CPR related injuries of the chest wall: direct and indirect fractures.

Background: Rib and sternum fractures are common injuries associated with cardiopulmonary resuscitation (CPR). The fracture mechanism is either direct by application of force on sternum and anterior ribs or indirect by bending through compression of the thorax. The aim of this study was to determine morphologies of rib fractures after CPR and to reevaluate prior findings on fracture localisation, type and degree of dislocation.

Do we have to redefine type B-fractures of the rib cartilage?

Objectives: Aim of this work was the evaluation and validation of the AO/OTA classification of the anterior chest wall, here especially for the rib cartilage.

Methods: Study design was a retrospective analysis of patients who were hospitalized with fractures of the thoracic wall in the years 2010-2016. This resulted in a collective of n = 124 patients.

Assessing novelty, feasibility and value of creative ideas with an unsupervised approach using GPT-4.

Creativity is defined by three key factors: novelty, feasibility and value. While many creativity tests focus primarily on novelty, they often neglect feasibility and value, thereby limiting their reflection of real-world creativity. In this study, we employ GPT-4, a large language model, to assess these three dimensions in a Japanese-language Alternative Uses Test (AUT).

Skin treatment with non-thermal plasma modulates the immune system through miR-223-3p and its target genes.

Non-thermal plasma, a partially ionized gas, holds significant potential for clinical applications, including wound-healing support, oral therapies, and anti-tumour treatments. While its applications showed promising outcomes, the underlying molecular mechanisms remain incompletely understood. We thus apply non-thermal plasma to mouse auricular skin and conducted non-coding RNA sequencing, as well as single-cell blood sequencing.

Mibianto: ultra-efficient online microbiome analysis through k-mer based metagenomics.

Quantifying microbiome species and composition from metagenomic assays is often challenging due to its time-consuming nature and computational complexity. In Bioinformatics, k-mer-based approaches were long established to expedite the analysis of large sequencing data and are now widely used to annotate metagenomic data. We make use of k-mer counting techniques for efficient and accurate compositional analysis of microbiota from whole metagenome sequencing.