Integrated antibody and cellular immunity monitoring are required for assessment of the long term protection that will be essential for effective next generation vaccine development
Bibliographic record
Abstract
The COVID pandemic exposed the critical role T cells play in initial immunity, the establishment and maintenance of long term protection, and of durable responsiveness against novel viral variants. A growing body of evidence indicates that adding measures of cellular immunity will fill an important knowledge gap in vaccine clinical trials, likely leading to improvements in the effectiveness of the next generation vaccines against current and emerging variants. In depth cellular immune monitoring in Phase II trials, particularly for high risk populations such as the elderly or immune compromised, should result in better understanding of the dynamics and requirements for establishing effective long term protection. Such analyses can result in cellular immunity correlates that can then be deployed in Phase III studies using appropriate, scalable technologies. Measures of cellular immunity are less established than antibodies as correlates of clinical immunity, and some misconceptions persist about cellular immune monitoring usefulness, cost, complexity, feasibility, and scalability. We outline the currently available cellular immunity assays, review their readiness for use in clinical trials, their logistical requirements, and the type of information each assay generates. The objective is to provide a reliable source of information that could be leveraged to develop a rational approach for comprehensive immune monitoring during vaccine development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".