Dynamic Characteristics of Cerebral Lipid Microemboli: Videomicroscopy Studies in Rats
Bibliographic record
Abstract
UNLABELLED: Cerebral lipid microemboli (LME) may cause postoperative cognitive dysfunction after orthopedic and cardiovascular surgery. In 13 anesthetized rats, we created a cranial window to study LME using orthogonal polarization spectral imaging videomicroscopy. All rats received 0.2 mL of human marrow fat, obtained from surgical waste during arthroplasty, injected into the superior vena cava. Five rats died within seconds of this injection, despite resuscitation efforts. Seven minutes later, we injected an additional 0.1 mL in 6 of the 8 surviving rats. We observed the videomicroscopy for 1 h in all 8 rats. Arterial blood pressure (BP) was continuously measured. No LME were observed in the first 7 min (n = 8); however, within seconds of the additional 0.1 mL injection, mean BP decreased from 79 +/- 31 mm Hg to 28 +/- 12 mm Hg (n = 6; P < 0.02). Epinephrine and crystalloid infusion increased BP to 161 +/- 9 mm Hg and 20-100 LME were seen within 5 min. LME changed shape and fragmentation, erosion, and streaming patterns were noted, with transient arteriolar occlusion (10-220 s). Increasing BP resulted in reperfusion of occluded arterioles. No venous LME were noted. Postmortem, brain and lung LME were found with no patent foramen ovale. This model may be useful in studying cerebral LME. IMPLICATIONS: Marrow lipid may pass through the lung during orthopedic surgery, creating cerebral lipid microemboli (LME). We created a cranial window in rats to study LME flowing through pial-cortical vessels. Cerebral LME appeared after resuscitation from hypotension and vessel occlusion was transient. This model may be useful in studying cerebral LME.
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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 | 0.001 |
| 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".