Comprehensive Overview of Computational Modeling and Artificial Intelligence in Pediatric Neurosurgery.

In this chapter, we give an overview of artificial intelligence tools and their use thus far in pediatric neurosurgery. We discuss different machine learning algorithms from a data-driven approach in order to guide clinicians and scientists as they apply them to real-world datasets. We provide examples of their successful application as well as evaluate limitations and pitfalls specific to clinical use.

Hospitalised COVID-19 outcomes are predicted by hypoxaemia and pneumonia phenotype irrespective of the timing of their emergence.

Unlabelled: Despite the known clinical importance of hypoxemia and pneumonia, there is a paucity of evidence for these variables with respect to risk of mortality and short-term outcomes among those hospitalised with COVID-19.

Objective: Describe the prevalence and clinical course of patients hospitalised with COVID-19 based on oxygenation and pneumonia status at presentation and determine the incidence of emergent hypoxaemia or radiographic pneumonia during admission.

Methods: A retrospective study was conducted using a Canadian regional registry.

Systematic Exploration in Tissue-Pathway Associations of Complex Traits Using Comprehensive eQTLs Catalog.

The collection of expression quantitative trait loci (eQTLs) is an important resource to study complex traits through understanding where and how transcriptional regulations are controlled by genetic variations in the non-coding regions of the genome. Previous studies have focused on associating eQTLs with traits to identify the roles of trait-related eQTLs and their corresponding target genes involved in trait determination. Since most genes function as a part of pathways in a systematic manner, it is crucial to explore the pathways' involvements in complex traits to test potentially novel hypotheses and to reveal underlying mechanisms of disease pathogenesis.