➢ Natural language processing with large language models is a subdivision of artificial intelligence (AI) that extracts meaning from text with use of linguistic rules, statistics, and machine learning to generate appropriate text responses. Its utilization in medicine and in the field of orthopaedic surgery is rapidly growing. ➢ Large language models can be utilized in generating scientific manuscript texts of a publishable quality; however, they suffer from AI hallucinations, in which untruths or half-truths are stated with misleading confidence. Their use raises considerable concerns regarding the potential for research misconduct and for hallucinations to insert misinformation into the clinical literature. ➢ Current editorial processes are insufficient for identifying the involvement of large language models in manuscripts. Academic publishing must adapt to encourage safe use of these tools by establishing clear guidelines for their use, which should be adopted across the orthopaedic literature, and by implementing additional steps in the editorial screening process to identify the use of these tools in submitted manuscripts.

Emerging evidence suggests that extracellular vesicles (EVs) play an essential role in mediating intercellular communication and inter-organ crosstalk both at normal physiological conditions and in the pathogenesis of human diseases. EV cargos are made up of a broad spectrum of molecules including lipids, proteins, and nucleic acids such as DNA, RNA, and microRNAs. The complex EV cargo composition is cell type-specific. A dynamic change in EV cargos occurs along with extracellular stimuli and a change in the pathophysiological status of the host. Currently, the underlying mechanisms by which EVs are formed and EV cargos are selected in the absence and presence of noxious stimuli and pathogens remain incompletely explored. The term EVs refers to a heterogeneous group of vesicles generated via different mechanisms. Some EVs are formed via direct membrane budding, while the others are produced through multivesicular bodies (MVBs) or during apoptosis. Despite the complexity of EV formation and EV cargo selection, recent studies suggest that caveolin-1, a well-known structural protein of caveolae, regulates the formation and cargo selection of some EVs, such as microvesicles (MVs). In this article, we will review the current understanding of this emerging and novel role of cav-1.

Objective: This study aims to evaluate ChatGPT’s performance in addressing real-world otolaryngology patient questions, focusing on accuracy, comprehensiveness, and patient safety, to assess its suitability for integration into healthcare. Methods: A cross-sectional study was conducted using patient questions from the public online forum Reddit’s r/AskDocs, where medical advice is sought from healthcare professionals. Patient questions were input into ChatGPT (GPT-3.5), and responses were reviewed by 5 board-certified otolaryngologists. The evaluation criteria included difficulty, accuracy, comprehensiveness, and bedside manner/empathy. Statistical analysis explored the relationship between patient question characteristics and ChatGPT response scores. Potentially dangerous responses were also identified. Results: Patient questions averaged 224.93 words, while ChatGPT responses were longer at 414.93 words. The accuracy scores for ChatGPT responses were 3.76/5, comprehensiveness scores were 3.59/5, and bedside manner/empathy scores were 4.28/5. Longer patient questions did not correlate with higher response ratings. However, longer ChatGPT responses scored higher in bedside manner/empathy. Higher question difficulty correlated with lower comprehensiveness. Five responses were flagged as potentially dangerous. Conclusion: While ChatGPT exhibits promise in addressing otolaryngology patient questions, this study demonstrates its limitations, particularly in accuracy and comprehensiveness. The identification of potentially dangerous responses underscores the need for a cautious approach to AI in medical advice. Responsible integration of AI into healthcare necessitates thorough assessments of model performance and ethical considerations for patient safety.

Emerging evidence suggests that Gram-negative bacteria release bacterial outer membrane vesicles (OMVs) and that these play an important role in the pathogenesis of bacterial infection-mediated inflammatory responses and organ damage. Despite the fact that scattered reports have shown that OMVs released from Gram-negative bacteria may function via the TLR2/4-signaling pathway or induce pyroptosis in macrophages, our study reveals a more complex role of OMVs in the development of inflammatory lung responses and macrophage pro-inflammatory activation. We first confirmed that various types of Gram-negative bacteria release similar OMVs which prompt pro-inflammatory activation in both bone marrow-derived macrophages and lung alveolar macrophages. We further demonstrated that mice treated with OMVs via intratracheal instillation developed significant inflammatory lung responses. Using mouse inflammation and autoimmune arrays, we identified multiple altered cytokine/chemokines in both bone marrow-derived macrophages and alveolar macrophages, suggesting that OMVs have a broader spectrum of function compared to LPS. Using TLR4 knock-out cells, we found that OMVs exert more robust effects on activating macrophages compared to LPS. We next examined multiple signaling pathways, including not only cell surface antigens, but also intracellular receptors. Our results confirmed that bacterial OMVs trigger both surface protein-mediated signaling and intracellular signaling pathways, such as the S100-A8 protein-mediated pathway. In summary, our studies confirm that bacterial OMVs strongly induced macrophage pro-inflammatory activation and inflammatory lung responses via multi-signaling pathways. Bacterial OMVs should be viewed as a repertoire of pathogen-associated molecular patterns (PAMPs), exerting more robust effects than Gram-negative bacteria-derived LPS.

Circular RNA (circRNA) is a type of single-stranded RNA that forms a covalently closed continuous loop, unlike linear RNA. The expression of circRNAs in mammals is often conserved across species and shows tissue and cell specificity. Some circRNA serve as gene regulators. However, the biological function of most circRNAs is unclear. CircRNA does not have 5′ or 3′ ends. The unique structure of circRNAs provides them with a much longer half-life and more resistance to RNase R than linear RNAs. Inflammatory lung responses occur in the pathogenesis and recovery of many lung diseases. Macrophages form the first line of host defense/innate immune responses and initiate/mediate lung inflammation. For example, in bacterial pneumonia, upon pro-inflammatory activation, they release early response cytokines/chemokines that recruit neutrophils, macrophages, and lymphocytes to sites of infection and clear pathogens. The functional effects and mechanisms by which circRNAs exert physiological or pathological roles in macrophage activation and lung inflammation remain poorly understood. In this article, we will review the current understanding and progress of circRNA biogenesis, regulation, secretion, and degradation. Furthermore, we will review the current reports on the role of circRNAs in macrophage activation and polarization, as well as in the process of inflammatory lung responses.