Multidimensional clinical phenotyping of an adult cystic fibrosis patient population

Effect of Candida albicans' supernatant on biofilm formation and virulence factors of Pseudomonas aeruginosa through las/rhl System

Pseudomonas aeruginosa (P. aeruginosa) and Candida albicans (C. albicans) are opportunistic pathogens whose mixed infections can exacerbate microbial dissemination and drug resistance, contributing to high mortality and morbidity rates among infected individuals. Few studies have explored the impact of C. albicans supernatant on P. aeruginosa, and the underlying mechanisms of such mixed infections remain unclear. In this study, we investigated the effects of C. albicans supernatant on biofilm formation and virulence factor activity in wild-type P. aeruginosa PAO1 and its quorum sensing-deficient mutants, ΔlasIrhlI and ΔlasRrhlR. Our results demonstrated that the biofilm formation capability and virulence were significantly higher in the PAO1 group compared to the ΔlasIrhlI and ΔlasRrhlR groups. Furthermore, exposure to C. albicans supernatant significantly enhanced both the biofilm formation and virulence of PAO1, whereas no significant changes were observed in the ΔlasIrhlI and ΔlasRrhlR mutants relative to their respective controls. These findings suggest that C. albicans supernatant may modulate P. aeruginosa biofilm formation and virulence via the las/rhl quorum sensing system.

ESPEN-ESPGHAN-ECFS guideline on nutrition care for cystic fibrosis

BACKGROUND

Nutritional status is paramount in Cystic Fibrosis (CF) and is directly correlated with morbidity and mortality. The first ESPEN-ESPGHAN-ECFS guidelines on nutrition care for infants, children, and adults with CF were published in 2016. An update to these guidelines is presented.

METHODS

The study was developed by an international multidisciplinary working group in accordance with officially accepted standards. Literature since 2016 was reviewed, PICO questions were discussed and the GRADE system was utilized. Statements were discussed and submitted for on-line voting by the Working Group and by all ESPEN members.

RESULTS

The Working Group updated the nutritional guidelines including assessment and management at all ages. Supplementation of vitamins and pancreatic enzymes remains largely the same. There are expanded chapters on pregnancy, CF-related liver disease, and CF-related diabetes, bone disease, nutritional and mineral supplements, and probiotics. There are new chapters on nutrition with highly effective modulator therapies and nutrition after organ transplantation.

Identifying Clinical Phenotypes in People Who Are Hispanic/Latino With Chronic Low Back Pain: Use of Sensor-Based Measures of Posture and Movement, Pain, and Psychological Factors

OBJECTIVE

The aim of this study was to identify clinical phenotypes using sensor-based measures of posture and movement, pain behavior, and psychological factors in Hispanic/Latino people with chronic low back pain (CLBP).

METHODS

Baseline measures from an ongoing clinical trial were analyzed for 81 Hispanic/Latino people with CLBP. Low back posture and movement were measured using commercial sensors during in-person testing and 8 hours of ecological monitoring. Magnitude, frequency, and duration of lumbar movements, sitting and standing postures were measured. Movement-evoked pain was assessed during in-person movement testing. Psychological measures included the Pain Catastrophizing Scale and the Fear Avoidance Beliefs Questionnaire. Random forest analysis was conducted to generate 2 groups and identify important variables that distinguish groups. Group differences in demographics, pain, psychological, and posture and movement variables were examined using t-tests and chi-square analyses.

RESULTS

Two subgroups of Hispanic/Latino people with CLBP were identified with minimal error (7.4% misclassification ["out-of-bag" error]). Ecological posture and movement measures best distinguished groups, although most movement-evoked pain and psychological measures did not. Group 1 had greater height and weight, lower movement frequency, more time in sitting, and less time in standing. Group 2 had a greater proportion of women than men, longer low back pain duration, higher movement frequency, more time in standing, and less time in sitting.

