Meningothelial Cells React to Elevated Pressure and Oxidative Stress

BACKGROUND: Meningothelial cells (MECs) are the cellular components of the meninges enveloping the brain. Although MECs are not fully understood, several functions of these cells have been described. The presence of desmosomes and tight junctions between MECs hints towards a barrier function protecting the brain. In addition, MECs perform endocytosis and, by the secretion of cytokines, are involved in immunological processes in the brain. However, little is known about the influence of pathological conditions on MEC function; e.g., during diseases associated with elevated intracranial pressure, hypoxia or increased oxidative stress. METHODS: We studied the effect of elevated pressure, hypoxia, and oxidative stress on immortalized human as well as primary porcine MECs. We used MTS (3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium) bioreduction assays to assess the proliferation of MECs in response to treatment and compared to untreated control cells. To assess endocytotic activity, the uptake of fluorescently labeled latex beads was analyzed by fluorescence microscopy. RESULTS: We found that exposure of MECs to elevated pressure caused significant cellular proliferation and a dramatic decrease in endocytotic activity. In addition, mild oxidative stress severely inhibited endocytosis. CONCLUSION: Elevated pressure and oxidative stress impact MEC physiology and might therefore influence the microenvironment of the subarachnoid space and thus the cerebrospinal fluid within this compartment with potential negative impact on neuronal function

A full pipeline to analyze lung histopathology images

Histopathology images involve the analysis of tissue samples to diagnose several diseases, such as cancer. The analysis of tissue samples is a time-consuming procedure, manually made by medical experts, namely pathologists. Computational pathology aims to develop automatic methods to analyze Whole Slide Images (WSI), which are digitized histopathology images, showing accurate performance in terms of image analysis. Although the amount of available WSIs is increasing, the capacity of medical experts to manually analyze samples is not expanding proportionally. This paper presents a full automatic pipeline to classify lung cancer WSIs, considering four classes: Small Cell Lung Cancer (SCLC), non-small cell lung cancer divided into LUng ADenocarcinoma (LUAD) and LUng Squamous cell Carcinoma (LUSC), and normal tissue. The pipeline includes a self-supervised algorithm for pre-training the model and Multiple Instance Learning (MIL) for WSI classification. The model is trained with 2,226 WSIs and it obtains an AUC of 0.8558 ± 0.0051 and a weighted f1-score of 0.6537 ± 0.0237 for the 4-class classification on the test set. The capability of the model to generalize was evaluated by testing it on the public The Cancer Genome Atlas (TCGA) dataset on LUAD and LUSC classification. In this task, the model obtained an AUC of 0.9433 ± 0.0198 and a weighted f1-score of 0.7726 ± 0.0438

The changing gender landscape of pediatric urology fellowship: results from a survey of fellows and recent graduates

© 2018 Journal of Pediatric Urology Company Introduction: Women are entering the subspecialty of pediatric urology at an accelerated rate. Gender differences affecting fellowship and job selection have been identified in other fields of medicine. Objective: The objective of this study was to understand gender differences in pediatric urology fellowship and job selection and how they may affect the workforce. Study design: A 47-question electronic survey consisting of questions regarding demographics, residency training, and factors influencing fellowship and job selection was distributed to current fellows and recent graduates in pediatric urology in May 2017. Results: A total of 111 recent and current fellows were contacted, and 72% completed the survey (55% female [F] and 45% male [M]; 61% current fellows and 39% recent fellows). Respondents rated factors important in choosing pediatric urology on a scale of 1–5 (1, not important and 5, extremely important), and the top three for both genders were 1-working with children, 2-influential mentors, and 3-bread and butter cases such as inguinal orchiopexy. During residency, 93% of respondents reported having influential mentors in pediatric urology. However, mentorship was more important in fellowship choice for males than females (3.6 F, 4.1 M; P-value = 0.048), and 45% reported having only male mentors. Rating factors important in job choice on a scale of 1–5, respondents reported the top factors as 1-rapport with partners/mentorship (4.5), 2-geography/family preferences (4.3), and 3-participation in mentoring/teaching (3.8). Although most job selection criteria were rated similarly between genders, females rated call schedule higher than males (3.5 F, 2.9 M, P-value = 0.009). Although most females and males (79% of F, 78% of M, P-value = 0.868) sought primarily academic positions, a smaller proportion of females accepted academic positions (52% of F, 72% of M, P-value 0.26), and females reported lower satisfaction regarding the availability of jobs on a scale of 1–5 (1, very dissatisfied and 5, very satisfied; 3.1 F, 3.7 M; P-value = 0.034), particularly in academic positions (3.1 F, 3.7 M; P-value = 0.06). This difference was more pronounced in current fellows than recent graduates and may represent a worsening trend. Conclusion: Although significant gender differences in fellowship and job selection may exist in other fields, we found that women and men choose pediatric urology fellowships and jobs using similar criteria, which include work–life balance. Gender differences exist in the influence of mentors, indicating a need for more female mentors. While men and women sought similar types of jobs, women were less satisfied with the availability of jobs, particularly academic jobs, than men, which warrants further investigation

Longterm Photometry of Variables at ESO - Part Two - the Second Data Catalogue 1986-1990

In this paper we present the second catalogue of photometric data in the Strömgren system obtained during the period October 1986- September 1990 in the framework of the Long-Term Photometry of Variables (LTPV) program at the European Southern Observatory. The catalogue is available in computer readable form at the Centre de Données de Strasbourg