Understanding lymphocyte metabolism for use in cancer immunotherapy
Bibliographic record
Abstract
/M. This methodical series of steps ensures fidelity in the generation of two identical T cells during a single round of division. To achieve this, T cells must activate or inactivate metabolic pathways at discrete times during each phase of the cell cycle. This permits the generation of substrates to support biosynthesis, bioenergetics and the epigenetic changes required for proper differentiation and function. The precursors that feed into these pathways are often shared, highlighting the complex relationship between metabolism and cellular processes that are essential to lymphocytes. It is therefore not surprising that different T cell subtypes exhibit unique metabolic dependencies that change as they mature and go through specialized differentiation programmes. The importance of the influence of metabolism on T cells is underscored by the emerging field of cancer immunotherapy, where autologous T cells can be manufactured ex vivo then infused as a form of curative treatment for human cancers. This review will highlight some of the recent knowledge on T lymphocyte metabolism and give a perspective on the practical implications for cellular-based immunotherapy.
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 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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".