Neutrophils and Influenza: A Thin Line between Helpful and Harmful
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Influenza viruses are one of the most prevalent respiratory pathogens known to humans and pose a significant threat to global public health each year. Annual influenza epidemics are responsible for 3-5 million infections worldwide and approximately 500,000 deaths. Presently, yearly vaccinations represent the most effective means of combating these viruses. In humans, influenza viruses infect respiratory epithelial cells and typically cause localized infections of mild to moderate severity. Neutrophils are the first innate cells to be recruited to the site of the infection and possess a wide range of effector functions to eliminate viruses. Some well-described effector functions include phagocytosis, degranulation, the production of reactive oxygen species (ROS), and the formation of neutrophil extracellular traps (NETs). However, while these mechanisms can promote infection resolution, they can also contribute to the pathology of severe disease. Thus, the role of neutrophils in influenza viral infection is nuanced, and the threshold at which protective functions give way to immunopathology is not well understood. Moreover, notable differences between human and murine neutrophils underscore the need to exercise caution when applying murine findings to human physiology. This review aims to provide an overview of neutrophil characteristics, their classic effector functions, as well as more recently described antibody-mediated effector functions. Finally, we discuss the controversial role these cells play in the context of influenza virus infections and how our knowledge of this cell type can be leveraged in the design of universal influenza virus vaccines.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it