O efeito conjunto de gemelaridade e prematuridade no aleitamento materno exclusivo na alta hospitalar / The joint effect of twinning and prematurity on exclusive breastfeeding at hospital discharge / El efecto conjunto de la gemelaridad y la prematuridad en la lactancia materna exclusiva al momento del alta hospitalaria

Objetivo: estimar a prevalência de Aleitamento Materno Exclusivo (AME) entre gemelares pré-termos e investigar o efeito de nascer gemelar e pré-termo no AME na alta hospitalar. Método: coorte prospectiva de recém-nascidos em uma instituição localizada no Rio de Janeiro, no período de 13 de março de 2017 a 12 de outubro de 2018. Dados coletados em questionário e prontuário médico. Foi utilizado DAG para construção do modelo conceitual, análise exploratória dos dados e regressão logística múltipla. Resultados: a prevalência de AME na alta hospitalar de gemelares pré-termos foi de 47,8%. Pré-termos apresentaram maior chance de não estarem em AME na alta hospitalar. Não gemelares apresentaram maior chance de não estarem em AME na alta hospitalar. Conclusão: pouco mais da metade dos gemelares pré-termo não estavam em AME na alta hospitalar. Prematuros tiveram maior chance de não estarem em AME. Não gemelares pré-termo apresentaram maior chance de não estarem em AME.

Objective: to estimate the prevalence of Exclusive Breastfeeding (EBF) in preterm twins and to investigate the effect of twin and preterm birth on EBF at hospital discharge. Method: prospective cohort of newborns in an institution located in Rio de Janeiro, from March 13, 2017, to October 12, 2018. Data collected through a questionnaire and medical records. A DAG was used to build the conceptual model, exploratory data analysis and multiple logistic regression. Results: prevalence of EBF at hospital discharge of preterm twins of 47.8%. Preterm infants were more likely to not be on EBF at hospital discharge. Non-twins were more likely to not be on EBF at hospital discharge. Conclusion: just over half of preterm twins were not on EBF at hospital discharge. Preterm infants had a greater chance of not being on EBF. Preterm non-twins were more likely to not be on EBF.

Objetivo: estimar la prevalencia de Lactancia Materna Exclusiva (LME) entre gemelos prematuros y investigar el efecto de nacer gemelo y prematuro en la LME al momento del alta hospitalaria. Método: cohorte prospectiva de recién nacidos en una institución ubicada en Rio de Janeiro, entre 13//marzo/2017 y 12/octubre/2018. Los datos se recolectaron mediante cuestionario y expediente médico. Se utilizó DAG para la construcción del modelo conceptual, análisis exploratorio de los datos y regresión logística múltiple. Resultados: la prevalencia de LME en el alta hospitalaria de gemelos prematuros fue del 47,8%. Los prematuros tuvieron mayor probabilidad de no estar en LME en el alta hospitalaria. Los no gemelares tuvieron mayor probabilidad de no estar en LME en el alta hospitalaria. Conclusión: poco más de la mitad de los gemelos prematuros no estaban en LME en el alta hospitalaria. Los prematuros tuvieron mayor probabilidad de no estar en LME. Los no gemelos prematuros presentaron mayor probabilidad de no estar en LME.

Genetic Influence on Social Support: A Twin Study.

Social support is often considered an environmental factor affecting health, especially in aging populations. However, its genetic underpinnings suggest a more complex origin. This study investigates the heritability of social support through applying a threshold model on data of a large adult sample of twins (N = 8019) from the Netherlands Twin Register, collected between 2009 and 2011. The study employed the Duke - UNC Functional Social Support Questionnaire to assess social support quality. Our analysis revealed genetic contributions to social support, with heritability estimated at 37%, without a contribution of shared environment and no differences between men and women in heritability. The study's results underscore the complexity of social support as a trait influenced by genetic and environmental factors, challenging the notion that it is solely an environmental construct.

Twin Data Support a Sensitive Period for Singing Ability.

As with many other musical traits, the social environment is a key influence on the development of singing ability. While the familial singing environment is likely to be formative, its role relative to other environmental influences such as training is unclear. We used structural equation modeling to test relationships among demographic characteristics, familial environmental variables (early and current singing with family), vocal training, and singing ability in a large, previously documented sample of Australian twins (N = 1163). Notably, early singing with family, and to a lesser extent vocal training, predicted singing ability, whereas current singing with family did not. Early familial singing also mediated the relationship between sex and singing ability, with men who sang less with family during childhood showing poorer ability. Bivariate twin models between early familial singing and singing ability showed the phenotypic correlation was largely explained by shared environmental influences. This raises the possibility of a sensitive period for singing ability, with sociocultural expectations around singing potentially differentiating the developmental trajectories of this skill for men and women.

