Preclinical characterization of endotoxin-induced uveitis models using OCT, PET/CT and proteomics.

Uveitis is a group of inflammatory ocular pathologies. Endotoxin-Induced Uveitis (EIU) model represent a well-known model induced by administration of Lipopolysaccharide (LPS). The aim is to characterize two models of EIU through two routes of administration with novel noninvasive imaging techniques. 29 rats underwent Intraocular Pressure (IOP) measurements, Optical Coherence Tomography (OCT), proteomic analysis, and Positron Emission Tomography and Computed Tomography (PET/CT). Groups included healthy controls (C), BSS administered controls (Ci), systemically induced EIU with LPS (LPSs), and intravitreally induced EIU with LPS (LPSi) for IOP, OCT, and proteomic studies. For 18F-FDG PET/CT study, animals were divided into FDG-C, FDG-LPSs and FDG-LPSi groups and scanned using a preclinical PET/CT system. LPSi animals exhibited higher IOP post-induction compared to C and LPSs groups. LPSi showed increased cellular infiltrate, fibrotic membranes, and iris inflammation. Proinflammatory proteins were more expressed in EIU models, especially LPSi. PET/CT indicated higher eye uptake in induced models compared to FDG-C. FDG-LPSi showed higher eye uptake than FDG-LPSs but systemic uptake was higher in FDG-LPSs due to generalized inflammation. OCT is valuable for anterior segment assessment in experimental models. 18F-FDG PET/CT shows promise as a noninvasive biomarker for ocular inflammatory diseases. Intravitreal induction leads to higher ocular inflammation. These findings offer insights for future inflammatory disease research and drug studies.

Are artificial intelligence chatbots a reliable source of information about contact lenses?

INTRODUCTION: Artificial Intelligence (AI) chatbots are able to explain complex concepts using plain language. The aim of this study was to assess the accuracy of three AI chatbots answering common questions related to contact lens (CL) wear. METHODS: Three open access AI chatbots were compared: Perplexity, Open Assistant and ChatGPT 3.5. Ten general CL questions were asked to all AI chatbots on the same day in two different countries, with the questions asked in Spanish from Spain and in English from the U.K. Two independent optometrists with experience working in each country assessed the accuracy of the answers provided. Also, the AI chatbots' responses were assessed if their outputs showed any bias towards (or against) any eye care professional (ECP). RESULTS: The answers obtained by the same AI chatbots were different in Spain and the U.K. Also, statistically significant differences were found between the AI chatbots for accuracy. In the U.K., ChatGPT 3.5 was the most and Open Assistant least accurate (p < 0.01). In Spain, Perplexity and ChatGPT were statistically more accurate than Open Assistant (p < 0.01). All the AI chatbots presented bias, except ChatGPT 3.5 in Spain. CONCLUSIONS: AI chatbots do not always consider local CL legislation, and their accuracy seems to be dependent on the language used to interact with them. Hence, at this time, although some AI chatbots might be a good source of information for general CL related questions, they cannot replace an ECP.