Equilibrium free energy measurement of a confined electron driven out of equilibrium

We study out-of equilibrium properties of a quantum dot in a GaAs/AlGaAs two-dimensional electron gas. By means of single electron counting experiments, we measure the distribution of work and dissipated heat of the driven quantum dot and relate these quantities to the equilibrium free energy change, as it has been proposed by C. Jarzynski [Phys. Rev. Lett. {\bf78}, 2690 (1997)]. We discuss the influence of the degeneracy of the quantized energy state on the free energy change as well as its relation to the tunnel rates between the dot and the reservoir.Comment: 5 pages, 4 figure

Morphologie et propriétés rhéologiques de mélanges ternaires PE/PA/argile : influence du mode d'élaboration et de la fraction d'argile.

L'amélioration des propriétés des mélanges de polymères immiscibles par l'ajout d'argile fait l'objet d'un intérêt croissant. Les systèmes ternaires étudiés sont constitués de polyéthylène, de polyamide et d'une nanocharge argileuse présentant une bonne affinité avec le polyamide. Deux modes d'élaboration sont confrontés : mélange d'un nanocomposite PA/argile et du polyéthylène d'une part, et mélange simultanément des trois composants d'autre part. L'influence du taux d'argile sur les propriétés morphologiques et rhéologiques de chaque système est étudiée. La morphologie des systèmes ternaires est très dépendante du mode d'élaboration et de la fraction de la phase dispersée. Les propriétés viscoélastiques sont étudiées en relation avec les propriétés morphologiques. Le comportement viscoélastique a été confronté aux prédictions du modèle de Palierne. Les différences dues au mode d'élaboration sont attribuées à la localisation préférentielle de l'argile dans la phase polyamide et/ou à l'interface PE/PA

Automated segmentation and labeling of subcutaneous mouse implants at 14.1T

Magnetic resonance imaging (MRI) is a valuable tool for studying subcutaneous implants in rodents, providing non-invasive insight into biomaterial conformability and longitudinal characterization. However, considerable variability in existing image analysis techniques, manual segmentation and labeling, as well as the lack of reference atlases as opposed to brain imaging, all render the manual implant segmentation task tedious and extremely time-consuming. To this end, the development of automated and robust segmentation pipelines is a necessary addition to the tools available in rodent imaging research. In this work, we presented and compared commonly used image processing contrast-based segmentation approaches—namely, Canny edge detection, Otsu’s single and multi-threshold methods, and a combination of the latter with morphological operators—with more recently introduced convolutional neural network (CNN-) based models, such as the U-Net and nnU-Net (“no-new-net”). These fully automated end-to-end state-of-the-art neural architectures have shown great promise in online segmentation challenges. We adapted them to the implant segmentation task in mice MRI, with both 2D and 3D implementations. Our results demonstrated the superiority of the 3D nnU-Net model, which is able to robustly segment the implants with an average Dice accuracy of 0.915, and an acceptable absolute volume prediction error of 5.74%. Additionally, we provide researchers in the field with an automated segmentation pipeline in Python, leveraging these CNN-based implementations, and allowing to drastically reduce the manual labeling time from approximately 90 min to less than 5 min (292.959 s ± 6.49 s, N = 30 predictions). The latter addresses the bottleneck of constrained animal experimental time in pre-clinical rodent research

Diagnostic reliability of the Berlin classification for complex MCA aneurysms—usability in a series of only giant aneurysms

Background and objective The main challenge of bypass surgery of complex MCA aneurysms is not the selection of the bypass type but the initial decision-making of how to exclude the affected vessel segment from circulation. To this end, we have previously proposed a classification for complex MCA aneurysms based on the preoperative angiography. The current study aimed to validate this new classification and assess its diagnostic reliability using the giant aneurysm registry as an independent data set. Methods We reviewed the pretreatment neuroimaging of 51 patients with giant (> 2.5 cm) MCA aneurysms from 18 centers, prospectively entered into the international giant aneurysm registry. We classified the aneurysms according to our previously proposed Berlin classification for complex MCA aneurysms. To test for interrater diagnostic reliability, the data set was reviewed by four independent observers. Results We were able to classify all 51 aneurysms according to the Berlin classification for complex MCA aneurysms. Eight percent of the aneurysm were classified as type 1a, 14% as type 1b, 14% as type 2a, 24% as type 2b, 33% as type 2c, and 8% as type 3. The interrater reliability was moderate with Fleiss's Kappa of 0.419. Conclusion The recently published Berlin classification for complex MCA aneurysms showed diagnostic reliability, independent of the observer when applied to the MCA aneurysms of the international giant aneurysm registry.Peer reviewe

KCNQ2 R144 variants cause neurodevelopmental disability with language impairment and autistic features without neonatal seizures through a gain-of-function mechanism

Prior studies have revealed remarkable phenotypic heterogeneity in KCNQ2-related disorders, correlated with effects on biophysical features of heterologously expressed channels. Here, we assessed phenotypes and functional properties associated with KCNQ2 missense variants R144W, R144Q, and R144G. We also explored in vitro blockade of channels carrying R144Q mutant subunits by amitriptyline

Antimicrobial resistance among migrants in Europe: a systematic review and meta-analysis

