Bibliographic record
Abstract
To the Editor The letter to the Editor, and the data in J. Immunol. Meth. 2007; 324: 74–83 from W. Schamel et al. suggest that one reason for the difference in calculating molecular weight of the TCR/CD3 complex ∼220 kDa versus the experimentally estimated one, ∼450 kDa, may be due to additional detergent micelle in the native complex. Our results were obtained using 1% Brij 98 detergent solubilized TCR/CD3 molecules and gel-filtration on Superose 6 HR. We used this detergent because it can be used with similar results at 4 and 37 °C (more physiological conditions) (personal communication with Dr H.T. He, CIML, Marseille, France), see also [1]. We have no arguments for or against the idea that detergent might increase the apparent molecular weight of the TCR/CD3 complexes. However, according to previous work from Schamel et al. [2], antigen-activation of T cells should aggregate TCR/CD3 complexes on the T cells. The main purpose of our gel-filtration experiments in Rubin et al. [3] was to compare apparent molecular weights of TCR/CD3 complexes from naïve, activated or hybridoma T cells. Accordingly, we expected to find aggregated TCR/CD3 complexes from the Vβ8+, activated T cells in the void volume (analysed by anti-Vβ8 mAb ELISA), or at least some of them. But no difference in molecular weight distribution was found between TCR/CD3 complexes from naïve (Vβ12+), hybridoma (Vβ2+) or activated T cells (Vβ8+). Though we have no idea, why we find results different from the Schamel group (except differences in technology), it is very important to discuss these issues and to design experiments, which could distinguish between different hypotheses. We thank Wolfgang Schamel et al. for their scientific initiative and wish them good luck in finding the ‘truth’. We also hope that this discussion might challenge other scientists in the field of TCR/CD3 structure/function.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.015 | 0.017 |
| Insufficient payload (model declined to judge) | 0.039 | 0.029 |
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".