ArticleBiosensors2023
Detection of Advanced Glycosylation End Products in the Cornea Based on Molecular Fluorescence and Machine Learning.
Article in Biosensors, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it, 4 citations in OpenAlex.
- Advanced Glycation End Products and Health: A Systematic Review.Annals of biomedical engineering · 2024Pooled it
- Pigment-Resistant, Portable Corneal Fluorescence Device for Non-Invasive AGEs Monitoring in Diabetes.Biosensors · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
Advanced glycosylation end products (AGEs) are continuously produced and accumulated in the bodies of diabetic patients. To effectively predict disease trends in diabetic patients, a corneal fluorescence detection device was designed based on the autofluorescence properties of AGEs, and corneal fluorescence measurements were performed on 83 volunteers. Multiple linear regression (MLR), extreme gradient boosting (XGBoost), support vector regression (SVR), and back-propagation neural network (BPNN) were used to predict the human AGE content. Physiological parameters which may affect corneal AGE content were collected for a correlation analysis to select the features that had a strong correlation with the corneal concentration of AGEs to participate in modeling. By comparing the predictive effects of the four models in the two cases of a single-input feature and a multi-input feature, it was found that the model with the single-input feature had a better predictive effect. In this case, corneal AGE content was predicted by a single-input SVR model, with the average error rate (AER), mean square error (MSE), and determination coefficient R-squared (
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.