Biomarkers troponin and procalcitonin in addition to CRB-65 enhance risk stratification in patients with community-acquired pneumonia.

Background: Community-acquired pneumonia (CAP) remains a leading cause of infectious disease mortality globally, necessitating intensive care unit (ICU) admission for ∼10% of hospitalised patients. Accurate prediction of disease severity facilitates timely therapeutic interventions.

Methods: Our study aimed to enhance the predictive capacity of the clinical CRB-65 score by evaluating eight candidate biomarkers: troponin T high-sensitive (TnT-hs), procalcitonin (PCT), N-terminal pro-brain natriuretic peptide, angiopoietin-2, copeptin, endothelin-1, lipocalin-2 and mid-regional pro-adrenomedullin.

A critical comparative study of the performance of three AI-assisted programs for bone age determination.

Objectives: To date, AI-supported programs for bone age (BA) determination for medical use in Europe have almost only been validated separately, according to Greulich and Pyle (G&P). Therefore, the current study aimed to compare the performance of three programs, namely BoneXpert, PANDA, and BoneView, on a single Central European population.

Materials And Methods: For this retrospective study, hand radiographs of 306 children aged 1-18 years, stratified by gender and age, were included.

Acceleration of skeletal maturation in Central Europe over the last two decades: insights from two cohorts of healthy children.

Background: Deviations between the determination of bone age (BA) according to Greulich and Pyle (G&P) and chronological age (CA) are common in Caucasians. Assessing these discrepancies in a population over time requires analysis of large samples and low intra-observer variability in BA estimation, both can be achieved with artificial intelligence-based software. The latest software-based reference curve contrasting the BA determined by G&P to the CA of Central European children dates back over two decades.

Development of a long noncoding RNA-based machine learning model to predict COVID-19 in-hospital mortality.

Tools for predicting COVID-19 outcomes enable personalized healthcare, potentially easing the disease burden. This collaborative study by 15 institutions across Europe aimed to develop a machine learning model for predicting the risk of in-hospital mortality post-SARS-CoV-2 infection. Blood samples and clinical data from 1286 COVID-19 patients collected from 2020 to 2023 across four cohorts in Europe and Canada were analyzed, with 2906 long non-coding RNAs profiled using targeted sequencing.