CONCLUSION

Two distinct clinical phenotypes of Hispanic/Latino people with CLBP were identified. One group was distinguished by greater height and weight and more sedentary posture and movement behavior; the second group had more women, longer duration of low back pain, higher lumbar spine movement frequency, and longer duration of standing postures.

IMPACT

Ecological measures of posture and movement are important for identifying 2 clinical phenotypes in Hispanic/Latino people with CLBP and may provide a basis for a more personalized plan of care.

LAY SUMMARY

Wearable sensors were used to measure low back posture and movement in Hispanic/Latino people with chronic low back pain. These posture and movement measures helped to identify 2 different clinical subgroups that will give physical therapists more information to better personalize treatment for chronic low back pain in Hispanic/Latino patients.

Cystic Fibrosis Screen Positive, Inconclusive Diagnosis Genotypes in People with Cystic Fibrosis from the U.S. Patient Registry

Rationale: Variants within the cystic fibrosis (CF) transmembrane conductance regulator gene, CFTR, that are of unknown significance or are categorized as non-CF causing may be observed in persons with CF. These variants are frequently detected in children with inconclusive newborn screen results and, in some cases, may be associated with a benign presentation in early childhood that progresses to a CF phenotype later in life. Objectives: To analyze data from individuals enrolled in the U.S. Cystic Fibrosis Foundation Patient Registry who have received a diagnosis of CF and who have variants found in a population of children with a CF screen positive, inconclusive diagnosis (CFSPID). Methods: This retrospective review analyzed registry data from individuals with a diagnosis of CF who also harbor one or more variants of interest because of their frequency within a CFSPID population and/or their interpretation as non-CF causing. Three groups were defined by the number of CF-causing variants identified (CF-Cx2, CF-Cx1, and CF-Cx0), which were reported in addition to the variant(s) of interest. Multivariate quantile regression modeling of the outcome for forced expiratory volume in 1 second (FEV1) generated a disease severity score for each person determined by six selected variables. Median scores were calculated for the three groups. Results: Patients carrying one CF-causing variant and at least one variant of interest (CF-Cx1) had higher median disease severity scores compared with those carrying CF-Cx2, suggesting a milder phenotype (P < 0.05). However, there was no statistically significant difference in scores between CF-Cx2 and the two other groups combined (CF-Cx1 and CF-Cx0; P = 0.33). Analysis revealed that the CF-Cx1 and CF-Cx0 groups, when compared with the CF-Cx2 group, had later median diagnoses (8 years vs. newborn; P < 0.0001), lower median sweat chloride (48 mmol/L vs. 94.5 mmol/L; P < 0.0001), lower prevalence of pancreatic insufficiency (29% vs. 78%; P < 0.0001), and higher median FEV1% predicted (95% vs. 87%; P = 0.0002). Conclusions: Individuals with CF who have specific variants frequently identified in children with CFSPID have a similar range of disease severity scores compared with those who have two CF-causing variants, but a milder phenotype overall. Variants that should be given careful scrutiny because of their high prevalence are G576A+R668C, T854T, R75Q, F1052V, R1070W, R31C, and L967S.

A machine learning approach using partitioning around medoids clustering and random forest classification to model groups of farms in regard to production parameters and bulk tank milk antibody status of two major internal parasites in dairy cows

