Meningococcus Hijacks a β2-Adrenoceptor/β-Arrestin Pathway to Cross Brain Microvasculature Endothelium

SummaryFollowing pilus-mediated adhesion to human brain endothelial cells, meningococcus (N. meningitidis), the bacterium causing cerebrospinal meningitis, initiates signaling cascades, which eventually result in the opening of intercellular junctions, allowing meningeal colonization. The signaling receptor activated by the pathogen remained unknown. We report that N. meningitidis specifically stimulates a biased β2-adrenoceptor/β-arrestin signaling pathway in endothelial cells, which ultimately traps β-arrestin-interacting partners, such as the Src tyrosine kinase and junctional proteins, under bacterial colonies. Cytoskeletal reorganization mediated by β-arrestin-activated Src stabilizes bacterial adhesion to endothelial cells, whereas β-arrestin-dependent delocalization of junctional proteins results in anatomical gaps used by bacteria to penetrate into tissues. Activation of β-adrenoceptor endocytosis with specific agonists prevents signaling events downstream of N. meningitidis adhesion and inhibits bacterial crossing of the endothelial barrier. The identification of the mechanism used for hijacking host cell signaling machineries opens perspectives for treatment and prevention of meningococcal infection.PaperFlic

Financement : les AP-DRG en Suisse

Développés il y a plus d'une quinzaine d'années aux USA, les AP-DRG (All patient Diagnosis related groups) ont permis, malgré leur ancienneté, de conduire des expériences très utiles et de préparer divers cantons suisses à un futur système de financement, mieux que toutes les théories invoquées à ce sujet n'auraient pu le faire. [Auteurs]]]> Diagnosis-Related Groups fre oai:serval.unil.ch:BIB_BCF3DD7A8FAB 2022-02-19T02:29:46Z <oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:xs="http://www.w3.org/2001/XMLSchema" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"> https://serval.unil.ch/notice/serval:BIB_BCF3DD7A8FAB La situation des minorités linguistiques en Suisse Wilson, B. info:eu-repo/semantics/conferenceObject inproceedings 2003 La Charte européenne des langues régionales ou minoritaires et la France, Quelle(s) langue(s) pour la République ? Le dilemme diversité/unicité, Actes du Colloque des 11 et 12 avril 2002 organisé par le Conseil de l'Europe et l'Université Robert-Schuman, pp. 99-102 fre oai:serval.unil.ch:BIB_BCF4831F22D6 2022-02-19T02:29:46Z <oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:xs="http://www.w3.org/2001/XMLSchema" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"> https://serval.unil.ch/notice/serval:BIB_BCF4831F22D6 Les troubles des conduites alimentaires (TCA) à l'adolescence Ambresin, A.E. Vust, S. info:eu-repo/semantics/article article 2010 Dépendances, no. 41, pp. 9-11 info:eu-repo/semantics/altIdentifier/pissn/1422-3368 fre oai:serval.unil.ch:BIB_BCF4F619FAE1 2022-02-19T02:29:46Z <oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:xs="http://www.w3.org/2001/XMLSchema" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"> https://serval.unil.ch/notice/serval:BIB_BCF4F619FAE1 Evidence-Based Recommendations for Optimal Dietary Protein Intake in Older People: A Position Paper From the PROT-AGE Study Group. info:doi:10.1016/j.jamda.2013.05.021 info:eu-repo/semantics/altIdentifier/doi/10.1016/j.jamda.2013.05.021 info:eu-repo/semantics/altIdentifier/pmid/23867520 Bauer, J. Biolo, G. Cederholm, T. Cesari, M. Cruz-Jentoft, A.J. Morley, J.E. Phillips, S. Sieber, C. Stehle, P. Teta, D. Visvanathan, R. Volpi, E. Boirie, Y. info:eu-repo/semantics/article article 2013 Journal of the American Medical Directors Association, vol. 14, no. 8, pp. 542-559 info:eu-repo/semantics/altIdentifier/eissn/1538-9375 urn:issn:1525-8610 <![CDATA[New evidence shows that older adults need more dietary protein than do younger adults to support good health, promote recovery from illness, and maintain functionality. Older people need to make up for age-related changes in protein metabolism, such as high splanchnic extraction and declining anabolic responses to ingested protein. They also need more protein to offset inflammatory and catabolic conditions associated with chronic and acute diseases that occur commonly with aging. With the goal of developing updated, evidence-based recommendations for optimal protein intake by older people, the European Union Geriatric Medicine Society (EUGMS), in cooperation with other scientific organizations, appointed an international study group to review dietary protein needs with aging (PROT-AGE Study Group). To help older people (&gt;65 years) maintain and regain lean body mass and function, the PROT-AGE study group recommends average daily intake at least in the range of 1.0 to 1.2 g protein per kilogram of body weight per day. Both endurance- and resistance-type exercises are recommended at individualized levels that are safe and tolerated, and higher protein intake (ie, ≥1.2 g/kg body weight/d) is advised for those who are exercising and otherwise active. Most older adults who have acute or chronic diseases need even more dietary protein (ie, 1.2-1.5 g/kg body weight/d). Older people with severe kidney disease (ie, estimated GFR &lt;30 mL/min/1.73m(2)), but who are not on dialysis, are an exception to this rule; these individuals may need to limit protein intake. Protein quality, timing of ingestion, and intake of other nutritional supplements may be relevant, but evidence is not yet sufficient to support specific recommendations. Older people are vulnerable to losses in physical function capacity, and such losses predict loss of independence, falls, and even mortality. Thus, future studies aimed at pinpointing optimal protein intake in specific populations of older people need to include measures of physical function

