Expression of microRNA-1234 related signal transducer and activator of transcription 3 in patients with diffuse large B-cell lymphoma of activated B-cell like type from high and low infectious disease areas
Bibliographic record
Abstract
Abstract Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous disease, and infectious agents are suspected to be involved in the tumorigenesis of DLBCL. MicroRNAs (miRNAs) are non-coding RNAs modulating protein expression. We compared miRNA expression profiles in lymph node tissues of patients with DLBCL of the activated B-cell like (ABC) type from two geographical areas with different background exposures, Sweden and Egypt. We showed previously that DLBCL tissues of the ABC-type in Swedish patients had a higher expression of signal transducer and activator of transcription 3 (STAT3) compared to Egyptian patients. Here, we analyzed the involvement of miRNAs in STAT3 regulation. miR-1234 was significantly up-regulated in Egyptian patients with DLBCL compared to Swedish patients (p < 0.03). The miR-1234 expression level correlated inversely with the expression of STAT3. The Stat3 protein was down-regulated in cells transfected with miR-1234, suggesting that STAT3 might be a potential target for miR-1234. miR-1234 and STAT3 might be involved in the tumorigenesis of DLBCL of ABC type and possibly associated with environmental background exposure.
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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 | 0.001 | 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".