Ureaplasma respiratory tract colonization is associated with bronchopulmonary dysplasia (BPD) in preterm infants. Previously, we demonstrated that a single intravenous (i.v.) dose of azithromycin (10 mg/kg of body weight) is safe but inadequate to eradicate Ureaplasma spp. in preterm infants. We performed a nonrandomized, single-arm open-label study of the pharmacokinetics (PK) and safety of intravenous 20-mg/kg single-dose azithromycin in 13 mechanically ventilated neonates with a gestational age between 24 weeks 0 days and 28 weeks 6 days. Pharmacokinetic data from 25 neonates (12 dosed with 10 mg/kg i.v. and 13 dosed with 20 mg/kg i.v.) were analyzed using a population modeling approach. Using a two-compartment model with allometric scaling of parameters on body weight (WT), the population PK parameter estimates were as follows: clearance, 0.21 liter/h × WT(kg)(0.75) [WT(kg)(0.75) indicates that clearance was allometrically scaled on body weight (in kilograms) with a fixed exponent of 0.75]; intercompartmental clearance, 2.1 liters/h × WT(kg)(0.75); central volume of distribution (V), 1.97 liters × WT (kg); and peripheral V, 17.9 liters × WT (kg). There was no evidence of departure from dose proportionality in azithromycin exposure over the tested dose range. The calculated area under the concentration-time curve over 24 h in the steady state divided by the MIC90 (AUC24/MIC90) for the single dose of azithromycin (20 mg/kg) was 7.5 h. Simulations suggest that 20 mg/kg for 3 days will maintain azithromycin concentrations of >MIC50 of 1 μg/ml for this group of Ureaplasma isolates for ≥ 96 h after the first dose. Azithromycin was well tolerated with no drug-related adverse events. One of seven (14%) Ureaplasma-positive subjects and three of six (50%) Ureaplasma-negative subjects developed physiologic BPD. Ureaplasma was eradicated in all treated Ureaplasma-positive subjects. Simulations suggest that a multiple-dose regimen may be efficacious for microbial clearance, but the effect on BPD remains to be determined.

Feature selection is a typical search problem where each state in the search space represents a subset of features candidate for selection. Out of n features, 2n subsets can be constructed, hence, an exhaustive search of all subsets becomes infeasible when n is relatively large. Therefore, Feature selection is done by employing a heuristic search algorithm that tries to reach the optimal feature subset. Here, we propose a new wrapper feature selection and weighting algorithm called Artificial Immune Feature Selection Algorithm (AIFSA); the algorithm is based on the metaphors of the Clonal Selection Algorithm (CSA). AIFSA, by itself, is not a classification algorithm, rather it utilizes well-known classifiers to evaluate and promote candidate feature subset. Experiments were performed on textual datasets like WebKB and Syskill&Webert web page ratings. Experimental results showed AIFSA competitive performance over traditional well-known filter feature selection approaches as well as some wrapper approaches existing in literature.