Identification of differentially expressed miRNA 48 h after cerebral ischemia–reperfusion injury in mice by the technique of miRNA microarray
Bibliographic record
Abstract
The objective was to identify the differential expressed miRNA during cerebral ischemia-reperfusion injury (CIRI) process, thereby assisting in elucidating the mechanism of CIRI development and providing a potential target for CIRI prevention and treatment. Six mice were randomly assigned to two groups: control group and CIRI model group. A global cerebral IR model by four-vessel occlusion was prepared among the CIRI model group. Brain tissues were collected 48 h after reperfusion. Total RNA was extracted for each sample. miRNA microarrays were employed to detect the differentially expressed miRNA between the CIRI group and the control group. One differentially expressed miRNA was selected for verification by PCR. Compared with the control group, 69 miRNAs were significantly differential expressed in samples of the CIRI group, among which 50 miRNAs were upregulated and 19 miRNAs were downregulated. The real-time qPCR results indicated that the results of the miRNA microarray were reliable. A number of miRNAs were significantly regulated in the CIRI model, which suggested that miRNA was closely associated with the pathological alterations after ischemia. These identified miRNAs may provide directions and targets for the future pathological research of CIRI.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".