Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Sepsis is defined as a state of multisystem organ dysfunction secondary to a dysregulated host response to infection and causes millions of deaths worldwide annually. Novel ways to counteract this disease are needed and such tools may be heralded by a detailed understanding of its molecular pathogenesis. MiRNAs are small RNA molecules that target mRNAs to inhibit or degrade their translation and have important roles in several disease processes including sepsis. MAIN BODY: The current review adopted a strategic approach to analyzing the widespread literature on the topic of miRNAs and sepsis. A pubmed search of "miRNA or microRNA or small RNA and sepsis not review" up to and including January 2021 led to 1140 manuscripts which were reviewed. Two hundred and thirty-three relevant papers were scrutinized for their content and important themes on the topic were identified and subsequently discussed, including an in-depth look at deregulated miRNAs in sepsis in peripheral blood, myeloid derived suppressor cells and extracellular vesicles. CONCLUSION: Our analysis yielded important observations. Certain miRNAs, namely miR-150 and miR-146a, have consistent directional changes in peripheral blood of septic patients across numerous studies with strong data supporting a role in sepsis pathogenesis. Furthermore, a large body of literature show miRNA signatures of clinical relevance, and lastly, many miRNAs deregulated in sepsis are associated with the process of endothelial dysfunction. This review offers a widespread, up-to-date and detailed discussion of the role of miRNAs in sepsis and is meant to stimulate further work in the field due to the potential of these small miRNAs in prompt diagnostics, prognostication and therapeutic agency.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 it