T cells from burn-injured mice demonstrate a loss of sensitivity to glucocorticoids
Bibliographic record
Abstract
Glucocorticoids (GC) are steroid hormones that modulate T cell functions and restrain their hyperresponsiveness following stimulation. Naive T lymphocytes are sensitive to GC but become more resistant when they are activated. A balance between activation and inhibition signals is important for a targeted and effective T cell response. Thermal injury is characterized by an immune dysfunction and hyperactive T cells visible at day 10 postburn. In this study, our objective was to evaluate T cell sensitivity to GC following thermal injury and to identify mechanisms that could modulate their sensitivity. One mechanism that we hypothesized was increased p38 mitogen-activated protein kinase (MAPK) activity that could lead to GC resistance. Male C57BL/6 mice underwent a full-thickness 20% total body surface area. At 10 days postinjury, splenic T cells were isolated. Glucocorticoid receptor (GR) expression was higher in T cells from burn-injured mice. Interestingly, these cells were also less sensitive to GC-induced apoptosis prior to and poststimulation. Furthermore, anti-CD3-activated T cells from burn-injured mice showed increased proliferation and CD25 expression, which resisted corticosterone's (CORT) suppressive effect. Anti-CD3-activated CD4(+)CD44(+) memory cells from burn-injured mice expressed the highest level of CD25 and were resistant to CORT. Increased phosphorylation of p38 MAPK was also noted in activated T cells from burn-injured mice. Pharmacological inhibition of p38 MAPK decreased cell proliferation and normalized interferon-gamma (IFNgamma) production. In conclusion, we demonstrate that a unique event like burn injury induces a loss of sensitivity to GC in splenic T cells and have identified p38 MAPK as a key modulator for this resistance.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".