Avian orthoavulaviruses type-1 (AOaV-1) have recently transitioned from animal vaccine vector to a bona fide vaccine delivery vehicle in human. Owing to induction of robust innate and adaptive immune responses in mucus membranes in both birds and mammals, AOaVs offer an attractive vaccine against respiratory pathogens. The unique features of AOaVs include over 50 years of safety profile, stable expression of foreign genes, high infectivity rates in avian and mammalian hosts, broad host spectrum, limited possibility of recombination and lack of pre-existing immunity in humans. Additionally, AOaVs vectors allow the production of economical and high quantities of vaccine antigen in chicken embryonated eggs and several GMP-grade mammalian cell lines. In this review, we describe the biology of AOaVs and define protocols to manipulate AOaVs genomes in effectively designing vaccine vectors. We highlighted the potential and established portfolio of AOaV-based vaccines for multiple respiratory and non-respiratory viruses of veterinary and medical importance. We comment on the limitations of AOaV-based vaccines and propose mitigations strategies. The exploitation of AOaVs vectors is expanding at an exciting pace; thus, we have limited the scope to their use as vaccines against viral pathogens in both animals and humans.

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.