Evidence map›Paper›PMID 41540914›Full record

ArticleDatabase : the journal of biological databases and curation2026

Tear fluid database: a reference website for tear fluid proteomics.

Drew Mayernik, Saleh Ahmed, Eliza Williams, Tae Jin Lee, Amy Estes, Pamela Martin, Wenbo Zhi, Vishal Jhanji, Shruti Sharma, Ashok Sharma

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Database : the journal of biological databases and curation, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Drew MayernikCenter for Biotechnology and Genomic Medicine, Medical College of Georgia, Augusta University, 1120 15th Street, Augusta, GA 30912, USA.
Saleh AhmedCenter for Biotechnology and Genomic Medicine, Medical College of Georgia, Augusta University, 1120 15th Street, Augusta, GA 30912, USA.
Eliza WilliamsCenter for Biotechnology and Genomic Medicine, Medical College of Georgia, Augusta University, 1120 15th Street, Augusta, GA 30912, USA.
Tae Jin LeeCenter for Biotechnology and Genomic Medicine, Medical College of Georgia, Augusta University, 1120 15th Street, Augusta, GA 30912, USA.
Amy EstesDepartment of Ophthalmology, Medical College of Georgia, Augusta University, 1120 15th Street, Augusta, GA 30912, USA.
Pamela MartinDepartment of Ophthalmology, Medical College of Georgia, Augusta University, 1120 15th Street, Augusta, GA 30912, USA.
Wenbo ZhiCenter for Biotechnology and Genomic Medicine, Medical College of Georgia, Augusta University, 1120 15th Street, Augusta, GA 30912, USA.
Vishal JhanjiDepartment of Ophthalmology, University of Pittsburgh School of Medicine, 1622 Locust Street, Pittsburgh, PA 15219, United States.
Shruti SharmaCenter for Biotechnology and Genomic Medicine, Medical College of Georgia, Augusta University, 1120 15th Street, Augusta, GA 30912, USA.
Ashok SharmaCenter for Biotechnology and Genomic Medicine, Medical College of Georgia, Augusta University, 1120 15th Street, Augusta, GA 30912, USA.ORCID 0000-0001-9597-4374

Funding

Sex-specific Influences on tear microRNAs in dry eye diseaseR01EY036114 · NEI · AUGUSTA UNIVERSITY · PI Ashok Sharma, Shruti Sharma · 2024 to 2026
$1.6M
NEI NIH HHS R01 EY036114NIH HHS R01 EY036114
6 · The paper itself

Abstract

Tear fluid is a clinically accessible, minimally invasive biofluid with a complex and dynamic proteome. Molecular alterations in tear composition have been linked to a broad spectrum of ocular and systemic diseases; however, the small volume of tear samples presents substantial challenges for obtaining high-quality proteomic data. To overcome this limitation, we developed a highly sensitive mass spectrometry workflow capable of identifying more than 1,000 proteins from individual tear samples. Applying this workflow to a large and diverse cohort, we generated a representative and comprehensive profile of the human tear fluid proteome and established reference abundance ranges for proteins commonly detected in tear fluid. In parallel with protein quantification, we collected detailed clinical annotations for each participant. As the database continues to grow, these analyses will increasingly support the identification of disease-associated proteomic signatures, deepen our understanding of underlying biological mechanisms, and accelerate the discovery of clinically relevant biomarkers. To make these data broadly accessible, we created a user-friendly website for exploring protein measurements alongside associated clinical metadata. The current release includes proteomic profiles from 74 human tear samples, encompassing 2,134 unique proteins. The TearFluid Database serves as a foundational resource for biomarker discovery, comparative proteomics, and systems-level investigations in tear biology, offering the scientific community a robust and expandable platform for advancing tear fluid proteomics research. Database URL: https://tearfluid.org/.

Indexed as

Databases, ProteinEye ProteinsInternetProteomeProteomicsTearsHumansEye ProteinsProteome

Identifiers

PMID41540914
PMCPMC12808846

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.