increased significantly (P<0.01). Pre-treatment of HUVECs with <0.01). Pre-treatment of HUVECs with PrG (0.1-5 mmol/L) significantly suppressed the adherence of PMN to VECs induced by TNF-alpha (PrG (0.1-5 mmol/L) significantly suppressed the adherence of PMN to VECs induced by TNF-alpha (P<0.05). PrG <0.05). PrG (1-5 mmol/L) inhibited the VEC surface expression of CD62E and CD54 in a dose-dependent way ( (1-5 mmol/L) inhibited the VEC surface expression of CD62E and CD54 in a dose-dependent way (P<0.05). PrG <0.05). PrG at lower concentrations (0.001-0.1 mmol/L) showed no effect on CD54 expression, while it showed a slightly at lower concentrations (0.001-0.1 mmol/L) showed no effect on CD54 expression, while it showed a slightly increasing trend in CD62E expression (increasing trend in CD62E expression (P>0.05). ASA at 10 mmol/L had no obvious effect on the positive rate of >0.05). ASA at 10 mmol/L had no obvious effect on the positive rate of CD62E and CD54. CD62E and CD54.

CONCLUSIONS

High concentrations of PrG (0.1-5 mmol/L) exert its inhibitory effect on cellular High concentrations of PrG (0.1-5 mmol/L) exert its inhibitory effect on cellular adherence of PMN to HUVECs, and its mechanism may be related to inhibiting surface expression of CD54 and adherence of PMN to HUVECs, and its mechanism may be related to inhibiting surface expression of CD54 and CD62E in HUVECs. Its action concentration was lower than that of ASA. CD62E in HUVECs. Its action concentration was lower than that of ASA.

Survey of the year 2005 commercial optical biosensor literature

We identified 1113 articles (103 reviews, 1010 primary research articles) published in 2005 that describe experiments performed using commercially available optical biosensors. While this number of publications is impressive, we find that the quality of the biosensor work in these articles is often pretty poor. It is a little disappointing that there appears to be only a small set of researchers who know how to properly perform, analyze, and present biosensor data. To help focus the field, we spotlight work published by 10 research groups that exemplify the quality of data one should expect to see from a biosensor experiment. Also, in an effort to raise awareness of the common problems in the biosensor field, we provide side-by-side examples of good and bad data sets from the 2005 literature.