Nebulizer-spray chamber apparatus and method for production of multi-component particle types and their use in affecting cellular responses
Bibliographic record
Abstract
A particle generation apparatus and methodology is described for its application to prepare heterogeneous particles of defined chemical composition and their subsequent delivery to human lung cell cultures. A concentric type nebulizer was used together with a single pass spray chamber that was operated at two different temperature settings, either room temperature or to produce intact particles, at nonuniform temperature with the central axis measuring 54°C. Silica (SiO2) particles in the micrometer size range were produced from the aggregation of nanoparticles. Tumor necrosis factor (TNF)-α, a well-characterized cytokine, was incorporated as a secondary soluble component within host silica particles for application as a proof-of-concept heterogeneous particle type. The overall particle deposition and wetting efficiency into media was 2.0 ± 0.4%, and the average size of particles that impacted and sank in the growth medium was 3.4 µm. The particle dose, reported as particle-to-cell ratio, spanned 0.1 to ∼2.5. Co-cultures of A549 and differentiated human monocytic cells (THP-1*) exposed to silica particles caused IL-6 upregulation, but not IL-8. For the silica plus TNF-α particle type, ICAM-1 signal increased in A549 mono-cultures as the particle-to-cell ratio increased, yet an estimated 90% of the TNF-α was denatured or possibly bound to the silica particle host, and was therefore bio-unavailable. Method quantitation using particles having two different chemical compositions has been described, illustrating capability for subsequent systematic investigation of the role of particles having different chemical composition in the human health aspects of the particulate air pollution issue.Copyright © 2016 American Association for Aerosol Research
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 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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".