MACROscopic imaging of tumor xenografts using fluorescence, phase contrast, and transmitted light
Bibliographic record
Abstract
Recent advances in imaging technology have contributed greatly to biological science. Confocal fluorescence microscopy (CFM) facilitates high-contrast 2D and 3D images of biological samples such as living cells, and frozen or fixed tissue sections. However, to date, imaging with existing confocal microscopes has been limited by practicality, especially when samples are large and overfill the field of view (FOV) of typical microscope objectives (e.g., ~10 mm2 tissue section). In this case, image-tiling must be employed to cover the entire specimen. This can be time consuming and cause artifacts in the composite image. The MACROscope® system (Biomedical Photometrics Inc, Waterloo, Canada), is a confocal device with a 2x7 cm2 FOV, and is ideal for imaging large tissue sections in a single frame. The system used in this work is a prototype capable of simultaneous acquisition from two detection channels. Reflected light (RL), transmitted light (TL) and differential phase contrast (DPC) images of whole cut mouse tumor xenografts were collected with the same system. Preliminary results demonstrate that the MACROscope® can produce high quality images of large tissue samples; comparable in resolution and contrast to those obtained with conventional CFM using low-power (5-10x) objectives, but at increased imaging speeds (>10x), and FOV (>20x). This new device avoids the need for image-tiling and provides simultaneous imaging of multiple tissue-specific fluorescent labels in large biological samples with high resolution and contrast; thereby allowing time- and cost-efficient high-throughput screening of immunohistopathological samples. This device may also serve in the imaging of high-throughput DNA and tissue arrays.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".