Modeling Neoplastic Growth in Renal Cell Carcinoma and Polycystic Kidney Disease
Bibliographic record
Abstract
Renal cell carcinoma (RCC) and autosomal dominant polycystic kidney disease (ADPKD) share several characteristics, including neoplastic cell growth, kidney cysts, and limited therapeutics. As well, both exhibit impaired vasculature and compensatory VEGF activation of angiogenesis. The PI3K/AKT/mTOR and Ras/Raf/ERK pathways play important roles in regulating cystic and tumor cell proliferation and growth. Both RCC and ADPKD result in hypoxia, where HIF-α signaling is activated in response to oxygen deprivation. Primary cilia and altered cell metabolism may play a role in disease progression. Non-coding RNAs may regulate RCC carcinogenesis and ADPKD through their varied effects. Drosophila exhibits remarkable conservation of the pathways involved in RCC and ADPKD. Here, we review the progress towards understanding disease mechanisms, partially overlapping cellular and molecular dysfunctions in RCC and ADPKD and reflect on the potential for the agile Drosophila genetic model to accelerate discovery science, address unresolved mechanistic aspects of these diseases, and perform rapid pharmacological screens.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".