Cyst Reduction in a Polycystic Kidney Disease Drosophila Model Using Smac Mimics
Bibliographic record
Abstract
Autosomal dominant polycystic kidney disease (ADPKD) is an inherited malady affecting 12.5 million people worldwide. Therapeutic options to treat PKD are limited, due in part to lack of precise knowledge of underlying pathological mechanisms. Mimics of the second mitochondria-derived activator of caspases (Smac) have exhibited activity as antineoplastic agents and reported recently to ameliorate cysts in a murine ADPKD model, possibly by differentially targeting cystic cells and sparing the surrounding tissue. A first-in-kind Drosophila PKD model has now been employed to probe further the activity of novel Smac mimics. Substantial reduction of cystic defects was observed in the Malpighian (renal) tubules of treated flies, underscoring mechanistic conservation of the cystic pathways and potential for efficient testing of drug prototypes in this PKD model. Moreover, the observed differential rescue of the anterior and posterior tubules overall, and within their physiologically diverse intermediate and terminal regions implied a nuanced response in distinct tubular regions contingent upon the structure of the Smac mimic. Knowledge gained from studying Smac mimics reveals the capacity for the Drosophila model to precisely probe PKD pharmacology highlighting the value for such critical evaluation of factors implicated in renal function and pathology.
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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 | 0.001 | 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".