Laser speckle microscopy measurement of arteriolar blood flow in many individual nephrons simultaneously
Bibliographic record
Abstract
Tubular pressure oscillations in cortical nephron pairs often synchronize. To determine the extent and duration of synchronization we used laser speckle microscopy on the renal surface of rats. The camera measures changes in surface roughness associated with blood flow changes in 442K pixels at 1/sec. A 15×15 pixel matrix captures one star vessel, and up to 100 star vessels are sampled simultaneously for 30 min or longer. Star vessel locations are identified on the renal surface by ordinary digital microscopy during intravenous dye injection. The digital camera image is overlaid on the time averaged speckle image in Photoshop; star vessel locations are registered on the speckle image. Time series are sampled from the speckle image at each star vessel location. Single star vessel flow changes in response to angiotensin II and acetyl choline mimic those in whole kidney blood flow. The time series were analyzed with wavelet transforms and show oscillations associated with both tubuloglomerular feedback and the myogenic mechanism. Synchronization is present among nephrons but persists for variable periods of time; the size of synchronization clusters also varies with time.
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 | 0.000 | 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".