BACKGROUND: Rates of antimicrobial resistance (AMR) are rising globally and there is concern that increased migration is contributing to the burden of antibiotic resistance in Europe. However, the effect of migration on the burden of AMR in Europe has not yet been comprehensively examined. Therefore, we did a systematic review and meta-analysis to identify and synthesise data for AMR carriage or infection in migrants to Europe to examine differences in patterns of AMR across migrant groups and in different settings. METHODS: For this systematic review and meta-analysis, we searched MEDLINE, Embase, PubMed, and Scopus with no language restrictions from Jan 1, 2000, to Jan 18, 2017, for primary data from observational studies reporting antibacterial resistance in common bacterial pathogens among migrants to 21 European Union-15 and European Economic Area countries. To be eligible for inclusion, studies had to report data on carriage or infection with laboratory-confirmed antibiotic-resistant organisms in migrant populations. We extracted data from eligible studies and assessed quality using piloted, standardised forms. We did not examine drug resistance in tuberculosis and excluded articles solely reporting on this parameter. We also excluded articles in which migrant status was determined by ethnicity, country of birth of participants' parents, or was not defined, and articles in which data were not disaggregated by migrant status. Outcomes were carriage of or infection with antibiotic-resistant organisms. We used random-effects models to calculate the pooled prevalence of each outcome. The study protocol is registered with PROSPERO, number CRD42016043681. FINDINGS: We identified 2274 articles, of which 23 observational studies reporting on antibiotic resistance in 2319 migrants were included. The pooled prevalence of any AMR carriage or AMR infection in migrants was 25·4% (95% CI 19·1-31·8; I2 =98%), including meticillin-resistant Staphylococcus aureus (7·8%, 4·8-10·7; I2 =92%) and antibiotic-resistant Gram-negative bacteria (27·2%, 17·6-36·8; I2 =94%). The pooled prevalence of any AMR carriage or infection was higher in refugees and asylum seekers (33·0%, 18·3-47·6; I2 =98%) than in other migrant groups (6·6%, 1·8-11·3; I2 =92%). The pooled prevalence of antibiotic-resistant organisms was slightly higher in high-migrant community settings (33·1%, 11·1-55·1; I2 =96%) than in migrants in hospitals (24·3%, 16·1-32·6; I2 =98%). We did not find evidence of high rates of transmission of AMR from migrant to host populations. INTERPRETATION: Migrants are exposed to conditions favouring the emergence of drug resistance during transit and in host countries in Europe. Increased antibiotic resistance among refugees and asylum seekers and in high-migrant community settings (such as refugee camps and detention facilities) highlights the need for improved living conditions, access to health care, and initiatives to facilitate detection of and appropriate high-quality treatment for antibiotic-resistant infections during transit and in host countries. Protocols for the prevention and control of infection and for antibiotic surveillance need to be integrated in all aspects of health care, which should be accessible for all migrant groups, and should target determinants of AMR before, during, and after migration. FUNDING: UK National Institute for Health Research Imperial Biomedical Research Centre, Imperial College Healthcare Charity, the Wellcome Trust, and UK National Institute for Health Research Health Protection Research Unit in Healthcare-associated Infections and Antimictobial Resistance at Imperial College London

The ANTENATAL multicentre study to predict postnatal renal outcome in fetuses with posterior urethral valves: objectives and design

Abstract Background Posterior urethral valves (PUV) account for 17% of paediatric end-stage renal disease. A major issue in the management of PUV is prenatal prediction of postnatal renal function. Fetal ultrasound and fetal urine biochemistry are currently employed for this prediction, but clearly lack precision. We previously developed a fetal urine peptide signature that predicted in utero with high precision postnatal renal function in fetuses with PUV. We describe here the objectives and design of the prospective international multicentre ANTENATAL (multicentre validation of a fetal urine peptidome-based classifier to predict postnatal renal function in posterior urethral valves) study, set up to validate this fetal urine peptide signature. Methods Participants will be PUV pregnancies enrolled from 2017 to 2021 and followed up until 2023 in >30 European centres endorsed and supported by European reference networks for rare urological disorders (ERN eUROGEN) and rare kidney diseases (ERN ERKNet). The endpoint will be renal/patient survival at 2 years postnatally. Assuming α = 0.05, 1–β = 0.8 and a mean prevalence of severe renal outcome in PUV individuals of 0.35, 400 patients need to be enrolled to validate the previously reported sensitivity and specificity of the peptide signature. Results In this largest multicentre study of antenatally detected PUV, we anticipate bringing a novel tool to the clinic. Based on urinary peptides and potentially amended in the future with additional omics traits, this tool will be able to precisely quantify postnatal renal survival in PUV pregnancies. The main limitation of the employed approach is the need for specialized equipment. Conclusions Accurate risk assessment in the prenatal period should strongly improve the management of fetuses with PUV

Maternal outcomes and risk factors for COVID-19 severity among pregnant women.

Pregnant women may be at higher risk of severe complications associated with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which may lead to obstetrical complications. We performed a case control study comparing pregnant women with severe coronavirus disease 19 (cases) to pregnant women with a milder form (controls) enrolled in the COVI-Preg international registry cohort between March 24 and July 26, 2020. Risk factors for severity, obstetrical and immediate neonatal outcomes were assessed. A total of 926 pregnant women with a positive test for SARS-CoV-2 were included, among which 92 (9.9%) presented with severe COVID-19 disease. Risk factors for severe maternal outcomes were pulmonary comorbidities [aOR 4.3, 95% CI 1.9-9.5], hypertensive disorders [aOR 2.7, 95% CI 1.0-7.0] and diabetes [aOR2.2, 95% CI 1.1-4.5]. Pregnant women with severe maternal outcomes were at higher risk of caesarean section [70.7% (n = 53/75)], preterm delivery [62.7% (n = 32/51)] and newborns requiring admission to the neonatal intensive care unit [41.3% (n = 31/75)]. In this study, several risk factors for developing severe complications of SARS-CoV-2 infection among pregnant women were identified including pulmonary comorbidities, hypertensive disorders and diabetes. Obstetrical and neonatal outcomes appear to be influenced by the severity of maternal disease