Fasciola hepatica and Ostertagia ostertagi are internal parasites of cattle compromising physiology, productivity, and well-being. Parasites are complex in their effect on hosts, sometimes making it difficult to identify clear directions of associations between infection and production parameters. Therefore, unsupervised approaches not assuming a structure reduce the risk of introducing bias to the analysis. They may provide insights which cannot be obtained with conventional, supervised methodology. An unsupervised, exploratory cluster analysis approach using the k–mode algorithm and partitioning around medoids detected two distinct clusters in a cross-sectional data set of milk yield, milk fat content, milk protein content as well as F. hepatica or O. ostertagi bulk tank milk antibody status from 606 dairy farms in three structurally different dairying regions in Germany. Parasite–positive farms grouped together with their respective production parameters to form separate clusters. A random forests algorithm characterised clusters with regard to external variables. Across all study regions, co–infections with F. hepatica or O. ostertagi, respectively, farming type, and pasture access appeared to be the most important factors discriminating clusters (i.e. farms). Furthermore, farm level lameness prevalence, herd size, BCS, stage of lactation, and somatic cell count were relevant criteria distinguishing clusters. This study is among the first to apply a cluster analysis approach in this context and potentially the first to implement a k–medoids algorithm and partitioning around medoids in the veterinary field. The results demonstrated that biologically relevant patterns of parasite status and milk parameters exist between farms positive for F. hepatica or O. ostertagi, respectively, and negative farms. Moreover, the machine learning approach confirmed results of previous work and shed further light on the complex setting of associations a between parasitic diseases, milk yield and milk constituents, and management practices.

Inter-Provincial Electricity Trading and Its Effects on Carbon Emissions from the Power Industry

Electricity trading is an effective measure to minimize carbon emissions and alleviate the imbalance between reverse distribution of regional energy resources and power load. However, the effects of China’s electricity trading on carbon emissions have not been fully explored due to lack of complete and balanced inter-provincial power transmission data. Therefore, the electricity generation–consumption downscaling model, logarithmic mean Divisia index (LMDI) model, and random forest clustering algorithm within a general framework were used in the present study to explore the effect of electricity trading on level of carbon emissions. Comprehensive inter-provincial electricity transmission data were generated, driving factors including electricity imports and exports were decomposed at the national and provincial scales, and clustered provincial policy implications were evaluated. The results revealed that: (i) although economic activities were the main driving factor for increase in carbon emissions at the national level, 382.95 million tons carbon emissions were offset from 2005 to 2019 due to inter-provincial electricity importation, whereas electricity export increased carbon emission by 230.30 million tons; (ii) analysis at the provincial level showed that electricity exports from Sichuan and Yunnan provinces accounted for more than 20% of the nation’s total electricity flow. Notably, this high level of exports did not significantly increase carbon emissions in these provinces owing to the abundant hydropower resources; (iii) emission reductions were only observed at the national level if the carbon intensity of the exporting provinces was lower compared with that of importing provinces, or if the electricity trading was generated from renewable sources; (iv) the effect of electricity import on emissions reduction was markedly higher relative to the effect of electricity export in most provinces, which reflected the actual situation of sustaining optimization of electricity generation structure in provincial grids of China. These findings provide a basis for decision makers to understand the contributions of electricity trading to the changes in carbon emissions from electricity generation, as well as form a foundation to explore practicable carbon emission mitigation strategies in the power industry.

Lung clearance index to characterize clinical phenotypes of children and adolescents with cystic fibrosis

Background

Lung clearance index (LCI) is accepted as an early marker of lung disease in cystic fibrosis (CF), however the utility of LCI to identify subgroups of CF disease in the paediatric age group has never been explored. The aim of the study was to characterize phenotypes of children with CF using LCI as a marker of ventilation inhomogeneity and to investigate whether these phenotypes distinguished patients based on time to pulmonary exacerbation (PE).

Methods

Data were collected on patients with CF aged < 18 years old, attending the CF Center of Milan during outpatient follow-up visits between October 2014 and September 2019. Cluster analysis using agglomerative nesting hierarchical method was performed to generate distinct phenotypes. Time-to-recurrent event analysis investigated association of phenotypes with PE.

Results

We collected 313 multiple breath washout tests on 125 children aged 5.5–16.8 years. Cluster analysis identified two divergent phenotypes in children and adolescents of same age, presenting with almost normal FEV₁ but with substantial difference in markers of ventilation inhomogeneity (mean LCI difference of 3.4, 95% Confidence Interval [CI] 2.6–4.2). A less severe phenotype was associated with a lower risk of PE relapse (Hazard Ratio 0.45, 95% CI 0.34–0.62).

