Publications by authors named "M Csalanosine Nemeth"
Background: Managing preoperative anxiety in pediatric anesthesia is challenging, as it impacts patient cooperation and postoperative outcomes. Both pharmacological and nonpharmacological interventions are used to reduce children's anxiety levels. However, the optimal approach remains debated, with evidence-based guidelines still lacking.
View Article and Find Full Text PDFGraphitic carbon nitride (g-CN) proved to be a promising semiconductor for the photocatalytic degradation of various organic pollutants. However, its efficacy is limited by a fast electron hole recombination, a restricted quantity of active sites, and a modest absorption in the visible range. To overcome these limitations, g-CN-BiS and g-CN-ZnS composites were effectively produced utilizing a starch-assisted technique.
View Article and Find Full Text PDFMore than three billion years of evolution have produced an image of biology encoded into the space of natural proteins. Here we show that language models trained at scale on evolutionary data can generate functional proteins that are far away from known proteins. We present ESM3, a frontier multimodal generative language model that reasons over the sequence, structure, and function of proteins.
View Article and Find Full Text PDFGiven that perioperative normothermia represents a quality parameter in pediatric anesthesia, numerous studies have been conducted on temperature measurement, albeit with heterogeneous measurement intervals, ranging from 30 s to fifteen minutes. We aimed to determine the minimum time interval for reporting of intraoperative core body temperature across commonly used measurement intervals in children. Data were extracted from the records of 65 children who had participated in another clinical study and analyzed using a quasibinomial mixed linear model.
View Article and Find Full Text PDFArticle Synopsis
- The text discusses the challenges of analyzing survey data from clinical trials, particularly dealing with subjective measures like well-being or pain, which typically use limited discrete response options.
- It critiques the common use of ordinary linear regression for such data, as it may violate key assumptions, potentially leading to biased predictions and insights focused only on average responses.
- The authors advocate for ordinal regression models, which better handle the nature of the data by providing probabilistic estimates across all response categories and discuss their application, strengths, and limitations in a pediatric anesthesia context.