A rule-based method for predicting the electrical activation of the heart with cardiac resynchronization therapy from non-invasive clinical data

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Quantifying atrial anatomy uncertainty from clinical data and its impact on electro-physiology simulation predictions

Patient-specific computational models of structure and function are increasingly being used to diagnose disease and predict how a patient will respond to therapy. Models of anatomy are often derived after segmentation of clinical images or from mapping systems which are affected by image artefacts, resolution and contrast. Quantifying the impact of uncertain anatomy on model predictions is important, as models are increasingly used in clinical practice where decisions need to be made regardless of image quality. We use a Bayesian probabilistic approach to estimate the anatomy and to quantify the uncertainty about the shape of the left atrium derived from Cardiac Magnetic Resonance images. We show that we can quantify uncertain shape, encode uncertainty about the left atrial shape due to imaging artefacts, and quantify the effect of uncertain shape on simulations of left atrial activation times

Predicting Atrial Fibrillation Recurrence by Combining Population Data and Virtual Cohorts of Patient-Specific Left Atrial Models.

BACKGROUND: Current ablation therapy for atrial fibrillation is suboptimal, and long-term response is challenging to predict. Clinical trials identify bedside properties that provide only modest prediction of long-term response in populations, while patient-specific models in small cohorts primarily explain acute response to ablation. We aimed to predict long-term atrial fibrillation recurrence after ablation in large cohorts, by using machine learning to complement biophysical simulations by encoding more interindividual variability. METHODS: Patient-specific models were constructed for 100 atrial fibrillation patients (43 paroxysmal, 41 persistent, and 16 long-standing persistent), undergoing first ablation. Patients were followed for 1 year using ambulatory ECG monitoring. Each patient-specific biophysical model combined differing fibrosis patterns, fiber orientation maps, electrical properties, and ablation patterns to capture uncertainty in atrial properties and to test the ability of the tissue to sustain fibrillation. These simulation stress tests of different model variants were postprocessed to calculate atrial fibrillation simulation metrics. Machine learning classifiers were trained to predict atrial fibrillation recurrence using features from the patient history, imaging, and atrial fibrillation simulation metrics. RESULTS: We performed 1100 atrial fibrillation ablation simulations across 100 patient-specific models. Models based on simulation stress tests alone showed a maximum accuracy of 0.63 for predicting long-term fibrillation recurrence. Classifiers trained to history, imaging, and simulation stress tests (average 10-fold cross-validation area under the curve, 0.85±0.09; recall, 0.80±0.13; precision, 0.74±0.13) outperformed those trained to history and imaging (area under the curve, 0.66±0.17) or history alone (area under the curve, 0.61±0.14). CONCLUSION: A novel computational pipeline accurately predicted long-term atrial fibrillation recurrence in individual patients by combining outcome data with patient-specific acute simulation response. This technique could help to personalize selection for atrial fibrillation ablation

Near-field Electrical Detection of Optical Plasmons and Single Plasmon Sources

Photonic circuits can be much faster than their electronic counterparts, but they are difficult to miniaturize below the optical wavelength scale. Nanoscale photonic circuits based on surface plasmon polaritons (SPs) are a promising solution to this problem because they can localize light below the diffraction limit. However, there is a general tradeoff between the localization of an SP and the efficiency with which it can be detected with conventional far-field optics. Here we describe a new all-electrical SP detection technique based on the near-field coupling between guided plasmons and a nanowire field-effect transistor. We use the technique to electrically detect the plasmon emission from an individual colloidal quantum dot coupled to an SP waveguide. Our detectors are both nanoscale and highly efficient (0.1 electrons/plasmon), and a plasmonic gating effect can be used to amplify the signal even higher (up to 50 electrons/plasmon). These results enable new on-chip optical sensing applications and are a key step towards "dark" optoplasmonic nanocircuits in which SPs can be generated, manipulated, and detected without involving far-field radiation.Comment: manuscript followed by supplementary informatio

