Characterization of a <i>CLCF1</i> conditional knock-out mice model
Bibliographic record
Abstract
Abstract The Cardiotrophin-like Cytokine Factor 1 (CLCF1) is a cytokine of the IL6 family with important pro-neurotrophic and immuno-modulating functions. However, the mechanisms behind CLCF1 activities and CLCF1 properties in pathological models remain poorly understood. This is partly due to a lack of tools for the study of CLCF1 functions in vivo. Indeed, the complete knock-out of CLCF1 in mice is lethal at P1: underdeveloped motor neurons of the face and jaw prevent the pups from suckling. To overcome this obstacle, we obtained a CLCF1 conditional knock-out mice model generated using the CRISPR-Cas9 technology. We then bred these mice with Vav-Cre mice to induce CLCF1 deletion in hematopoietic cells. We are now conducting immuno-phenotyping experiments of the main immune populations in those animals. Considering the activities of CLCF1 that were previously shown in vitro or in vivo using overexpression models, we hypothesize that the knock-out of CLCF1 in immune cells will lead to decreased numbers of B and myeloid cells. This new CLCF1 conditional knock-out mice model will be a potent tool to confirm and further study the activities, mechanisms, and pathological roles of CLCF1 in vivo. Supported by grants from CIHR (Canadian Institutes of Health Research)
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".