Conclusions

LCI is useful in clinical practice to characterize distinct phenotypes of children and adolescents with mild/normal FEV₁. A less severe phenotype translates into a lower risk of PE relapse.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12890-022-01903-5.

Unsupervised phenotypic clustering for determining clinical status in children with cystic fibrosis

BACKGROUND

Cystic fibrosis (CF) is a multisystem disease in which the assessment of disease severity based on lung function alone may not be appropriate. The aim of the study was to develop a comprehensive machine-learning algorithm to assess clinical status independent of lung function in children.

METHODS

A comprehensive prospectively collected clinical database (Toronto, Canada) was used to apply unsupervised cluster analysis. The defined clusters were then compared by current and future lung function, risk of future hospitalisation, and risk of future pulmonary exacerbation treated with oral antibiotics. A k-nearest-neighbours (KNN) algorithm was used to prospectively assign clusters. The methods were validated in a paediatric clinical CF dataset from Great Ormond Street Hospital (GOSH).

RESULTS

The optimal cluster model identified four (A-D) phenotypic clusters based on 12 200 encounters from 530 individuals. Two clusters (A and B) consistent with mild disease were identified with high forced expiratory volume in 1 s (FEV₁), and low risk of both hospitalisation and pulmonary exacerbation treated with oral antibiotics. Two clusters (C and D) consistent with severe disease were also identified with low FEV₁. Cluster D had the shortest time to both hospitalisation and pulmonary exacerbation treated with oral antibiotics. The outcomes were consistent in 3124 encounters from 171 children at GOSH. The KNN cluster allocation error rate was low, at 2.5% (Toronto) and 3.5% (GOSH).

CONCLUSION

Machine learning derived phenotypic clusters can predict disease severity independent of lung function and could be used in conjunction with functional measures to predict future disease trajectories in CF patients.

Machine learning for subtype definition and risk prediction in heart failure, acute coronary syndromes and atrial fibrillation: systematic review of validity and clinical utility

BACKGROUND

Machine learning (ML) is increasingly used in research for subtype definition and risk prediction, particularly in cardiovascular diseases. No existing ML models are routinely used for cardiovascular disease management, and their phase of clinical utility is unknown, partly due to a lack of clear criteria. We evaluated ML for subtype definition and risk prediction in heart failure (HF), acute coronary syndromes (ACS) and atrial fibrillation (AF).

METHODS

For ML studies of subtype definition and risk prediction, we conducted a systematic review in HF, ACS and AF, using PubMed, MEDLINE and Web of Science from January 2000 until December 2019. By adapting published criteria for diagnostic and prognostic studies, we developed a seven-domain, ML-specific checklist.

RESULTS

Of 5918 studies identified, 97 were included. Across studies for subtype definition (n = 40) and risk prediction (n = 57), there was variation in data source, population size (median 606 and median 6769), clinical setting (outpatient, inpatient, different departments), number of covariates (median 19 and median 48) and ML methods. All studies were single disease, most were North American (n = 61/97) and only 14 studies combined definition and risk prediction. Subtype definition and risk prediction studies respectively had limitations in development (e.g. 15.0% and 78.9% of studies related to patient benefit; 15.0% and 15.8% had low patient selection bias), validation (12.5% and 5.3% externally validated) and impact (32.5% and 91.2% improved outcome prediction; no effectiveness or cost-effectiveness evaluations).

CONCLUSIONS

Studies of ML in HF, ACS and AF are limited by number and type of included covariates, ML methods, population size, country, clinical setting and focus on single diseases, not overlap or multimorbidity. Clinical utility and implementation rely on improvements in development, validation and impact, facilitated by simple checklists. We provide clear steps prior to safe implementation of machine learning in clinical practice for cardiovascular diseases and other disease areas.

