Hepatitis B Virus Vaccine–Induced Cell-Mediated Immunity Correlates with Humoral Immune Response following Primary Vaccination during Infancy
Bibliographic record
Abstract
Abstract Data on hepatitis B virus (HBV) vaccine–induced cell-mediated immunity (CMI) and humoral immune response during infancy is scarce. We assessed HBV vaccine–induced CMI among infants stratified as nonresponders (Ab against HBV surface Ag [anti-HBs] levels <10 IU/l), low-responders (anti-HBs levels 10–100 IU/l), and high-responders (anti-HBs levels ≥100 IU/l) following their primary vaccination against HBV. Moreover, we assessed the association between HBV vaccine–induced CMI and anti-HBs levels. Infants were immunized with HBV vaccine at ages 2, 4, and 6 mo. Hepatitis B surface Ag (HBsAg)-specific proliferation, intracellular cytokine production, and bulk cytokine secretion were assessed on PBMCs collected at 1 y and anti-HBs levels were measured at 1 and 2 y of age. Infants classified at 2 y of age as low-responders (n = 28) had lower median levels of secreted IFN-γ than high-responders (n = 29), 17.15 pg/ml versus 33.16 pg/ml, respectively, p = 0.009. Infants classified at 2 y of age as nonresponders (n = 15) had lower median levels of secreted TNF-α than high-responders (n = 29), 116.11 pg/ml versus 162.27 pg/ml, respectively, p = 0.032. There was a positive correlation between HBsAg-specific secreted IFN-γ levels at 1 y and anti-HBs levels at 1 and 2 y of age, rho = 0.269 and 0.302, respectively, (p = 0.019 and p = 0.01, respectively). There was a positive correlation between anti-HBs levels at age 1 y and the levels of secreted IL-10, rho = 0.297, p = 0.009. HBsAg-specific IFN-γ, IL-10, and TNF-α secretion correlated with HBV vaccine–induced humoral immune response. Assessment of CMI is a useful adjunct in demonstrating the persistence of HBV vaccine–induced memory immune response.
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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 | 0.001 | 0.001 |
| 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".