CRISPR-Cas9 as a gene editing tool using cardiac glycoside reductase operon for digoxin metabolism
Bibliographic record
Abstract
Gene editing technology has gained popularity over the past two decades with the scientific advancements being made in genetics and computational biology. CRISPR-Cas9 technique allows us to target genetic material and perform accurate manipulation of targeted gene function. Cgr operon, commonly found in the gut microbiome bacteria of E. lenta has several attributes which can be modified using the CRISPR-Cas9 methodology. Here, we briefly describe how the Cas9 nuclease can be used to prevent Digoxin from being suppressed by the cgr gene, directly correlating to improved cardiac function. The editing tool is described to assist in mutating the genetic material of cgr gene discussing the inhibition of Na+/K+ ATPase in cardiac myocytes and CG-content. The principle of operation behind CRISPR-Cas9 is described using non-homologous end joining or homology-directed repair cellular mechanisms. Target site selection, designing sgRNAs and introducing various mutations are discussed with limitations of current technology and possible applications in the field of point of care diagnostics and biosensing developing therapeutic interventions using cgr operon.
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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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".