High-dose Oral Ambroxol for Early Treatment of Pulmonary Acute Respiratory Distress Syndrome: an Exploratory, Randomized, Controlled Pilot Trial.

OBJECTIVE: To evaluate efficacy of high-dose oral ambroxol in acute respiratory distress syndrome (ARDS) with respect to ventilator-free days (VFD). DESIGN: Prospective, randomized, placebo-controlled, blinded pilot trial. PATIENTS: Sixty-six mechanically ventilated patients (1 month to 12 years) with ARDS who were hand-ventilated for <24 hr before pediatric intensive care unit admission. INTERVENTIONS: Patients randomized to oral ambroxol (40 mg/kg/day, in four divided doses) (n = 32) or placebo (n = 34) until 10 days, extubation or death whichever is earlier. MEASUREMENTS AND MAIN RESULTS: Majority (91%) had pneumonia and bronchiolitis. Two study groups were similar in baseline characteristics. Mean partial pressure of arterial oxygen/fraction of inspired oxygen and oxygenation index were >175 and <10, respectively, with no difference in the two study groups. VFD were similar in the two study groups. Overall mortality was 26%. No adverse events were noted with ambroxol. CONCLUSIONS: Among ventilated pulmonary ARDS patients with oxygenation index of <10, mortality was 26%. Ambroxol did not improve VFD. Study with higher and more frequently administered doses of ambroxol in larger sample is suggested after having generated relevant pharmacokinetic data among critically ill children.

Machine Learning Based Healthcare System for Investigating the Association Between Depression and Quality of Life.

New technological innovations are changing the future of healthcare system. Identification of factors that are responsible for causing depression may lead to new experiments and treatments. Because depression as a disease is becoming a leading community health concern worldwide. Using machine learning techniques this article presents a complete methodological framework to process and explore the heterogenous data and to better understand the association between factors related to quality of life and depression. Subsequently, the experimental study is mainly divided into two parts. In the first part, a data consolidation process is presented. The relationship of data is formed and to uniquely identify each relation in data the concept of the Secure Hash Algorithm is adopted. Hashing is used to locate and index the actual items in the data. The second part proposed a model using both unsupervised and supervised machine learning techniques. The consolidation approach helped in providing a base for formulation and validation of the research hypothesis. The Self organizing map provided 08 cluster solution and the classification problems were taken from the clustered data to further validate the performance of the posterior probability multi-class Support Vector Machine. The expectations of the importance sampling resulted in factors responsible for causing depression. The proposed model was adopted to improve the classification performance, and the result showed classification accuracy of 91.16%.