Broad spectrum <scp>SARS‐CoV</scp>‐2‐specific immunity in hospitalized First Nations peoples recovering from <scp>COVID</scp>‐19
Bibliographic record
Abstract
Abstract Indigenous peoples globally are at increased risk of COVID‐19‐associated morbidity and mortality. However, data that describe immune responses to SARS‐CoV‐2 infection in Indigenous populations are lacking. We evaluated immune responses in Australian First Nations peoples hospitalized with COVID‐19. Our work comprehensively mapped out inflammatory, humoral and adaptive immune responses following SARS‐CoV‐2 infection. Patients were recruited early following the lifting of strict public health measures in the Northern Territory, Australia, between November 2021 and May 2022. Australian First Nations peoples recovering from COVID‐19 showed increased levels of MCP‐1 and IL‐8 cytokines, IgG‐antibodies against Delta‐RBD and memory SARS‐CoV‐2‐specific T cell responses prior to hospital discharge in comparison with hospital admission, with resolution of hyperactivated HLA‐DR + CD38 + T cells. SARS‐CoV‐2 infection elicited coordinated ASC, Tfh and CD8 + T cell responses in concert with CD4 + T cell responses. Delta and Omicron RBD‐IgG, as well as Ancestral N‐IgG antibodies, strongly correlated with Ancestral RBD‐IgG antibodies and Spike‐specific memory B cells. We provide evidence of broad and robust immune responses following SARS‐CoV‐2 infection in Indigenous peoples, resembling those of non‐Indigenous COVID‐19 hospitalized patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".