A whole-organ multi-scale in silico framework for human kidney haemodynamics informed by hierarchical phase-contrast tomography
Bibliographic record
Abstract
Abstract Studying human kidney haemodynamics has been limited by the absence of complete vascular maps of the whole organ. Here we utilise previously generated multi-resolution hierarchical phase-contrast tomography (HiP-CT) coupled with a hybrid anatomically-grounded synthetic reconstruction to generate a full arterial–glomerular network of an intact human kidney comprising 1.6 million vessels and over 800,000 glomeruli. Using this anatomically comprehensive structure, we apply physics-based zero-dimensional haemodynamic modelling to quantify blood pressure, flow and simulated filtration rate across the entire organ. We show that the reconstructed human kidney vascular network exhibits order-dependent branching behaviour similar to that of rat kidneys, and that physiologically plausible pressure and flow patterns are recovered only when the full vascular network is represented. We further demonstrate how the kidney responds to macrovascular and microvascular perturbations, including stenosis of the large renal arteries and narrowing or ablation of afferent arterioles. Stenosis and arteriole narrowing exhibit threshold-type behaviour, with kidney perfusion and simulated glomerular filtration rate remaining largely preserved up to ∼50% narrowing, followed by sharp nonlinear declines beyond ∼70%. These predictions emerge in the absence of autoregulatory mechanisms, indicating that vascular geometry and resistance scaling alone contribute to kidney functional deterioration. Together, our framework provides the first organ-wide, data-driven model of human kidney haemodynamics and offers a foundation for future studies of kidney physiology and disease.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".