Blocking Lymph Flow by Suturing Afferent Lymphatic Vessels in Mice
Bibliographic record
Abstract
Lymphatic vessels are critical in maintaining tissue fluid balance and optimizing immune protection by transporting antigens, cytokines, and cells to draining lymph nodes (LNs). Interruption of lymph flow is an important method when studying the function of lymphatic vessels. The afferent lymphatic vessels from the murine footpad to the popliteal lymph nodes (pLNs) are well-defined as the only routes for lymph drainage into the pLNs. Suturing these afferent lymphatic vessels can selectively prevent lymph flow to the pLNs. This method allows for interference in lymph flow with minimal damage to the lymphatic endothelial cells in the draining pLN, the afferent lymphatic vessels, as well as other lymphatic vessels around the area. This method has been used to study how lymph impacts high endothelial venules (HEV) and chemokine expression in the LN, and how lymph flows through the adipose tissue surrounding the LN in the absence of functional lymphatic vessels. With the growing recognition of the importance of lymphatic function, this method will have broader applications to further unravel the function of lymphatic vessels in regulating the LN microenvironment and immune responses.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".