Determining Expression Levels of the Inflammatory Marker IL-8 in Human Alveolar Basal Epithelial A549 cells following exposure to amorphous Silicon Dioxide Micro- and Nanoparticles in the presence of IL-1B
Bibliographic record
Abstract
Exposure to nanoparticles and micro particles can have adverse health effects, particularly for the increasing population with pre-existing inflammatory lung conditions such as COPD or asthma. This is of concern in the light of an increased usage of fine particles in the workplace and in consumer products. We studied how pre-existing inflammation and particle exposure are interdependent in cell cultures of A549 cells. A549 cells are immortalized human alveolar basal cells that closely resemble Type II lung epithelial cells. IL-1β was used to induce an inflammatory response and was assessed by measuring interleukin 8 (IL-8). IL-8 is an important pro-inflammatory cytokine expressed in respiratory cells in the human lung, including environmental insults by particles and is associated with neutrophil recruitment. The results indicated that the amorphous silicon dioxide nanoparticles and microparticles alone did not elicit the release of IL-8 or produce inflammation in A549 cells. Also, it was determined that in the presence of inflammation, even brief particle exposure has no enhancement on the inflammatory effect. We speculate that uptake and transcytosis is an efficient clearance mechanism for particles small enough to reach the peripheral lung, where the “mucocilliary elevator” is absent. This clearance mechanism is not associated with inflammation, and thus, prevents unnecessary damage to the lungs.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".