Efficacy and safety of targeted therapies in VEXAS syndrome: retrospective study from the FRENVEX.

OBJECTIVES: Vacuoles, E1 enzyme, X-linked, autoinflammatory and somatic (VEXAS) syndrome is an adult-onset autoinflammatory disease associated with somatic ubiquitin-like modifier-activating enzyme 1 (UBA1) mutations. We aimed to evaluate the efficacy and safety of targeted therapies. METHODS: Multicentre retrospective study including patients with genetically proven VEXAS syndrome who had received at least one targeted therapy. Complete response (CR) was defined by a clinical remission, C-reactive protein (CRP) ≤10 mg/L and a ≤10 mg/day of prednisone-equivalent therapy, and partial response (PR) was defined by a clinical remission and a 50% reduction in CRP levels and glucocorticoid dose. RESULTS: 110 patients (median age 71 (68-79) years) who received 194 targeted therapies were included: 78 (40%) received Janus kinase (JAK) inhibitors (JAKi), 51 (26%) interleukin (IL)-6 inhibitors, 33 (17%) IL-1 inhibitors, 20 (10%) tumour necrosis factor (TNFα) blockers and 12 (6%) other targeted therapies. At 3 months, the overall response (CR and PR) rate was 24% with JAKi, 32% with IL-6 inhibitors, 9% with anti-IL-1 and 0% with TNFα blockers or other targeted therapies. At 6 months, the overall response rate was 30% with JAKi and 26% with IL-6 inhibitors. Survival without treatment discontinuation was significantly longer with JAKi than with the other targeted therapies. Among patients who discontinued treatment, causes were primary failure, secondary failure, serious adverse event or death in 43%, 14%, 19% and 19%, respectively, with JAKi and 46%, 11%, 31% and 9%, respectively, with IL-6 inhibitors. CONCLUSIONS: This study shows the benefit of JAKi and IL-6 inhibitors, whereas other therapies have lower efficacy. These results need to be confirmed in prospective trials.

COVID-19 or not COVID-19? Compared characteristics of patients hospitalized for suspected COVID-19.

During an epidemic period, we compared patients hospitalized for initial suspicion of COVID-19 but for whom an alternative diagnosis was finally retained (n = 152) with those who had COVID-19 (n = 222). Most common diagnoses were another infectious disease and heart failure. COVID-19-negative patients were more often active smokers had less often cough, fever, and digestive symptoms, as compared to the 222 COVID-19-positive patients. They had higher median neutrophil and lymphocyte counts and lower CRP level. In multivariate analysis, no current smoking, neurocognitive disorder, myalgia, and fibrinogen ≥4g/L were independently associated with a final diagnosis of COVID-19.

Surface perturbation inverted from angle variations of eigenbeams in an ultrasonic waveguide.

Ocean acoustic tomography is traditionally performed using the travel-time variations of an acoustic path between a source and a receiver. In the context of shallow-water tomography and multipath propagation, the different acoustic paths can be correctly identified if the source and the receiver are arrays of transducers. Here, a double-beamforming algorithm can be applied to extract a collection of eigenbeams from the raw acoustic dataset. In this study, four observables can be measured for each eigenbeam: the travel-time, the amplitude, and the emitting and receiving angles. In this study, the sensitivity kernel (SK) formulation is used to establish a quantitative relation between a perturbation of the surface of an ultrasonic waveguide and the emitting and receiving angles of each eigenbeam. This theoretical relation is experimentally demonstrated using a forward model experiment designed to measure the SK. The SK formulation is then used in a second experiment to quantitatively and dynamically image the propagation of a surface wave traveling across the surface of the waveguide. The inversion results show that the quality of the joint inversion of the emitting and receiving angles is higher than previous results based on amplitude or travel-time observables.

Dynamic imaging of a capillary-gravity wave in shallow water using amplitude variations of eigenbeams.

