Plasma Amino Acids May Improve Prediction Accuracy of Cerebral Vasospasm after Aneurysmal Subarachnoid Haemorrhage.

Aneurysmal subarachnoid haemorrhages (aSAH) account for 5% of strokes and continues to place a great burden on patients and their families. Cerebral vasospasm (CVS) is one of the main causes of death after aSAH, and is usually diagnosed between day 3 and 14 after bleeding. Its pathogenesis remains poorly understood. To verify whether plasma concentration of amino acids have prognostic value in predicting CVS, we analysed data from 35 patients after aSAH (median age 55 years, IQR 39-62; 20 females, 57.1%), and 37 healthy volunteers (median age 50 years, IQR 38-56; 19 females, 51.4%). Fasting peripheral blood samples were collected on postoperative day one and seven. High performance liquid chromatography-mass spectrometry (HPLC-MS) analysis was performed. The results showed that plasma from patients after aSAH featured a distinctive amino acids concentration which was presented in both principal component analysis and direct comparison. No significant differences were noted between postoperative day one and seven. A total of 18 patients from the study group (51.4%) developed CVS. Hydroxyproline (AUC = 0.7042, 95%CI 0.5259-0.8826, p = 0.0248) and phenylalanine (AUC = 0.6944, 95%CI 0.5119-0.877, p = 0.0368) presented significant CVS prediction potential. Combining the Hunt-Hess Scale and plasma levels of hydroxyproline and phenylalanine provided the model with the best predictive performance and the lowest leave-one-out cross-validation of performance error. Our results suggest that plasma amino acids may improve sensitivity and specificity of Hunt-Hess scale in predicting CVS.

Associations of Arginine with Gestational Diabetes Mellitus in a Follow-Up Study.

In the reported study we applied the targeted metabolomic profiling employing high pressure liquid chromatography coupled with triple quadrupole tandem mass spectrometry (HPLC-MS/MS) to understand the pathophysiology of gestational diabetes mellitus (GDM), early identification of women who are at risk of developing GDM, and the differences in recovery postpartum between these women and normoglycemic women. We profiled the peripheral blood from patients during the second trimester of pregnancy and three months, and one year postpartum. In the GDM group Arg, Gln, His, Met, Phe and Ser were downregulated with statistical significance in comparison to normoglycemic (NGT) women. From the analysis of the association of all amino acid profiles of GDM and NGT women, several statistical models predicting diabetic status were formulated and compared with the literature, with the arginine-based model as the most promising of the screened ones (area under the curve (AUC) = 0.749). Our research results have shed light on the critical role of arginine in the development of GDM and may help in precisely distinguishing between GDM and NGT and earlier detection of GDM but also in predicting women with the increased type 2 diabetes mellitus (T2DM) risk.

Experimental versus Calculated Proton Affinities for Aromatic Carboxylic Acid Anions and Related Phenide Ions.

Herein, we present the comparison of a large set of experimentally measured proton affinity (PA) values for 65 aromatic carboxylate anions with the values calculated by using selected popular DFT (B3LYP, PBE0, and M05-2X) and composite [G3(MP2), G4(MP2)] quantum chemistry methods. The root-mean-square error (RMSE) values for the chosen methods are RMSEPBE0 =1.7, RMSEB3LYP =4.6, RMSEM05-2X =6.6, RMSEG3MP2 =6.3, RMSEG4MP2 =4.5 kJ mol(-1) . In the second part of the study, 82 PA values for substituted phenide ions and a few heteroaromatic anions were calculated. Again, very good agreement between the calculated and experimental values has been observed: RMSEPBE0 =1.9, RMSEB3LYP =4.5, RMSEM05-2X =6.3, RMSEG3MP2 =4.9, RMSEG4MP2 =5.5 kJ mol(-1) . Our results show that, for medium-sized carboxylate anions, all tested methods give reliable results and, surprisingly, much more computationally demanding composite methods do not perform significantly better than the time-efficient DFT methods.