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The slow growth of Mycobacterium tuberculosis (Mtb), the causative agent of tuberculosis (TB), hinders development of new diagnostics, therapeutics and vaccines. Using non-invasive real-time imaging technologies to monitor the disease process in live animals would facilitate TB research in all areas. We developed fluorescent protein (FP) expressing Mycobacterium bovis BCG strains for in vivo imaging, which can be used to track bacterial location, and to quantify bacterial load in live animals. We selected an optimal FP for in vivo imaging, by first cloning six FPs: tdTomato, mCherry, mPlum, mKate, Katushka and mKeima, into mycobacteria under either a mycobacterial Hsp60 or L5 promoter, and compared their fluorescent signals in vitro and in vivo. Fluorescence from each FP-expressing strain was measured with a multimode reader using the optimal excitation and emission wavelengths for the FP. After normalizing bacterial numbers with optical density, the strain expressing L5-tdTomato displayed the highest fluorescence. We used the tdTomato-labeled M. bovis BCG to obtain real-time images of pulmonary infections in living mice and rapidly determined the number of bacteria present. Further comparison between L5-tdTomato and Hsp60-tdTomato revealed that L5-tdTomato carried four-fold more tdTomato gene copies than Hsp60-tdTomato, which eventually led to higher protein expression of tdTomato. Evaluating anti-TB efficacy of rifampicin and isoniazid therapy in vitro and in vivo using the L5-tdTomato strain demonstrated that this strain can be used to identify anti-TB therapeutic efficacy as quickly as 24 h post-treatment. These M. bovis BCG reporter strains represent a valuable new tool for evaluation of therapeutics, vaccines and virulence. [ABSTRACT FROM AUTHOR]

Background: Invasive fractional flow reserve (FFR) is a standard tool for identifying ischemia-producing coronary stenosis. However, in clinical practice, over 70% of treatment decisions still rely on visual estimation of angiographic stenosis, which has limited accuracy (about 60%-65%) for the prediction of FFR < 0.80. One of the reasons for the visual-functional mismatch is that myocardial ischemia can be affected by the supplied myocardial size, which is not always evident by coronary angiography. The aims of this study were to develop an angiography-based machine learning (ML) algorithm for predicting the supplied myocardial volume for a stenosis, as measured using coronary computed tomography angiography (CCTA), and then to build an angiography-based classifier for the lesions with an FFR < 0.80 versus ≥ 0.80.Methods and Findings: A retrospective study was conducted using data from 1,132 stable and unstable angina patients with 1,132 intermediate lesions who underwent invasive coronary angiography, FFR, and CCTA at the Asan Medical Center, Seoul, Korea, between 1 May 2012 and 30 November 2015. The mean age was 63 ± 10 years, 76% were men, and 72% of the patients presented with stable angina. Of these, 932 patients (assessed before 31 January 2015) constituted the training set for the algorithm, and 200 patients (assessed after 1 February 2015) served as a test cohort to validate its diagnostic performance. Additionally, external validation with 79 patients from two centers (CHA University, Seongnam, Korea, and Ajou University, Suwon, Korea) was conducted. After automatic contour calibration using the caliber of guiding catheter, quantitative coronary angiography was performed using the edge-detection algorithms (CAAS-5, Pie-Medical). Clinical information was provided by the Asan BiomedicaL Research Environment (ABLE) system. The CCTA-based myocardial segmentation (CAMS)-derived myocardial volume supplied by each vessel (right coronary artery [RCA], left anterior descending [LAD], left circumflex [LCX]) and the myocardial volume subtended to a stenotic segment (CAMS-%Vsub) were measured for labeling. The ML for (1) predicting vessel territories (CAMS-%LAD, CAMS-%LCX, and CAMS-%RCA) and CAMS-%Vsub and (2) identifying the lesions with an FFR < 0.80 was constructed. Angiography-based ML, employing a light gradient boosting machine (GBM), showed mean absolute errors (MAEs) of 5.42%, 8.57%, and 4.54% for predicting CAMS-%LAD, CAMS-%LCX, and CAMS-%RCA, respectively. The percent myocardial volumes predicted by ML were used to predict the CAMS-%Vsub. With 5-fold cross validation, the MAEs between ML-predicted percent myocardial volume subtended to a stenotic segment (ML-%Vsub) and CAMS-%Vsub were minimized by the elastic net (6.26% ± 0.55% for LAD, 5.79% ± 0.68% for LCX, and 2.95% ± 0.14% for RCA lesions). Using all attributes (age, sex, involved vessel segment, and angiographic features affecting the myocardial territory and stenosis degree), the ML classifiers (L2 penalized logistic regression, support vector machine, and random forest) predicted an FFR < 0.80 with an accuracy of approximately 80% (area under the curve [AUC] = 0.84-0.87, 95% confidence intervals 0.71-0.94) in the test set, which was greater than that of diameter stenosis (DS) > 53% (66%, AUC = 0.71, 95% confidence intervals 0.65-0.78). The external validation showed 84% accuracy (AUC = 0.89, 95% confidence intervals 0.83-0.95). The retrospective design, single ethnicity, and the lack of clinical outcomes may limit this prediction model's generalized application.Conclusion: We found that angiography-based ML is useful to predict subtended myocardial territories and ischemia-producing lesions by mitigating the visual-functional mismatch between angiographic and FFR. Assessment of clinical utility requires further validation in a large, prospective cohort study. [ABSTRACT FROM AUTHOR]

