Estimation of Total Glomerular Number Using an Integrated Disector Method in Embryonic and Postnatal Kidneys
Bibliographic record
Abstract
Congenital Anomalies of the Kidney and Urinary Tract (CAKUT) are a polymorphic group of clinical disorders comprising the major cause of renal failure in children. Included within CAKUT is a wide spectrum of developmental malformations ranging from renal agenesis, renal hypoplasia and renal dysplasia (maldifferentiation of renal tissue), each characterized by varying deficits in nephron number. First presented in the Brenner Hypothesis, low congenital nephron endowment is becoming recognized as an antecedent cause of adult-onset hypertension, a leading cause of coronary heart disease, stroke, and renal failure in North America. Genetic mouse models of impaired nephrogenesis and nephron endowment provide a critical framework for understanding the origins of human kidney disease. Current methods to quantitate nephron number include (i) acid maceration (ii) estimation of nephron number from a small number of tissue sections (iii) imaging modalities such as MRI and (iv) the gold standard physical disector/fractionator method. Despite its accuracy, the physical disector/fractionator method is rarely employed because it is labour-intensive, time-consuming and costly to perform. Consequently, less rigourous methods of nephron estimation are routinely employed by many laboratories. Here we present an updated, digitized version of the physical disector/fractionator method using free open source Fiji software, which we have termed the integrated disector method. This updated version of the gold standard modality accurately, rapidly and cost-effectively quantitates nephron number in embryonic and post-natal mouse kidneys, and can be easily adapted for stereological measurements in other organ systems.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".