The role of posterior aortopexy in the treatment of left mainstem bronchus compression

OBJECTIVES: We reviewed the role of posterior aortopexy for left mainstem bronchus compression in infants and children. METHODS: Eighteen children with respiratory symptoms were enrolled between 2005 and 2015 for surgical decompression of the left mainstem bronchus. The children were managed from diagnosis to follow-up by a dedicated tracheal team. Primary outcomes were the complete relief of symptoms or improvement with respect to preoperative clinical status. RESULTS: The median age was 4 years (0.3-15.4) and the median weight was 13.2 kg (3, 1-40). Symptoms or indications for bronchoscopy included difficult weaning from mechanical ventilation (n = 3, 17%), difficult weaning from tracheotomy (n = 4, 22%), recurrent pneumonia (n = 4, 22%), wheezing (n = 3, 17%), atelectasis (n = 1, 5.5%), bitonal cough (n = 1, 5.5%) and stridor (n = 2, 11%). Associated malformations were present in 88.7%. The diagnosis was made by bronchoscopy and computed tomography. Indication for surgery was the presence of pulsations and reduction in the diameter of the left mainstem bronchus compression of more than 70%. Surgery was performed by left posterolateral thoracotomy. Aortopexy was done under bronchoscopic control. No early or late deaths were observed, nor were reoperations necessary. Residual malacia was observed in 8 children (44%). Median follow-up was 4.1 years (0.1-7.1). At last follow-up, 17/18 (94.4%) children showed adequate airway patency. CONCLUSIONS: The intrathoracic location of the left mainstem bronchus predisposes it to compression. Vascular anomalies represent the most frequent causes. Aortopexy has been advocated as a safe and useful method to relieve the compression, and our results confirmed these findings. Management of these patients is challenging and requires a multidisciplinary team

Can Bayesian Network empower propensity score estimation from Real World Data?

A new method, based on Bayesian Networks, to estimate propensity scores is proposed with the purpose to draw causal inference from real world data on the average treatment effect in case of a binary outcome and discrete covariates. The proposed method ensures maximum likelihood properties to the estimated propensity score, i.e. asymptotic efficiency, thus outperforming other available approach. Two point estimators via inverse probability weighting are then proposed, and their main distributional properties are derived for constructing confidence interval and for testing the hypotheses of absence of the treatment effect. Empirical evidence of the substantial improvements offered by the proposed methodology versus standard logistic modelling of propensity score is provided in simulation settings that mimic the characteristics of a real dataset of prostate cancer patients from Milan San Raffaele Hospital

Emission measure distribution in loops impulsively heated at the foot-points

This work is prompted by the evidence of sharply peaked emission measure distributions in active stars, and by the claims of isothermal loops in solar coronal observations, at variance with the predictions of hydrostatic loop models with constant cross-section and uniform heating. We address the problem with loops heated at the foot-points. Since steady heating does not allow static loop models solutions, we explore whether pulse-heated loops can exist and appear as steady loops, on a time average. We simulate pulse-heated loops, using the Palermo-Harvard 1-D hydrodynamic code, for different initial conditions corresponding to typical coronal temperatures of stars ranging from intermediate to active ($T \sim 3$--$10 \times 10^6$ K). We find long-lived quasi-steady solutions even for heating concentrated at the foot-points over a spatial region of the order of $\sim 1/5$ of the loop half length and broader.These solutions yield an emission measure distribution with a peak at high temperature, and the cool side of the peak is as steep as $\sim T^{5}$, in contrast to the usual $\sim T^{3/2}$ of hydrostatic models with constant cross-section and uniform heating. Such peaks are similar to those found in the emission measure distribution of active stars around $10^7$ K.Comment: 9 pages, 6 figures, accepted for publication in The Astrophysical Journa

Combined IASI-NG and MWS observations for the retrieval of cloud liquid and ice water path: a deep learning artificial intelligence approach

