Robust and prototypical immune responses towards COVID-19 BNT162b2 vaccines in Indigenous people
Bibliographic record
Abstract
Abstract SARS-CoV-2 has led to >270 million infections and >5 million deaths globally. Indigenous people are disproportionately affected by infectious diseases, therefore also more susceptible to the COVID-19 pandemic. There are an estimated 476 million indigenous people globally, including an estimated 798,365 Aboriginal and Torres Strait Islander in Australia. With the high vulnerability to COVID-19, this knowledge is urgently needed to better protect indigenous populations. We evaluated a breadth of immune responses in indigenous (n=57) and non-indigenous (n=49) individuals after COVID-19 vaccination. We tested RBD antibodies, spike/RBD-probe-specific B cells, peptide stimulations with activation-induced marker (AIM) assay and intracellular cytokine staining. We found 22% and 34% seroconversion rates after 1st dose of BNT162b2 vaccine for Indigenous and non-indigenous individuals, respectively, which increased to 100% at 1-mth after 2nd dose for both groups. RBD-specific IgG levels in indigenous individuals at 1-mth after 2nd dose positively correlated with their body mass index. At 1-mth after the 2nd COVID-19 vaccination, CD4+ and CD8+ T cell responses via AIM expression and IFN-γ+ TNF+ production was comparable between indigenous and non-indigenous individuals. We are also going to assess the longevity of antibodies and T cells. Therefore, COVID-19 vaccination induced similar immune responses in indigenous and non-indigenous individuals.
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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) | 0.002 | 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".