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Paiva, T.

Publications and source records attributed to Paiva, T..

2 recordsLinked to original sources

Functional Redundancy in Candida auris Cell Surface Adhesins Crucial for Cell-Cell Interaction and Aggregation

Candida auris is an emerging nosocomial fungal pathogen associated with life-threatening invasive disease due to its persistent colonization, high level of transmissibility and multi-drug resistance. Aggregative and non-aggregative growth phenotypes for C. auris strains with different biofilm forming abilities, drug susceptibilities and virulence characteristics have been described. Using comprehensive transcriptional analysis we identified key cell surface adhesins that were highly upregulated in the aggregative phenotype during in vitro and in vivo grown biofilms using a mouse model of catheter infection. Phenotypic and functional evaluations of generated null mutants demonstrated crucial roles for the adhesins Als5 and Scf1 in mediating cell-cell adherence, coaggregation and biofilm formation. While individual mutants were largely non-aggregative, in combination cells were able to co-adhere and aggregate, as directly demonstrated by measuring cell adhesion forces using single-cell atomic force spectroscopy. This co-adherence indicates their role as complementary adhesins, which despite their limited similarity, may function redundantly to promote cell-cell interaction and biofilm formation. Functional diversity of cell wall proteins may be a form of regulation that provides the aggregative phenotype of C. auris with flexibility and rapid adaptation to the environment, potentially impacting persistence and virulence.

microbiology↗

Validation of the Munich Actimetry Sleep Detection Algorithm for estimating sleep-wake patterns from activity recordings

Periods of sleep and wakefulness can be estimated from wrist-locomotor activity recordings via algorithms that identify periods of relative activity and inactivity. Here, we evaluated the performance of our Munich Actimetry Sleep Detection Algorithm (MASDA). MASDA uses a moving 24-hour-threshold and correlation procedure estimating relatively consolidated periods of sleep and wake. MASDA was validated against sleep logs and polysomnography. Sleep-log validation was performed on 2 field samples collected over 54 and 34 days (median) in 34 adolescents and 28 young adults. Polysomnographic validation was performed on a clinical sample of 23 individuals undergoing 1 night of polysomnography. Epoch-by-epoch analyses were conducted and comparisons of sleep measures via Bland-Altman plots and correlations. Compared with sleep logs, MASDA classified sleep with a median sensitivity of 80% (IQR = 75-86%) and specificity of 91% (87-92%). Mean onset and offset times were highly correlated (r = 0.86-0.91). Compared with polysomnography, MASDA reached a median sensitivity of 92% (85-100%), but low specificity of 33% (10-98%), owing to the low frequency of wake episodes in the nighttime polysomnographic recordings. MASDA overestimated sleep onset (~21 min) and underestimated wake after sleep onset (~26 min), while not performing systematically different from polysomnography in other sleep parameters. These results demonstrate the validity of MASDA to faithfully estimate sleep-wake patterns in field studies. With its good performance across day- and nighttime, it enables analyses of sleep-wake patterns in long recordings performed to assess circadian and sleep regularity and is therefore an excellent objective alternative to sleep logs in field settings.

neuroscience↗