Aeroparticles, Composition, and Lung Diseases
Bibliographic record
Abstract
Urban air pollution is a serious worldwide problem due to its impact on human health. In the past 60 years, growing evidence established a correlation between exposure to air pollutants and the developing of severe respiratory diseases. Recently particulate matter (PM) is drawing more public attention to various aspects including historical backgrounds, physicochemical characteristics, and its pathological role. Therefore, this review is focused on these aspects. The most famous air pollution disaster happened in London on December 1952; it has been calculated that more than 4,000 deaths occurred during this event. Air pollution is a complex mix of gases and particles. Gaseous pollutants disseminate deeply into the alveoli, allowing its diffusion through the blood-air barrier to several organs. Meanwhile, PM is a mix of solid or liquid particles suspended in the air. PM is deposited at different levels of the respiratory tract, depending on its size: coarse particles (PM10) in upper airways and fine particles (PM2.5) can be accumulated in the lung parenchyma, inducing several respiratory diseases. Additionally to size, the composition of PM has been associated with different toxicological outcomes on clinical and epidemiological, as well as in vivo and in vitro animal and human studies. PM can be constituted by organic, inorganic, and biological compounds. All these compounds are capable of modifying several biological activities, including alterations in cytokine production, coagulation factors balance, pulmonary function, respiratory symptoms, and cardiac function. It can also generate different modifications during its passage through the airways, like inflammatory cells recruitment, with the release of cytokines and reactive oxygen species (ROS). These inflammatory mediators can activate different pathways, such as MAP kinases, NF-κB, and Stat-1, or induce DNA adducts. All these alterations can mediate obstructive or restrictive respiratory diseases like asthma, COPD, pulmonary fibrosis, and even cancer. In 2013, outdoor air pollution was classified as Group 1 by IARC based on all research studies data about air pollution effects. Therefore, it is important to understand how PM composition can generate several pulmonary pathologies.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".