Effects of an aged tissue niche on the immune potency of dendritic cells using simulated microgravity
Bibliographic record
Abstract
Microgravity accelerates the aging of various physiological systems, and it is well acknowledged that aged individuals and astronauts both have increased susceptibility to infections and poor response to vaccination. Immunologically, dendritic cells (DCs) are the key players in linking innate and adaptive immune responses. Their distinct and optimized differentiation and maturation phases play a critical role in presenting antigens and mounting effective lymphocyte responses for long-term immunity. Despite their importance, no studies to date have effectively investigated the effects of microgravity on DCs in their native microenvironment, which is primarily located within tissues. Here, we address a significantly outstanding research gap by examining the effects of simulated microgravity via a random positioning machine on both immature and mature DCs cultured in biomimetic collagen hydrogels, a surrogate for tissue matrices. Furthermore, we explored the effects of loose and dense tissues via differences in collagen concentration. Under these various environmental conditions, the DC phenotype was characterized using surface markers, cytokines, function, and transcriptomic profiles. Our data indicate that aged or loose tissue and exposure to RPM-induced simulated microgravity both independently alter the immunogenicity of immature and mature DCs. Interestingly, cells cultured in denser matrices experience fewer effects of simulated microgravity at the transcriptome level. Our findings are a step forward to better facilitate healthier future space travel and enhance our understanding of the aging immune system on Earth.
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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 | 0.000 |
| 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".