Morphometric analysis of retinal arterioles in control and hypertensive population using adaptive optics imaging

Purpose: To measure the wall-to-lumen ratio (WLR) and the vascular wall cross-sectional area (WCSA) of retinal arterioles by an Adaptive Optics (AO) retinal camera using semi-automated software and comparing them between control and hypertensive population. Methods: This was a cross-sectional observational study including a hypertensive group and a control group. Subjects were examined and their medical history recorded. Retinal arteriolar morphometry was assessed by rtx1 AO retinal camera using AOdetect Artery semiautomated software. Main Outcome Measures: WLR and WCSA were measured on the basis of retinal arteriolar wall thickness (W1, W2), lumen diameter (LD) and vessel diameter (VD). Influence of age and arterial hypertension on the WLR and WCSA were examined. Results: A total of 150 human subjects were included out of which 110 were controls and 40 were hypertensives under treatment. There was statistically significant difference in the age, systolic and diastolic blood pressures between the control and hypertensive groups (P < 0.01). We found no significant correlation between age and WLR (R2 = 0.049, P > 0.05) or age and WCSA (R2 = 0.045, P > 0.05). We observed a significant difference in WLR and WCSA measurements between control and hypertensive groups (P < 0.01). On measuring intra-observer variability (IOV) we found excellent consistency. Conclusion: AO retinal imaging allows a direct measurement of the retinal vessel wall and LD with excellent IOV. WLR and WCSA reflect the remodelling process and can be used to further aid the early detection and monitoring of systemic hypertension.

Use of Raman spectroscopy to decrease time for identifying the species of candida growth in cultures

The objective of this study is to establish Raman signatures from pure cultures of different Candida species using Raman Spectroscopy [RS] and use these signatures for rapid identification of unknown Candida species. Pure cultures of five Candida species were evaluated using RS to build a limited signature library. 'Raman Processing' [RP] software was used for Principal Component Analysis [PCA] and Differential Functional Analysis [DFA]. Eleven principal components described at least 95% variance in the spectra. Raman signatures from these known Candida species were able to identify the species of unknown Candida cultures with 100% accuracy. Raman spectroscopy can improve early identification of Candida species and may facilitate early optimal antifungal therapy