Spatiotemporal analysis of renal autoregulation
Bibliographic record
Abstract
Laser speckle perfusion imaging was used to study vascular interactions among nephrons in relation to renal autoregulation on physiologically relevant spatial and temporal scales. Surface images of stabilized rat kidneys were collected with a moorFLPI imager (Moor Instruments, UK) at a sampling rate of 1 Hz. High resolution time‐frequency spectra (TFS) were generated for each pixel after application of a 2D spatial filter. The maximum amplitude from each TFS within the two autoregulation frequency ranges (tubuloglomerular feedback: 0.02 – 0.05 Hz and the myogenic mechanism: 0.1 – 0.3 Hz) were found at each time point, and the corresponding instantaneous frequency and phase were extracted. Frequency maps at each time point were constructed to visualize how autoregulation is organized across the kidney surface. Time‐varying, wide‐radius synchronization in the form of frequency‐locked pixels was observed. The reported methods can be used to study the synchronization of nephrons and the autoregulatory components involved. It can also be used to study pathophysiological consequences of loss of synchronization. This research was supported by CIHR MOP‐102694 and the Intramural Research Program of the National Institute of Biomedical Imaging and Bioengineering, NIH.
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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 | 0.000 | 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".