GaSbBi alloys and heterostructures: fabrication and properties

International audienceDilute bismuth (Bi) III-V alloys have recently attracted great attention, due to their properties of band-gap reduction and spin-orbit splitting. The incorporation of Bi into antimonide based III-V semiconductors is very attractive for the development of new optoelectronic devices working in the mid-infrared range (2-5 µm). However, due to its large size, Bi does not readily incorporate into III-V alloys and the epitaxy of III-V dilute bismides is thus very challenging. This book chapter presents the most recent developments in the epitaxy and characterization of GaSbBi alloys and heterostructures

Increased Expression of Fatty-Acid and Calcium Metabolism Genes in Failing Human Heart

Heart failure (HF) involves alterations in metabolism, but little is known about cardiomyopathy-(CM)-specific or diabetes-independent alterations in gene expression of proteins involved in fatty-acid (FA) uptake and oxidation or in calcium-(Ca(2+))-handling in the human heart.RT-qPCR was used to quantify mRNA expression and immunoblotting to confirm protein expression in left-ventricular myocardium from patients with HF (n = 36) without diabetes mellitus of ischaemic (ICM, n = 16) or dilated (DCM, n = 20) cardiomyopathy aetiology, and non-diseased donors (CTL, n = 6).Significant increases in mRNA of genes regulating FA uptake (CD36) and intracellular transport (Heart-FA-Binding Protein (HFABP)) were observed in HF patients vs CTL. Significance was maintained in DCM and confirmed at protein level, but not in ICM. mRNA was higher in DCM than ICM for peroxisome-proliferator-activated-receptor-alpha (PPARA), PPAR-gamma coactivator-1-alpha (PGC1A) and CD36, and confirmed at the protein level for PPARA and CD36. Transcript and protein expression of Ca(2+)-handling genes (Two-Pore-Channel 1 (TPCN1), Two-Pore-Channel 2 (TPCN2), and Inositol 1,4,5-triphosphate Receptor type-1 (IP3R1)) increased in HF patients relative to CTL. Increases remained significant for TPCN2 in all groups but for TPCN1 only in DCM. There were correlations between FA metabolism and Ca(2+)-handling genes expression. In ICM there were six correlations, all distinct from those found in CTL. In DCM there were also six (all also different from those found in CTL): three were common to and three distinct from ICM.DCM-specific increases were found in expression of several genes that regulate FA metabolism, which might help in the design of aetiology-specific metabolic therapies in HF. Ca(2+)-handling genes TPCN1 and TPCN2 also showed increased expression in HF, while HF- and CM-specific positive correlations were found among several FA and Ca(2+)-handling genes

Gas source molecular beam epitaxy growth and characterization of Ga0.51In0.49P/InxGa1-xAs/GaAs modulation-doped field-effect transistor structures

Lattice-matched Ga0.51In0.49P/GaAs and strained Ga0.51In0.49P/InxGa1-xAs/GaAs (0.1 less than or equal to x less than or equal to 0.25) modulation-doped field-effect transistor structures were grown by gas source molecular beam epitaxy by using Si as dopant. Detailed electrical characterization results are presented, The Ga0.5In0.49P/In0.25Ga0.75As/GaAs sample yielded dark two-dimensional electron gas densities of 3.75 x 10(12) cm(-2) (300 K) and 2.3 x 10(12) cm(-2) (77 K) which are comparable to the highest sheet electron densities reported in AlGaAs/InGaAs/GaAs and InAlAs/InGaAs/InP modulation-doped heterostructures. Persistent photoconductivity was observed in the strained samples only. A 0.797 eV deep lever has been detected in the undoped GaInP layers of the structures. Another lever, with DLTS peak height dependent an the filling pulse width, has been detected at the interface of the strained samples. Based on the DLTS and Hall effect measurement results, this level, which seems to be the origin of persistent photoconductivity, can be attributed to the strain relaxation related defects