Tracking the Recruitment of Diabetogenic CD8+ T-Cells to the Pancreas in Real Time
Bibliographic record
Abstract
Development of autoimmune diabetes in both humans and mice is preceded by a prolonged period of inflammation of pancreatic islets by autoreactive T-cells. Noninvasive imaging techniques, including positron-emission tomography and optical or magnetic resonance imaging, have been used to track the recruitment of lymphocytes to sites of inflammation. These techniques, however, rely on labeling strategies that are non-antigen specific and do not allow specific tracking of the recruitment of autoreactive lymphocytes. Here we describe an antigen-specific magnetic label to selectively target a prevalent population of diabetogenic CD8(+) T-cells that contribute to the progression of insulitis to overt diabetes in NOD mice. Superparamagnetic nanoparticles coated with multiple copies of a high-avidity peptide/major histocompatibility complex ligand of these T-cells (NRP-V7/K(d)) are endocytosed by CD8(+) T-cells in an antigen-specific manner. Using these T-cells as probes, we show that inflammation of pancreatic islets by autoreactive T-cells can be detected in real time by magnetic resonance imaging. This study demonstrates the feasibility of visualizing the presence of ongoing autoimmune responses noninvasively.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".