Dust events, pulmonary diseases and immune system.
Bibliographic record
Abstract
Incidences of sand storms have increased in recent years and there is evidence that these dusts can move across long distances. Sand dusts have different adverse effects on health, but one of the most important of them is pulmonary disease. After inhalation of dust, many dust particles are moved to the airways. Dust particles can be sensed by airways epithelial cells, activate macrophages, dendritic cells and innate immune cells and then initiate responses in various populations of specific immune cells such as T helper cells subsets (Th1, Th2, Th17), T cytotoxic cells and B cells. Initiation of inflammatory immune responses, activation of immune cells and releases of many cytokines, chemokines and other inflammatory molecules, have variable pathologic affects on lung in different respiratory diseases. Unfortunately control of desert dusts is more difficult than control of air pollution. For prevention and treatment of respiratory diseases that are caused by desert dusts, researchers need well-designed epidemiological studies, combined with analysis of the precise composition of sand dusts, and the precise mechanisms of the immune responses. Recognizing the exact cellular and molecular immune mechanisms would be very useful to find new approaches for treatment of desert dust associated pulmonary diseases.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".