The Connection between MiR-122 and Lymphocytes in Patients Receiving Treatment for Chronic Hepatitis B Virus Infection
Bibliographic record
Abstract
New molecular predictors for the response to treatment in HBV (hepatitis B virus) infection are assessed. Among them is miR-122. Our article searches the connection between miR-122 and the counts of lymphocytes in chronic HBV patients receiving treatment. We included the sera of 38 Romanian subjects with chronic HBV infection (20 receiving treatment and 18 not receiving treatment) and 5 healthy controls. The expression of miR-122 was determined using RT-PCR (real-time PCR) and a 2−ΔΔCT method. Two systematic analyses were also performed on databases (PUBMED, Web of Science, and Science Direct), eliminating systematic reviews, editorials, letters to editors, meta-analyses, reviews, conference proceedings, or pre-print manuscripts. We included human-based articles following the PRISMA criteria and the Newcastle Ottawa Assessment Scale for Case–Control and Cohort studies. R 4.2.2 was used for statistics, and MIENTURNET and STRING were used for the bioinformatic analysis. Our results showed a link between the variations in the expression of miR-122 and the counts of lymphocytes in HBV Romanian patients receiving therapy. Treatment influenced miR-122 and the lymphocyte numbers. This is the first study with these results, and it may lead to a new perspective on the inter-relationships between microRNAs and therapy in HBV 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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".