Human Airway Epithelium Responses to Invasive Fungal Infections: A Critical Partner in Innate Immunity
Bibliographic record
Abstract
The lung epithelial lining serves as the primary barrier to inhaled environmental toxins, allergens, and invading pathogens. Pulmonary fungal infections are devastating and carry high mortality rates, particularly in those with compromised immune systems. While opportunistic fungi infect primarily immunocompromised individuals, endemic fungi cause disease in immune competent and compromised individuals. Unfortunately, in the case of inhaled fungal pathogens, the airway epithelial host response is vastly understudied. Furthering our lack of understanding, very few studies utilize primary human models displaying pseudostratified layers of various epithelial cell types at air-liquid interface. In this review, we focus on the diversity of the human airway epithelium and discuss the advantages and disadvantages of oncological cell lines, immortalized epithelial cells, and primary epithelial cell models. Additionally, the responses by human respiratory epithelial cells to invading fungal pathogens will be explored. Future investigations leveraging current human in vitro model systems will enable identification of the critical pathways that will inform the development of novel vaccines and therapeutics for pulmonary fungal infections.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".