Dynamic acoustic imaging of a surface wave propagating at an air-water interface is a complex task that is investigated here at the laboratory scale through an ultrasonic experiment in a shallow water waveguide. Using a double beamforming algorithm between two source-receiver arrays, the authors isolate and identify each multi-reverberated eigenbeam that interacts with the air-water and bottom interfaces. The waveguide transfer matrix is recorded 100 times per second while a low-amplitude gravity wave is generated by laser-induced breakdown at the middle of the waveguide, just above the water surface. The controlled, and therefore repeatable, breakdown results in a blast wave that interacts with the air-water interface, which creates ripples at the surface that propagate in both directions. The amplitude perturbations of each ultrasonic eigenbeam are measured during the propagation of the gravity-capillary wave. Inversion of the surface deformation is performed from the amplitude variations of the eigenbeams using a diffraction-based sensitivity kernel approach. The accurate ultrasonic imaging of the displacement of the air-water interface is compared to simultaneous measurements with an optical camera, which provides independent validation.

Using the trapped energy ratio for source depth discrimination with a horizontal line array: Theory and experimental results.

The problem of acoustic source depth discrimination was introduced as a way to get basic information on source depth in configurations where accurate depth estimation is not feasible. It is a binary classification problem, aiming to evaluate whether the source is near the surface or submerged. Herein, the classification relies on a signal measured with a horizontal line array in shallow water. Knowing the source-array distance is not required but the source bearing has to be close to the array endfire. Signal processing relies on a normal-mode propagation model, and thus requires prior knowledge of the mode characteristics. The decision relies on an estimation of the trapped energy ratio in mode space. The performance is predicted with simulations and Monte Carlo methods, allowing one to compare several estimators based on different mode filters, and to choose an appropriate decision threshold. The impact on performance of frequency, noise level, horizontal aperture, and environmental mismatch is numerically studied. Finally, the approach is validated on experimental data acquired with a horizontal line array deployed off the coast of New Jersey, and the impact of errors in the environmental model is illustrated. The investigated approach successfully identifies a surface ship and a submerged towed source.

Source depth discrimination with a vertical line array.

Source depth estimation with a vertical line array generally involves mode filtering, then matched-mode processing. Because mode filtering is an ill-posed problem if the water column is not well-sampled, concerns for robustness motivate a simpler approach: source depth discrimination considered as a binary classification problem. It aims to evaluate whether the source is near the surface or submerged. These two hypotheses are formulated in terms of normal modes, using the concept of trapped and free modes. Decision metrics based on classic mode filters are proposed. Monte Carlo methods are used to predict performance and set the parameters of a classifier accordingly.

Inverting for a deterministic surface gravity wave using the sensitivity-kernel approach.

The dynamic imaging of a deterministic gravity wave propagating at an air-water interface requires continuous sampling of every point at this interface. This sampling can be done acoustically using waves that propagate in the water column but have specular reflection points that fully scan the air-water interface. This study aims to perform this complex task experimentally, with identical ultrasonic source and receiver arrays that face each other in a 1-m-long, 5-cm-deep fluid waveguide, and with frequencies in the MHz range. The waveguide transfer matrix is recorded 100 times per second between the source-receiver arrays, while a gravity wave is generated at the air-water interface. Through the beamforming process, a large set of acoustic multi-reverberated beams are isolated and identified that interact with the air-water interface. The travel-time and amplitude modulations of each eigenbeam are measured when the surface gravity wave travels through the source-receiver plane. Linear inversion of the travel-time and amplitude perturbations is performed from a few thousand eigenbeams using diffraction-based sensitivity kernels. Inversion results using travel-times, amplitudes, or these two observables together, lead to accurate spatial-temporal patterns of the surface deformation. The advantages and limitations of the method are discussed.

3-D Passive Cavitation Imaging Using Adaptive Beamforming and Matrix Array Transducer With Random Apodization.

