A GMCSF and IL7 fusion cytokine leads to functional thymic‐dependent T‐cell regeneration in age‐associated immune deficiency
Bibliographic record
Abstract
The competence of cellular immunity depends on a diverse T-cell receptor (TCR) repertoire arising from thymic output. Normal thymopoiesis arises from marrow-derived CD3(-)CD4(-)CD8(-) triple-negative T-cell progenitors (TN), which develop into mature single-positive (SP) CD4 or CD8 T cells after expressing both CD4 and CD8 (double-positive, DP) transiently, leading to de novo T-cell production. Interleukin-7 (IL7) is a singularly important common γ-chain IL involved in normal thymic development. Our previous work has demonstrated that γc cytokines fused with granulocyte-macrophage colony stimulating factor (GMCSF) at the N-terminus acquire unheralded biological properties. Therefore, to enhance thymopoiesis, we developed a novel biopharmaceutical based on the fusion of GMCSF and IL7, hereafter GIFT7. Systemic administration of GIFT7 leads to cortical thymic hyperplasia including the specific expansion of CD44(int)CD25(-) double-negative 1 (DN1) thymic progenitors. During murine cytomegalovirus (mCMV) infection of aged animals, GIFT7-mediated neo-thymopoiesis led to increased absolute numbers of viral-specific CD8(+) T cell. Our work demonstrated that thymic precursors can be therapeutically repopulated and its reconstitution leads to meaningful central and peripheral T-cell neogenesis, correcting immune dysfunction arising from age-associated thymic atrophy.
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 | 0.000 | 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 | 0.000 |
| 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".