Modulation of pancreatic islet function in a microgravity analog
Bibliographic record
Abstract
Endocrines, cytokines, and nutrients play essential roles in maintaining muscle in extended space flight. We determined the influence of a microgravity analog upon nutritional and immunologic function of pancreatic endocrine cells. High Aspect Ratio Vessel (HARV) was used as a microgravity analog. Human islets of Langerhans were cultivated in the HARV or in static plate (PLT) up to 48 hours at 37 O C in 5% CO 2 and 95% H 2 O. Islets were treated with hydrocortisone (HCS) and a mixture of 11 essential amino acids (AA). Insulin, glucagon, and 41 amino acids metabolites were assessed in supernatants. In islet tissue we determined gene expression of insulin, glucagon, somatostatin, tumor necrosis factor (TNF)‐α, TNF‐α receptor (rTNF‐α), interleukin (IL)‐1â, IL‐1 receptor (rIL‐1), and IL‐1 receptor antagonist (raIL‐1). We observed significantly (P<0.05) lower glutamine concentration in PLT (0.63±0.2ìmol/mL) compare to HARV (1.3±0.2 ìmol/mL). Additionally, increased arginine/ornithine ratio was observed in PLT but not HARV. We conclude that a microgravity analog alters insulin gene expression. A combination of cell culture and endocrine factors altered gene expression of glucagon, somatostatin, IL‐1β, rIL‐1, raIL‐1, and TNF‐α, but not TNF‐α receptor. HCS treatment is able to recover suppressed insulin, glucagon, somatostatin, and IL‐1â gene expression in both HARV and PLT cultures. Supported by NIH R15 GM62795‐03 and NSBRI through Cooperative Agreement NCC 9 58 with NASA.
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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".