Comparison of Cluster Analysis Methodologies for Characterization of Classroom Observation Protocol for Undergraduate STEM (COPUS) Data

The Classroom Observation Protocol for Undergraduate STEM (COPUS) provides descriptive feedback to instructors by capturing student and instructor behaviors occurring in the classroom. Due to the increasing prevalence of COPUS data collection, it is important to recognize how researchers determine whether groups of courses or instructors have unique classroom characteristics. One approach uses cluster analysis, highlighted by a recently developed tool, the COPUS Analyzer, that enables the characterization of COPUS data into one of seven clusters representing three groups of instructional styles (didactic, interactive, and student centered). Here, we examine a novel 250 course data set and present evidence that a predictive cluster analysis tool may not be appropriate for analyzing COPUS data. We perform a de novo cluster analysis and compare results with the COPUS Analyzer output and identify several contrasting outcomes regarding course characterizations. Additionally, we present two ensemble clustering algorithms: 1) k-means and 2) partitioning around medoids. Both ensemble algorithms categorize our classroom observation data into one of two clusters: traditional lecture or active learning. Finally, we discuss implications of these findings for education research studies that leverage COPUS data.

Personalized Web-Based Cognitive Rehabilitation Treatments for Patients with Traumatic Brain Injury: Cluster Analysis

Background

Traumatic brain injury (TBI) is a leading cause of disability worldwide. TBI is a highly heterogeneous disease, which makes it complex for effective therapeutic interventions. Cluster analysis has been extensively applied in previous research studies to identify homogeneous subgroups based on performance in neuropsychological baseline tests. Nevertheless, most analyzed samples are rarely larger than a size of 100, and different cluster analysis approaches and cluster validity indices have been scarcely compared or applied in web-based rehabilitation treatments.

Objective

The aims of our study were as follows: (1) to apply state-of-the-art cluster validity indices to different cluster strategies: hierarchical, partitional, and model-based, (2) to apply combined strategies of dimensionality reduction by using principal component analysis and random forests and perform stability assessment of the final profiles, (3) to characterize the identified profiles by using demographic and clinically relevant variables, and (4) to study the external validity of the obtained clusters by considering 3 relevant aspects of TBI rehabilitation: Glasgow Coma Scale, functional independence measure, and execution of web-based cognitive tasks.

Methods

This study was performed from August 2008 to July 2019. Different cluster strategies were executed with Mclust, factoextra, and cluster R packages. For combined strategies, we used the FactoMineR and random forest R packages. Stability analysis was performed with the fpc R package. Between-group comparisons for external validation were performed using 2-tailed t test, chi-square test, or Mann-Whitney U test, as appropriate.

Results

We analyzed 574 adult patients with TBI (mostly severe) who were undergoing web-based rehabilitation. We identified and characterized 3 clusters with strong internal validation: (1) moderate attentional impairment and moderate dysexecutive syndrome with mild memory impairment and normal spatiotemporal perception, with almost 66% (111/170) of the patients being highly educated (P<.05); (2) severe dysexecutive syndrome with severe attentional and memory impairments and normal spatiotemporal perception, with 49.2% (153/311) of the patients being highly educated (P<.05); (3) very severe cognitive impairment, with 45.2% (42/93) of the patients being highly educated (P<.05). We externally validated them with severity of injury (P=.006) and functional independence assessments: cognitive (P<.001), motor (P<.001), and total (P<.001). We mapped 151,763 web-based cognitive rehabilitation tasks during the whole period to the 3 obtained clusters (P<.001) and confirmed the identified patterns. Stability analysis indicated that clusters 1 and 2 were respectively rated as 0.60 and 0.75; therefore, they were measuring a pattern and cluster 3 was rated as highly stable.

Conclusions

Cluster analysis in web-based cognitive rehabilitation treatments enables the identification and characterization of strong response patterns to neuropsychological tests, external validation of the obtained clusters, tailoring of cognitive web-based tasks executed in the web platform to the identified profiles, thereby providing clinicians a tool for treatment personalization, and the extension of a similar approach to other medical conditions.

