Engineered biomimetic cisplatin-polyphenol nanocomplex for chemo-immunotherapy of glioblastoma by inducing pyroptosis.

Glioblastoma multiforme (GBM) is characterized by pronounced immune escape and resistance to chemotherapy-induced apoptosis. Preliminary investigations revealed a marked overexpression of gasdermin E (GSDME) in GBM. Notably, cisplatin (CDDP) demonstrated a capacity of inducing pyroptosis by activating caspase-3 to cleave GSDME, coupled with the release of proinflammatory factors, indicating the potential as a viable approach of inducing anti-tumor immune activation.

Voices of Nanomedicine: Blueprint Guidelines for Collaboration in Addressing Global Unmet Medical Needs.

The "" under this Perspective underline the importance of interdisciplinary collaboration and partnerships across several disciplines, such as medical science and technology, medicine, bioengineering, and computational approaches, in bridging the gap between research, manufacturing, and clinical applications. Effective communication is key to bridging team gaps, enhancing trust, and resolving conflicts, thereby fostering teamwork and individual growth toward shared goals. Drawing from the success of the COVID-19 vaccine development, we advocate the application of similar collaborative models in other complex health areas such as nanomedicine and biomedical engineering.

Establishing vocabulary tests as a benchmark for evaluating large language models.

Vocabulary tests, once a cornerstone of language modeling evaluation, have been largely overlooked in the current landscape of Large Language Models (LLMs) like Llama 2, Mistral, and GPT. While most LLM evaluation benchmarks focus on specific tasks or domain-specific knowledge, they often neglect the fundamental linguistic aspects of language understanding. In this paper, we advocate for the revival of vocabulary tests as a valuable tool for assessing LLM performance.

Using large language models to estimate features of multi-word expressions: Concreteness, valence, arousal.

This study investigates the potential of large language models (LLMs) to provide accurate estimates of concreteness, valence, and arousal for multi-word expressions. Unlike previous artificial intelligence (AI) methods, LLMs can capture the nuanced meanings of multi-word expressions. We systematically evaluated GPT-4o's ability to predict concreteness, valence, and arousal.

AI-generated estimates of familiarity, concreteness, valence, and arousal for over 100,000 Spanish words.

This study investigates whether estimates of familiarity, valence, arousal, and concreteness based on artificial intelligence (AI) are useful alternatives to word counts and human ratings in Spanish. We replicate and extend previous findings in English and show that GPT-4o is effective in estimating these word features. Validity checks even suggest that AI-generated estimates sometimes outperform traditional measurements.