Quantitative studies on the movement of fluid and lymphocytes through periodontal tissue and into the draining lymph
Bibliographic record
Abstract
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.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".