Lung Recruitment Before Surfactant Administration in Extremely Preterm Neonates: 2-Year Follow-Up of a Randomized Clinical Trial.

Importance: A multicenter randomized clinical trial (RCT) showed a lung recruitment maneuver using high-frequency oscillatory ventilation just before surfactant administration (ie, intubate-recruit-surfactant-extubate [IN-REC-SUR-E]) improved the efficacy of treatment compared with the standard intubate-surfactant-extubate (IN-SUR-E) technique without increasing the risk of adverse neonatal outcomes.

Objective: To examine follow-up outcomes at corrected postnatal age (cPNA) 2 years of preterm infants previously enrolled in an RCT and treated with IN-REC-SUR-E or IN-SUR-E in 35 tertiary neonatal intensive care units.

Design, Setting, And Participants: This was a follow-up study of infants recruited into the primary RCT from 2015 to 2018 at 35 tertiary neonatal intensive care units (NICUs) in Italy.

Corrigendum: Correlation between antioxidant and anticancer activity and phenolic profile of new Apulian table grape genotypes ( L.).

[This corrects the article DOI: 10.3389/fpls.2022.

Correlation between antioxidant and anticancer activity and phenolic profile of new Apulian table grape genotypes ( L.).

Grapes represent a significant source of phenolic compounds known for their health-promoting properties, such as antioxidant capacity on normal cells and prooxidant activity on tumor cells. The genotype highly affects the polyphenolic composition in grapes and, consequently, the nutritional quality of berries. This work aimed to characterize the phenolic composition, the antioxidant, and anticancer activity of grape skin extracts (GSEs) of nine new table grape genotypes selected from a breeding program to obtain new cultivars of seedless table grapes, well adapted to the climatic change and with higher nutraceutical properties.

Use of Artificial Neural Networks and NIR Spectroscopy for Non-Destructive Grape Texture Prediction.

In this article, a combination of non-destructive NIR spectroscopy and machine learning techniques was applied to predict the texture parameters and the total soluble solids content (TSS) in intact berries. The multivariate models obtained by building artificial neural networks (ANNs) and applying partial least squares (PLS) regressions showed a better prediction ability after the elimination of uninformative spectral ranges. A very good prediction was obtained for TSS and springiness (R 0.