Immune potency of bioreactor-aged dendritic cells in 3D collagen matrices
Bibliographic record
Abstract
Abstract The processes of aging and space travel both have significant adverse effects on the immune system, resulting in increased susceptibility to infections. Using simulated microgravity platforms, such as the random positioning machine (RPM), on Earth allows us to investigate these effects to better facilitate future space travel and our understanding of the aging immune system. Dendritic cells (DCs) are key players in linking the innate and adaptive immune responses. Their distinct differentiation and maturation phases play vital roles in presenting antigens and mounting effective T-cell responses. However, DCs primarily reside in tissues such as the skin and lymph nodes. To date, no studies have effectively investigated the effects of aging via RPM on DCs in their native microenvironment. With 3D biomimetic collagen hydrogels, we can study the effects on DCs in more physiologically relevant microenvironments. In this study, we investigated the effects of loose and dense culture matrices on the phenotype, function, and transcriptome profile of immature and mature DCs utilizing an RPM to simulate an accelerated aging model. Our data indicate that an aged, or loose tissue microenvironment, and exposure to RPM conditions decrease the immunogenicity of iDCs and mDCs. Interestingly, cells cultured in dense matrices experienced fewer effects by the RPM at the transcriptome level.
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".