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Objective: In preterm and very low birth weight (VLBW) infants, attention-related problems have been found to be more pronounced and emerge later as academic difficulties that may persist into school age. In response, based on three attention networks: alerting, orienting, and executive attention, we examined the development of attention functions at 42 months (not corrected for prematurity) as a follow-up study of VLBW ( = 23) and normal birth weight (NBW:  = 48) infants.

Method: The alerting and orienting attention networks were examined through an overlap task with or without warning signal.

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Very preterm infants (VPIs) often experience extrauterine growth failure. Therefore, aggressive nutritional management of VPIs is recommended with the goal of achieving the postnatal growth of an equivalent fetus. However, VPIs frequently present postnatal length growth restriction at term-corrected age that remains lower than the standard weight and have greater fat mass and lower lean and bone mass than term-born infants.

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Efficacy and Safety of Mydriatic Microdrops for Retinopathy of Prematurity Screening: The MyMiROPS Randomized Clinical Trial.

JAMA Ophthalmol

December 2024

Second Department of Ophthalmology, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Papageorgiou General Hospital, Thessaloniki, Greece.

Importance: Commercial mydriatics administered in preterm infants during retinopathy of prematurity (ROP) screening have been associated with various cardiorespiratory and gastrointestinal adverse events.

Objective: To examine whether microdrops of a combined mixture of 1.67% phenylephrine and 0.

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Background: Very preterm children are at greater risk of academic difficulties (AD). Some of them start school a year earlier than anticipated during pregnancy due to being born preterm. The aim of this study was to analyze the relationship between neurodevelopment, school-entry age, and AD at age seven.

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Preeclampsia is one of the leading causes of maternal morbidity, with consequences during and after pregnancy. Because of its diverse clinical presentation, preeclampsia is an adverse pregnancy outcome that is uniquely challenging to predict and manage. In this paper, we developed racial bias-free machine learning models that predict the onset of preeclampsia with severe features or eclampsia at discrete time points in a nulliparous pregnant study cohort.

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