Stress Hyperglycemia, Insulin Treatment, and Innate Immune Cells
Bibliographic record
Abstract
Hyperglycemia (HG) and insulin resistance are the hallmarks of a profoundly altered metabolism in critical illness resulting from the release of cortisol, catecholamines, and cytokines, as well as glucagon and growth hormone. Recent studies have proposed a fundamental role of the immune system towards the development of insulin resistance in traumatic patients. A comprehensive review of published literatures on the effects of hyperglycemia and insulin on innate immunity in critical illness was conducted. This review explored the interaction between the innate immune system and trauma-induced hypermetabolism, while providing greater insight into unraveling the relationship between innate immune cells and hyperglycemia. Critical illness substantially disturbs glucose metabolism resulting in a state of hyperglycemia. Alterations in glucose and insulin regulation affect the immune function of cellular components comprising the innate immunity system. Innate immune system dysfunction via hyperglycemia is associated with a higher morbidity and mortality in critical illness. Along with others, we hypothesize that reduction in morbidity and mortality observed in patients receiving insulin treatment is partially due to its effect on the attenuation of the immune response. However, there still remains substantial controversy regarding moderate versus intensive insulin treatment. Future studies need to determine the integrated effects of HG and insulin on the regulation of innate immunity in order to provide more effective insulin treatment regimen for these patients.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".