BCG vaccine and COVID-19: implications for infection prophylaxis and cancer immunotherapy
Bibliographic record
Abstract
The COVID-19 pandemic has killed over 400 000 people globally. Ecological evidence indicates that countries with national universal BCG vaccination programs for tuberculosis (TB) prevention have a much lower incidence of severe COVID-19 and mortality compared with those that do not have such programs. BCG is a century old vaccine used for TB prevention via infant/childhood vaccination in lowto middle-income countries with high infection prevalence rate and is known to reduce all-cause neonatal mortality. BCG remains the standard immunotherapy treatment for patients with high-risk non-muscle invasive bladder cancer globally for more than 44 years. Several trials are, therefore, investigating BCG as a prophylactic against COVID-19 in healthcare workers and the elderly. In this commentary, we discuss the potential mechanisms that may underlie BCG associated heterologous protection with a focus on tertiary lymphoid structure (TLS) organogenesis. Given the significance of TLSs in mucosal immunity, their association with positive prognosis and response to immune checkpoint blockade with a critical role of Type I interferon (IFN-1) in inducing these, we also discuss potentiating TLS formation as a promising approach to enhance anti-tumor immunity. We propose that lessons learned from BCG immunotherapy success could be applied to not only augment such microbe-based therapeutics but also lead to similar adjunctive IFN-1 activating approaches to improve response to immune checkpoint blockade therapy in cancer.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".