Novel method for the development of regulatory T cells (166.22)
Bibliographic record
Abstract
Abstract Background: Regulatory T cells (Treg) are important in the inhibition of inflammatory disorders, like asthma. Unfortunately, current protocols used to develop them in vitro for study rely on complicated cloning methods that may not represent biologically relevant Treg. LPS-free ovalbumin (OVA) given intranasal (i.n.) can induce mucosal tolerance in mice. Therefore, we hypothesized that Dendritic cells (DCs) taken from the draining lymph nodes (LNs) of mice given LPS-free OVA would induce Treg in vitro. Methods: Mice were given i.n. LPS-free OVA. 24 h later cervical and bronchial LNs were excised and DCs were isolated (~98% purity). DCs were cultured in vitro with transgenic OVA-specific T cells for four days. The development of Treg was assessed by flowcytometry (Foxp3 expression), ELISA (IL-10 production), and inhibition of T cell proliferation (CFSE staining/BrdU uptake). Finally, we studied the effectiveness of these cells to inhibit the development of AHR in a mouse model of asthma. Results: T cells from mice treated with LPS-free OVA showed high levels of Foxp3 expression, high IL-10 production, and the ability to inhibit OVA specific T cells in vitro. compared to control mice. Furthermore, T cells from LPS-free OVA treated mice prevented the development of AHR when given to mice before OVA challenge (i.n.). Conclusion: We believe our protocol could represent an alternative method to develop Treg cells for study.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".