Pre-Clinical Studies of MicroRNA-Based Therapies for Sepsis: A Scoping Review
Bibliographic record
Abstract
Background: Sepsis is a severe and life-threatening condition triggered by a dysregulated response to infection, leading to organ failure and, often, death. The syndrome is expensive to treat, with survivors frequently experiencing reduced quality of life and enduring various long-term disabilities. The increasing understanding of RNA, RNA biology, and therapeutic potential offers an unprecedented opportunity to develop innovative therapy. Objective: This study is a scoping review focusing on pre-clinical studies of microRNA (miRNA)-based therapies for sepsis. Methodology: A scoping review. The search strategy identified papers published in PubMed until 15 October 2023, using the keywords (microRNA) AND (sepsis) AND (animal model). Inclusion criteria included papers that used either gain- or loss-of-function approaches, excluding papers that did not focus on microRNAs as therapy targets, did not include animal models, did not show organ failure-specific assessments, and focused on microRNAs as biomarkers. The PRISMA-ScR guideline was used in this study. Results: A total of 199 articles were identified that featured the terms “microRNA/miRNA/miR”, “Sepsis”, and “animal model”. Of these, 51 articles (25.6%) employed miRNA-based therapeutic interventions in animal models of sepsis. Of these, 15 studies extended their inquiry to include or reference human clinical data. Key microRNAs of interest and their putative mechanisms of action in sepsis are highlighted. Conclusions: The body of work examined herein predominantly addresses various dimensions of sepsis-induced organ dysfunction, supporting the emerging role of miRNAs as potential therapeutic candidates. However, nearly 5% of papers on miR-based therapy have been retracted over the past 5 years, raising important concerns regarding the quality and complexity of the biology and models for assessing therapeutic potential.
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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 | 0.023 | 0.084 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.022 | 0.018 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".