A metabolic perspective of the neutrophil life cycle: new avenues in immunometabolism
Bibliographic record
Abstract
Neutrophils are the most abundant innate immune cells. Multiple mechanisms allow them to engage a wide range of metabolic pathways for biosynthesis and bioenergetics for mediating biological processes such as development in the bone marrow and antimicrobial activity such as ROS production and NET formation, inflammation and tissue repair. We first discuss recent work on neutrophil development and functions and the metabolic processes to regulate granulopoiesis, neutrophil migration and trafficking as well as effector functions. We then discuss metabolic syndromes with impaired neutrophil functions that are influenced by genetic and environmental factors of nutrient availability and usage. Here, we particularly focus on the role of specific macronutrients, such as glucose, fatty acids, and protein, as well as micronutrients such as vitamin B3, in regulating neutrophil biology and how this regulation impacts host health. A special section of this review primarily discusses that the ways nutrient deficiencies could impact neutrophil biology and increase infection susceptibility. We emphasize biochemical approaches to explore neutrophil metabolism in relation to development and functions. Lastly, we discuss opportunities and challenges to neutrophil-centered therapeutic approaches in immune-driven diseases and highlight unanswered questions to guide future discoveries.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.007 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".