ss of wound healing after administration ofmitomycin-C, which inhibits granulation tissue formationand collagen synthesis, resulting in chronic wounds. Thevitreous gel of cow eyeballs contains a high level ofhyaluronic acid, which has a role in inflammation,granulation, re-epithelialization, and remodelling. This studyaims to understand the effect of 1% povidone iodine andvitreous gel of cow eyeballs on wound healing afteradministration of mitomycin-C.Methods: This was an invivostudy with quasi-experimentalmethods on 32 Wistar mice. Full-thickness wounds weremade and then treated with mitomicyn-C. The mice weredivided into 4 groups: a control group with NaCl 0.9%vitreous gel of cow eyeball (VGCE), 1% povidone-iodine, anda combination of VGCE and 1% povidone-iodine groups.Macroscopic and microscopic observations of the processof wound healing were performed on days 3, 7, and 14.Results: Vitreous gel administration produced significantwound healing rates within the first three days, andhistological analysis revealed an increased number offibroblasts and polymorphonuclear cells. However, thepovidone iodine group and the combination group withvitreous gel did not produce significant results.Conclusion: The single administration of VGCE canaccelerate the wound healing process, increase the numberof fibroblasts, and reduce inflammation in a chronic woundmodel.

Objectives: Disability weights require regular updates, as they are influenced by both diseases and societal perceptions. Consequently, it is necessary to develop an up-to-date list of the causes of diseases and establish a survey panel for estimating disability weights. Accordingly, this study was conducted to calculate, assess, modify, and validate disability weights suitable for Korea, accounting for its cultural and social characteristics. Methods: The 380 causes of disease used in the survey were derived from the 2019 Global Burden of Disease Collaborative Network and from 2019 and 2020 Korean studies on disability weights for causes of disease. Disability weights were reanalyzed by integrating the findings of an earlier survey on disability weights in Korea with those of the additional survey conducted in this study. The responses were transformed into paired comparisons and analyzed using probit regression analysis. Coefficients for the causes of disease were converted into predicted probabilities, and disability weights in 2 models (model 1 and 2) were rescaled using a normal distribution and the natural logarithm, respectively. Results: The mean values for the 380 causes of disease in models 1 and 2 were 0.488 and 0.369, respectively. Both models exhibited the same order of disability weights. The disability weights for the 300 causes of disease present in both the current and 2019 studies demonstrated a Pearson correlation coefficient of 0.994 (p=0.001 for both models). This study presents a detailed add-on approach for calculating disability weights. Conclusions This method can be employed in other countries to obtain timely disability weight estimations.