Although hemodynamic influence of the subprosthetic tissue, termed as pannus, may contribute to prosthetic aortic valve dysfunction, the relationship between pannus extent and hemodynamics in the prosthetic valve has rarely been reported. We investigated the fluid dynamics of pannus formation using in vitro experiments with particle image velocimetry. Subvalvular pannus formation caused substantial changes in prosthetic valve transvalvular peak velocity, transvalvular pressure gradient (TPG) and opening angle. Maximum flow velocity and corresponding TPG were mostly affected by pannus width. When the pannus width was 25% of the valve diameter, pannus formation elevated TPG to >2.5 times higher than that without pannus formation. Opening dysfunction was observed only for a pannus involvement angle of 360°. Although circumferential pannus with an involvement angle of 360° decreased the opening angle of the valve from approximately 82° to 58°, eccentric pannus with an involvement angle of 180° did not induce valve opening dysfunction. The pannus involvement angle largely influenced the velocity flow field at the aortic sinus and corresponding hemodynamic indices, including wall shear stress, principal shear stress and viscous energy loss distributions. Substantial discrepancy between the velocity-based TPG estimation and direct pressure measurements was observed for prosthetic valve flow with pannus formation. [ABSTRACT FROM AUTHOR]

Background and objective: Studies investigating the impact of white matter hyperintensities (WMHs) on the response of acetylcholinesterase inhibitors in patients with Alzheimer’s disease (AD) have presented inconsistent results. We aimed to compare the effects of the rivastigmine patch between patients with AD with minimal WMHs and those with moderate WMHs. Methods: Three hundred patients with mild to moderate AD were enrolled in this multicenter prospective open-label study and divided into two groups. Group 1 comprised patients with AD with minimal WMHs and group 2 comprised those with moderate WMHs. The patients were treated with a rivastigmine patch for 24 weeks. Efficacy measures were obtained at baseline and after 24 weeks. The primary endpoint was the change in the AD Assessment Scale-Cognitive subscale (ADAS-Cog) from the baseline to the end of the study. Results: Of the 300 patients, there were 206 patients in group 1 and 94 patients in group 2. The intention-to-treat group comprised 198 patients (group 1, n = 136; group 2, n = 46) during the 24-week study period. Demographic factors did not differ between group 1 and group 2. There were no significant differences in change in ADAS-cog between group 1 (-0.62±5.70) and group 2 (-0.23±5.98) after the 24-week rivastigmine patch therapy (p = 0.378). The patients in group 1 had a 0.63-point improvement from baseline on the Frontal Assessment Battery, while group 2 had a 0.16-point decline compared to baseline at the end of the study (p = 0.037). The rates of adverse events (AEs) (42.6 vs. 40.3%) and discontinuation due to AEs (10.3% vs. 4.3%) did not differ between the groups. Conclusions: Although the efficacy and tolerability of rivastigmine patch therapy were not associated with WMH severity in patients with AD, some improvement in frontal function was observed in those with minimal WMHs. Trial registration: ClinicalTrials.gov [ABSTRACT FROM AUTHOR]

Background: Although the measurement of turbulence kinetic energy (TKE) by using magnetic resonance imaging (MRI) has been introduced as an alternative index for quantifying energy loss through the cardiac valve, experimental verification and clinical application of this parameter are still required. Objectives: The goal of this study is to verify MRI measurements of TKE by using a phantom stenosis with particle image velocimetry (PIV) as the reference standard. In addition, the feasibility of measuring TKE with MRI is explored. Methods: MRI measurements of TKE through a phantom stenosis was performed by using clinical 3T MRI scanner. The MRI measurements were verified experimentally by using PIV as the reference standard. In vivo application of MRI-driven TKE was explored in seven patients with aortic valve disease and one healthy volunteer. Transvalvular gradients measured by MRI and echocardiography were compared. Results: MRI and PIV measurements of TKE are consistent for turbulent flow (0.666 < R2 < 0.738) with a mean difference of −11.13 J/m3 (SD = 4.34 J/m3). Results of MRI and PIV measurements differ by 2.76 ± 0.82 cm/s (velocity) and −11.13 ± 4.34 J/m3 (TKE) for turbulent flow (Re > 400). The turbulence pressure drop correlates strongly with total TKE (R2 = 0.986). However, in vivo measurements of TKE are not consistent with the transvalvular pressure gradient estimated by echocardiography. Conclusions: These results suggest that TKE measurement via MRI may provide a potential benefit as an energy-loss index to characterize blood flow through the aortic valve. However, further clinical studies are necessary to reach definitive conclusions regarding this technique. [ABSTRACT FROM AUTHOR]