Neuroimmune recognition and regulation in the respiratory system
Bibliographic record
Abstract
Neuroimmune recognition and regulation in the respiratory system is a complex and highly coordinated process involving interactions between the nervous and immune systems to detect and respond to pathogens, pollutants and other potential hazards in the respiratory tract. This interaction helps maintain the health and integrity of the respiratory system. Therefore, understanding the complex interactions between the respiratory nervous system and immune system is critical to maintaining lung health and developing treatments for respiratory diseases. In this review, we summarise the projection distribution of different types of neurons (trigeminal nerve, glossopharyngeal nerve, vagus nerve, spinal dorsal root nerve, sympathetic nerve) in the respiratory tract. We also introduce several types of cells in the respiratory epithelium that closely interact with nerves (pulmonary neuroendocrine cells, brush cells, solitary chemosensory cells and tastebuds). These cells are primarily located at key positions in the respiratory tract, where nerves project to them, forming neuroepithelial recognition units, thus enhancing the ability of neural recognition. Furthermore, we summarise the roles played by these different neurons in sensing or responding to specific pathogens (influenza, severe acute respiratory syndrome coronavirus 2, respiratory syncytial virus, human metapneumovirus, herpes viruses, Sendai parainfluenza virus, Mycobacterium tuberculosis , Pseudomonas aeruginosa , Staphylococcus aureus , amoebae), allergens, atmospheric pollutants (smoking, exhaust pollution), and their potential roles in regulating interactions among different pathogens. We also summarise the prospects of bioelectronic medicine as a third therapeutic approach following drugs and surgery, as well as the potential mechanisms of meditation breathing as an adjunct therapy.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".