17β-Estradiol Prevents Early-Stage Atherosclerosis in Estrogen Receptor-Alpha Deficient Female Mice

Estrogen is atheroprotective and a high-affinity ligand for both known estrogen receptors, ERα and ERβ. However, the role of the ERα in early-stage atherosclerosis has not been directly investigated and is incompletely understood. ERα-deficient (ERα−/−) and wild-type (ERα+/+) female mice consuming an atherogenic diet were studied concurrent with estrogen replacement to distinguish the actions of 17β-estradiol (E2) from those of ERα on the development of early atherosclerotic lesions. Mice were ovariectomized and implanted with subcutaneous slow-release pellets designed to deliver 6 or 8 μg/day of exogenous 17β-estradiol (E2) for a period of up to 4 months. Ovariectomized mice (OVX) with placebo pellets (E2-deficient controls) were compared to mice with endogenous E2 (intact ovaries) and exogenous E2. Aortas were analyzed for lesion area, number, and distribution. Lipid and hormone levels were also determined. Compared to OVX, early lesion development was significantly (p < 0.001) attenuated by E2 with 55–64% reduction in lesion area by endogenous E2 and >90% reduction by exogenous E2. Compared to OVX, a decline in lesion number (2- to 4-fold) and lesser predilection (~4-fold) of lesion formation in the proximal aorta also occurred with E2. Lesion size, development, number, and distribution inversely correlated with circulating plasma E2 levels. However, atheroprotection was independent of ERα status, and E2 athero-protection in both genotypes was not explained by changes in plasma lipid levels (total cholesterol, triglyceride, and high-density lipoprotein cholesterol). The ERα is not essential for endogenous/exogenous E2-mediated protection against early-stage atherosclerosis. These observations have potentially significant implications for understanding the molecular and cellular mechanisms and timing of estrogen action in different estrogen receptor (ER) deletion murine models of atherosclerosis, as well as implications to human studies of ER polymorphisms and lipid metabolism. Our findings may contribute to future improved clinical decision-making concerning the use of hormone therapy

Comparative Lipidomics in Clinical Isolates of Candida albicans Reveal Crosstalk between Mitochondria, Cell Wall Integrity and Azole Resistance

Prolonged usage of antifungal azoles which target enzymes involved in lipid biosynthesis invariably leads to the development of multi-drug resistance (MDR) in Candida albicans. We had earlier shown that membrane lipids and their fluidity are closely linked to the MDR phenomenon. In one of our recent studies involving comparative lipidomics between azole susceptible (AS) and azole resistant (AR) matched pair clinical isolates of C. albicans, we could not see consistent differences in the lipid profiles of AS and AR strains because they came from different patients and so in this study, we have used genetically related variant recovered from the same patient collected over a period of 2-years. During this time, the levels of fluconazole (FLC) resistance of the strain increased by over 200-fold. By comparing the lipid profiles of select isolates, we were able to observe gradual and statistically significant changes in several lipid classes, particularly in plasma membrane microdomain specific lipids such as mannosylinositolphosphorylceramides and ergosterol, and in a mitochondrial specific phosphoglyceride, phosphatidyl glycerol. Superimposed with these quantitative and qualitative changes in the lipid profiles, were simultaneous changes at the molecular lipid species levels which again coincided with the development of resistance to FLC. Reverse transcriptase-PCR of the key genes of the lipid metabolism validated lipidomic picture. Taken together, this study illustrates how the gradual corrective changes in Candida lipidome correspond to the development of FLC tolerance. Our study also shows a first instance of the mitochondrial membrane dysfunction and defective cell wall (CW) in clinical AR isolates of C. albicans, and provides evidence of a cross-talk between mitochondrial lipid homeostasis, CW integrity and azole tolerance

Clinically applicable deep learning for diagnosis and referral in retinal disease

The volume and complexity of diagnostic imaging is increasing at a pace faster than the availability of human expertise to interpret it. Artificial intelligence has shown great promise in classifying two-dimensional photographs of some common diseases and typically relies on databases of millions of annotated images. Until now, the challenge of reaching the performance of expert clinicians in a real-world clinical pathway with three-dimensional diagnostic scans has remained unsolved. Here, we apply a novel deep learning architecture to a clinically heterogeneous set of three-dimensional optical coherence tomography scans from patients referred to a major eye hospital. We demonstrate performance in making a referral recommendation that reaches or exceeds that of experts on a range of sight-threatening retinal diseases after training on only 14,884 scans. Moreover, we demonstrate that the tissue segmentations produced by our architecture act as a device-independent representation; referral accuracy is maintained when using tissue segmentations from a different type of device. Our work removes previous barriers to wider clinical use without prohibitive training data requirements across multiple pathologies in a real-world setting