Objective To observe the effect of high glucose on the expression of activating transcription factor 4 (ATF4) in cultured retinal Muuml;ller glia cells. Methods The retinal tissue of Sprague-Dawley (SD) rats was collected, and Muuml;ller cells were isolated and cultured. The glial fibrillary acidic protein (GFAP) and glutamine synthetase (GS) of Muuml;ller cells were identified by streptavidin-biotin-peroxidase complex. Cultured rat Muuml;ller cells were divided into control group (5.5 mmol/L glucose), group A (20 mmol/L glucose), group B (30 mmol/L glucose) and group C (40 mmol/L glucose). ATF4 protein expressions in Muuml;ller cells of four groups were measured by Western blot four days after cultured. Results GFAP and GS expressed in more than 95% of Muuml;ller cells. Over 95% of Muuml;ller cells of group A, B and C were positive for GFAP and GS. Western blots indicated that ATF4 protein in group A, B and C increased obviously compared with the control group (q=0.293, 0.754,0.484;P＜0.05). Conclusion High glucose can increase the expression of ATF4 protein and cause endoplasmic reticulum stress in retinal Muuml;ller glia cells in vitro.
Objective Methods of evidence-based medicine were used to make an individualized treatment plan concerning newly diagnosed open-angle glaucoma patient. Methods After clinical problems were put forward, evidence was collected from Cochrane Library (Issue 4, 2009), PubMed (1990 -2009), MEDLINE (1990-2009), EMbase (1990-2009), CBM (1990-2009), and CNKI (1990-2009) according to the search strategy. Subject words were open-angle glaucoma, timolol, latanoprost, trabeculectomy, intraocular pressure, randomized controlled trials, human, meta-analysis, systematic review. Results A total of 221 randomized controlled trials, and 19 systematic reviews were identified. A rational treatment plan was made upon a serious evaluation of the data. After one year follow-up, the plan was proved optimal. Conclusion The treatment efficacy in newly diagnosed open-angle glaucoma has been improved by determining an individualized treatment plan according to evidence-based methods.
Objective To systematically review the accuracy and consistency of large language models (LLMs) in assessing risk of bias in analytical studies. Methods The cohort and case-control studies related to COVID-19 based on the team's published systematic review of clinical characteristics of COVID-19 were included. Two researchers independently screened the studies, extracted data, and assessed risk of bias of the included studies with the LLM-based BiasBee model (version Non-RCT) used for automated evaluation. Kappa statistics and score differences were used to analyze the agreement between LLM and human evaluations, with subgroup analysis for Chinese and English studies. Results A total of 210 studies were included. Meta-analysis showed that LLM scores were generally higher than those of human evaluators, particularly in representativeness of exposed cohorts (△=0.764) and selection of external controls (△=0.109). Kappa analysis indicated slight agreement in items such as exposure assessment (κ=0.059) and adequacy of follow-up (κ=0.093), while showing significant discrepancies in more subjective items, such as control selection (κ=−0.112) and non-response rate (κ=−0.115). Subgroup analysis revealed higher scoring consistency for LLMs in English-language studies compared to that of Chinese-language studies. Conclusion LLMs demonstrate potential in risk of bias assessment; however, notable differences remain in more subjective tasks. Future research should focus on optimizing prompt engineering and model fine-tuning to enhance LLM accuracy and consistency in complex tasks.