Implementation and assessment of an animal management system for small-animal micro-CT / micro-SPECT imaging
Bibliographic record
Abstract
Advances in laboratory imaging systems for CT, SPECT, MRI, and PET facilitate routine micro-imaging during pre-clinical investigations. Challenges still arise when dealing with immune-compromised animals, biohazardous agents, and multi-modality imaging. These challenges can be overcome with an appropriate animal management system (AMS), with the capability for supporting and monitoring a rat or mouse during micro-imaging. We report the implementation and assessment of a new AMS system for mice (PRA-3000 / AHS-2750, ASI Instruments, Warren MI), designed to be compatible with a commercial micro-CT / micro-SPECT imaging system (eXplore speCZT, GE Healthcare, London ON). The AMS was assessed under the following criteria: 1) compatibility with the imaging system (i.e. artifact generation, geometric dimensions); 2) compatibility with live animals (i.e. positioning, temperature regulation, anesthetic supply); 3) monitoring capabilities (i.e. rectal temperature, respiratory and cardiac monitoring); 4) stability of co-registration; and 5) containment. Micro-CT scans performed using a standardized live-animal protocol (90 kVp, 40 mA, 900 views, 16 ms per view) exhibited low noise (±19 HU) and acceptable artifact from high-density components within the AMS (e.g. ECG pad contacts). Live mice were imaged repeatedly (with removal and replacement of the AMS) and spatial registration was found to be stable to within ±0.07 mm. All animals tolerated enclosure within the AMS for extended periods (i.e. > one hour) without distress, based on continuous recordings of rectal temperature, ECG waveform and respiratory rate. A sealed AMS system extends the capability of a conventional micro-imaging system to include immune-compromised and biosafety level 2 mouse-imaging protocols.
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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 | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".