Role of Serine Protease Inhibitors A1 and A3 in Ocular Pathologies.

Serine protease inhibitors A1 (SerpinA1) and A3 (SerpinA3) are important members of the serpin family, playing crucial roles in the regulation of serine proteases and influencing various physiological processes. SerpinA1, also known as α-1-antitrypsin, is a versatile glycoprotein predominantly synthesized in the liver, with additional production in inflammatory and epithelial cell types. It exhibits multifaceted functions, including immune modulation, complement activation regulation, and inhibition of endothelial cell apoptosis. SerpinA3, also known as α-1-antichymotrypsin, is expressed both extracellularly and intracellularly in various tissues, particularly in the retina, kidney, liver, and pancreas. It exerts anti-inflammatory, anti-angiogenic, antioxidant, and antifibrotic activities. Both SerpinA1 and SerpinA3 have been implicated in conditions such as keratitis, diabetic retinopathy, age-related macular degeneration, glaucoma, cataracts, dry eye disease, keratoconus, uveitis, and pterygium. Their role in influencing metalloproteinases and cytokines, as well as endothelial permeability, and their protective effects on Müller cells against oxidative stress further highlight their diverse and critical roles in ocular pathologies. This review provides a comprehensive overview of the etiology and functions of SerpinA1 and SerpinA3 in ocular diseases, emphasizing their multifaceted roles and the complexity of their interactions within the ocular microenvironment.

Evaluating the Artificial Intelligence Performance Growth in Ophthalmic Knowledge.

OBJECTIVE: We aim to compare the capabilities of Chat Generative Pre-Trained Transformer (ChatGPT)-3.5 and ChatGPT-4.0 (OpenAI, San Francisco, CA, USA) in addressing multiple-choice ophthalmic case challenges. METHODS AND ANALYSIS: Both models' accuracy was compared across different ophthalmology subspecialties using multiple-choice ophthalmic clinical cases provided by the American Academy of Ophthalmology (AAO) "Diagnosis This" questions. Additional analysis was based on image content, question difficulty, character length of models' responses, and model's alignment with responses from human respondents. χ2 test, Fisher's exact test, Student's t-test, and one-way analysis of variance (ANOVA) were conducted where appropriate, with p<0.05 considered significant. RESULTS: GPT-4.0 significantly outperformed GPT-3.5 (75% versus 46%, p<0.01), with the most noticeable improvement in neuro-ophthalmology (100% versus 38%, p=0.03). While both models struggled with uveitis and refractive questions, GPT-4.0 excelled in other areas, such as pediatric questions (82%). In image-related questions, GPT-4.0 also displayed superior accuracy that trended toward significance (73% versus 46%, p=0.07). GPT-4.0 performed better with easier questions (93.8% (least difficult) versus 76.2% (middle) versus 53.3% (most), p=0.03) and generated more concise answers than GPT-3.5 (651.7±342.9 versus 1,112.9±328.8 characters, p<0.01). Moreover, GPT-4.0's answers were more in line with those of AAO respondents (57.3% versus 41.4%, p<0.01), showing a strong correlation between its accuracy and the proportion of AAO respondents who selected GPT-4.0's answer (ρ=0.713, p<0.01). CONCLUSION AND RELEVANCE: Our study demonstrated that GPT-4.0 significantly outperforms GPT-3.5 in addressing ophthalmic case challenges, especially in neuro-ophthalmology, with improved accuracy even in image-related questions. These findings underscore the potential of advancing artificial intelligence (AI) models in enhancing ophthalmic diagnostics and medical education.

Assessment of Dentist Participation in Public Insurance Programs for Children in the US.

Importance: Evaluating the availability of dentists to provide dental care services to children is important for identifying interventions for improving access. Objective: To assess dental care availability for children in the US by public insurance participation, rural-urban setting, and dentist taxonomy (general, pediatric, or specialized). Design, Setting, and Participants: This cross-sectional study analyzed the availability of dentists from matching 3 data sets: the 2020 National Plan and Provider Enumeration System, the 2019-2020 State Board of Dentistry information acquired from each state, and the 2019 InsureKidsNow.org database. Data on active dentists in most states (including the District of Columbia [combined hereinafter with states] and excluding Hawaii and Washington) were included in the analysis. The study was conducted from January 2019 to March 2022. Main Outcomes and Measures: The number and percentage of dentists participating in public insurance programs (Medicaid and/or Children's Health Insurance Program [CHIP]) were aggregated at the dental office and stratified by the rurality of their practice and taxonomy. State-level comparisons were derived between this study and reports from the Health Policy Institute of the American Dental Association, along with maps and summary statistics disseminated through a data portal and state reports. Results: Among 204 279 active dentists, participation in public insurance varied widely across states, especially for the states that manage the Medicaid and CHIP programs separately. Participation rates in Medicaid and CHIP varied substantially from those of the Health Policy Institute of the American Dental Association. Participation in Medicaid and CHIP was lowest among urban dentists (Medicaid, 26%; CHIP, 29%) and highest among rural dentists (Medicaid, 39%; CHIP, 40%), while urban dentists accounted for most of the dentist population (urban, 84%; rural, 5%). Similarly, participation in Medicaid and CHIP was substantially lower among general dentists (Medicaid, 28%; CHIP, 29%) vs pediatric dentists (57% in both programs), while each state's dentist population consisted of notably more general (84%) than pediatric (3%) dentists. Nearly half of the states revealed wide variations in Medicaid and CHIP participation between counties, ranging from no participation (21 states) to full participation (22 states). Conclusions and Relevance: The findings of this study suggest that disparities in the availability of dentists for pediatric dental care are extensive, particularly for Medicaid- and CHIP-insured children, those living in rural communities, and those receiving specialized care. Lack of dentist availability for Medicaid- and CHIP-insured children appears to deter access to receiving dental care.