An introduction to superhydrophobicity

This paper is derived from a training session prepared for COST P21. It is intended as an introduction to superhydrophobicity to scientists who may not work in this area of physics or to students. Superhydrophobicity is an effect where roughness and hydrophobicity combine to generate unusually hydrophobic surfaces, causing water to bounce and roll off as if it were mercury and is used by plants and animals to repel water, stay clean and sometimes even to breathe. The effect is also known as The Lotus Effect® and Ultrahydrophobicity. In this paper we introduce many of the theories used, some of the methods used to generate surfaces and then describe some of the implications of the effect

A Multivariate Model for Predicting Respiratory Status in Patients with Chronic Obstructive Pulmonary Disease

OBJECTIVE: To develop and validate a multivariate model for predicting respiratory status in patients with advanced chronic obstructive pulmonary disease (COPD). DESIGN: Prospective, double-blind study of peak flow monitoring. SETTING: Albuquerque Veterans Affairs Medical Center. PATIENTS: Male veterans with an irreversible component of airflow obstruction on baseline pulmonary function tests. MEASUREMENTS: This study was conducted between January 1995 and May 1996. At entry, subjects were instructed in the use of the modified Medical Research Council Dyspnea Scale and a mini–Wright peak flow meter equipped with electronic storage. For the next 6 months, they recorded their dyspnea scores once daily and peak expiratory flow rates twice daily, before and after the use of bronchodilators. Patients were blinded to their peak expiratory flow rates, and medical care was provided in the customary manner. Readings were aggregated into 7-day sampling intervals, and interval means were calculated for dyspnea score and peak expiratory flow rate parameters. Intervals from all subjects were then pooled and randomized to separate groups for model development (training set) and validation (test set). In the training set, logistic regression was used to identify variables that predicted future respiratory status. The dependent variable was the log odds that the subject would attain his highest level of dyspnea in the next 7 days. The final model was used to stratify the test set into “high-risk” and “low-risk” categories. The analysis was repeated for 3-day intervals. MAIN RESULTS: Of the 40 patients considered eligible for study, 8 declined to participate, 4 could not master the technique of peak flow monitoring, and 6 had no fluctuations in their dyspnea level. The remaining 22 subjects form the basis of this report. Fourteen (64%) of the latter completed the 6-month protocol. Data from the 8 who were dropped or died were included up to the point of withdrawal. For 7-day forecasts, mean dyspnea score and mean daily prebronchodilator peak expiratory flow rate were identified as predictor variables. The adjusted odds ratio (OR) for mean dyspnea score was 2.71 (95% confidence interval [CI] 1.79, 4.12) per unit. For mean prebronchodilator peak expiratory flow rate, it was 1.05 (95% CI 1.01, 1.09) per percentage predicted. For 3-day forecasts, the model was composed of mean dyspnea score and mean daily bronchodilator response. The ORs for these terms were 2.66 (95% CI 2.06, 3.44) per unit and 0.980 (95% CI 0.962, 0.998) per percentage of improvement over baseline, respectively. For a given level of dyspnea, higher prebronchodilator peak expiratory flow rate and lower bronchodilator response were poor prognostic findings. When the models were applied to the test sets, “high-risk” intervals were 4 times more likely to be followed by maximal symptoms than “low-risk” intervals. CONCLUSIONS: Dyspnea scores and certain peak expiratory flow rate parameters are independent predictors of respiratory status in patients with COPD. However, our results suggest that monitoring is of little benefit except in patients with the most advanced form of this disease, and its contribution to their management is modest at best