Elastin Shapes Small Molecule Distribution in Lymph Node Conduits
Bibliographic record
Abstract
The spatial and temporal Ag distribution determines the subsequent T cell and B cell activation at the distinct anatomical locations in the lymph node (LN). It is well known that LN conduits facilitate small Ag distribution in the LN, but the mechanism of how Ags travel along LN conduits remains poorly understood. In C57BL/6J mice, using FITC as a fluorescent tracer to study lymph distribution in the LN, we found that FITC preferentially colocalized with LN capsule-associated (LNC) conduits. Images generated using a transmission electron microscope showed that LNC conduits are composed of solid collagen fibers and are wrapped with fibroblastic cells. Superresolution images revealed that high-intensity FITC is typically colocalized with elastin fibers inside the LNC conduits. Whereas tetramethylrhodamine isothiocyanate appears to enter LNC conduits as effectively as FITC, fluorescently-labeled Alexa-555-conjugated OVA labels significantly fewer LNC conduits. Importantly, injection of Alexa-555-conjugated OVA with LPS substantially increases OVA distribution along elastin fibers in LNC conduits, indicating immune stimulation is required for effective OVA traveling along elastin in LN conduits. Finally, elastin fibers preferentially surround lymphatic vessels in the skin and likely guide fluid flow to the lymphatic vessels. Our studies demonstrate that fluid or small molecules are preferentially colocalized with elastin fibers. Although the exact mechanism of how elastin fibers regulate Ag trafficking remains to be explored, our results suggest that elastin can be a potentially new target to direct Ag distribution in the LN during vaccine design.
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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".