Humoral and T cell responses to SARS-CoV-2 reveal insights into immunity during the early pandemic period in Pakistan
Bibliographic record
Abstract
BACKGROUND: Protection against SARS-CoV-2 is mediated by humoral and T cell responses. Pakistan faced relatively low morbidity and mortality from COVID-19 through the pandemic. To examine the role of prior immunity in the population, we studied IgG antibody response levels, virus neutralizing activity and T cell reactivity to Spike protein in a healthy control group (HG) as compared with COVID-19 cases and individuals from the pre-pandemic period (PP). METHODS: HG and COVID-19 participants were recruited between October 2020 and May 2021. Pre-pandemic sera was collected before 2018. IgG antibodies against Spike and its Receptor Binding Domain (RBD) were determined by ELISA. Virus neutralization activity was determined using a PCR-based micro-neutralization assay. T cell - IFN-γ activation was assessed by ELISpot. RESULTS: Overall, the magnitude of anti-Spike IgG antibody levels as well as seropositivity was greatest in COVID-19 cases (90%) as compared with HG (39.8%) and PP (12.2%). During the study period, Pakistan experienced three COVID-19 waves. We observed that IgG seropositivity to Spike in HG increased from 10.3 to 83.5% during the study, whilst seropositivity to RBD increased from 7.5 to 33.3%. IgG antibodies to Spike and RBD were correlated positively in all three study groups. Virus neutralizing activity was identified in sera of COVID-19, HG and PP. Spike reactive T cells were present in COVID-19, HG and PP groups. Individuals with reactive T cells included those with and without IgG antibodies to Spike. CONCLUSIONS: Antibody and T cell responses to Spike protein in individuals from the pre-pandemic period suggest prior immunity against SARS-CoV-2, most likely from cross-reactive responses. The rising seroprevalence observed in healthy individuals through the pandemic without known COVID-19 may be due to the activation of adaptive immunity from cross-reactive memory B and T cells. This may explain the more favourable COVID-19 outcomes observed in this population.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".