The Role of Hyperemia in Cellular Hypersensitivity Reactions
Bibliographic record
Abstract
The three physiological processes vascular permeability, blood flow and lymphocyte migration were all enhanced in tuberculin reactions induced in guinea pigs and sheep and also in normal lymphocyte transfer reactions in sheep. Microspheres labelled with 85Sr were used to measure blood flow to dermal sites and it was found that cellular hypersensitivity reactions had blood flows 7-25 times that of normal skin at the reaction peak. Vascular permeability was measured as an increase in the flow rate of afferent lymph or, in guinea pigs, as the enhanced leakage of intravascular 125I-albumin. When the permeability-inducing peptide bradykinin was injected directly into tuberculin reaction, the resulting permeability was greater than the sum of the tuberculin and bradykinin permeability taken individually and it was concluded that the hyperemia enhanced the permeability-inducing capacity of bradykinin. When the traffic of lymphocytes through hypersensitivity lesions was measured in sheep by cannulating the regional afferent lymph vessels and continuously collecting the lymph, the increase in lymphocyte traffic was of the same order of magnitude as the increase in blood flow. It is suggested than the antigen-induced enhancement of blood flow caused the increase in lymphocyte traffic and that the mechanism was similar to that occurring within lymph nodes during the immune response to all antigens.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".