<scp>mi</scp><scp>RNA</scp>in the Regulation of Ion Channel/Transporter Expression
Bibliographic record
Abstract
Ion channels and transporters are expressed in every living cell, where they participate in controlling a plethora of biological processes and physiological functions, such as excitation of cells in response to stimulation, electrical activities of cells, excitation-contraction coupling, cellular osmolarity, and even cell growth and death. Alterations of ion channels/transporters can have profound impacts on the cellular physiology associated with these proteins. Expression of ion channels/transporters is tightly regulated and expression deregulation can trigger abnormal processes, leading to pathogenesis, the channelopathies. While transcription factors play a critical role in controlling the transcriptome of ion channels/transporters at the transcriptional level by acting on the 5'-flanking region of the genes, microribonucleic acids (miRNAs), a newly discovered class of regulators in the gene network, are also crucial for expression regulation at the posttranscriptional level through binding to the 3'untranslated region of the genes. These small noncoding RNAs fine tune expression of genes involved in a wide variety of cellular processes. Recent studies revealed the role of miRNAs in regulating expression of ion channels/transporters and the associated physiological functions. miRNAs can target ion channel genes to alter cardiac excitability (conduction, repolarization, and automaticity) and affect arrhythmogenic potential of heart. They can modulate circadian rhythm, pain threshold, neuroadaptation to alcohol, brain edema, etc., through targeting ion channel genes in the neuronal systems. miRNAs can also control cell growth and tumorigenesis by acting on the relevant ion channel genes. Future studies are expected to rapidly increase to unravel a new repertoire of ion channels/transporters for miRNA regulation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.086 | 0.044 |
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".