The kinetochore protein, CENPF, is mutated in human ciliopathy and microcephaly phenotypes

Background: Mutations in microtubule-regulating genes are associated with disorders of neuronal migration and microcephaly. Regulation of centriole length has been shown to underlie the pathogenesis of certain ciliopathy phenotypes. Using a next-generation sequencing approach, we identified mutations in a novel centriolar disease gene in a kindred with an embryonic lethal ciliopathy phenotype and in a patient with primary microcephaly. Methods and results Whole exome sequencing data from a non-consanguineous Caucasian kindred exhibiting mid-gestation lethality and ciliopathic malformations revealed two novel non-synonymous variants in CENPF, a microtubule-regulating gene. All four affected fetuses showed segregation for two mutated alleles [IVS5-2A>C, predicted to abolish the consensus splice-acceptor site from exon 6; c.1744G>T, p.E582X]. In a second unrelated patient exhibiting microcephaly, we identified two CENPF mutations [c.1744G>T, p.E582X; c.8692 C>T, p.R2898X] by whole exome sequencing. We found that CENP-F colocalised with Ninein at the subdistal appendages of the mother centriole in mouse inner medullary collecting duct cells. Intraflagellar transport protein-88 (IFT-88) colocalised with CENP-F along the ciliary axonemes of renal epithelial cells in age-matched control human fetuses but did not in truncated cilia of mutant CENPF kidneys. Pairwise co-immunoprecipitation assays of mitotic and serum-starved HEKT293 cells confirmed that IFT88 precipitates with endogenous CENP-F. Conclusions: Our data identify CENPF as a new centriolar disease gene implicated in severe human ciliopathy and microcephaly related phenotypes. CENP-F has a novel putative function in ciliogenesis and cortical neurogenesis

La tuberculose dans le Loir-et-Cher de 1992 à 1999 (à propos de 108 observations)

TOURS-BU Médecine (372612103) / SudocPARIS-BIUM (751062103) / SudocSudocFranceF

Description de méningoradiculites présumées infectieuses à Borrelia burgdorferi (maladie de Lyme) dans un service de médecine interne du Centre hospitalier de Blois (à propos de 22 observations)

TOURS-BU Médecine (372612103) / SudocPARIS-BIUM (751062103) / SudocSudocFranceF

Approche du traitement antithrombotique de la fibrillation auriculaire en médecine générale (à propos de 30 dossiers)

TOURS-BU Médecine (372612103) / SudocPARIS-BIUM (751062103) / SudocSudocFranceF

Association d'un mélanome sur nævus spilus et d'une polyradiculonévrite chronique inflammatoire (à propos d'un cas et revue de la littérature)

ANGERS-BU Médecine-Pharmacie (490072105) / SudocPARIS-BIUM (751062103) / SudocSudocFranceF

Prise en charge des insuffisants cardiaques de plus de 65 ans dans quatre clientèles de médecine générale

TOURS-BU Médecine (372612103) / SudocPARIS-BIUM (751062103) / SudocSudocFranceF

Prévalence de la dénutrition et du risque de dénutrition dans la clientèle âgée de plus de 80 ans vivant à domicile de cinq médecins généralistes d'Indre-Et-Loire (influence de l'état cognitif, de l'autonomie et du mode de vie)

TOURS-BU Médecine (372612103) / SudocTOURS-Inst.Eur.Hist.Alimentation (372615207) / SudocSudocFranceF

Analyse des aides à domicile dispensées dans six clientèles de médecins généralistes du Loir et Cher

TOURS-BU Médecine (372612103) / SudocPARIS-BIUM (751062103) / SudocSudocFranceF

Home care aides’ observations and machine learning algorithms for the prediction of visits to emergency departments by older community-dwelling individuals receiving home care assistance: A proof of concept study

International audienceBACKGROUND: Older individuals receiving home assistance are at high risk for emergency visits and unplanned hospitalization. Anticipating their health difficulties could prevent these events. This study investigated the effectiveness of an at-home monitoring method using social workers' observations to predict risk for 7- and 14-day emergency department (ED) visits.METHODS: This was a prospective cohort study of persons ≥75 years, living at home and receiving assistance from home care aides (HCA) at 6 French facilities. After each home visit, HCAs reported on participants' functional status using a smartphone application that recorded 27 functional items about each participant (e.g., ability to stand, move, eat, mood, loneliness). We recorded ED visits. Finally, we used machine learning techniques (i.e., leveraging random forest predictors) to develop a 7- and 14-day predictive algorithm for the risk of ED visit.RESULTS: The study included 301 participants, and the HCA made 9,987 observations. Over the mean 10-month follow-up, 97 participants (32%) had at least one ED visit. Modeling techniques identified 9 contributory factors from the longitudinal records of the HCA and developed a predictive algorithm for the risk of ED visit. The predictive performance (i.e., the area under the ROC curve) was 0.70 at 7 days and 0.67 at 14 days.INTERPRETATION: For frail elders receiving in-home care, information on functional status collected by HCA helps predict the risk of ED visits 7 to 14 days in advance. A survey system for real-time identification of risks could be developed using this exploratory work