Heterologous Immunity: Role in Natural and Vaccine-Induced Resistance to Infections
Bibliographic record
Abstract
The central paradigm of vaccination is to generate resistance to infection by a specific pathogen when the vacinee is re-exposed to that pathogen. This paradigm is based on two fundamental characteristics of the adaptive immune system, specificity and memory. These characteristics come from the clonal specificity of T and B cells and the long-term survival of previously-encountered memory cells which can rapidly and specifically expand upon re-exposure to the same specific antigen. However, there is an increasing awareness of the concept, as well as experimental documentation of, heterologous immunity and cross-reactivity of adaptive immune lymphocytes in protection from infection. This awareness is supported by a number of human epidemiological studies in vaccine recipients and/or individuals naturally-resistant to certain infections, as well as studies in mouse models of infections, and indeed theoretical considerations regarding the disproportional repertoire of available T and B cell clonotypes compared to antigenic epitopes found on pathogens. Heterologous immunity can broaden the protective outcomes of vaccinations, and natural resistance to infections. Besides exogenous microbes/pathogens and/or vaccines, endogenous microbiota can also impact the outcomes of an infection and/or vaccination through heterologous immunity. Moreover, utilization of viral and/or bacterial vaccine vectors, capable of inducing heterologous immunity may also influence the natural course of many infections/diseases. This review article will briefly discuss these implications and redress the central dogma of specificity in the immune system.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".