3D microenvironment attenuates simulated microgravity-mediated changes in T cell transcriptome
Bibliographic record
Abstract
Abstract Human space travel and exploration are of interest to both the industrial and scientific community. However, there are many adverse effects of spaceflight on human physiology. In particular, there is a lack of understanding to the extent by which microgravity affects the immune system. T cells, key players of the adaptive immune system and long term immunity, are present not only in blood circulation, but also reside within tissue. As of yet, studies investigating the effects of microgravity on T cells are limited to peripheral blood or traditional 2D cell culture that recapitulates circulating blood. To better mimic interstitial tissue, 3D cell culture has been well established for physiologically and pathologically relevant models. In this work, we utilize 2D and 3D cell culture to gain an understanding of how simulated microgravity affects both circulating and tissue resident T cells. T cells were studied in both resting and activated stages. We found that 3D cell culture attenuates the effects of simulated microgravity on the T cells transcriptome and nuclear irregularities compared to 2D cell culture. Interestingly, simulated microgravity appears to have less effect on activated T cells compared to those in the resting stage. Overall, our work provides novel insights into the effects of simulated microgravity on circulating and tissue resident T cells which could provide benefits for the health of space travelers.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".