Diagnosing pulmonary hypertension due to left heart disease using diastolic echo markers: The National Echo Database of Australia (NEDA) PH-LHD predictive formula

Aims: Pulmonary hypertension (PH) is commonly due to left heart disease caused by ischaemic heart disease, hypertension and valvular heart disease. It is under diagnosed and associated with a high mortality. PH diagnosed using echo requires a measurable tricuspid regurgitation velocity (TRV) to estimate the pulmonary artery systolic pressure (PH = PASP \u3e40mmHg). However, up to 40% of studies have insufficient TRV to calculate a PASP. This can lead to significant delays in the diagnosis of pulmonary hypertension, increased morbidity and delays in the initiation of treatment. This thesis seeks to determine the prevalence of PH and the diastolic echo markers related to the development of PH in left heart disease (PH-LHD) and create a predictive model using diastolic echo markers to diagnose PH in the absence of a TRV. Methods: This study is a retrospective observational cohort study with data derived from the National Echo Database of Australia (NEDA). Using PH as the dependent variable and markers of diastolic function as the independent variables we performed univariate and multivariate analysis on the entire cohort to identify predictive diastolic markers that correlates with PH. To create a predictive formula to diagnose PH-LHD, the entire cohort was randomised 1:1 into a development (DD) and validation database (VD). Using logistic regression analysis on diastolic markers and the presence of PH in the DD, we derived a constant (con) that could be used to predict the probability of PH. Using probability analysis, the Receiver Operating Characteristic (ROC) curve was generated using a 0.5 cut off to evaluate the accuracy of the model. The accuracy of the model was then tested using the VD. Results: Of the 174,229 patients in the NEDA, 75,204 (43%) had insufficient TRV to calculate a PASP. Of the 99,025 patients with a PASP, 19,767 (20%) had PH. Patients with PH were older (76 vs 62 yr) (p = The DD (150,979 echos) had 5,181 valid studies to create the NEDA PH-LHD Constant (Con) = -6.649 + (0.035 x Age) + (0.072 x E’) + (0.077 x E/E’) + (0.509 x E/A) + (0.03 x LAVI) to predict the probability of PH. The DD model AUC ROC is 75% accurate in 14 diagnosing PH-LHD. Applying our formula to the VD (151,767 echos), the AUC of the ROC curve is 0.742. Conclusion: Using the NEDA, 20% of patients were diagnosed with PH. Using Age, E’, E/E’ ratio, E/A ratio and LAVI, the NEDA PH-LHD formula can diagnose PH-LHD in 75% of cases in the absence of TRV

Optofluidic fabrication for 3D-shaped particles.

Complex three-dimensional (3D)-shaped particles could play unique roles in biotechnology, structural mechanics and self-assembly. Current methods of fabricating 3D-shaped particles such as 3D printing, injection moulding or photolithography are limited because of low-resolution, low-throughput or complicated/expensive procedures. Here, we present a novel method called optofluidic fabrication for the generation of complex 3D-shaped polymer particles based on two coupled processes: inertial flow shaping and ultraviolet (UV) light polymerization. Pillars within fluidic platforms are used to deterministically deform photosensitive precursor fluid streams. The channels are then illuminated with patterned UV light to polymerize the photosensitive fluid, creating particles with multi-scale 3D geometries. The fundamental advantages of optofluidic fabrication include high-resolution, multi-scalability, dynamic tunability, simple operation and great potential for bulk fabrication with full automation. Through different combinations of pillar configurations, flow rates and UV light patterns, an infinite set of 3D-shaped particles is available, and a variety are demonstrated

Learning Hard Alignments with Variational Inference

There has recently been significant interest in hard attention models for tasks such as object recognition, visual captioning and speech recognition. Hard attention can offer benefits over soft attention such as decreased computational cost, but training hard attention models can be difficult because of the discrete latent variables they introduce. Previous work used REINFORCE and Q-learning to approach these issues, but those methods can provide high-variance gradient estimates and be slow to train. In this paper, we tackle the problem of learning hard attention for a sequential task using variational inference methods, specifically the recently introduced VIMCO and NVIL. Furthermore, we propose a novel baseline that adapts VIMCO to this setting. We demonstrate our method on a phoneme recognition task in clean and noisy environments and show that our method outperforms REINFORCE, with the difference being greater for a more complicated task

Effects of NHC-Backbone Substitution on Efficiency in Ruthenium-Based Olefin Metathesis

series of ruthenium olefin metathesis catalysts bearing N-heterocyclic carbene (NHC) ligands with varying degrees of backbone and N-aryl substitution have been prepared. These complexes show greater resistance to decomposition through C−H activation of the N-aryl group, resulting in increased catalyst lifetimes. This work has utilized robotic technology to examine the activity and stability of each catalyst in metathesis, providing insights into the relationship between ligand architecture and enhanced efficiency. The development of this robotic methodology has also shown that, under optimized conditions, catalyst loadings as low as 25 ppm can lead to 100% conversion in the ring-closing metathesis of diethyl diallylmalonate