<i>Retracted:</i> Novel approaches for gene‐specific interference via manipulating actions of microRNAs: Examination on the pacemaker channel genes <i>HCN2</i> and <i>HCN4</i>
Post-publication record
Source: Retraction Watch, joined by DOI. OpenAlex records retraction as is_retracted, a boolean over a state space with at least four values, so it cannot express an expression of concern, a correction or a reinstatement; it reports them as false, which reads as “fine”.
Bibliographic record
Abstract
Recent evidence has suggested microRNAs as viable therapeutic targets for a wide range of human disease. However, lack of gene-specificity of microRNA actions may hinder this application. Here we developed two new approaches, the gene-specific microRNA mimic and microRNA-masking antisense approaches, to explore the possibility of using microRNA's principle of actions in a gene-specific manner. We examined the value of these strategies as rational approaches to develop heart rate-reducing agents and "biological pacemakers" by manipulating the expression of the cardiac pacemaker channel genes HCN2 and HCN4. We showed that the gene-specific microRNA mimics, 22-nt RNAs designed to target the 3'untranslated regions (3'UTRs) of HCN2 and HCN4, respectively, were efficient in abrogating expression and function of HCN2 and HCN4. The gene-specific microRNA mimics repressed protein levels, accompanied by depressed f-channel conductance and the associated rhythmic activity, without affecting mRNA levels of HCN2 and HCN4. Meanwhile, we also designed the microRNA-masking antisense based on the miR-1 and miR-133 target sites in the 3'UTRs of HCN2 and HCN4 and found that these antisense oligodeoxynucleotides markedly enhanced HCN2/HCN4 expression and function, as reflected by increased protein levels of HCN2/HCN4 and If conductance, by removing the repression of HCN2/HCN4 expression induced by endogenous miR-1/miR-133. The experimental examination of these techniques and the resultant findings not only indicate feasibility of interfering miRNA action in a gene-specific fashion but also may provide a new research tool for studying function of miRNAs. The new approaches also have the potential of becoming alternative gene therapy strategies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".