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    Application of axisymmetric drop shape analysis and brewster angle microscopy for assessment of clinical samples from prematurely born infants with NRDS

    Year: 2017

    Journal: Colloid Surf. A-Physicochem. Eng. Asp., Volume 519, APR 20, page 187–191

    Authors: Bangyozova, Maya; Jordanova, Albena; Tsanova, Asya; Stoyanova, Vishnya; Tasheva, Elena; Ivanova, Krasimira; Todorov, Roumen; Hristova, Emilia; Lalchev, Zdravko

    Keywords: Gastric aspirates; Neonatal respiratory distress syndrome; Surface tension; Axisymmetric drop shape analysis; Brewster angle microscopy

    Axisymmetric drop shape analysis (ADSA) is a powerful technique for surface tension measurement of a pendant drop, requiring only 50 microliters of the tested clinical sample. In addition, Brewster angle microscopy (BAM) is a well-established and useful method for visualization of ultra-thin surface films, such as Langmuir monolayers, on air-water interface. In the present study we analysed the surface properties of gastric aspirates (GAs) from prematurely born infants with neonatal respiratory distress syndrome (NRDS) and healthy full term infants with regard to find an approachable method for assessment of surfactant maturity at birth. The measurement of dynamic surface characteristics of GA by ADSA showed significantly higher mean values of the minimal surface tension in the premature infants as compared to the full term babies. This parameter proves to be the most reliable parameter for lung maturity assessment. In addition, the hysteresis curves of the surface tension vs. drop surface area indicated more stable surface behavior of alveolar surfactant from healthy children, as compared to the NRDS infants. The morphology of the monolayers composed of GAs also showed considerable differences between the samples from both groups studied. In conclusion, ADSA and BAM methods could be used into the clinical practice for rapid and sensitive surfactant's assessment in premature infants with regard to administration of exogenous surfactant. (C) 2016 Elsevier B.V. All rights reserved.
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