Zygosity Effects on Human Voice: Fundamental Frequency Analysis of Brazilian Twins' Speech.

Voice production can be influenced by interindividual variations related to genetic, physiological, behavioral, and several environmental factors. Here we examined the effect of zygosity on speaking fundamental frequency (F0) statistical descriptors. Our aims were: (1) to determine whether the genetic similarity between monozygotic (MZ) and dizygotic (DZ) twins affects F0 characteristics, and (2) to quantify the contribution of genetic factors to these characteristics. The study involved 79 same-sex twin pairs of Brazilian Portuguese speakers, comprising 65 MZ and 14 DZ twins, aged 18 to 66 years (31.7 ± 11.6 years), with 21 male and 58 female pairs. Participants were recorded while uttering a greeting phrase and the Brazilian Portuguese version of the 'Happy Birthday to You' song. Speech segments were analyzed using Praat free software, and F0 measures were automatically extracted in both Hertz and semitone scales. Statistical descriptors, including centrality, dispersion, and extreme values of F0 were examined, and the ACE model (i.e., total genetic effects, A; shared environmental influences, C; and nonshared environmental influences, E) was employed to estimate the additive effect;ts of monozygosity. As anticipated, we observed a zygosity effect on several F0 parameters, with more similarity between MZ twins compared to DZ twins. We discuss the genetic influences on F0 parameters and the absence of a monozygosity effect in two of them. Additionally, we briefly address potential biases associated with the selected measurement scale for statistical modeling. Finally, we explore the influence of genetic factors on F0 patterns, as well as environmental, life history and linguistic factors, particularly concerning F0 variation in speech.

Network-level enrichment provides a framework for biological interpretation of machine learning results.

Machine learning algorithms are increasingly being utilized to identify brain connectivity biomarkers linked to behavioral and clinical outcomes. However, research often prioritizes prediction accuracy at the expense of biological interpretability, and inconsistent implementation of ML methods may hinder model accuracy. To address this, our paper introduces a network-level enrichment approach, which integrates brain system organization in the context of connectome-wide statistical analysis to reveal network-level links between brain connectivity and behavior. To demonstrate the efficacy of this approach, we used linear support vector regression (LSVR) models to examine the relationship between resting-state functional connectivity networks and chronological age. We compared network-level associations based on raw LSVR weights to those produced from the forward and inverse models. Results indicated that not accounting for shared family variance inflated prediction performance, the k-best feature selection via Pearson correlation reduced accuracy and reliability, and raw LSVR model weights produced network-level associations that deviated from the significant brain systems identified by forward and inverse models. Our findings offer crucial insights for applying machine learning to neuroimaging data, emphasizing the value of network enrichment for biological interpretation.

Developing Inclusive Community Platforms: A Catalyst for DEI Community.

Developing inclusive communities is important to enhance individuals' well-being yet this brings the challenge of actively engaging and leveraging the diversity of residents in communities. Such significant social challenges are prominent in Japan, a focus of this article, as the most advanced aging society in the world and thus relevant to European and other countries. This paper explains a major government initiative that takes an innovative approach through leveraging a social technology, a Digital Twin of a community, to understand and address inclusiveness of a community leveraging population approach. The purpose of this letter is to provide researchers and policy makers insights into the approach taken to stimulate reflection on the potential for adaptation and replication.

The dual journey: The development of twins' relationships throughout childhood.

Twin bonds, likely the most enduring of human relationships, provide both solace and rivalry for twins. Using an evolutionary psychology perspective, this chapter scrutinizes twins' bonds from prenatal stages to childhood to better understand their unique relationships. Twins' interactions, which begin in the womb, establish patterns of cooperation and competition. The initial years pose parenting challenges that shape the twins' experiences of dependency and rivalry. As twins grow, five dimensions-closeness, dependence, conflict, rivalry, and dominance-emerge, evolving distinctly between monozygotic twins (MZ: sharing close to 100% of their genes) and dizygotic twins (DZ: sharing on average 50% of their genetic variance). The chapter notes the closer relationship MZ twins share compared to DZ twins. While the closeness and dependence among DZ twins decline throughout childhood, these elements remain stable in MZ twins. The effect of zygosity on conflict and rivalry is less clear. For both MZ and DZ twins, conflict stays steady, while rivalry intensifies with school entry, probably driven by external comparisons, but lessens as twins develop into late childhood. Unlike singletons, where birth order dictates dominance dynamics, in twins, this dynamic is more variable and becomes more defined by around 6.5 years of age. Several factors are presented as impacting the nature of the twins' relationships: the evolvement of 'twin language', the parenting style and the differential parenting they receive. This exploration into the development of twins' relationships underlines the importance of tailored caregiving and invites further research into the genetic and environmental factors that shape close bonds.

