Role of computed tomography angiography in the evaluation of haemoptysis in children: Decoding the abnormal vessels

Background & objectives: Haemoptysis in children is potentially life-threatening. In most cases, the bleeding arises from the systemic circulation, and in 5-10 per cent of cases, it arises from the pulmonary circulation. The role of computed tomography angiography (CTA) in this setting is important. This study was undertaken (i) to study the role of single-phase split-bolus dual energy contrast-enhanced multidetector row CTA (DECTA) in the evaluation of haemoptysis in children; (ii) to analyze the patterns of abnormal vascular supply in the various aetiologies encountered. Methods: A retrospective study of 86 patients who underwent split bolus DECTA for the evaluation of haemoptysis was performed. Final diagnoses were categorized as normal computed tomography, active tuberculosis (TB), post-infectious sequelae, non-TB active infection, cystic fibrosis (CF), non-CF bronchiectasis, congenital heart disease (CHD), interstitial lung disease, vasculitis, pulmonary thromboembolism and idiopathic pulmonary haemosiderosis. Abnormal bronchial arteries (BAs) and non-bronchial systemic collateral arteries (NBSCs) were assessed for number and site and their correlation with underlying aetiologies. Results: A total of 86 patients (45 males, age from 0.3 to 18 yr, mean 13.88 yr) were included in the study; among these only two patients were less than five years of age. The most common cause of haemoptysis was active infection (n=30), followed by bronchiectasis (n=18), post-infectious sequelae (n=17) and CHD (n=7). One hundred and sixty five abnormal arteries were identified (108 BA and 57 NBSC), and were more marked in bronchiectasis group. Interpretation & conclusions: Active infections and bronchiectasis are the most common causes of haemoptysis in children. While post-infectious sequelae are less common, in patients with haemoptysis, the presence of any abnormal arteries correlates with a more frequent diagnosis of bronchiectasis. NBSCs are more common in post-infectious sequelae and CHD

Growth Characteristics Modeling of Mixed Culture of Bifidobacterium bifidum and Lactobacillus acidophilus using Response Surface Methodology and Artificial Neural Network

Different culture conditions viz. additional carbon and nitrogen content, inoculum size and age, temperature and pH of the mixed culture of Bifidobacterium bifidum and Lactobacillus acidophilus were optimized using response surface methodology (RSM) and artificial neural network (ANN). Kinetic growth models were fitted for the cultivations using a Fractional Factorial (FF) design experiments for different variables. This novel concept of combining the optimization and modeling presented different optimal conditions for the mixture of B. bifidum and L. acidophilus growth from their one variable at-a-time (OVAT) optimization study. Through these statistical tools, the product yield (cell mass) of the mixture of B. bifidum and L. acidophilus was increased. Regression coefficients (R2) of both the statistical tools predicted that ANN was better than RSM and the regression equation was solved with the help of genetic algorithms (GA). The normalized percentage mean squared error obtained from the ANN and RSM models were 0.08 and 0.3%, respectively. The optimum conditions for the maximum biomass yield were at temperature 38°C, pH 6.5, inoculum volume 1.60 mL, inoculum age 30 h, carbon content 42.31% (w/v), and nitrogen content 14.20% (w/v). The results demonstrated a higher prediction accuracy of ANN compared to RSM.

Growth characteristics modeling of Lactobacillus acidophilus using RSM and ANN

The culture conditions viz. additional carbon and nitrogen content, inoculum size, age, temperature and pH of Lactobacillus acidophilus were optimized using response surface methodology (RSM) and artificial neural network (ANN). Kinetic growth models were fitted to cultivations from a Box-Behnken Design (BBD) design experiments for different variables. This concept of combining the optimization and modeling presented different optimal conditions for L. acidophilus growth from their original optimization study. Through these statistical tools, the product yield (cell mass) of L. acidophilus was increased. Regression coefficients (R²) of both the statistical tools predicted that ANN was better than RSM and the regression equation was solved with the help of genetic algorithms (GA). The normalized percentage mean squared error obtained from the ANN and RSM models were 0.06 and 0.2%, respectively. The results demonstrated a higher prediction accuracy of ANN compared to RSM.