Systematic reviews (SR) synthesize evidence-based medical literature, but they involve labor-intensive manual article screening. Large language models (LLMs) can select relevant literature, but their quality and efficacy are still being determined compared to humans. We evaluated the overlap between title- and abstract-based selected articles of 18 different LLMs and human-selected articles for three SR.
View Article and Find Full Text PDFPersonalized medicine aims to tailor medical treatments to individual patients, and predicting drug responses from molecular profiles using machine learning is crucial for this goal. However, the high dimensionality of the molecular profiles compared to the limited number of samples presents significant challenges. Knowledge-based feature selection methods are particularly suitable for drug response prediction, as they leverage biological insights to reduce dimensionality and improve model interpretability.
View Article and Find Full Text PDFNon-healing bone defects are a pressing public health concern accounting for one main cause for decreased life expectancy and quality. An aging population accompanied with increasing incidence of comorbidities, foreshadows a worsening of this socio-economic problem. Conventional treatments for non-healing bone defects prove ineffective for 5%-10% of fractures.
View Article and Find Full Text PDFThe axolotl, known for its remarkable regenerative abilities, is an excellent model for studying regenerative therapies. Nevertheless, the precise molecular mechanisms governing its regenerative potential remain uncertain. In this study, we collected samples from axolotls of different ages, including 8-year-old individuals and 8-month-old juveniles, obtaining their blastemas 10 days after amputation.
View Article and Find Full Text PDF