In vivo micro-computed tomography imaging in liver tumor study of mice using Fenestra VC and Fenestra HDVC
Bibliographic record
Abstract
Contrast agents are used to enhance the visibility of rodent organs during in vivo micro-computed tomography imaging. Specifically, this non-invasive technique can study liver tumor growth and progression in small animals. Fenestra VC and the novel Fenestra HDVC were compared for enhancement in the liver of healthy and tumor-bearing mice, and the images were compared for their ability to define the tumor border, volume and quantity of tumors. Fenestra VC and Fenestra HDVC were injected into healthy eight-week-old female mice (C57BL/6) via the tail vein then imaged at seven different time points. The experimental results showed that 0.005 mL/g of Fenestra HDVC resulted in the same enhancement for all eight organs as 0.01 mL/g of Fenestra VC across all time points. For the tumor study, B16F10 tumors were surgically introduced into ten eight-week-old female mice (C57BL/6) then imaged in vivo over a 3 day period. Ex vivo micro-CT images of the excised livers were also obtained. The tumor volume and quantity were measured in each image, and the tumour progression observed over 3 days. We showed Fenestra HDVC is effective for in vivo imaging in rodents because the optimal enhancement level in organs is maintained at a reduced injection volume.
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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) | 0.001 | 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 | 0.001 | 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".