Human Leukocyte Transcriptome Changes During The Transition To And From 60 Days Of Bed Rest
Bibliographic record
Abstract
PURPOSE: We sought to investigate the effect of unloading and physical inactivity typical of the bed rest model by identifying leukocyte transcriptome changes in participants that underwent 60 days of bed rest followed by reambulation. Previous work from our lab utilized a time course analysis and identified temporal expression changes in 2,415 protein-coding transcripts (Stratis et al., 2022). Our current work is focused on selective time-point comparisons to reveal expression changes in both coding and non-coding genes specific to the bed rest and reambulation study phases. METHODS: This longitudinal study design collected ten blood samples from twenty healthy male participants. We measured gene expression through RNA sequencing of leukocytes and applied linear mixed modelling to assess differential expression at the following time-points: model 1, baseline data collection (BDC) (BDC-12 and BDC-11 combined) vs head-down tilt (HDT) bed rest (HDT1, HDT2, HDT30, HDT60); and model 2, HDT60 vs reambulation (R1, R2, R12, R30). RESULTS: Model 1 found 30/44 (68%) differentially expressed genes (α < 0.05 & log fold change>|1|) were between baseline and early bed rest (BDC-12/-11 vs HDT2). Model 2 found 24/37 (65%) differentially expressed genes were between late bed rest and early reambulation (HDT60 vs R1). CONCLUSIONS: Major transcriptome changes occurred early at the transitions to and from bed rest. Current findings can guide future work on the complex responses and adaptation mechanisms experienced during physical inactivity and unloaded environments.
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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 | 0.001 |
| 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 | 0.000 |
| 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".