Search

Introduction: Our aim was to determine which foetal or neonatal growth curves discriminate the probability of dying of newborns with low birth weight for their gestational age (small for gestational age, SGA) and sex (weight < 10th percentile) and to establish the curves that are presumably most useful for monitoring growth through age 10 years. Material and methods: The analysis included every neonate (15 122) managed in our hospital (2013–2022) and all neonates born preterm before 32 weeks (6913) registered in the SEN1500 database (2019–2022). We considered most useful those curves with the highest likelihood ratio (LR) for dying with or without a history of SGA in each subgroup of gestational ages. Theoretically, the optimal curves for monitoring growth would be those with a higher R2 in the quantile regression formulas for the 50th percentile. Results: The growth curves exhibiting the strongest association between SGA and hospital mortality are the Intergrowth fetal curves and the Fenton neonatal curves in infants born preterm before 32 weeks. However, the optimal curves for premature babies and neonates overall were those of Olsen and Intergrowth. The most useful curves to monitor anthropometric values alone until age 10 years of age are the longitudinal Intergrowth curves followed by the WHO standards, but if a single reference is desired from birth through age 10 years, the best option is the Fenton curves followed by the WHO standards. Conclusions: The Intergrowth reference provides the most discriminating foetal growth curves. In neonatal clinical practice, the optimal references are the Fenton followed by the WHO charts. Resumen: Introducción: Se pretende determinar qué curvas de crecimiento fetal o neonatal discriminan la probabilidad de fallecer de los recién nacidos de bajo peso para su edad gestacional (BPEG) y sexo (peso inferior al percentil 10) y conocer las curvas teóricamente más útiles para seguir el crecimiento hasta los 10 años. Material y método: Se estudian todos los neonatos (15.122) atendidos en nuestro hospital (2013-2022) y todos los prematuros menores de 32 semanas (6.913) incluidos en la base de datos SEN1500 (2019-2022). Las curvas más útiles serán las que tengan un mayor cociente de probabilidad (LHR=likelihood ratio) de fallecer siendo o no BPEG en cada subgrupo de edades gestacionales. Las curvas más indicadas teóricamente en el seguimiento serán las que tengan una mayor R2 en las fórmulas de regresión de las tendencias del percentil 50. Resultados: Las curvas de crecimiento que mejor relacionan BPEG con mortalidad hospitalaria son las fetales de Intergrowth y las neonatales de Fenton en los prematuros menores de 32 semanas. Sin embargo, en todos los prematuros y neonatos en su conjunto, son las de Olsen e Intergrowth. Las curvas más útiles para seguir sólo la evolución antropométrica hasta los 10 años son las longitudinales de Intergrowth más OMS, pero si se desea una curva única desde el nacimiento hasta los 10 años son las de Fenton más OMS. Conclusiones: Las curvas de crecimiento fetal más discriminante son las de Intergrowth fetales. En la práctica clínica del neonatólogo, las curvas más indicadas son las de Fenton más OMS.

Data capturing multiple axes of tree size and shape, such as a tree's stem diameter, height and crown size, underpin a wide range of ecological research-from developing and testing theory on forest structure and dynamics, to estimating forest carbon stocks and their uncertainties, and integrating remote sensing imagery into forest monitoring programmes. However, these data can be surprisingly hard to come by, particularly for certain regions of the world and for specific taxonomic groups, posing a real barrier to progress in these fields. To overcome this challenge, we developed the Tallo database, a collection of 498,838 georeferenced and taxonomically standardized records of individual trees for which stem diameter, height and/or crown radius have been measured. These data were collected at 61,856 globally distributed sites, spanning all major forested and non-forested biomes. The majority of trees in the database are identified to species (88%), and collectively Tallo includes data for 5163 species distributed across 1453 genera and 187 plant families. The database is publicly archived under a CC-BY 4.0 licence and can be access from: https://doi.org/10.5281/zenodo.6637599. To demonstrate its value, here we present three case studies that highlight how the Tallo database can be used to address a range of theoretical and applied questions in ecology-from testing the predictions of metabolic scaling theory, to exploring the limits of tree allometric plasticity along environmental gradients and modelling global variation in maximum attainable tree height. In doing so, we provide a key resource for field ecologists, remote sensing researchers and the modelling community working together to better understand the role that trees play in regulating the terrestrial carbon cycle.

Forests that regrow naturally on abandoned fields are important for restoring biodiversity and ecosystem services, but can they also preserve the distinct regional tree floras? Using the floristic composition of 1215 early successional forests (≤20 years) in 75 human-modified landscapes across the Neotropic realm, we identified 14 distinct floristic groups, with a between-group dissimilarity of 0.97. Floristic groups were associated with location, bioregions, soil pH, temperature seasonality, and water availability. Hence, there is large continental-scale variation in the species composition of early successional forests, which is mainly associated with biogeographic and environmental factors but not with human disturbance indicators. This floristic distinctiveness is partially driven by regionally restricted species belonging to widespread genera. Early secondary forests contribute therefore to restoring and conserving the distinctiveness of bioregions across the Neotropical realm, and forest restoration initiatives should use local species to assure that these distinct floras are maintained.

ObjectiveTo develop predictive clinical models of bronchopulmonary dysplasia (BPD) through competing risk analysis.MethodsRetrospective observational cohort study, including preterm newborns ≤32 weeks gestational age, conducted between January 1, 2013 and September 30, 2022 in a third-level Neonatal Intensive Care Unit in Spain. A prediction study was carried out using competing risk models, where the event of interest was BPD and the competing event was death. A multivariate competing risk model was developed separately for each postnatal day (days 1, 3, 7 and 14). Nomograms to predict BPD risk were developed from the coefficients of the final models and internally validated.ResultsA total of 306 patients were included in the study, of which 73 (23.9%) developed BPD and 29 (9.5%) died. On day 1, the model with the greatest predictive capacity was that including birth weight, days since rupture of membranes, and surfactant requirement (area under the receiver operating characteristic (ROC) curve (AUC), 0.896; 95% CI, 0.792–0.999). On day 3, the final predictive model was based on the variables birth weight, surfactant requirement, and Fraction of Inspired Oxygen (FiO2) (AUC, 0.891; 95% CI, 0.792–0.989).ConclusionsCompeting risk analysis allowed accurate prediction of BPD, avoiding the potential bias resulting from the exclusion of deceased newborns or the use of combined outcomes. The resulting models are based on clinical variables measured at bedside during the first 3 days of life, can be easily implemented in clinical practice, and can enable earlier identification of patients at high risk of BPD.