TCR and CD3 antibody cross‐reactivity in 44 species
Bibliographic record
Abstract
The production of monoclonal antibodies is very costly, and antibodies are only available for a limited number of species. Until a more cost effective method of antibody production is found, identification of cross-reactive antibodies is an alternative approach that can provide investigators studying immunity in minor species with valuable antibody reagents. Flow cytometry was used to test 21 monoclonal antibodies (mAb), raised against alphabeta and gammadelta T cell receptors and CD3 from human and five animal species, for cross-reactivity in 44 different species including 16 species of nonhuman primates, marsupials, carnivores, lagomorphs, rodents, ruminants, swine, cetacean, horse, birds, a reptile, and fish. Fifteen of the mAbs cross-reacted with orthologous molecules in one or more species. Two antibodies, anti-human TCR gammadelta (B1.1), and anti-human CD3 (SP34) were found to costain in 13 species of nonhuman primates. This study has identified valuable new reagents for studying T cell populations in different animal species and for the first time characterized antibodies useful for studying gammadelta T cell populations in many species of primates. These antibodies may be used for further immunity research in species with less well-characterized immune systems.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".