An End-to-End Natural Language Processing Application for Prediction of Medical Case Coding Complexity: Algorithm Development and Validation

BackgroundMedical coding is the process that converts clinical documentation into standard medical codes. Codes are used for several key purposes in a hospital (eg, insurance reimbursement and performance analysis); therefore, their optimization is crucial. With the rapid growth of natural language processing technologies, several solutions based on artificial intelligence have been proposed to aid in medical coding by automatically suggesting relevant codes for clinical documents. However, their effectiveness is still limited to simple cases, and it is not yet clear how much value they can bring in improving coding efficiency and accuracy. ObjectiveThis study aimed to bring more efficiency to the coding process to improve the selection of codes by medical coders. To achieve this, we developed an innovative multimodal machine learning–based solution that, instead of predicting codes, detects the degree of coding complexity before coding is performed. The notion of coding complexity was used to better dispatch work among medical coders to eventually minimize errors and improve throughput. MethodsTo train and evaluate our approach, we collected 2060 cases rated by coders in terms of coding complexity from 1 (simplest) to 4 (most complex). We asked 2 expert coders to rate 3.01% (62/2060) of the cases as the gold standard. The agreements between experts were used as benchmarks for model evaluation. A case contains both clinical text and patient metadata from the hospital electronic health record. We extracted both text features and metadata features, then concatenated and fed them into several machine learning models. Finally, we selected 2 models. The first used cross-validated training on 1751 cases and testing on 309 cases aiming to assess the predictive power of the proposed approach and its generalizability. The second model was trained on 1998 cases and tested on the gold standard to validate the best model performance against human benchmarks. ResultsOur first model achieved a macro–F1-score of 0.51 and an accuracy of 0.59 on classifying the 4-scale complexity. The model distinguished well between the simple (combined complexity 1-2) and complex (combined complexity 3-4) cases with a macro–F1-score of 0.65 and an accuracy of 0.71. Our second model achieved 61% agreement with experts’ ratings and a macro–F1-score of 0.62 on the gold standard, whereas the 2 experts had a 66% (41/62) agreement ratio with a macro–F1-score of 0.67. ConclusionsWe propose a multimodal machine learning approach that leverages information from both clinical text and patient metadata to predict the complexity of coding a case in the precoding phase. By integrating this model into the hospital coding system, distribution of cases among coders can be done automatically with performance comparable with that of human expert coders, thus improving coding efficiency and accuracy at scale

Meningococcal type IV pili recruit the polarity complex to cross the brain endothelium

Type IV pili mediate the initial interaction of many bacterial pathogens with their host cells. In Neisseria meningitidis, the causative agent of cerebrospinal meningitis, type IV pili-mediated adhesion to brain endothelial cells is required for bacteria to cross the blood-brain barrier. Here, Type IV pili-mediated adhesion of N. meningitidis to human brain endothelial cells was found to recruit the Par3/Par6/PKCzeta polarity complex that plays a pivotal role in the establishment of eukaryotic cell polarity and the formation of intercellular junctions. This recruitment leads to the formation of ectopic intercellular junctional domains at the site of bacterial-cell interaction and a subsequent depletion of junctional proteins at the cell-cell interface with opening of the intercellular junctions of the brain-endothelial interface