Complexed hyaluronic acid-based nanoparticles in cancer therapy and diagnosis: Research trends by natural language processing.

Hyaluronic acid (HA) is a popular surface modifier in targeted cancer delivery due to its receptor-binding abilities. However, HA alone faces limitations in lipid solubility, biocompatibility, and cell internalization, making it less effective as a standalone delivery system. This comprehensive study aimed to explore a dynamic landscape of complexation in HA-based nanoparticles in cancer therapy, examining diverse aspects from influential modifiers to emerging trends in cancer diagnostics. We discovered that certain active substances, such as 5-aminolevulinic acid, adamantane, and protamine, have been on trend in terms of their usage over the past decade. Dextran, streptavidin, and catechol emerge as intriguing conjugates for HA, coupled with nanostar, quantum dots, and nanoprobe structures for optimal drug delivery and diagnostics. Strategies like hypoxic conditioning, dual responsiveness, and pulse laser activation enhance controlled release, targeted delivery, and real-time diagnostic techniques like ultrasound imaging and X-ray computed tomography (X-ray CT). Based on our findings, conventional bibliometric tools fail to highlight relevant topics in this area, instead producing merely abstract and broad-meaning keywords. Extraction using Named Entity Recognition and topic search with Latent Dirichlet Allocation successfully revealed five representative topics with the ability to exclude irrelevant keywords. A shift in research focuses from optimizing chemical toxicity to particular targeting tactics and precise release mechanisms is evident. These findings reflect the dynamic landscape of HA-based nanoparticle research in cancer therapy, emphasizing advancements in targeted drug delivery, therapeutic efficacy, and multimodal diagnostic approaches to improve overall patient outcomes.