Fluid balance and phase angle as assessed by bioelectrical impedance analysis in critically ill patients:a multicenter prospective cohort study

Background: Bioelectrical impedance analysis (BIA) is a validated method to assess body composition in persons with fluid homeostasis and reliable body weight. This is not the case during critical illness. The raw BIA markers resistance, reactance, phase angle, and vector length are body weight independent. Phase angle reflects cellular health and has prognostic significance. We aimed to assess the course of phase angle and vector length during intensive care unit (ICU) admission, and determine the relation between their changes (Δ) and changes in body hydration. Methods: A prospective, dual-center observational study of adult ICU patients was conducted. Univariate and multivariable regression analyses were performed, including reactance as a marker of cellular mass and integrity and total body water according to the Biasioli equation (TBWBiasioli) and fluid balance as body weight independent markers of hydration. Results: One hundred and fifty-six ICU patients (mean ± SD age 62.5 ± 14.5 years, 67% male) were included. Between days 1 and 3, there was a significant decrease in reactance/m (−2.6 ± 6.0 Ω), phase angle (−0.4 ± 1.1°), and vector length (−12.2 ± 44.3 Ω/m). Markers of hydration significantly increased. Δphase angle and Δvector length were both positively related to Δreactance/m (r2 = 0.55, p < 0.01; r2 = 0.38, p < 0.01). Adding ΔTBWBiasioli as explaining factor strongly improved the association between Δphase angle and Δreactance/m (r2 = 0.73, p < 0.01), and Δvector length and Δreactance/m (r2 = 0.77, p < 0.01). Conclusions: Our results show that during critical illness, changes in phase angle and vector length partially reflect changes in hydration

2nd ICMI Workshop on Bridging Social Sciences and AI for Understanding Child Behaviour

Child behavior is a topic of great scientific interest across a wide range of disciplines, including social and behavioral sciences, as well as artificial intelligence (AI). The first workshop had a significant impact, and in this workshop, we aimed to bring together researchers from these fields to discuss topics such as using AI to better understand and model child behavioral and developmental processes, challenges and opportunities for AI in large-scale child behavior analysis, and implementing explainable ML/AI on sensitive child data. The workshop was a successful second step toward this objective, attracting contributions from many academic fields on child behavior analysis. This document summarizes the workshop’s events as well as the accepted papers and abstracts

Understanding print: Early reading development and the contributions of home literacy experiences

Abstract This study explored the development of children&apos;s early understanding of visual and orthographic aspects of print and how this is related to early reading acquisition. A total of 474 children, ages 48 to 83 months, completed standardized measures of phonological awareness and early reading skills. They also completed experimental tasks that tapped their understanding of what constitutes &quot;readable&quot; print. The parents of participants completed a questionnaire regarding their children&apos;s home literacy experiences. The data showed systematic development in children&apos;s understanding of print conventions and English orthography and spelling. Regression analyses indicated that print knowledge was related to early reading skill, even after accounting for variance due to age and phonological awareness. Furthermore, parents&apos; ratings of the extent of their children&apos;s involvement in activities that led to practice in reading and writing most consistently predicted the development of emerging literacy skills, including understanding of the conventions of the English writing system. Little relation between print knowledge and the frequency of storybook reading by adults was observed

Multicenter Optimization and Validation of a 2-Gene mRNA Urine Test for Detection of Clinically Significant Prostate Cancer before Initial Prostate Biopsy

Contains fulltext : 206224.pdf (publisher's version ) (Open Access

Bridging Social Sciences and AI for Understanding Child Behaviour

Child behaviour is a topic of wide scientific interest among many different disciplines, including social and behavioural sciences and artificial intelligence (AI). In this workshop, we aimed to connect researchers from these fields to address topics such as the usage of AI to better understand and model child behavioural and developmental processes, challenges and opportunities for AI in large-scale child behaviour analysis and implementing explainable ML/AI on sensitive child data. The workshop served as a successful first step towards this goal and attracted contributions from different research disciplines on the analysis of child behaviour. This paper provides a summary of the activities of the workshop and the accepted papers and abstracts

2nd ICMI Workshop on Bridging Social Sciences and AI for Understanding Child Behaviour

Child behavior is a topic of great scientific interest across a wide range of disciplines, including social and behavioral sciences, as well as artificial intelligence (AI). The first workshop had a significant impact, and in this workshop, we aimed to bring together researchers from these fields to discuss topics such as using AI to better understand and model child behavioral and developmental processes, challenges and opportunities for AI in large-scale child behavior analysis, and implementing explainable ML/AI on sensitive child data. The workshop was a successful second step toward this objective, attracting contributions from many academic fields on child behavior analysis. This document summarizes the workshop’s events as well as the accepted papers and abstracts