Deep CRISPR mutagenesis characterizes the functional diversity of TP53 mutations.

The mutational landscape of TP53, a tumor suppressor mutated in about half of all cancers, includes over 2,000 known missense mutations. To fully leverage TP53 mutation status for personalized medicine, a thorough understanding of the functional diversity of these mutations is essential. We conducted a deep mutational scan using saturation genome editing with CRISPR-mediated homology-directed repair to engineer 9,225 TP53 variants in cancer cells.

Automatic classification of fungal-fungal interactions using deep leaning models.

Fungi provide valuable solutions for diverse biotechnological applications, such as enzymes in the food industry, bioactive metabolites for healthcare, and biocontrol organisms in agriculture. Current workflows for identifying new biocontrol fungi often rely on subjective visual observations of strains' performance in microbe-microbe interaction studies, making the process time-consuming and difficult to reproduce. To overcome these challenges, we developed an AI-automated image classification approach using machine learning algorithm based on deep neural network.

Predictors of outcome in patients with cerebral palsy following selective dorsal rhizotomy.

Background: To identify outcome predictors of selective dorsal rhizotomy (SDR) in children with spastic cerebral palsy (CP) using logistic regression models.

Methods: A retrospective single-center study was conducted on children with spastic CP who had undergone SDR. Two outcomes were defined, one representing children not improving in motor function and spasticity and one representing children improving in motor function two years after surgery.