ArticleBMC ophthalmology2013
Tear fluid proteomics multimarkers for diabetic retinopathy screening.
Article in BMC ophthalmology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
What it found
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The trial behind it
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Who cites it
31 citing papers in PubMed, 58 citations in OpenAlex.
- Nanosuspension-based Ocular Therapeutics for Infection Control: Integrating Mucin Interactions and Tear Film Dynamics.AAPS PharmSciTech · 2026Review
- Alterations in Tear Proteomes of Adults with Pre-Diabetes and Type 2 Diabetes Mellitus but Without Diabetic Retinopathy.Proteomes · 2025Article
- Tear-derived extracellular vesicles as diagnostic biomarkers for ocular and neurodegenerative diseases: opportunities and challenges.Extracellular vesicles and circulating nucleic acids · 2025Review
- New insights of potential biomarkers in diabetic retinopathy: integrated multi-omic analyses.Frontiers in endocrinology · 2025Review
- Retinopathy of Prematurity (ROP): An Overview of Biomarkers in Various Samples for Prediction, Diagnosis, and Prognosis.Clinical ophthalmology (Auckland, N.Z.) · 2025Review
- Wearable Sensors for Physiological Condition and Activity Monitoring.Small science · 2024Article
- Applications of artificial intelligence and bioinformatics methodologies in the analysis of ocular biofluid markers: a scoping review.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2024Article
- In vitro Assay to Examine the Function of Tears on Corneal Epithelial Cells.Bio-protocol · 2024Article
- Mass spectrometry-based tear proteomics for noninvasive biomarker discovery.Mass spectrometry reviews · 2022Review
- Colorimetric and Electrochemical Screening for Early Detection of Diabetes Mellitus and Diabetic Retinopathy-Application of Sensor Arrays and Machine Learning.Sensors (Basel, Switzerland) · 2022Review
- A Meta-Analysis of Wearable Contact Lenses for Medical Applications: Role of Electrospun Fiber for Drug Delivery.Polymers · 2022Review
- Metabolomics of various samples advancing biomarker discovery and pathogenesis elucidation for diabetic retinopathy.Frontiers in endocrinology · 2022Review
- Putative Biomarkers in Tears for Diabetic Retinopathy Diagnosis.Frontiers in medicine · 2022Article
- Comparison of VEGF level in tears post phacoemulsification between non-proliferative diabetic retinopathy and non-diabetic patients.Journal of diabetes and metabolic disorders · 2021Article
- Reduced Level of Tear Antimicrobial and Immunomodulatory Proteins as a Possible Reason for Higher Ocular Infections in Diabetic Patients.Pathogens (Basel, Switzerland) · 2021Article
- The acute phase response protein SERPINA3 is increased in tear fluid from the unaffected eyes of patients with unilateral acute anterior uveitis.Journal of ophthalmic inflammation and infection · 2021Article
- Machine Learning Techniques for Personalised Medicine Approaches in Immune-Mediated Chronic Inflammatory Diseases: Applications and Challenges.Frontiers in pharmacology · 2021Review
- Proteomic Analysis of Tear Film Obtained from Diabetic Dogs.Animals : an open access journal from MDPI · 2020Article
- Tear Proteomic Predictive Biomarker Model for Ocular Graft Versus Host Disease Classification.Translational vision science & technology · 2020Observational
- Proteomic Biomarkers of Retinal Inflammation in Diabetic Retinopathy.International journal of molecular sciences · 2019Review
Corrections and comments
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Authors and funding
12 authors at 3 institutions in 3 countries.
Funding
Abstract
backgroundThe aim of the project was to develop a novel method for diabetic retinopathy screening based on the examination of tear fluid biomarker changes. In order to evaluate the usability of protein biomarkers for pre-screening purposes several different approaches were used, including machine learning algorithms.
methodsAll persons involved in the study had diabetes. Diabetic retinopathy (DR) was diagnosed by capturing 7-field fundus images, evaluated by two independent ophthalmologists. 165 eyes were examined (from 119 patients), 55 were diagnosed healthy and 110 images showed signs of DR. Tear samples were taken from all eyes and state-of-the-art nano-HPLC coupled ESI-MS/MS mass spectrometry protein identification was performed on all samples. Applicability of protein biomarkers was evaluated by six different optimally parameterized machine learning algorithms: Support Vector Machine, Recursive Partitioning, Random Forest, Naive Bayes, Logistic Regression, K-Nearest Neighbor.
resultsOut of the six investigated machine learning algorithms the result of Recursive Partitioning proved to be the most accurate. The performance of the system realizing the above algorithm reached 74% sensitivity and 48% specificity.
conclusionsProtein biomarkers selected and classified with machine learning algorithms alone are at present not recommended for screening purposes because of low specificity and sensitivity values. This tool can be potentially used to improve the results of image processing methods as a complementary tool in automatic or semiautomatic systems.
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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.