Biobanking of Human Aqueous and Vitreous Liquid Biopsies for Molecular Analyses
Bibliographic record
Abstract
A critical challenge in translational research is establishing a viable and efficient interface between patient care in the operating room (OR) and the research laboratory. Here, we developed a protocol for acquiring high-quality liquid biopsies for molecular analyses from the aqueous humor and the vitreous from patients undergoing eye surgery. In this workflow, a Mobile Operating Room Lab Interface (MORLI) cart equipped with a computer, a barcode scanner, and lab instruments, including onboard cold storage, is used to obtain and archive human biological samples. A web-based data privacy-compliant database enables annotating each sample over its lifetime, and a cartesian coordinate system allows tracking each barcoded specimen in storage, enabling quick and accurate retrieval of samples for downstream analyses. Molecular characterization of human tissue samples not only serves as a diagnostic tool (e.g., to distinguish between infectious endophthalmitis and other non-infectious intraocular inflammation) but also represents an important component of translational research, allowing the identification of new drug targets, development of new diagnostic tools, and personalized therapeutics.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".