Detecting physiological systems with laser speckle perfusion imaging of the renal cortex
Bibliographic record
Abstract
Laser speckle perfusion imaging (LSPI) has become an increasingly popular technique for monitoring vascular perfusion over a tissue surface. However, few studies have utilized the full range of spatial and temporal information generated by LSPI to monitor spatial properties of physiologically relevant dynamics. In this study, we extend the use of LSPI to analyze renal perfusion dynamics over a spatial surface of ~5 × 7 mm of renal cortex. We identify frequencies related to five physiological systems that induce temporal changes in renal vascular perfusion (cardiac flow pulse, respiratory-induced oscillations, baroreflex components, the myogenic response, and tubuloglomerular feedback) across the imaged surface and compare the results with those obtained from renal blood flow measurements. We find that dynamics supplied from global sources (cardiac, respiration, and baroreflex) present with the same frequency at all locations across the imaged surface, but the local renal autoregulation dynamics can be heterogeneous in their distribution across the surface. Moreover, transfer function analysis with forced blood pressure as the input yields the same information with laser speckle imaging or renal blood flow as the output during control, intrarenal infusion of N(ω)-nitro-L-arginine methyl ester to enhance renal autoregulation, and intrarenal infusion of the rho-kinase inhibitor Y-27632 to inhibit vasomotion. We conclude that LSPI measurements can be used to analyze local as well as global renal perfusion dynamics and to study the properties of physiological systems across the renal cortex.
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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 | 0.001 | 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".