Hemodynamic and electrophysiological progression of the Rose Bengal photothrombotic stroke model in mice: vasoactive properties of Rose Bengal, tissue heating, wavelength optimization, and sex differences in lesion volume
Bibliographic record
Abstract
Abstract The photothrombotic stroke model is gaining popularity due to its relative simplicity, minimal invasiveness, and clinical relevance. Photothrombosis involves the delivery of an intravascular photosensitizer (Rose Bengal) followed by its photoactivation, resulting in vessel occlusion and ischemia. Using a combination of complementary optical and non-optical techniques, we characterized the physiological changes in mice undergoing photothrombosis. We report that Rose Bengal acts as a rapid vasoconstrictor, inducing hypoemia both in the brain and periphery even in the absence of its photoactivation. Conversely, we find that light, when used at photothrombosis-appropriate intensities and durations, induces large amounts of tissue heating and hyperemia even in the distal non-illuminated hemisphere. Furthermore, we show that use of the optimal photothrombotic wavelength based on the Rose Bengal absorption spectrum (yellow-561nm) produces a more consistent and pronounced drop in blood flow, and a shorter latency to the initial spreading depolarization (SD), ultimately resulting in a larger stroke. Similarly, when yellow light is used to induce a stroke in ChR2-expressing mice, the electrophysiological and hemodynamic confounds from green light cross activation of ChR2 are eliminated. Finally, we observe across cohorts that male mice have larger strokes than females. Altogether, we extensively describe important caveats and confounds concerning photothrombosis and provide a detailed characterization of its early ischemic events. Significance statement Photothrombosis is a powerful model of ischemic stroke which uses light to photoactivate an intravascular dye (Rose Bengal). However, little is known about the independent effects of both the Rose Bengal and the light used to activate it. We show that both manipulations introduce separate confounds relevant to stroke outcomes, something which should be considered and accounted for when using this technique. In addition, we demonstrate that by using the optimal Rose Bengal excitation wavelength, the blood flow drop is more pronounced and consistent, resulting in larger strokes and perhaps better modelling human injury. Furthermore, we show that precautions can be taken to avoid spectral overlap when integrating photothrombosis in optogenetic experiments. Finally, we explore sex differences in lesion volume.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".