RESUMEN
BACKGROUND: Preterm birth is associated with long-term cardiovascular morbidity and mortality. In adults, fibroblast growth factor-23 (FGF-23), α-Klotho, and secretoneurin have all garnered attention as cardiovascular biomarkers, but their utility in pediatric populations has not yet been ascertained. The aim of this pilot study was to evaluate these novel cardiovascular biomarkers and their association with indicators of cardiovascular impairment in the highly vulnerable population of former very preterm infants. METHODS: Five- to seven-year-old children born at < 32 weeks' gestation were eligible for the study. Healthy same-aged children born at term served as controls. Biomarkers were quantified in fasting blood samples, and echocardiographic measurements including assessment of aortic elastic properties were obtained. RESULTS: We included 26 former very preterm infants and 21 term-born children in the study. At kindergarten age, former very preterm infants exhibited significantly higher plasma concentrations of biologically active intact FGF-23 (iFGF-23; mean 43.2 pg/mL vs. 29.1 pg/mL, p = 0.003) and secretoneurin (median 93.8 pmol/L vs. 70.5 pmol/L, p = 0.046). iFGF-23 inversely correlated with distensibility of the descending aorta. CONCLUSION: In preterm-born children, iFGF-23 and secretoneurin both offer prospects as valuable cardiovascular biomarkers, potentially allowing for risk stratification and timely implementation of preventive measures. IMPACT: Former very preterm infants have increased plasma concentrations of the novel cardiovascular biomarkers intact fibroblast growth factor-23 (iFGF-23) and secretoneurin at kindergarten age. Increases in iFGF-23 concentrations are associated with decreased distensibility of the descending aorta even at this early age. Monitoring of cardiovascular risk factors is essential in individuals with a history of preterm birth. Both iFGF-23 and secretoneurin hold promise as clinically valuable biomarkers for risk stratification, enabling the implementation of early preventive measures.
RESUMEN
Language models based on artificial neural networks increasingly capture key aspects of how humans process sentences. Most notably, model-based surprisals predict event-related potentials such as N400 amplitudes during parsing. Assuming that these models represent realistic estimates of human linguistic experience, their success in modeling language processing raises the possibility that the human processing system relies on no other principles than the general architecture of language models and on sufficient linguistic input. Here, we test this hypothesis on N400 effects observed during the processing of verb-final sentences in German, Basque, and Hindi. By stacking Bayesian generalised additive models, we show that, in each language, N400 amplitudes and topographies in the region of the verb are best predicted when model-based surprisals are complemented by an Agent Preference principle that transiently interprets initial role-ambiguous noun phrases as agents, leading to reanalysis when this interpretation fails. Our findings demonstrate the need for this principle independently of usage frequencies and structural differences between languages. The principle has an unequal force, however. Compared to surprisal, its effect is weakest in German, stronger in Hindi, and still stronger in Basque. This gradient is correlated with the extent to which grammars allow unmarked NPs to be patients, a structural feature that boosts reanalysis effects. We conclude that language models gain more neurobiological plausibility by incorporating an Agent Preference. Conversely, theories of human processing profit from incorporating surprisal estimates in addition to principles like the Agent Preference, which arguably have distinct evolutionary roots.
RESUMEN
The COVID-19 pandemic has highlighted the role of breastfeeding in providing passive immunity to infants via specific anti-SARS-CoV-2 antibodies in breast milk. We aimed to quantify these antibodies across different lactation stages and identify influencing factors. This prospective study involved mother-child dyads from Innsbruck University Hospital, Austria, with a positive maternal SARS-CoV-2 test during pregnancy or peripartum between 2020 and 2023. We collected breast milk samples at various lactation stages and analyzed anti-Spike S1 receptor-binding domain (S1RBD) immunoglobulins (Ig). Maternal and neonatal data were obtained from interviews and medical records. This study included 140 mothers and 144 neonates. Anti-S1RBD-IgA (72.0%), -IgG (86.0%), and -IgM (41.7%) were highly present in colostrum and decreased as milk matured. Mothers with natural infection and vaccination exhibited higher anti-S1RBD-IgA and -IgG titers in all milk stages. Mothers with moderate to severe infections had higher concentrations of anti-S1RBD-IgA and -IgG in transitional milk and higher anti-S1RBD-IgA and -IgM in mature milk compared to those with mild or asymptomatic infections. Variations in antibody responses were also observed with preterm birth and across different virus waves. This study demonstrates the dynamic nature of breast milk Ig and underscores the importance of breastfeeding during a pandemic.