Minimum information for reporting on the TEER (trans-epithelial/endothelial electrical resistance) assay (MIRTA)
Bibliographic record
Abstract
Standard information reporting helps to ensure that assay conditions and data are consistently reported and to facilitate inter-laboratory comparisons. Here, we present recommendations on minimum information for reporting on the TEER (trans-epithelial/endothelial electrical resistance) assay (MIRTA). The TEER assay is extensively used to evaluate the health of an epithelial/endothelial cell culture model and as an indicator of the potential toxicity of a test substance. This publication is the result of an international collaboration─called the RespTox (Respiratory Toxicity) Collaborative─through which twelve laboratories shared their protocols for assessing the barrier function of respiratory epithelial cells using the TEER assay following exposure to substances. The protocols from each laboratory were reviewed to identify general steps for performing the TEER assay, interlaboratory differences between steps, the rationale for differences, whether these differences impact results or cross-laboratory comparisons between TEER measurements. While the MIRTA recommendations are focused on respiratory epithelial cell systems, these recommendations can be adapted for other cell systems that form barriers. The use of these recommendations will support data transparency and reproducibility, reduce challenges in data interpretation, enable cross-laboratory comparisons, help assess study quality, and facilitate the incorporation of the TEER assay into national and international testing guidance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.081 | 0.117 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.012 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.009 | 0.004 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.019 | 0.019 |
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".