AI models in clinical neonatology: a review of modeling approaches and a consensus proposal for standardized reporting of model performance.

Artificial intelligence (AI) is a rapidly advancing area with growing clinical applications in healthcare. The neonatal intensive care unit (NICU) produces large amounts of multidimensional data allowing AI and machine learning (ML) new avenues to improve early diagnosis, enhance monitoring, and provide highly-targeted treatment approaches. In this article, we review recent clinical applications of AI to important neonatal problems, including sepsis, retinopathy of prematurity, bronchopulmonary dysplasia, and others.

Comparing machine learning techniques for neonatal mortality prediction: insights from a modeling competition.

Background: Predicting mortality risk in neonatal intensive care units (NICUs) is challenging due to complex, variable clinical and physiological data. Machine learning (ML) offers potential for more accurate risk stratification.

Objective: To compare the performance of various ML models in predicting NICU mortality using a team-based modeling competition.

Estradiol deficiency as a consequence of aging contributes to the depletion of the satellite cell pool in female mice.

The effects of aging on the satellite cell pool have primarily been studied in male mice, where the role of cell-intrinsic versus environmental changes on satellite cell function remains contentious. Estradiol is necessary for maintenance of satellite cell pool size in adult female mice-here we investigate the hypothesis that in females, estradiol is a major environmental driver of age-associated effects on satellite cells. In 24-26 month-old ovarian senescent mice, we find the satellite cell pool size is severely diminished in certain muscles (TA and EDL) but only marginally affected in others (soleus and gastrocnemius).

Legacy Effects of Plant Community Structure Are Manifested in Microbial Biofilm Development With Consequences for Ecosystem CO Emissions.

To better understand linkages between hydrology and ecosystem carbon flux in northern aquatic ecosystems, we evaluated the relationship between plant communities, biofilm development, and carbon dioxide (CO) exchange following long-term changes in hydrology in an Alaskan fen. We quantified seasonal variation in biofilm composition and CO exchange in response to lowered and raised water table position (relative to a control) during years with varying levels of background dissolved organic carbon (DOC). We then used nutrient-diffusing substrates (NDS) to evaluate cause-effect relationships between changes in plant subsidies (i.