Simulating the ideal geometrical and biomechanical parameters of the pulmonary autograft to prevent failure in the Ross operation

OBJECTIVES: Reinforcements for the pulmonary autograft (PA) in the Ross operation have been introduced to avoid the drawback of conduit expansion and failure. With the aid of an in silico simulation, the biomechanical boundaries applied to a healthy PA during the operation were studied to tailor the best implant technique to prevent reoperation. METHODS: Follow-up echocardiograms of 66 Ross procedures were reviewed. Changes in the dimensions and geometry of reinforced and non-reinforced PAs were evaluated. Miniroot and subcoronary implantation techniques were used in this series. Mechanical stress tests were performed on 36 human pulmonary and aortic roots explanted from donor hearts. Finite element analysis was applied to obtain high-fidelity simulation under static and dynamic conditions of the biomechanical properties and applied stresses on the PA root and leaflet and the similar components of the native aorta. RESULTS: The non-reinforced group showed increases in the percentages of the mean diameter that were significantly higher than those in the reinforced group at the level of the Valsalva sinuses (3.9%) and the annulus (12.1%). The mechanical simulation confirmed geometrical and dimensional changes detected by clinical imaging and demonstrated the non-linear biomechanical behaviour of the PA anastomosed to the aorta, a stiffer behaviour of the aortic root in relation to the PA and similar qualitative and quantitative behaviours of leaflets of the 2 tissues. The annulus was the most significant constraint to dilation and affected the distribution of stress and strain within the entire complex, with particular strain on the sutured regions. The PA was able to evenly absorb mechanical stresses but was less adaptable to circumferential stresses, potentially explaining its known dilatation tendency over time. CONCLUSIONS: The absence of reinforcement leads to a more marked increase in the diameter of the PA. Preservation of the native geometry of the PA root is crucial; the miniroot technique with external reinforcement is the most suitable strategy in this context

Evaluation of MRI features of neuromas in oncological amputees, and the relation to pain

Objective: The impact of time on neuroma growth and morphology on pain intensity is unknown. This study aims to assess magnetic resonance imaging (MRI) differences between symptomatic and non-symptomatic neuromas in oncological amputees, and whether time influences MRI-detected neuroma dimensions and their association with pain. Material and methods: Oncological patients who underwent traditional extremity amputation were included. Post-amputation MRIs were assessed before decision for neuroma surgery. Chart review was performed for residual limb pain (numeric rating scale, 0–10) and the presence of neuropathic symptoms. Neuromas were classified as symptomatic or non-symptomatic, with neuroma size expressed as radiological neuroma-to-nerve-ratio (NNR). Results: Among 78 neuromas in 60 patients, the median NNR was 2.0, and 56 neuromas (71.8%) were symptomatic with a median pain score of 3.5. NNR showed no association with symptomatology or pain intensity but correlated with a longer time-to-neuroma-excision interval and a smaller nerve caliber. Symptomatic neuromas were associated with lower extremity amputation, T2 heterogeneity, and the presence of heterotopic ossification. Lower extremity amputation, T2 heterogeneity, perineural edema, and presence of heterotopic ossification were associated with more painful neuromas. Conclusion: MRI features associated with symptomatic neuromas and pain intensity were identified. Awareness of the potential clinical significance of these imaging features may help in the interpretation of MRI exams and may aid clinicians in patient selection for neuroma surgery in oncological amputees.</p

The three hundred project. A machine learning method to infer clusters of galaxy mass radial profiles from mock Sunyaev–Zel’dovich maps

We develop a machine learning algorithm to infer the three-dimensional cumulative radial profiles of total and gas masses in galaxy clusters from thermal Sunyaev–Zel’dovich effect maps. We generate around 73 000 mock images along various lines of sight using 2522 simulated clusters from THE THREE HUNDRED project at redshift z &lt; 0.12 and train a model that combines an auto-encoder and a random forest. Without making any prior assumptions about the hydrostatic equilibrium of the clusters, the model is capable of reconstructing the total mass profile as well as the gas mass profile, which is responsible for the Sunyaev–Zel’dovich effect. We show that the recovered profiles are unbiased with a scatter of about 10 per cent, slightly increasing towards the core and the outskirts of the cluster. We selected clusters in the mass range of 1013.5 ≤ M200/(h−1 M) ≤ 1015.5, spanning different dynamical states, from relaxed to disturbed haloes. We verify that both the accuracy and precision of this method show a slight dependence on the dynamical state, but not on the cluster mass. To further verify the consistency of our model, we fit the inferred total mass profiles with a Navarro–Frenk–White model and contrast the concentration values with those of the true profiles. We note that the inferred profiles are unbiased for higher concentration values, reproducing a trustworthy mass–concentration relation. The comparison with a widely used mass estimation technique, such as hydrostatic equilibrium, demonstrates that our method recovers the total mass that is not biased by non-thermal motions of the gas

-PBR28 positron emission tomography signal as an imaging marker of joint inflammation in knee osteoarthritis

