Protocol to extract tear fluid for proteomics using Schirmer strips
Bibliographic record
Abstract
Schirmer strips are widely regarded as the gold standard for tear fluid collection. However, their use presents several challenges for proteomic analysis. Here, we present a protocol for extracting tear proteins from Schirmer strips. We describe steps for acquisition and handling of strips, extraction buffer preparation, strip preparation, and protein extraction. This protocol is designed to improve protein yield and facilitate proteomic workflows and is adaptable for various protein-based studies, particularly in the context of ocular disease research and diagnostics. • Protocol for quantifying tear volume for proteomic analysis using Schirmer strips • Procedures for protein extraction through a diffusion-based workflow • Guidance on optimizing for high-yield protein recovery and minimizing protein loss Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics. Schirmer strips are widely regarded as the gold standard for tear fluid collection. However, their use presents several challenges for proteomic analysis. Here, we present a protocol for extracting tear proteins from Schirmer strips. We describe steps for acquisition and handling of strips, extraction buffer preparation, strip preparation, and protein extraction. This protocol is designed to improve protein yield and facilitate proteomic workflows and is adaptable for various protein-based studies, particularly in the context of ocular disease research and diagnostics.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.044 | 0.043 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".