Algorithms For The Optimal Hamiltonian Path In Halin Graphs.
Bibliographic record
Abstract
In order to study the in vivo role of E-selectin in human inflammation, we have developed a model in which human skin is transplanted onto severe combined immunodeficient (SCID) mice. The grafted skin closely resembles normal skin and retains its human vasculature. After intradermal injection of rTNF-alpha, human E-selectin was rapidly up-regulated on dermal microvessels, with significant expression (determined immunohistochemically) at 1 h postinjection and maximum expression at 2 h postinjection. To study the functional role of E-selectin, a murine Ab against human E-selectin (mAb HEL 3/2) was developed that inhibited the in vitro adhesion of both human U937 cells and murine 32D cells to TNF-alpha-stimulated human endothelial cells. After intradermal injection of TNF-alpha, large numbers of murine leukocytes migrated into the grafts within 2 h. Intravenous injection of the antihuman E-selectin mAb 3/2 completely inhibited murine white blood cell (WBC) transmigration into the skin grafts, but an isotype-matched control Ab that also bound to human endothelium had no effect. Antihuman E-selectin mAb 3/2 was also able to inhibit the migration of i.v. 51Cr-labeled human neutrophils. These findings demonstrate that E-selectin is important in early white blood cell adhesion events and is required for TNF-alpha-induced white blood cell transmigration in the human/SCID mouse chimeric model.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.003 |
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".