High‐resolution optical imaging of synchronization in the renal circulation.
Bibliographic record
Abstract
Study of nephron to nephron interaction and of the corresponding signaling pathways is fundamental for understanding how the kidney operates and why it malfunctions. Micropuncture studies have shown that nephrons signal to each other over short distances and can synchronize their activity accordingly. Because micropuncture can be applied to no more than 2 or 3 nephrons simultaneously, however, the technique is incapable of estimating the size of the synchronized field. In our earlier studies, we used a commercial laser speckle imaging camera to measure blood flow dynamics over the renal surface, but limitations of the technology available at the time restricted spatiotemporal resolution, making it impossible to capture signals from efferent arterioles of single nephrons at reasonable signal‐to‐noise ratio. To resolve signals from separate nephrons in a sufficiently large field of view we developed a new methodology for renal laser speckle imaging. Our setup provides spatial resolution down to 0.8□m per pixel and imaging frequency up to 160Hz. We applied the new technology to record ~1.5x1.5 mm 2 sections of the renal surface in control and under IV stimuli, resolving 30–70 “star” vessels per section. We analyzed spectral properties of recorded signals in time and studied inter‐nephron magnitude coherence as well as frequency and phase locking. The analysis showed the dynamic evolution of clusters of various sizes, including the formation of large (>30 vessels) long‐lived TGF frequency locked clusters (>10 periods). To our knowledge, it is the first study to estimate of the number of nephrons in the superficial synchronized field and to show the evolution of synchronous clusters in time. Support or Funding Information Supported by Novo Nordisk Foundation. Project number NNF17OC0025224. This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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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 | 0.001 |
| 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".