The regulated long-term delivery of therapeutic proteins by using antigen-specific B lymphocytes
Bibliographic record
Abstract
Memory lymphocytes are important mediators of the immune response. These cells are long-lived and undergo clonal expansion upon reexposure to specific antigen, differentiating into effector cells that secrete Ig or cytokines while maintaining a residual pool of memory T and B lymphocytes. Here, the ability of antigen-specific lymphocytes to undergo repeated cycles of antigen-driven clonal expansion and contraction is exploited in a therapeutic protocol aimed at regulating protein delivery. The principle of this strategy is to introduce genes encoding proteins of therapeutic interest into a small number of antigen-specific B lymphocytes. Output of therapeutic protein can then be regulated in vivo by manipulating the size of the responder population by antigen challenge. To evaluate whether such an approach is feasible, we developed a mouse model system in which Emu- and Iglambda-based vectors were used to express human erythropoietin (hEPO) gene in B lymphocytes. These mice were then immunized with the model antigen phycoerythrin (PE), and immune splenocytes (or purified PE-specific B lymphocytes) were adoptively transferred to normal or mutant (EPO-deficient) hosts. High levels of hEPO were detected in the serum of adoptively transferred normal mice after PE administration, and this responsiveness was maintained for several months. Similarly, in EPO-deficient anemic recipients, antigen-driven hEPO expression was shown to restore hematocrit levels to normal. These results show that antigen-mediated regulation of memory lymphocytes can be used as a strategy for delivering therapeutic proteins in vivo.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".