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Record W2049382010 · doi:10.1002/jemt.10004

Quantitative studies on the movement of fluid and lymphocytes through periodontal tissue and into the draining lymph

2002· review· en· W2049382010 on OpenAlexaff
Binh Au, Christopher A. McCulloch, John B. Hay

Bibliographic record

VenueMicroscopy Research and Technique · 2002
Typereview
Languageen
FieldMedicine
TopicCell Adhesion Molecules Research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsLymphPeriodontiumPathologyLymphatic systemCervical lymph nodesLymphocyteMedicineImmunologyInternal medicineDentistry

Abstract

fetched live from OpenAlex

Chronic lymph drainage techniques in sheep have been used to map the pathways and to quantify the fluid and cell traffic through periodontal tissues. The continuous collection of cervical and prescapular lymph has demonstrated that 65% of labelled protein tracer injected into the periodontal tissues could be found in lymph over a period of 7.5 hours. Nearly 90% of the total radioactivity could be accounted for between the lymph and the tissue site. When silk was impregnated with radiolabelled albumin and a tooth ligated, the kinetics of the subsequent appearance of the tracer in lymph emphasized the ease with which macromolecules surrounding the teeth gain access to the lymphatics, regional lymph nodes, and immune apparatus. Animals were primed with BCG and then tuberculin (delayed hypersensitivity) lesions were simultaneously induced in the skin, bowel, and periodontium. When T cells were labelled with radioisotopes and their migration from blood to lymph measured, the periodontal tissue traffic pattern was distinct from the traffic pattern through DTH in the skin and also distinct from the pattern through the small intestine. This indicates that the lymphocyte traffic through the inflamed periodontium has unique features. This tissue specificity was not apparent when lesions were induced with TNFalpha. The static assessment of lymphocyte subsets within the tissues was also assessed with immunohistochemistry.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.810
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.003
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.207
GPT teacher head0.499
Teacher spread0.292 · 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.

Study designOther design
Domainnot available
GenreReview

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

Citations3
Published2002
Admission routes1
Has abstractyes

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