CRISPR-Cas9 system in autosomal dominant polycystic kidney disease: a comprehensive review
Bibliographic record
Abstract
Genetic kidney diseases are caused by mutations in specific genes that significantly affect kidney development and function. Although the underlying pathogenic genes of many kidney diseases have been identified, an understanding of their mechanisms and effective treatments remains limited. Gene editing, particularly using clustered regularly interspaced short palindromic repeats (CRISPR), has recently become a promising approach for studying genetic diseases and the CRISPR/CRISPR-associated protein 9 (CRISPR-Cas9) method has become a prominent research method. It has been shown that CRISPR-Cas9 can be targeted to knock out specific genomic sites, which enables researchers to correct gene mutations, prevent inheritance, and better understand the function of genes and the effectiveness of drugs. However, the application of CRISPR-Cas9 technology in the development of therapeutic agents against genetic kidney disease has been overlooked compared with other genetic diseases. In this paper, we provide an overview of the current research advancements in genetic kidney diseases using CRISPR technology, as well as the diverse preclinical research methods implemented, with particular emphasis on 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".