A High‐Throughput AngioPlate Platform with Integrated AngioTEER for Modeling and Monitoring Renal Proximal Tubule Injury
Bibliographic record
Abstract
Abstract Renal tubular injury is the leading cause of acute and chronic kidney diseases. This condition occurs when renal proximal tubular epithelial cells sustain damage from exposure to nephrotoxins, infections, or ischemia leading to tubular interstitial fibrosis and eventually organ failure. Despite its severity, the pathophysiology of several renal tubular injuries remains inadequately understood with no treatment due to lack of predictive preclinical models. Here a model of renal proximal tubules is reported on an AngioPlate platform integrated with Trans Electrical Epithelial Resistance measurements (AngioTEER) for automated, real‐time monitoring of tubular barrier integrity in 128 tissues in health and in response to injury. The platform is used to successfully model drug and hypoxia‐induced tubular injuries. In addition, the platform's use of amenable extracellular matrices is leveraged to model renal fibrosis by co‐culturing fibroblasts with renal proximal tubules. Given the lack of approved treatments for tubulointerstitial fibrosis, the possibility of repurposing pirfenidone is explored, a drug currently approved for lung fibrosis, and found that it may offer a potential therapeutic effect for this challenging condition. Overall, this work demonstrates the versatility of our engineered 3D renal proximal tubule model to study renal disease mechanisms and screen potential treatment options.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".