Editorial: Immune determinants of COVID-19 protection and disease: A focus on asymptomatic COVID and long COVID
Bibliographic record
Abstract
Immune determinants of COVID-19 protection and disease: A focus on asymptomatic COVID and long COVID SARS-CoV-2 infection manifests as variety of disease presentations, from asymptomatic to mild-moderate COVID-19 symptoms, life-threatening disease, or even persistent debilitating symptoms in some cases (1).Asymptomatic infection occurs in a significant fraction of individuals, and as many as half of all transmission events were reported to occur from pre-symptomatic and asymptomatic individuals (2).One of the most important determinants of disease severity is age as individuals over 65 years have the greatest risk of requiring intensive care, while young children seem to be less severely affected (3, 4).The role of imbalanced immune responses in the overall severity of acute COVID-19 is still not clear.Within this context, we launched our Research Topic "Immune determinants of COVID-19 protection and disease: a Focus on Asymptomatic COVID and Long COVID" on May 31 st 2021 and invited researchers to contribute towards increasing the understanding of the immunological determinants of COVID-19 disease presentation and severity.We received diverse and insightful manuscript applications, of which Frontiers in Immunology published 15 articles from 232 authors of 12 countries.Despite the diversity of this collective venture, the contributions fall into two main areas of research: serological and cellular responses to SARS-CoV-2 infections.A first line of research includes contributions examining serological correlates of immune protection.In their perspective commentary, Narasimhan et al. highlighted the importance of following serological responses in asymptomatic individuals, as they could be silent reservoirs to propagate the infection.After evaluating the antibody profiles in 272 plasma samples collected from 59 COVID-19 patients (18 asymptomatic patients, 33 mildly ill patients and 8 severely ill), measuring the IgG against five viral structural proteins, different isotypes of immunoglobulins against the Receptor Binding Domain (RBD) protein, and neutralizing antibodies, Liao et al. concluded that the overall antibody response was lower in asymptomatic infections than in symptomatic infections throughout the disease course.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.013 | 0.017 |
| Insufficient payload (model declined to judge) | 0.021 | 0.018 |
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".