Reactive murine lymph nodes uniquely permit parenchymal access for T cells that enter via the afferent lymphatics
Bibliographic record
Abstract
Whereas naïve T cells access the lymph nodes predominantly via the high endothelial venules, their effector counterparts can also enter via the afferent lymphatics. It is unclear if such cells are confined to the lymphatic spaces during their transit through the lymph node or whether they can access the lymphocyte- and dendritic cell-rich parenchyma with its potentially stimulatory environment. We used a flank HSV inoculation model that featured neuronal-mediated movement of virus to distinct areas of skin to study the lymphatic-mediated transit of activated T cells between different skin-draining lymph nodes. These experiments showed that activated T cells released from the brachial lymph node, draining the primary site of inoculation, entered the downstream axillary lymph node. These activated T cells accessed the subcapsular areas of the axillary lymph node via lymphatic vessels exiting the upstream brachial node regardless of whether the former drained skin that was associated with active infection. However, T cells remained within the sinusoidal network of the axillary lymph node unless it was directly associated with peripheral infection. Thus, activated T cells that enter a given lymph node using the afferent lymphatics do not have automatic access to the parenchyma unless it is a reactive node involved with peripheral inflammation or infection.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".