An intricate link between autophagy and microRNAs in cystic fibrosis (HUM1P.262)
Bibliographic record
Abstract
Abstract Each year 1,000 children and adults are diagnosed with Cystic Fibrosis (CF), a fatal genetic disorder that critically affects the lungs. Autophagy, a highly-regulated biological process, normally functions to clear dysfunctional CFTR (CF transmembrane conductance regulator) proteins that aggregate within macrophages. However, this process is defective in CF patients and CF mice, as their macrophages express limited autophagy activity thus exacerbating inflammation. Present therapies to improve autophagy are ineffective. MicroRNAs (miRNAs, miRs) are non-coding RNAs that post-transcriptionally regulate targeted mRNA expression. The objective for this study is to elucidate the role of miRNAs in CF macrophages in an effort to restore autophagy. We hypothesize CF macrophages exhibit elevated cluster expression that downregulate autophagy targets thus contributing to autophagy dysfunction. We find that, CF macrophages exhibit decreased autophagy protein expression and elevated cluster expression compared to WT. When cluster expression is absent, autophagy protein expression is restored, suggesting the canonical inverse relationship between miRNA and protein expression. Predicted autophagy targets of specific miRs comprising the cluster were validated. In vivo downregulation of specific miRs comprising the cluster increases autophagy expression. Thus, this data demonstrates, microRNA cluster expression correlates to autophagy expression which modulates the pathophysiology of CF.
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.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".