With the development of promising cavitation-based treatments, the interest in cavitation monitoring with passive acoustic mapping (PAM) is significantly increasing. While most of studies regarding PAM are performed in 2-D, 3-D imaging modalities are getting more attention relying on either custom-made or commercial matrix probes. Unless specific phased-arrays are used for a specific application, limitations due to probe apertures often results in poor performances of the 3-D mapping, due to the use of a delay-and-sum (DAS) classic beamformer, which results in strong artifacts and large main lobe sizes. In this article, 3D-PAM is achieved by performing adaptive beamforming in the frequency domain (FD) in 3-D, and using a random sparse apodization of a commercial matrix array driving only 256 elements among the 1024 available. It reduces the computation time and makes use of only one 256-channel research platform. Three beamformers have been implemented in 3-D and in the FD: the DAS beamformer, which corresponds to the beamformer used in previous 3D-PAM studies, the robust capon beamformer (RCB), an adaptive algorithm widely used in 2D-PAM for its high performances, and the MidWay (MW) beamformer, an adaptive algorithm with a computation complexity equivalent to the one of DAS. These algorithms are evaluated both in simulations and experiments with a harmonic source at different positions, and are also applied to real cavitation signals. The results show that, in the case of matrix arrays of small aperture such as generic commercial matrix probes, the DAS beamformer leads to large main lobe sizes, while adaptive beamformers largely improve the performances of the mapping. The low computation time and its parameter-free character make MW beamformer a good compromise for 3D-PAM applications. It thus appears that a random sparse apodization combined with adaptive beamforming is a good solution to achieve high-performance 3D-PAM with manageable devices.

An inverse method using Cross spectral Matrix Fitting for passive cavitation imaging.

High intensity focused ultrasound (HIFU) can produce cavitation, which requires monitoring for specific applications such as sonoporation, targeted drug delivery or histotripsy. Passive acoustic mapping has been proposed in the literature as a method for monitoring cavitation, but it lacks spatial resolution, primarily in the axial direction, due to the absence of a time reference. This is a common issue with passive imaging compared to standard pulse-echo ultrasound. In order to improve the axial resolution, we propose an adaptation of the Cross spectral Matrix Fitting (CMF) method for passive cavitation imaging, which is based on the resolution of an inverse problem with different regularizations that promote sparsity in the reconstructed cavitation maps: Elastic Net (CMF-ElNet) and sparse Total Variation (CMF-spTV). The results from both simulated and experimental data are presented and compared to state-of-the-art approaches, such as the frequential Delay-and-Sum (DAS) and the frequential Robust Capon Beamformer (RCB). We show the interest of the method for improving the axial resolution, with an axial Full Width Half Maximum (FWHM) divided by 3 and 5 compared to RCB and DAS, respectively. Moreover, CMF based methods improve Contrast-to-Noise Ratio (CNR) by more than 15 dB in experimental conditions compared to RCB. We also show the advantage of the sparse Total Variation prior over Elastic Net when dealing with cloud shaped cavitation sources, that can be assumed as sparse grouped sources.

Time-angle sensitivity kernels for sound-speed perturbations in a shallow ocean.

Acoustic waves traveling in a shallow-water waveguide produce a set of multiple paths that can be characterized as a geometric approximation by their travel time (TT), direction of arrival (DOA), and direction of departure (DOD). This study introduces the use of the DOA and DOD as additional observables that can be combined to the classical TT to track sound-speed perturbations in an oceanic waveguide. To model the TT, DOA, and DOD variations induced by sound-speed perturbations, the three following steps are used: (1) In the first-order Born approximation, the Fréchet kernel provides a linear link between the signal fluctuations and the sound-speed perturbations; (2) a double-beamforming algorithm is used to transform the signal fluctuations received on two source-receiver arrays in the time, receiver-depth, and source-depth domain into the eigenray equivalent measured in the time, reception-angle and launch angle domain; and finally (3) the TT, DOA, and DOD variations are extracted from the double-beamformed signal variations through a first-order Taylor development. As a result, time-angle sensitivity kernels are defined and used to build a linear relationship between the observable variations and the sound-speed perturbations. This approach is validated with parabolic-equation simulations in a shallow-water ocean context.

Experimental measurement of the acoustic sensitivity kernel.

In the Born approximation, the acoustic scattering from a spherical obstacle of a size comparable to the acoustic wavelength can be evaluated in the framework of the sensitivity kernel approach, which describes the relationship between the pressure-field fluctuation and the position of a local change in the propagation medium. The spatial structure of the sensitivity kernel is here investigated through experimental observations made in a water tank at the ultrasonic scale and compared to an analytical model. The pattern of the sensitivity kernel is discussed in the case of a source-to-receiver wave field that includes a direct path and one surface reflection.

Shallow-water acoustic tomography from angle measurements instead of travel-time measurements.

