In vivo high spatiotemporal resolution visualization of circulating T lymphocytes in high endothelial venules of lymph nodes
Bibliographic record
Abstract
Lymph nodes (LN) are major checkpoints for circulating T lymphocytes to recognize foreign antigens collected from peripheral tissue. High endothelial venule (HEV) in LN facilitates the effective transmigration of circulating T lymphocytes from the blood into LN. There have been many efforts to visualize T lymphocytes trafficking across HEV to understand the underlying mechanism. However, due to insufficient spatiotemporal resolution and the lack of an in vivo labeling method, clear visualization of dynamic behaviors of rapidly flowing T lymphocytes in HEV and their transmigration have been difficult. In this work, we adapted a custom-designed video-rate laser scanning confocal microscopy system to track individual flowing T lymphocytes in the HEV in real time in vivo. We demonstrate that the HEVs in LN can be clearly identified in vivo with its distinctive cuboidal morphology of endothelial cells fluorescently labeled by intravenously injected anti-CD31 antibody conjugated with Alexa fluorophore. By visualizing the adaptively transferred T lymphocytes, we successfully analyzed dynamic flowing behaviors of T lymphocytes and their transendothelial migration while interacting with the endothelial cells in HEV.
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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) | 0.000 | 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) | 0.001 | 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".