The role of glutamate-NMDA-receptor in mice alpha beta T cells from intestinal mucosa
Bibliographic record
Abstract
Abstract The intestinal mucosa consists of a simple layer of epithelial tissue and loose connective tissue called the lamina propria that is formed by several immune cells mainly T cells that play important roles in local tolerance to microorganisms of the microbiota and food antigens. In addition to mucosa the small and large intestines also present the submucosal and the muscular layers that present ganglionic enteric plexuses formed by networks of interconnection between autonomic neurons of the enteric nervous system. These neurons secrete various neurotransmitters including glutamate and their innervations reach the lamina propria. It is known that T cells have receptors for neurotransmitters even the ionotropic glutamate receptor NMDAR. Thus, the present study intends to evaluate the role of NMDAR in αβ T cells from intestinal mucosa. C57BL/6 Grin1f/f and CD4crexGrin1flox mice of both sexes were used at 8 weeks of age. CD4+ T cells, CD8+ T cells, γδT cells, dendritic cells and macrophages present in mesenteric lymph nodes and at intraepithelial compartment of small intestine were evaluated. We found that CD8+ T cells were decreased and γδT cells were increased in mesenteric lymph nodes of CD4crexGrin1flox mice in comparison to the control group. Thus, intraepithelial CD4+ T cells and macrophages were increased, but intraepithelial CD8+ T cells were decreased in small intestine of CD4crexGrin1flox mice compared to the control group. Our preliminary results shows that the absence of NMDAR exclusively in αβ T cells alters the percentage of immune cells present both in mesenteric lymph nodes and intraepithelial compartment of small intestine suggesting a modulation by glutamate. New evaluations are still in progress to better understand that.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".