Tiny Guides, Big Impact: Focus on the Opportunities and Challenges of miR-Based Treatments for ARDS
Bibliographic record
Abstract
Acute Respiratory Distress Syndrome (ARDS) is characterized by lung inflammation and increased membrane permeability, which represents the leading cause of mortality in ICUs. Mechanical ventilation strategies are at the forefront of supportive approaches for ARDS. Recently, an increasing understanding of RNA biology, function, and regulation, as well as the success of RNA vaccines, has spurred enthusiasm for the emergence of novel RNA-based therapeutics. The most common types of RNA seen in development are silencing (si)RNAs, antisense oligonucleotide therapy (ASO), and messenger (m)RNAs that collectively account for 80% of the RNA therapeutics pipeline. These three RNA platforms are the most mature, with approved products and demonstrated commercial success. Most recently, miRNAs have emerged as pivotal regulators of gene expression. Their dysregulation in various clinical conditions offers insights into ARDS pathogenesis and offers the innovative possibility of using microRNAs as targeted therapy. This review synthesizes the current state of the literature to contextualize the therapeutic potential of miRNA modulation. It considers the potential for miR-based therapeutics as a nuanced approach that incorporates the complexity of ARDS pathophysiology and the multifaceted nature of miRNA interactions.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".