Lymphatic vessel‐independent lymph flow pattern from the peri‐nodal adipose tissue to the lymph node
Bibliographic record
Abstract
Rationale Lymph nodes (LNs) anatomically are distributed along the lymphatic vessels to survey materials carried by the lymph flow. Adipose tissue is a reservoir of hormones, cytokines, metabolites that can regulate immune responses and cell homeostasis in the LNs. Although lymphatic vessel and LNs are always surrounded by adipose tissue, lymph or cell communication between the peri‐nonal adipose tissue and the LNs remains unclear. Objective We aimed to understand how lymph flows from the peri‐nodal adipose tissue to the LNs. Methods and Results Using high‐resolution, super‐resolution and 3D reconstruction images, we studied the lymph distribution pattern from the adipose tissue to and through the LN. We found that a subset of LN conduits that are initiated at the LN capsule (LNC conduits), connect the afferent subcapsular sinus (SCS) and efferent medullary sinus (MS) and facilitate rapid lymph flow through the LN parenchyma. Super‐resolution images showed the unprecedented heterogeneous flow patterns inside LNC conduits. Surprisingly, we found that the LN capsule and its associated LNC conduits connect to the collagen channels in the fat tissue and provide an additional route supports lymph flow from the peri‐nodal adipose tissue to the LN (Fat‐LNC conduits). The type of materials entering the Fat‐LN conduits is dictated by molecular size. Finally, small molecules can enter LN after the afferent lymphatic vessels are sutured. Conclusions The lymph distribution pattern suggests that Fat‐LNC conduit support lymphatic vessel‐independent lymph flow from peri‐nodal adipose tissue to the LN, which provide a “highway” for fat‐derived factors to regulate LN microenvironment. Support or Funding Information Dianne & Irving Kipnes Foundation, Natural Sciences and Engineering Research Council of Canada (NSERC RGPIN‐2015‐03641), Canadian Institute of Health Research (CIHR) to SL and Canada Foundation for Innovation to SL.
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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".