Multi-dimensional clinical phenotyping of a national cohort of adult cystic fibrosis patients

BACKGROUND

Cystic Fibrosis (CF) is a multi-systemic disorder resulting from genetic variation in the Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) gene which can result in bronchiectasis, chronic sinusitis, pancreatic malabsorption, cholestatic liver disease and distal intestinal obstructive syndrome. This study generates multi-dimensional clinical phenotypes that capture the complexity and spectrum of the disease manifestations seen in adult CF patients using statistically robust techniques.

METHODS

Pre-transplant clinical data from adult (age ≥18 years) CF patients (n = 992) seen in six regionally distinct US CF centers between 1/1/2014 and 6/30/2015 were included. Demographic, spirometry, nutritional, microbiological and therapy data were used to generate clusters using the Random Forests statistical-learning and Partitioning around Medoids (PAM) clustering algorithms. Five commonly measured demographic, physiological and nutritional parameters were needed to create the final phenotypes that are highly similar to a regionally matched group of patients from the CF Foundation Patient Registry RESULTS: This approach identified high-risk phenotypes with expected characteristics including high rates of pancreatic insufficiency, diabetes and Pseudomonas aeruginosa colonization. It also identified unexpected populations including a) a male-dominated, well-nourished group with good lung function with a high prevalence of severe genotypes (i.e. 60% subjects had two minimal function CFTR variations), b) and an older, "survivor" phenotype that had high rates of chronic P. aeruginosa infection.

CONCLUSIONS

This study identified recognizable phenotypes that capture the clinical complexity in a statistically robust manner and which may aide in the identification of specific genetic and environmental factors responsible for these disease manifestation patterns.

Beyond Antagonism: The Interaction Between Candida Species and Pseudomonas aeruginosa

There are many examples of the interaction between prokaryotes and eukaryotes. One such example is the polymicrobial colonization/infection by the various opportunistic pathogenic yeasts belonging to the genus Candida and the ubiquitous bacterium, Pseudomonas aeruginosa. Although this interaction has simplistically been characterized as antagonistic to the yeast, this review highlights the complexity of the interaction with various factors influencing both microbes. The first section deals with the interactions in vitro, looking specifically at the role of cell wall components, quorum sensing molecules, phenazines, fatty acid metabolites and competition for iron in the interaction. The second part of this review places all these interactions in the context of various infection or colonization sites, i.e., lungs, wounds, and the gastrointestinal tract. Here we see that the role of the host, as well as the methodology used to establish co-infection, are important factors, influencing the outcome of the disease. Suggested future perspectives for the study of this interaction include determining the influence of newly identified participants of the QS network of P. aeruginosa, oxylipin production by both species, as well as the genetic and phenotypic plasticity of these microbes, on the interaction and outcome of co-infection.

Cluster and CART analyses identify large subgroups of adults with cystic fibrosis at low risk of 10-year death

Our goal was to identify subgroups of adults with cystic fibrosis (CF) at low risk of death within 10 years.Factor analysis for mixed data followed by Ward's cluster analysis was conducted using 25 variables from 1572 French CF adults in 2005. Rates of death by subgroups were analysed over 10 years. An algorithm was developed using CART (classification and regression tree) analysis to provide rules for the identification of subgroups of CF adults with low rates of death within 10 years. This algorithm was validated in 1376 Canadian CF adults.Seven subgroups were identified by cluster analysis in French CF adults, including two subgroups with low (∼5%) rates of death at 10 years: one subgroup (22% of patients) was composed of patients with nonclassic CF, the other subgroup (17% of patients) was composed of patients with classic CF but low rates of Pseudomonas aeruginosa infection and diabetes. An algorithm based on CART analysis of data in 2005 allowed us to identify most French adults with low rates of death. When tested using data from Canadian CF adults in 2005, the algorithm identified 287 out of 1376 (21%) patients at low risk (10-year death: 7.7%).Large subgroups of CF adults share low risk of 10-year mortality.

