Real-Time Imaging of Immune Modulation by Cannabinoids Using Intravital Fluorescence Microscopy
Bibliographic record
Abstract
Introduction: The endocannabinoid system (ECS) is an endogenous regulatory system involved in a wide range of physiologic and disease processes. Study of ECS regulation provides novel drug targets for disease treatment. Intravital microscopy (IVM), a microscopy-based imaging method that allows the observation of cells and cell-cell interactions within various tissues and organs in vivo, has been utilized to study tissues and cells in their physiologic microenvironment. This article reviews the current state of the IVM techniques used in ECS-related inflammation research. Methodological Aspects of IVM: IVM with focus on conventional fluorescent microscope has been introduced in investigation of microcirculatory function and the behavior of individual circulating cells in an in vivo environment. Experimental setting, tissue protection under physiologic condition, and microscopical observation are described. Application of IVM in Experimental Inflammatory Disorders: Using IVM to investigate the effects of immune modulation by cannabinoids is extensively reviewed. The inflammatory disorders include sepsis, arthritis, diabetes, interstitial cystitis, and inflammatory conditions in the central nervous system and eyes. Conclusion: IVM is a critical tool in cannabinoid and immunology research. It has been applied to investigate the role of the ECS in physiologic and disease processes. This review demonstrates that the IVM technique provides a unique means in understanding ECS regulation on immune responses in diseases under their physical conditions, which could not be achieved by other methods.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".