'Omics-Based Digital Twins for Personalised Paediatric Healthcare.

Digital twins offer potential to enhance the precision and personalisation of healthcare delivery. As part of a collaborative project between La Trobe University and the Murdoch Children's Research Institute, Melbourne, Australia, multidimensional 'omics-based digital twins of children are being developed. The aim is to explore their application to a range of health contexts in children. A pilot project is commencing that focuses on food allergy diagnosed at one year of age.

Cardiovascular care with digital twin technology in the era of generative artificial intelligence.

Digital twins, which are in silico replications of an individual and its environment, have advanced clinical decision-making and prognostication in cardiovascular medicine. The technology enables personalized simulations of clinical scenarios, prediction of disease risk, and strategies for clinical trial augmentation. Current applications of cardiovascular digital twins have integrated multi-modal data into mechanistic and statistical models to build physiologically accurate cardiac replicas to enhance disease phenotyping, enrich diagnostic workflows, and optimize procedural planning. Digital twin technology is rapidly evolving in the setting of newly available data modalities and advances in generative artificial intelligence, enabling dynamic and comprehensive simulations unique to an individual. These twins fuse physiologic, environmental, and healthcare data into machine learning and generative models to build real-time patient predictions that can model interactions with the clinical environment to accelerate personalized patient care. This review summarizes digital twins in cardiovascular medicine and their potential future applications by incorporating new personalized data modalities. It examines the technical advances in deep learning and generative artificial intelligence that broaden the scope and predictive power of digital twins. Finally, it highlights the individual and societal challenges as well as ethical considerations that are essential to realizing the future vision of incorporating cardiology digital twins into personalized cardiovascular care.

Digital twins for managing bridge climate change adaptation.

Background: Bridges are vital construction infrastructures that almost every nation needs in order to function. Climate change is a significant issue, that especially affects the construction industry. It is very important that bridges are able to withstand the impacts of climate change and adaptation measures will be required to achieve this. Methods: The digital twin will be developed using BIM to manage the climate change adaptation measures for the bridges. A 6D BIM model will be created that includes the 3D Revit model of a bridge featuring climate change measures, the climate change adaptation measures timeline schedule, climate change adaptation cost estimation, and carbon emission estimation, which will be produced using Revit software, Navisworks, and Granta EduPack. The results will show how 6D BIM can be used to support the adaptation of bridges to the effects of climate change. Results: The findings underscore the efficacy of 6D BIM in enhancing bridge resilience against climate change impacts. The 3D model demonstrates integration of adaptation measures without compromising bridge functionality. Moreover, the 4D model's timeline scheduling facilitates hazard anticipation, project planning, communication enhancement, collaborative efforts, and project visualization. Cost estimations from the 5D model reveal varying costs among adaptation measures, while the 6D model highlights differences in carbon footprints. These BIM dimensions enable stakeholders to analyse effects on project costs and energy consumption, aiding sustainability and cost-efficiency considerations. Conclusions: The study exhibits the literature review analysis, the risk assessment, research on climate change adaptation strategies, and implementation using Revit 2022, Navisworks 2022, and Granta EduPack software. By contributing to the adaptation of bridges to climate change effects, the research has provided valuable insights and practical implications for enhancing bridge resilience globally.

Bridges are vital construction infrastructures that almost every nation needs in order to function. Climate change is a significant issue, that especially affects the construction industry. It is very important that bridges are able to withstand the impacts of climate change and adaptation measures will be required to achieve this. The digital twin or virtual replica of the bridge will be developed to manage the climate change adaptation measures for the bridges. A 6D BIM model will be created that includes the 3D Revit model of a bridge featuring climate change measures, the climate change adaptation measures timeline schedule, climate change adaptation cost estimation, and carbon emission estimation, which will be produced using Revit software, Navisworks, and Granta EduPack. The results will show how an interactive 6D BIM (transforming into a digital twin) can be used to support the adaptation of bridges to the effects of climate change. Our study contributes to the adaptation of bridges to climate change effects, the research has provided valuable insights and practical implications for enhancing bridge resilience globally.