A Microperfusion and In-Bore Oxygenator System Designed for Magnetic Resonance Microscopy Studies on Living Tissue Explants
Bibliographic record
Abstract
Spectrometers now offer the field strengths necessary to visualize mammalian cells but were not designed to accommodate imaging of live tissues. As such, spectrometers pose significant challenges--the most evident of which are spatial limitations--to conducting experiments in living tissue. This limitation becomes problematic upon trying to employ commercial perfusion equipment which is bulky and--being designed almost exclusively for light microscopy or electrophysiology studies--seldom includes MR-compatibility as a design criterion. To overcome problems exclusive to ultra-high magnetic field environments with limited spatial access, we have designed microperfusion and in-bore oxygenation systems capable of interfacing with Bruker's series of micro surface-coils. These devices are designed for supporting cellular resolution imaging in MR studies of excised, living tissue. The combined system allows for precise control of both dissolved gas and pH levels in the perfusate thus demonstrating applicability for a wide range of tissue types. Its compactness, linear architecture, and MR-compatible material content are key design features intended to provide a versatile hardware interface compatible with any NMR spectrometer. Such attributes will ensure the microperfusion rig's continued utility as it may be used with a multitude of contemporary NMR systems in addition to those which are currently in development.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".