Bibliographic record
Abstract
In the coming decades, many developed countries in the world are expecting the "greying" of their populations. This phenomenon poses unprecedented challenges to healthcare systems. Aging is one of the most important risk factors for infections and a myriad of diseases such as cancer, cardiovascular and neurodegenerative diseases. A common denominator that is implicated in these diseases is the immune system. The immune system consists of the innate and adaptive arms that complement each other to provide the host with a holistic defense system. While the diverse interactions between multiple arms of the immune system are necessary for its function, this complexity is amplified in the aging immune system as each immune cell type is affected differently-resulting in a conundrum that is especially difficult to target. Furthermore, certain cell types, such as γδ T cells, do not fit categorically into the arms of innate or adaptive immunity. In this review, we will first introduce the human γδ T cell family and its ligands before discussing parallels in mice. By covering the ontogeny and homeostasis of γδ T cells during their lifespan, we will better capture their evolution and responses to age-related stressors. Finally, we will identify knowledge gaps within these topics that can advance our understanding of the relationship between γδ T cells and aging, as well as age-related diseases such as cancer.
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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 | 0.000 |
| 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 | 0.007 |
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; both teacher heads agree on what is shown here.
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".