Background:The Candida albicans efflux pump Cdr1p causes clinically significant antifungal resistance. Results: Biochemical mapping of Cdr1p transmembrane domain mutants reveals residues affecting drug transport. Conclusion: Functional characterization and homology modeling provide insight into the drug binding cavity and substrate promiscuity of Cdr1p. Significance: A platform is provided for systematic Cdr1p structure/function analysis and the rational design of transport modulators/inhibitors.

Aims Studies have demonstrated the ability of a new automated algorithm for volumetric analysis of 3D echocardiographic (3DE) datasets to provide accurate and reproducible measurements of left ventricular and left atrial (LV, LA) volumes at end-systole and end-diastole. Recently, this methodology was expanded using a machine learning (ML) approach to automatically measure chamber volumes throughout the cardiac cycle, resulting in LV and LA volume–time curves. We aimed to validate ejection and filling parameters obtained from these curves by comparing them to independent well-validated reference techniques. Methods and results We studied 20 patients referred for cardiac magnetic resonance (CMR) examinations, who underwent 3DE imaging the same day. Volume–time curves were obtained for both LV and LA chambers using the ML algorithm (Philips HeartModel), and independently conventional 3DE volumetric analysis (TomTec), and CMR images (slice-by-slice, frame-by-frame manual tracing). Automatically derived LV and LA volumes and ejection/filling parameters were compared against both reference techniques. Minor manual correction of the automatically detected LV and LA borders was needed in 4/20 and 5/20 cases, respectively. Time required to generate volume–time curves was 35 ± 17 s using ML algorithm, 3.6 ± 0.9 min using conventional 3DE analysis, and 96 ± 14 min using CMR. Volume–time curves obtained by all three techniques were similar in shape and magnitude. In both comparisons, ejection/filling parameters showed no significant inter-technique differences. Bland–Altman analysis confirmed small biases, despite wide limits of agreement. Conclusion The automated ML algorithm can quickly measure dynamic LV and LA volumes and accurately analyse ejection/filling parameters. Incorporation of this algorithm into the clinical workflow may increase the utilization of 3DE imaging.

Type 2 diabetes is a complex metabolic disorder characterized by insulin resistance and pancreatic β-cell dysfunction. Deregulated glucose and lipid metabolism are the primary underlying manifestations associated with this disease and its complications. Long non-coding RNAs (lncRNAs) are a novel class of functional RNAs that regulate a variety of biological processes by a diverse interplay of mechanisms including recruitment of epigenetic modifiers, transcriptional and post-transcriptional regulation, control of mRNA decay, and sequestration of transcription factors. Although the underlying causes that define the diabetic phenotype are extremely intricate, most of the studies in the last decades were mostly centered on protein-coding genes. However, current opinion in the recent past has authenticated the contributions of diverse lncRNAs as critical regulatory players during the manifestation of diabetes. The current review will highlight the importance of lncRNAs in regulating cellular processes that govern metabolic homeostasis in key metabolic tissues. A more in-depth understanding of lncRNAs may enable their exploitation as biomarkers or for therapeutic applications during diabetes and its associated complications.

Objectives We sought to: (1) determine the agreement in cardiovascular magnetic resonance (CMR) and speckle tracking echocardiography (STE) derived strain measurements, (2) compare their reproducibility, (3) determine which approach is best related to CMR late gadolinium enhancement (LGE). Background While STE-derived strain is routinely used to assess left ventricular (LV) function, CMR strain measurements are not yet standardized. Strain can be measured using dedicated pulse sequences (strain-encoding, SENC), or post-processing of cine images (feature tracking, FT). It is unclear whether these measurements are interchangeable, and whether strain can be used as an alternative to LGE. Methods Fifty patients underwent 2D echocardiography and 1.5 T CMR. Global longitudinal strain (GLS) was measured by STE (Epsilon), FT (NeoSoft) and SENC (Myocardial Solutions) and circumferential strain (GCS) by FT and SENC. Results GLS showed good inter-modality agreement ( r -values: 0.71–0.75), small biases (< 1%) but considerable limits of agreement (− 7 to 8%). The agreement between the CMR techniques was better for GLS than GCS (r = 0.81 vs 0.67; smaller bias). Repeated measurements showed low intra- and inter-observer variability for both GLS and GCS (intraclass correlations 0.86–0.99; coefficients of variation 3–13%). LGE was present in 22 (44%) of patients. Both SENC- and FT-derived GLS and GCS were associated with LGE, while STE-GLS was not. Irrespective of CMR technique, this association was stronger for GCS (AUC 0.77–0.78) than GLS (AUC 0.67–0.72) and STE-GLS (AUC = 0.58). Conclusion There is good inter-technique agreement in strain measurements, which were highly reproducible, irrespective of modality or analysis technique. GCS may better reflect the presence of underlying LGE than GLS.