Targeting immunosenescence for improved tumor immunotherapy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Tumor immunotherapy has significantly transformed the field of oncology over the past decade. An optimal tumor immunotherapy would ideally elicit robust innate and adaptive immune responses within tumor immune microenvironment (TIME). Unfortunately, immune system experiences functional decline with chronological age, a process termed "immunosenescence," which contributes to impaired immune responses against pathogens, suboptimal vaccination outcomes, and heightened vulnerability to various diseases, including cancer. In this context, we will elucidate hallmarks and molecular mechanisms underlying immunosenescence, detailing alterations in immunosenescence at molecular, cellular, organ, and disease levels. The role of immunosenescence in tumorigenesis and senescence-related extracellular matrix (ECM) has also been addressed. Recognizing that immunosenescence is a dynamic process influenced by various factors, we will evaluate treatment strategies targeting hallmarks and molecular mechanisms, as well as methods for immune cell, organ restoration, and present emerging approaches in immunosenescence for tumor immunotherapy. The overarching goal of immunosenescence research is to prevent tumor development, recurrence, and metastasis, ultimately improving patient prognosis. Our review aims to reveal latest advancements and prospective directions in the field of immunosenescence research, offering a theoretical basis for development of practical anti-immunosenescence and anti-tumor strategies.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it