by Keyword: psl
Liang Z, Nilsson M, Kragh KN, Hedal I, Alcàcer-Almansa J, Kiilerich RO, Andersen JB, Tolker-Nielsen T, (2023). The role of individual exopolysaccharides in antibiotic tolerance of Pseudomonas aeruginosa aggregates Frontiers In Microbiology 14, 1187708
The bacterium Pseudomonas aeruginosa is involved in chronic infections of cystic fibrosis lungs and chronic wounds. In these infections the bacteria are present as aggregates suspended in host secretions. During the course of the infections there is a selection for mutants that overproduce exopolysaccharides, suggesting that the exopolysaccharides play a role in the persistence and antibiotic tolerance of the aggregated bacteria. Here, we investigated the role of individual P. aeruginosa exopolysaccharides in aggregate-associated antibiotic tolerance. We employed an aggregate-based antibiotic tolerance assay on a set of P. aeruginosa strains that were genetically engineered to over-produce a single, none, or all of the three exopolysaccharides Pel, Psl, and alginate. The antibiotic tolerance assays were conducted with the clinically relevant antibiotics tobramycin, ciprofloxacin and meropenem. Our study suggests that alginate plays a role in the tolerance of P. aeruginosa aggregates toward tobramycin and meropenem, but not ciprofloxacin. However, contrary to previous studies we did not observe a role for Psl or Pel in the tolerance of P. aeruginosa aggregates toward tobramycin, ciprofloxacin, and meropenem.Copyright © 2023 Liang, Nilsson, Kragh, Hedal, Alcàcer-Almansa, Kiilerich, Andersen and Tolker-Nielsen.
JTD Keywords: aggregates, antibiotic tolerance, biofilm formation, extracellular matrix, genome, growth, lungs, molecular-mechanisms, mutations, polysaccharide, pseudomonas aeruginosa, psl, system, Aggregates, Antibiotic tolerance, Biofilm, Extracellular matrix, Pseudomonas aeruginosa, Small-colony variants
Jané, R., (2014). Engineering Sleep Disorders: From classical CPAP devices toward new intelligent adaptive ventilatory therapy IEEE Pulse , 5, (5), 29-32
Among the most common sleep disorders are those related to disruptions in airflow (apnea) or reductions in the breath amplitude (hypopnea) with or without obstruction of the upper airway (UA). One of the most important sleep disorders is obstructive sleep apnea (OSA). This sleep-disordered breathing, quantified by the apnea-hypopnea index (AHI), can produce a significant reduction of oxygen saturation and an abnormal elevation of carbon dioxide levels in the blood. Apnea and hypopnea episodes are associated with arousals and sleep fragmentation during the night and compensatory response of the autonomic nervous system.
JTD Keywords: Biomedical engineering, Biomedical measurements, Biomedical monitoring, Breathing disorders, Medical conditions, Medical treatment, Sleep, Sleep apnea
Arcentales, A., Voss, A., Caminal, P., Bayes-Genis, A., Domingo, M. T., Giraldo, B. F., (2013). Characterization of patients with different ventricular ejection fractions using blood pressure signal analysis CinC 2013 Computing in Cardiology Conference (CinC) , IEEE (Zaragoza, Spain) , 795-798
Ischemic and dilated cardiomyopathy are associated with disorders of myocardium. Using the blood pressure (BP) signal and the values of the ventricular ejection fraction, we obtained parameters for stratifying cardiomyopathy patients as low- and high-risk. We studied 48 cardiomyopathy patients characterized by NYHA ≥2: 19 patients with dilated cardiomyopathy (DCM) and 29 patients with ischemic cardiomyopathy (ICM). The left ventricular ejection fraction (LVEF) percentage was used to classify patients in low risk (LR: LVEF > 35%, 17 patients) and high risk (HR: LVEF ≤ 35%, 31 patients) groups. From the BP signal, we extracted the upward systolic slope (BPsl), the difference between systolic and diastolic BP (BPA), and systolic time intervals (STI). When we compared the LR and HR groups in the time domain analysis, the best parameters were standard deviation (SD) of 1=STI, kurtosis (K) of BPsl, and K of BPA. In the frequency domain analysis, very low frequency (VLF) and high frequency (HF) bands showed statistically significant differences in comaprisons of LR and HR groups. The area under the curve of power spectral density was the best parameter in all classifications, and particularly in the very-low-and high- frequency bands (p <; 0.001). These parameters could help to improve the risk stratification of cardiomyopathy patients.
JTD Keywords: blood pressure measurement, cardiovascular system, diseases, medical disorders, medical signal processing, statistical analysis, time-domain analysis, BP signal, HR groups, LR groups, blood pressure signal analysis, cardiomyopathy patients, diastolic BP, dilated cardiomyopathy, frequency domain analysis, high-frequency bands, ischemic cardiomyopathy, left ventricular ejection fraction, low-frequency bands, myocardium disorders, patient characterization, power spectral density curve, standard deviation, statistical significant differences, systolic BP, systolic slope, systolic time intervals, time domain analysis, ventricular ejection fraction, Abstracts, Databases, Parameter extraction, Telecommunication standards, Time-frequency analysis