Comparison of Dextran Perfusion and GSI-B4 Isolectin Staining in a Mouse Model of Oxygen-induced Retinopathy

Purpose: Oxygen-induced retinopathy(OIR) is a robust and widely used animal model for the study of retinal neovascularization(NV). Dextran perfusion and Griffonia simplicifolia isolectin B4(GSI-B4) staining are two common methods for examining the occurrence and extent of OIR. This study provides a quantitative comparison of the two for OIR detection.Methods: At postnatal day 7(PN7), fifteen C57 BL / 6J mice were exposed to a 75% hyperoxic condition for 5 days and then returned to room air conditions. At PN17, the mice received intravitreal injection of GSI-B4 Alexa Fluor 568 conjugate. After 10 hours, they were infused with FITC-dextran conjugate via the left ventricle. Retinal flat mounts were photographed by confocal microscopy. Areas with fluorescent signals and the total retinal areas were quantified by Image J software.Results:Both GSI-B4 and dextran detected the peripheral neovascular area. The mean hyper fluorescence area was 0.33 ±0.14% of whole retinal area determined by GSI-B4 staining and 0.25±0.28% determined by dextran perfusion. The difference between the two measures was 0.08%(95% CI:-0.59%,0.43%)..The Pearson correlation coefficient between the two methods was 0.386,P =0.035..The mean coincidence rates were 14.3 ±13.4% and 24.9 ±18.5% for GSI-B4 and dextran staining, respectively.Conclusion:.Both methods can complement each other indemonstrating and quantitatively evaluating retinal NV. A poor agreement was found between the two methods;.GSI-B4 isolectin was more effective than FITC-dextran perfusion in evaluating the extent of retinal NV in a mouse model of OIR.

Assessment of disability weights at the provincial and city levels based on 93,254 respondents in Fujian,China:Findings from the Fujian disability weight measurement study

To the Editor:Disability weights(DWs)are essential factors to quantify health losses relating to non-fatal outcomes for estimates of disability-adjusted life years(DALYs).Although national and subnational sets of DWs were published recently in the Chinese mainland,[1]data from only 4925 participants who responded to population health equivalence(PHE)questions used to anchor the 0-1 DWs scale did not include participants from Fujian province.Moreover,it remains unknown whether people living in different cities share the same DWs.This study provided an alternative approach using non-parametric regression to locate the DWs scale,as used in European surveys,[2]aimed at measuring DWs at the provincial and subprovincial levels in Fujian.

Advancing Underlying Cause of Death Inference Through Wide and Deep Model

Introduction:Accurately filling out death certificates is essential for death surveillance.However,manually determining the underlying cause of death is often imprecise.In this study,we investigate the Wide and Deep framework as a method to improve the accuracy and reliability of inferring the underlying cause of death.Methods:Death report data from national-level cause of death surveillance sites in Fujian Province from 2016 to 2022,involving 403,547 deaths,were analyzed.The Wide and Deep embedded with Convolutional Neural Networks(CNN)was developed.Model performance was assessed using weighted accuracy,weighted precision,weighted recall,and weighted area under the curve(AUC).A comparison was made with XGBoost,CNN,Gated Recurrent Unit(GRU),Transformer,and GRU with Attention.Results:The Wide and Deep achieved strong performance metrics on the test set:precision of 95.75%,recall of 92.08%,F1 Score of 93.78%,and an AUC of 95.99%.The model also displayed specific F1 Scores for different cause-of-death chain lengths:97.13%for single causes,95.08%for double causes,91.24%for triple causes,and 79.50%for quadruple causes.Conclusions:The Wide and Deep significantly enhances the ability to determine the root causes of death,providing a valuable tool for improving causeof-death surveillance quality.Integrating artificial intelligence(AI)in this field is anticipated to streamline death registration and reporting procedures,thereby boosting the precision of public health data.