In this paper, the fluorogenic property of vindoline was exploited and, as a probe, used to analyze the interaction of vindoline with HSA by fluorescence and absorption spectra in combination with molecular modeling under a simulated physiological conditions. The evidences from synchronous fluorescence and absorption spectroscopes showed the effect of vindoline on the microenvironment around HSA in aqueous solution. Data obtained by the fluorescence spectroscopy indicated that binding of vindoline with HSA leads to dramatic enhancement of the fluorescence emission intensity. The binding constants and the number of binding sites between vindoline and HSA at different temperatures (303, 310 and 317 K) were calculated according to the data obtained from fluorescence titration. Molecular docking was performed to reveal the possible binding mode or mechanism and suggested that vindoline can bind strongly to HSA. It is considered that vindoline binds to HSA mainly by a hydrophobic interaction and there are four hydrogen bonds interactions between the drug and the residues Ala291, Arg222, Arg218 and Lys195, separately. Fluorescent displacement measurements confirmed that vindoline bind HSA on site II. The thermodynamic parameters obtained (the enthalpy change deltaH0 and the entropy change deltaS0 were calculated to be -10.30 kJ x mol(-1) and 79.98 J x mol(-1) x K(-1), respectively, according to the Van't Hoff equation) suggested that hydrophobic and electrostatic interaction is the predominant intermolecular forces stabilizing the complex.

Objective: To investigate the correlations among striatal dopamine transporter (DAT) distribution, glucose metabolism and Parkinson′s disease (PD) clinical symptoms. Methods: Twenty-five clinically confirmed idiopathic PD patients (17 males, 8 females, age: (59.8±9.2) years) who underwent ¹¹C-2-beta-carbomethoxy-3-beta-(4-fluorophenyl)tropane (CFT) and ¹⁸F-fluorodeoxyglucose (FDG) PET imaging from January 2015 to December 2016 were reviewed. The detailed clinical scores were systematically collected from all patients. Correlations between DAT distribution, glucose metabolism and clinical symptoms were evaluated at global and voxel levels using Pearson correlation analysis. Results: There were significantly positive correlations between the PD-related pattern (PDRP) value and unified PD rating scale (UPDRS) motor scores, non-motor symptoms scale (NMSS) scores, activity of daily living scale (ADL) scores (r values: 0.580, 0.522, 0.557, all P<0.05). The CFT uptake of ipsilateral caudate nucleus, anterior putamen, and posterior putamen were negatively correlated with UPDRS motor scores (r values: -0.496, -0.492, -0.457, all P<0.05), while those had no significant correlations with NMSS scores (r values: -0.420, -0.402, -0.355, all P>0.05). The CFT uptake of ipsilateral caudate nucleus and anterior putamen were negatively correlated with ADL scores (r values: -0.502, -0.522, both P<0.05). There were no significant correlations between CFT uptake in contralateral striatal, anterior putamen, posterior putamen and PDRP values, UPDRS motor scores, NMSS scores and ADL scores(r values: from -0.466 to -0.129, all P>0.05). The presence of the significant correlations between UPDRS motor scores, ADL scores and the CFT radioactive count were confirmed in left caudate nucleus and left putamen (r values: from -0.90 to -0.47, all P<0.05). The metabolic PET imaging disclosed a set of brain regions correlating with the clinical symptoms. The presence of significant correlations between the metabolic PET imaging and CFT uptake were confirmed in bilateral caudate nucleus (r values: 0.47-0.90, both P<0.01), precentral gyrus and insula (r values: -0.90 to -0.47, all P<0.01). Conclusion The correlations between DAT distribution, glucose metabolism and PD clinical symptoms are complicated, which promote the understanding in the proper application of dopaminergic and metabolic PET imaging in PD and offer more evidences in PD pathophysiological mechanisms.