Infrared optical imaging of matrix metalloproteinases (MMPs) up regulation following ischemia reperfusion is ameliorated by hypothermia
Bibliographic record
Abstract
BACKGROUND: We investigated the use of a new MMP activatable probe MMPSense™ 750 FAST (MMPSense750) for in-vivo visualization of early MMP activity in ischemic stroke. Following middle cerebral artery occlusion (MCAO) optical imaging was performed. Near-infrared (NIR) fluorescent images of MMPSense activation were acquired using an Olympus fluorescent microscope, 1.25 x objective, a CCD camera and an appropriate filter cube for detecting the activated probe with peak excitation and emission at 749 and 775 nm, respectively. Images were acquired starting at 2 or 24 hours after reperfusion over the ipsilateral and contralateral cortex before and for 3 hours after, MMPSense750 was injected. RESULTS: Increased intensities ipsilaterally were observed following MMPSense750 injection with ischemic injury but not in sham animals. There were significant ipsilateral and contralateral differences at 15 minutes (P <0.05) in early ischemic reperfusion and at time 0 in 24 hours post ischemia (P <0.05) which persisted at 180 minutes in both these groups (P <0.01), but not following sham surgery. The increase in ipsilateral signal intensity was attenuated by hypothermia. These observations corresponded with a significant increase in the total MMP-9 protein levels, 5 and 24 hours following ischemia reperfusion (P <0.05) and their reduction by hypothermia. CONCLUSIONS: Matrix-metalloproteinase upregulation in ischemia reperfusion can be imaged acutely in-vivo with NIRF using MMPSense750. Hypothermia attenuated both the optical increase in intensity after MMPSense750 and the increase in MMP-9 protein expression supporting the proof of concept that NIRF imaging using MMPSense can be used to assess potential therapeutic strategies for stroke treatment.
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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 | 0.000 | 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) | 0.001 | 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".