MicroRNA Expression Patterns of CD8+ T Cells in Acute and Chronic Brucellosis
Bibliographic record
Abstract
Although our knowledge about Brucella virulence factors and the host response increase rapidly, the mechanisms of immune evasion by the pathogen and causes of chronic disease are still unknown. Here, we aimed to investigate the immunological factors which belong to CD8+ T cells and their roles in the transition of brucellosis from acute to chronic infection. Using miRNA microarray, more than 2000 miRNAs were screened in CD8+ T cells of patients with acute or chronic brucellosis and healthy controls that were sorted from peripheral blood with flow cytometry and validated through qRT-PCR. Findings were evaluated using GeneSpring GX (Agilent) 13.0 software and KEGG pathway analysis. Expression of two miRNAs were determined to display a significant fold change in chronic group when compared with acute or control groups. Both miRNAs (miR-126-5p and miR-4753-3p) were decreased (p <0.05 or fold change > 2). These miRNAs have the potential to be the regulators of CD8+ T cell-related marker genes for chronic brucellosis infections. The differentially expressed miRNAs and their predicted target genes are involved in MAPK signaling pathway, cytokine-cytokine receptor interactions, endocytosis, regulation of actin cytoskeleton, and focal adhesion indicating their potential roles in chronic brucellosis and its progression. It is the first study of miRNA expression analysis of human CD8+ T cells to clarify the mechanism of inveteracy in brucellosis.
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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) | 0.000 | 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) | 0.001 | 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".