A microRNA checkpoint for Ca2+ signaling and overload in acute pancreatitis
Bibliographic record
Abstract
Acute pancreatitis (AP) is a common digestive disease without specific treatment, and its pathogenesis features multiple deleterious amplification loops dependent on translation, triggered by cytosolic Ca 2+ ([Ca 2+ ] i ) overload; however, the underlying mechanisms in Ca 2+ overload of AP remains incompletely understood.Here we show that microRNA-26a (miR-26a) inhibits pancreatic acinar cell (PAC) store-operated Ca 2+ entry (SOCE) channel expression, Ca 2+ overload, and AP.We find that major SOCE channels are post-transcriptionally induced in PACs during AP, whereas miR-26a expression is reduced in experimental and human AP and correlated with AP severity.Mechanistically, miR-26a simultaneously targets Trpc3 and Trpc6 SOCE channels and attenuates physiological oscillations and pathological elevations of [Ca 2+ ] i in PACs.MiR-26a deficiency increases SOCE channel expression and [Ca 2+ ] i overload, and significantly exacerbates AP.Conversely, global or PAC-specific overexpression of miR-26a in mice ameliorates pancreatic edema, neutrophil infiltration, acinar necrosis, and systemic inflammation, accompanied with remarkable improvements on pathological determinants related with [Ca 2+ ] i overload.Moreover, pancreatic or systemic administration of an miR-26a mimic to mice significantly alleviates experimental AP.These findings reveal a previously unknown mechanism underlying AP pathogenesis, establish a critical role for miR-26a in Ca 2+ signaling in the exocrine pancreas, and identify a potential target for the treatment of AP.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".