Tumor distribution of bromodeoxyuridine-labeled cells is strongly dose dependent.
Bibliographic record
Abstract
Bromodeoxyuridine (BrdUrd) is used extensively to measure the fraction of proliferating cells in tumors. Unlike endogenous markers of proliferation such as proliferating cell nuclear antigen (PCNA) and Ki-67, BrdUrd is exogenously administered and reaches the tumor via vasculature where it must then diffuse throughout the tissue to label S-phase cells. In this study, we examine the dose dependence of BrdUrd on the tumor distribution of labeled cells in histological sections. Analysis of the distribution of labeled cells in SiHa tumor xenografts showed that a dose between 400 and 1000 mg/kg was required to label cells 150 micro m from blood vessels, approaching the border of necrosis. Lower doses resulted in only the cells close to blood vessels being labeled. Interestingly, cells residing furthest from blood vessels still labeled albeit at half the level of cells situated proximal to the tumor vasculature. Results were compared with the penetration of BrdUrd seen in vitro using multilayered cell culture (MCC), a three-dimensional tissue culture model of solid tumors. Using MCC, an exposure of 100 micro M BrdUrd for 1 h was required for labeling of S-phase cells 150 micro m into the tissue, whereas cells adjacent to the edge of the tissue could be adequately labeled with just 5 micro M BrdUrd for 1 h. The area under the curve for a 100 mg/kg BrdUrd dose in mice was found to be approximately 30 micro M x h.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".