For shallow-water waveguides and mid-frequency broadband acoustic signals, ocean acoustic tomography (OAT) is based on the multi-path aspect of wave propagation. Using arrays in emission and reception and advanced array processing, every acoustic arrival can be isolated and matched to an eigenray that is defined not only by its travel time but also by its launch and reception angles. Classically, OAT uses travel-time variations to retrieve sound-speed perturbations; this assumes very accurate source-to-receiver clock synchronization. This letter uses numerical simulations to demonstrate that launch-and-reception-angle tomography gives similar results to travel-time tomography without the same requirement for high-precision synchronization.

T-cell immune response predicts the risk of critical SARS-Cov2 infection in hospitalized COVID-19 patients.

INTRODUCTION: This study aimed to identify markers of disease worsening in patients hospitalized for SARS-Cov2 infection. PATIENTS AND METHODS: Patients hospitalized for severe recent-onset (<1 week) SARS-Cov2 infection were prospectively included. The percentage of T-cell subsets and plasma IL-6 at admission (before any steroid therapy) were compared between patients who progressed to a critical infection and those who did not. RESULTS: Thirty-seven patients (18 men, 19 women) were included; 11 (30%) progressed to critical infection. At admission, the critical infection patients were older (P = 0.021), had higher creatinine levels (P = 0.003), and decreased percentages of circulating B cells (P = 0.04), T cells (P = 0.009), and CD4+ T cells (P = 0.004) than those with a favorable course. Among T cell subsets, there was no significant difference between the two groups except for the percentage of Th17 cells, which was two-fold higher in patients who progressed to critical infection (P = 0.028). Plasma IL-6 at admission was also higher in this group (P = 0.018). In multivariate analysis, the percentage of circulating Th17 cells at admission was the only variable associated with higher risk of progression to critical SARS-Cov2 infection (P = 0.021). CONCLUSION: This study suggests that an elevated percentage of Th17 cells in patients hospitalized for SARS-Cov2 infection is associated with an increased risk of progression to critical disease. If these data are confirmed in a larger study, this marker could be used to better target the population of patients in whom tocilizumab could decrease the risk of progression to critical COVID-19.

Target detection and localization in shallow water: an experimental demonstration of the acoustic barrier problem at the laboratory scale.

This study demonstrates experimentally at the laboratory scale the detection and localization of a wavelength-sized target in a shallow ultrasonic waveguide between two source-receiver arrays at 3 MHz. In the framework of the acoustic barrier problem, at the 1/1000 scale, the waveguide represents a 1.1-km-long, 52-m-deep ocean acoustic channel in the kilohertz frequency range. The two coplanar arrays record in the time-domain the transfer matrix of the waveguide between each pair of source-receiver transducers. Invoking the reciprocity principle, a time-domain double-beamforming algorithm is simultaneously performed on the source and receiver arrays. This array processing projects the multireverberated acoustic echoes into an equivalent set of eigenrays, which are defined by their launch and arrival angles. Comparison is made between the intensity of each eigenray without and with a target for detection in the waveguide. Localization is performed through tomography inversion of the acoustic impedance of the target, using all of the eigenrays extracted from double beamforming. The use of the diffraction-based sensitivity kernel for each eigenray provides both the localization and the signature of the target. Experimental results are shown in the presence of surface waves, and methodological issues are discussed for detection and localization.

Travel-time tomography in shallow water: experimental demonstration at an ultrasonic scale.

Acoustic tomography in a shallow ultrasonic waveguide is demonstrated at the laboratory scale between two source-receiver arrays. At a 1/1,000 scale, the waveguide represents a 1.1-km-long, 52-m-deep ocean acoustic channel in the kilohertz frequency range. Two coplanar arrays record the transfer matrix in the time domain of the waveguide between each pair of source-receiver transducers. A time-domain, double-beamforming algorithm is simultaneously performed on the source and receiver arrays that projects the multi-reflected acoustic echoes into an equivalent set of eigenrays, which are characterized by their travel times and their launch and arrival angles. Travel-time differences are measured for each eigenray every 0.1 s when a thermal plume is generated at a given location in the waveguide. Travel-time tomography inversion is then performed using two forward models based either on ray theory or on the diffraction-based sensitivity kernel. The spatially resolved range and depth inversion data confirm the feasibility of acoustic tomography in shallow water. Comparisons are made between inversion results at 1 and 3 MHz with the inversion procedure using ray theory or the finite-frequency approach. The influence of surface fluctuations at the air-water interface is shown and discussed in the framework of shallow-water ocean tomography.