Neutrophilic proteolysis in the cystic fibrosis lung correlates with a pathogenic microbiome

BACKGROUND

Studies of the cystic fibrosis (CF) lung microbiome have consistently shown that lung function decline is associated with decreased microbial diversity due to the dominance of opportunistic pathogens. However, how this phenomenon is reflected in the metabolites and chemical environment of lung secretions remains poorly understood.

METHODS

Here we investigated the microbial and molecular composition of CF sputum samples using 16S rRNA gene amplicon sequencing and untargeted tandem mass spectrometry to determine their interrelationships and associations with clinical measures of disease severity.

RESULTS

The CF metabolome was found to exist in two states: one from patients with more severe disease that had higher molecular diversity and more Pseudomonas aeruginosa and the other from patients with better lung function having lower metabolite diversity and fewer pathogenic bacteria. The two molecular states were differentiated by the abundance and diversity of peptides and amino acids. Patients with severe disease and more pathogenic bacteria had higher levels of peptides. Analysis of the carboxyl terminal residues of these peptides indicated that neutrophil elastase and cathepsin G were responsible for their generation, and accordingly, these patients had higher levels of proteolytic activity from these enzymes in their sputum. The CF pathogen Pseudomonas aeruginosa was correlated with the abundance of amino acids and is known to primarily feed on them in the lung.

CONCLUSIONS

In cases of severe CF lung disease, proteolysis by host enzymes creates an amino acid-rich environment that P. aeruginosa comes to dominate, which may contribute to the pathogen's persistence by providing its preferred carbon source.

Median regression spline modeling of longitudinal FEV1 measurements in cystic fibrosis (CF) and chronic obstructive pulmonary disease (COPD) patients

Rationale

Clinical phenotyping, therapeutic investigations as well as genomic, airway secretion metabolomic and metagenomic investigations can benefit from robust, nonlinear modeling of FEV₁ in individual subjects. We demonstrate the utility of measuring FEV₁ dynamics in representative cystic fibrosis (CF) and chronic obstructive pulmonary disease (COPD) populations.

Methods

Individual FEV₁ data from CF and COPD subjects were modeled by estimating median regression splines and their predicted first and second derivatives. Classes were created from variables that capture the dynamics of these curves in both cohorts.

Results

Nine FEV₁ dynamic variables were identified from the splines and their predicted derivatives in individuals with CF (n = 177) and COPD (n = 374). Three FEV₁ dynamic classes (i.e. stable, intermediate and hypervariable) were generated and described using these variables from both cohorts. In the CF cohort, the FEV₁ hypervariable class (HV) was associated with a clinically unstable, female-dominated phenotypes while stable FEV₁ class (S) individuals were highly associated with the male-dominated milder clinical phenotype. In the COPD cohort, associations were found between the FEV₁ dynamic classes, the COPD GOLD grades, with exacerbation frequency and symptoms.

Conclusion

Nonlinear modeling of FEV₁ with splines provides new insights and is useful in characterizing CF and COPD clinical phenotypes.

Candida albicans and Pseudomonas aeruginosa Interaction, with Focus on the Role of Eicosanoids

Candida albicans is commonly found in mixed infections with Pseudomonas aeruginosa, especially in the lungs of cystic fibrosis (CF) patients. Both of these opportunistic pathogens are able to form resistant biofilms and frequently infect immunocompromised individuals. The interaction between these two pathogens, which includes physical interaction as well as secreted factors, is mainly antagonistic. In addition, research suggests considerable interaction with their host, especially with immunomodulatory lipid mediators, termed eicosanoids. Candida albicans and Pseudomonas aeruginosa are both able to utilize arachidonic acid (AA), liberated from the host cells during infection, to form eicosanoids. The production of these eicosanoids, such as Prostaglandin E₂, by the host and the pathogens may affect the dynamics of polymicrobial infection and the outcome of infections. It is of considerable importance to elucidate the role of host-produced, as well as pathogen-produced eicosanoids in polymicrobial infection. This review will focus on in vitro as well as in vivo interaction between C. albicans and P. aeruginosa, paying special attention to the role of eicosanoids in the cross-talk between host and the pathogens.