Brain-derived textiloma post glioblastoma resection and application of oxidized regenerated cellulose: A pilot, bedside-to-bench, translational study.

Oxidized regenerated cellulose (ORC; marketed as Surgicel® and Tabotamp®) is routinely used as an intraoperative hemostatic agent. Rarely, residual ORC has been associated with a foreign body reaction generating cystic or granulomatous lesions (i.e.

Prediction of peptide structural conformations with AlphaFold2.

Protein structure prediction artificial intelligence/machine learning (AI/ML) approaches has sparked substantial research interest in structural biology and adjacent disciplines. More recently, AlphaFold2 (AF2) has been adapted for the prediction of multiple structural conformations in addition to single-state structures. This novel avenue of research has focused on proteins (typically 50 residues in length or greater), while multi-conformation prediction of shorter peptides has not yet been explored in this context.

Generative artificial intelligence performs rudimentary structural biology modeling.

Natural language-based generative artificial intelligence (AI) has become increasingly prevalent in scientific research. Intriguingly, capabilities of generative pre-trained transformer (GPT) language models beyond the scope of natural language tasks have recently been identified. Here we explored how GPT-4 might be able to perform rudimentary structural biology modeling.

Generative artificial intelligence performs rudimentary structural biology modeling.

Natural language-based generative artificial intelligence (AI) has become increasingly prevalent in scientific research. Intriguingly, capabilities of generative pre-trained transformer (GPT) language models beyond the scope of natural language tasks have recently been identified. Here we explored how GPT-4 might be able to perform rudimentary structural biology modeling.

Mathematical modeling of cancer immunotherapy for personalized clinical translation.

Encouraging advances are being made in cancer immunotherapy modeling, especially in the key areas of developing personalized treatment strategies based on individual patient parameters, predicting treatment outcomes and optimizing immunotherapy synergy when used in combination with other treatment approaches. Here we present a focused review of the most recent mathematical modeling work on cancer immunotherapy with a focus on clinical translatability. It can be seen that this field is transitioning from pure basic science to applications that can make impactful differences in patients' lives.