Multiplane deconvolution in underwater acoustics: Simultaneous estimations of source level and position.

In many acoustic imaging applications, conventional beamforming (CBF) cannot provide both accurate position and source level estimates simultaneously. Also, the CBF acoustic maps suffer from many artifacts due to the spreading of large point-spread-functions. An original CLEAN deconvolution procedure, including an additional plane containing out-of-plane interfering sources, is proposed here to achieve simultaneous localization, source level estimation, and de-noising. The approach is illustrated using experimental data mimicking a challenging deep-sea mining configuration: an underwater acoustic source of interest is located 700 m below the sea surface, tens of meters from a 3 m-length array, with boat noise as the disturbing source.

Use of the Cross-Spectral Density Matrix for Enhanced Passive Ultrasound Imaging of Cavitation.

Passive ultrasound imaging is of great interest for cavitation monitoring. Spatiotemporal monitoring of cavitation bubbles in therapeutic applications is possible using an ultrasound imaging probe to passively receive the acoustic signals from the bubbles. Fourier-domain (FD) beamformers have been proposed to process the signals received into maps of the spatial localization of cavitation activity, with reduced computing times with respect to the time-domain approach, and to take advantage of frequency selectivity for cavitation regime characterization. The approaches proposed have been mainly nonadaptive, and these have suffered from low resolution and contrast, due to the many reconstruction artifacts. Inspired by the array-processing literature and in the context of passive ultrasound imaging of cavitation, we propose here a robust estimation of the second-order statistics of data through spatial covariance matrices in the FD or cross-spectral density matrices (CSMs). The benefits of such formalism are illustrated using advanced reconstruction algorithms, such as the robust Capon beamformer, the Pisarenko class beamformer, and the multiple signal classification approach. Through both simulations and experiments in a water tank, we demonstrate that enhanced localization of cavitation activity (i.e., improved resolution and contrast with respect to nonadaptive approaches) is compatible with the rapid and frequency-selective approaches of the FD. Robust estimation of the CSM and the derived adaptive beamformers paves the way to the development of powerful passive ultrasound imaging tools.

Estimation of modal group velocities with a single receiver for geoacoustic inversion in shallow water.

Due to the expense associated with at-sea sensor deployments, a challenge in underwater acoustics has been to develop methods requiring a minimal number of sensors. This paper introduces an adaptive time-frequency signal processing method designed for application to a single source-receiver sensor pair. The method involves the application of conjugate time-frequency warping transforms to improve the SNR and resolution of the time-frequency distribution (TFD) of the measured field. Such refined knowledge of the TFD facilitates efforts to extract tomographic information about the propagation medium. Here the method is applied to the case of modal propagation in a shallow ocean range independent environment to extract a refined TFD. Given knowledge of the source-receiver separation, the refined TFD is used to extract the frequency dependent group velocities of the individual modal components. The extracted group velocities are then incorporated into a computationally light tomographic inversion method. Simulated and experimental results are discussed.

Travel-time sensitivity kernels versus diffraction patterns obtained through double beam-forming in shallow water.

In recent years, the use of sensitivity kernels for tomographic purposes has been frequently discussed in the literature. Sensitivity kernels of different observables (e.g., amplitude, travel-time, and polarization for seismic waves) have been proposed, and relationships between adjoint formulation, time-reversal theory, and sensitivity kernels have been developed. In the present study, travel-time sensitivity kernels (TSKs) are derived for two source-receiver arrays in an acoustic waveguide. More precisely, the TSKs are combined with a double time-delay beam-forming algorithm performed on two source-receiver arrays to isolate and identify each eigenray of the multipath propagation between a source-receiver pair in the acoustic waveguide. A relationship is then obtained between TSKs and diffraction theory. It appears that the spatial shapes of TSKs are equivalent to the gradients of the combined direction patterns of the source and receiver arrays. In the finite-frequency regimes, the combination of TSKs and double beam-forming both simplifies the calculation of TSK and increases the domain of validity for ray theory in shallow-water ocean acoustic tomography.