OBJECTIVES: The aim of this study is to assess the role of Granulocyte Macrophage-Colony Stimulating Factor (GMCSF) in asthmatic airway hyper-responsiveness associated with RSV infections.
MATERIALS AND METHODS: Forty five asthmatic cases and 45 healthy individuals were studied in a cross-sectional design. All asthmatics underwent symptom score assessment.GMCSF concentrations in sputum and RSV-IgM/IgG in serum samples were measured for all participants by Enzyme Linked Immuno-Sorbent Assay (ELISA).
RESULTS: The GM-CSF concentration level was significantly higher in asthmatics (270.27± 194.87pg/mL) especially among moderate and severe disease with mean concentration of 197.33±98.47 and 521.08± 310.04 respectively, compared to healthy controls (22.20±21.27 pg/ mL) (p =0.0001). The sputum level of GM-CSF in asthmatics is highly significant associated with positive anti-RSV IgG sera which represents 35/45(77.8%) with mean GM-CSF concentration of (276.99± 86.42) compared with controls at about 31/45 (68.9%) with GM-CSF mean concentration of (22.84±23.47). On the other hand, positive anti-RSV IgM in asthma cases was 8 out of 45(17.8 %) with GM-CSF mean concentration of (307.25± 306.65). Furthermore, GM-CSF sputum level was significantly correlated with eosinophil count especially in moderate and severe asthma.
CONCLUSIONS: This study revealed that GM-CSF level is associated with eosinophilia and indicates asthma severity that might be evident during RSV infection .The distinctive GM-CSF features observed in the sputum from asthmatics with RSV may be useful as a diagnostic methods to help match patients with antibody therapy.
METHODS: Two stochastic models (SM2 and SM3) were developed using retrospective patient respiratory elastance (Ers) from two clinical cohorts which were averaged over time intervals of 10 and 30 min respectively. A stochastic model from a previous study (SM1) was used to benchmark performance. The stochastic models were clinically validated on an independent retrospective clinical cohort of 14 patients. Differences in predictive ability were evaluated using the difference in percentile lines and cumulative distribution density (CDD) curves.
RESULTS: Clinical validation shows all three models captured more than 98% (median) of future Ers data within the 5th - 95th percentile range. Comparisons of stochastic model percentile lines reported a maximum mean absolute percentage difference of 5.2%. The absolute differences of CDD curves were less than 0.25 in the ranges of 5 systems, providing guided, personalised, and safe MV treatment.