Method to characterize inorganic particulates in lung tissue biopsies using field emission scanning electron microscopy
Bibliographic record
Abstract
Humans accumulate large numbers of inorganic particles in their lungs over a lifetime. Whether this causes or contributes to debilitating disease over a normal lifespan depends on the type and concentration of the particles. We developed and tested a protocol for in situ characterization of the types and distribution of inorganic particles in biopsied lung tissue from three human groups using field emission scanning electron microscopy (FE-SEM) combined with energy dispersive spectroscopy (EDS). Many distinct particle types were recognized among the 13 000 particles analyzed. Silica, feldspars, clays, titanium dioxides, iron oxides and phosphates were the most common constituents in all samples. Particles were classified into three general groups: endogenous, which form naturally in the body; exogenic particles, natural earth materials; and anthropogenic particles, attributed to industrial sources. These in situ results were compared with those using conventional sodium hypochlorite tissue digestion and particle filtration. With the exception of clays and phosphates, the relative abundances of most common particle types were similar in both approaches. Nonetheless, the digestion/filtration method was determined to alter the texture and relative abundances of some particle types. SEM/EDS analysis of digestion filters could be automated in contrast to the more time intensive in situ analyses.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".