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Record W3028057673 · doi:10.3791/61178

Blocking Lymph Flow by Suturing Afferent Lymphatic Vessels in Mice

2020· article· en· W3028057673 on OpenAlexafffund
Yujia Lin, Jingna Xue, Shan Liao

Bibliographic record

VenueJournal of Visualized Experiments · 2020
Typearticle
Languageen
FieldMedicine
TopicLymphatic System and Diseases
Canadian institutionsUniversity of Calgary
FundersCanadian Institutes of Health Research
KeywordsLymphatic systemLymphHigh endothelial venulesLymph node stromal cellLymphatic vesselLymphatic EndotheliumPathologyLymphangiogenesisMedicineAfferentLymph nodeAnatomyBiologyInternal medicineCancerMetastasis

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.048
Threshold uncertainty score0.695

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.038
GPT teacher head0.398
Teacher spread0.360 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2020
Admission routes2
Has abstractyes

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