Insights into Autosomal Dominant Polycystic Kidney Disease from Genetic Studies
Bibliographic record
Abstract
Autosomal dominant polycystic kidney disease is the most common monogenic cause of ESKD. Genetic studies from patients and animal models have informed disease pathobiology and strongly support a “threshold model” in which cyst formation is triggered by reduced functional polycystin dosage below a critical threshold within individual tubular epithelial cells due to ( 1 ) germline and somatic PKD1 and/or PKD2 mutations, ( 2 ) mutations of genes ( e.g. , SEC63 , SEC61B , GANAB , PRKCSH , DNAJB11 , ALG8 , and ALG9 ) in the endoplasmic reticulum protein biosynthetic pathway, or ( 3 ) somatic mosaicism. Genetic testing has the potential to provide diagnostic and prognostic information in cystic kidney disease. However, mutation screening of PKD1 is challenging due to its large size and complexity, making it both costly and labor intensive. Moreover, conventional Sanger sequencing–based genetic testing is currently limited in elucidating the causes of atypical polycystic kidney disease, such as within-family disease discordance, atypical kidney imaging patterns, and discordant disease severity between total kidney volume and rate of eGFR decline. In addition, environmental factors, genetic modifiers, and somatic mosaicism also contribute to disease variability, further limiting prognostication by mutation class in individual patients. Recent innovations in next-generation sequencing are poised to transform and extend molecular diagnostics at reasonable costs. By comprehensive screening of multiple cystic disease and modifier genes, targeted gene panel, whole-exome, or whole-genome sequencing is expected to improve both diagnostic and prognostic accuracy to advance personalized medicine in autosomal dominant polycystic kidney disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".