Although inflammation is known to play a role in knee osteoarthritis (KOA), inflammation-specific imaging is not routinely performed. In this article, we evaluate the role of joint inflammation, measured using [11C]-PBR28, a radioligand for the inflammatory marker 18-kDa translocator protein (TSPO), in KOA. Twenty-one KOA patients and 11 healthy controls (HC) underwent positron emission tomography/magnetic resonance imaging (PET/MRI) knee imaging with the TSPO ligand [11C]-PBR28. Standardized uptake values were extracted from regions-of-interest (ROIs) semiautomatically segmented from MRI data, and compared across groups (HC, KOA) and subgroups (unilateral/bilateral KOA symptoms), across knees (most vs least painful), and against clinical variables (eg, pain and Kellgren-Lawrence [KL] grades). Overall, KOA patients demonstrated elevated [11C]-PBR28 binding across all knee ROIs, compared with HC (all P's &lt; 0.005). Specifically, PET signal was significantly elevated in both knees in patients with bilateral KOA symptoms (both P's &lt; 0.01), and in the symptomatic knee (P &lt; 0.05), but not the asymptomatic knee (P = 0.95) of patients with unilateral KOA symptoms. Positron emission tomography signal was higher in the most vs least painful knee (P &lt; 0.001), and the difference in pain ratings across knees was proportional to the difference in PET signal (r = 0.74, P &lt; 0.001). Kellgren-Lawrence grades neither correlated with PET signal (left knee r = 0.32, P = 0.19; right knee r = 0.18, P = 0.45) nor pain (r = 0.39, P = 0.07). The current results support further exploration of [11C]-PBR28 PET signal as an imaging marker candidate for KOA and a link between joint inflammation and osteoarthritis-related pain severity

A machine learning method to infer clusters of galaxies mass radial profiles from mock Sunyaev-Zel’dovich maps with The Three Hundred clusters

Our study introduces a new machine learning algorithm for estimating 3D cumulative radial profiles of total and gas mass in galaxy clusters from thermal Sunyaev-Zel’dovich (SZ) effect maps. We generate mock images from 2522 simulated clusters, employing an autoencoder and random forest in our approach. Notably, our model makes no prior assumptions about hydrostatic equilibrium. Our results indicate that the model successfully reconstructs unbiased total and gas mass profiles, with a scatter of approximately 10%. We analyse clusters in various dynamical states and mass ranges, finding that our method’s accuracy and precision are consistent. We verify the capabilities of our model by comparing it with the hydrostatic equilibrium technique, showing that it accurately recovers total mass profiles without any bias

The ERA-EDTA Registry Annual Report 2017 : a summary

Background. This article presents a summary of the 2017 Annual Report of the European Renal Association-European Dialysis and Transplant Association (ERA-EDTA) Registry and describes the epidemiology of renal replacement therapy (RRT) for end-stage renal disease (ESRD) in 37 countries. Methods. The ERA-EDTA Registry received individual patient data on patients undergoing RRT for ESRD in 2017 from 32 national or regional renal registries and aggregated data from 21 registries. The incidence and prevalence of RRT, kidney transplantation activity and survival probabilities of these patients were calculated. Results. In 2017, the ERA-EDTA Registry covered a general population of 694 million people. The incidence of RRT for ESRD was 127 per million population (pmp), ranging from 37 pmp in Ukraine to 252 pmp in Greece. A total of 62% of patients were men, 52% were >= 65 years of age and 23% had diabetes mellitus as the primary renal disease. The treatment modality at the onset of RRT was haemodialysis for 85% of patients. On 31 December 2017, the prevalence of RRT was 854 pmp, ranging from 210 pmp in Ukraine to 1965 pmp in Portugal. The transplant rate in 2017 was 33 pmp, ranging from 3 pmp in Ukraine to 103 pmp in the Spanish region of Catalonia. For patients commencing RRT during 2008-12, the unadjusted 5-year patient survival probability for all RRT modalities combined was 50.8%.Peer reviewe

The first HyDRA challenge for computational vibrational spectroscopy

Vibrational spectroscopy in supersonic jet expansions is a powerful tool to assess molecular aggregates in close to ideal conditions for the benchmarking of quantum chemical approaches. The low temperatures achieved as well as the absence of environment effects allow for a direct comparison between computed and experimental spectra. This provides potential benchmarking data which can be revisited to hone different computational techniques, and it allows for the critical analysis of procedures under the setting of a blind challenge. In the latter case, the final result is unknown to modellers, providing an unbiased testing opportunity for quantum chemical models. In this work, we present the spectroscopic and computational results for the first HyDRA blind challenge. The latter deals with the prediction of water donor stretching vibrations in monohydrates of organic molecules. This edition features a test set of 10 systems. Experimental water donor OH vibrational wavenumbers for the vacuum-isolated monohydrates of formaldehyde, tetrahydrofuran, pyridine, tetrahydrothiophene, trifluoroethanol, methyl lactate, dimethylimidazolidinone, cyclooctanone, trifluoroacetophenone and 1-phenylcyclohexane-cis-1,2-diol are provided. The results of the challenge show promising predictive properties in both purely quantum mechanical approaches as well as regression and other machine learning strategies