Cre-recombinase systems for induction of neuron-specific knockout models: a guide for biomedical researchers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Gene deletion has been a valuable tool for unraveling the mysteries of molecular biology. Early approaches included gene trapping and gene targetting to disrupt or delete a gene randomly or at a specific location, respectively. Using these technologies in mouse embryos led to the generation of mouse knockout models and many scientific discoveries. The efficacy and specificity of these approaches have significantly increased with the advent of new technology such as clustered regularly interspaced short palindromic repeats for targetted gene deletion. However, several limitations including unwanted off-target gene deletion have hindered their widespread use in the field. Cre-recombinase technology has provided additional capacity for cell-specific gene deletion. In this review, we provide a summary of currently available literature on the application of this system for targetted deletion of neuronal genes. This article has been constructed to provide some background information for the new trainees on the mechanism and to provide necessary information for the design, and application of the Cre-recombinase system through reviewing the most frequent promoters that are currently available for genetic manipulation of neurons. We additionally will provide a summary of the latest technological developments that can be used for targeting neurons. This may also serve as a general guide for the selection of appropriate models for biomedical research.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it