Genome-wide analysis of microRNAs expression profiling in patients with primary IgA nephropathy
Bibliographic record
Abstract
The aim of this study was to investigate the differential expression characteristics and the roles of the genome-wide microRNAs (miRNAs) in immunoglobulin A nephropathy (IgAN) kidney tissues. We used Illumina high-throughput sequencing technology to evaluate the miRNAs expression of six biopsy tissues from IgAN and six normal renal cortex specimens from patients with renal cell carcinoma. We observed a total of 85 miRNAs that were differentially expressed in the six IgAN patients, of which 11 miRNAs were up-regulated and 74 miRNAs were down-regulated in patients' tissues compared with control tissues. Additionally, we identified 55 candidate novel miRNAs in our study, which comprised seven candidates who were detected in the IgAN group and 49 candidates who were detected in the control group. Only one candidate (miR-n-9) was expressed in both groups. The bioinformatics showed that the regulated target genes of differentially expressed miRNAs were associated with immune and renal pathological changes. The identification of specific tissue miRNAs in our study not only helped clarify the genetics or immunology mechanisms involved in the pathogenesis of IgAN but also helped explain the pathological changes in the kidney tissues. We hypothesize that some significant miRNAs might potentially serve as novel diagnostic biomarkers in IgAN patients.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".