A neural network (NN) approach is proposed to combine future infrared (IASI-NG) and microwave (MWS) observations to retrieve cloud liquid and ice water path. The methodology is applied to simulated IASI-NG and MWS observations in the period January–October 2019. IASI-NG and MWS observations are simulated globally at synoptic hours (00:00, 06:00, 12:00, 18:00 UTC) and on a regular spatial grid (0.125° × 0.125°) from ECMWF 5-generation reanalysis (ERA5). The state-of-the-art σ-IASI and RTTOV radiative transfer codes are used to simulate IASI-NG and MWS observations, respectively, from the earth's state vector given by ERA5. A principal component analysis of the simulated IASI-NG observations is performed. Accordingly, a NN is developed to retrieve cloud liquid and ice water path from a combination of 24 MWS channels and 30 IASI-NG PCs. Validation indicates that this combination results in liquid and ice water path retrievals with overall accuracy of 1.85 10 −2 kg/m 2 and 1.18 10 −2 kg/m 2 , respectively, and 0.97 correlation with respect to reference values. The root-mean-square error (RMSE) for CLWP results in about 30% of the mean value (5.91 10 −2 kg/m 2 ) and 22% of the variability (1-sigma). Similarly, the RMSE for CIWP results in about 41% of the mean value (2.91 10 −2 kg/m 2 ) and 22% of the variability. Two more NN are developed, retrieving cloud liquid and ice water path from microwave observations only (24 MWS channels) and infrared observations only (30 IASI-NG PCs), demonstrating quantitatively the advantage of using the combination of infrared and microwave observations with respect to either one alone

Double Tracheoesophageal Fistula and Azygos Lobe

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A Feedforward Neural Network Approach for the Detection of Optically Thin Cirrus From IASI-NG

The identification of optically thin cirrus is crucial for their accurate parameterization in climate and Earth's system models. This study exploits the characteristics of the infrared atmospheric sounding interferometer-new generation (IASI-NG) to develop an algorithm for the detection of optically thin cirrus. IASI-NG has been designed for the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) polar system second-generation program to continue the service of its predecessor IASI from 2024 onward. A thin-cirrus detection algorithm (TCDA) is presented here, as developed for IASI-NG, but also in parallel for IASI to evaluate its performance on currently available real observations. TCDA uses a feedforward neural network (NN) approach to detect thin cirrus eventually misidentified as clear sky by a previously applied cloud detection algorithm. TCDA also estimates the uncertainty of "clear-sky" or "thin-cirrus" detection. NN is trained and tested on a dataset of IASI-NG (or IASI) simulations obtained by processing ECMWF 5-generation reanalysis (ERA5) data with the s-IASI radiative transfer model. TCDA validation against an independent simulated dataset provides a quantitative statistical assessment of the improvements brought by IASI-NG with respect to IASI. In fact, IASI-NG TCDA outperforms IASI TCDA by 3% in probability of detection (POD), 1% in bias, and 2% in accuracy, and the false alarm ratio (FAR) passes from 0.02 to 0.01. Moreover, IASI TCDA validation against state-of-the-art cloud products from Cloudsat/CPR and CALIPSO/Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) real observations reveals a tendency for IASI TCDA to underestimate the presence of thin cirrus (POD = 0.47) but with a low FAR (0.07), which drops to 0.0 for very thin cirrus

On estimating key cloud properties with satellite observations: An artificial intelligence based retrieval framework

This work builds on the analyses made within the EUMETSAT ComboCloud project (contract EUM/CO/19/4600002352/THH) whose purpose was to develop AI-based solutions to infer key cloud parameters exploiting the combination of innovative features offered by upcoming satellite sensors, namely the Next Generation Atmospheric Sounding Interferometer (IASI-NG), and the Microwave Sounder (MWS). We present the potential of the developed solutions applied to real observations, from the instruments flying onboard the EUMETSAT MetOp satellites such as the Atmospheric Sounding Interferometer (IASI), the Advanced Microwave Sounding Unit (AMSU), and the Microwave Humidity Sounder (MHS) and validated against cloud products from an independent dataset of real observations. The validation demonstrated good agreement between reference and retrieved cloud key parameters, showing consistent range and spatial patterns