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Record W3018503281 · doi:10.1177/0748233720918679

Tremolite–actinolite fiber coatings of sub-nanometer silica-rich particles in lungs from deceased Quebec miners

2020· article· en· W3018503281 on OpenAlexaboutno aff
Mark Germine, John H. Puffer

Bibliographic record

VenueToxicology and Industrial Health · 2020
Typearticle
Languageen
FieldMedicine
TopicOccupational and environmental lung diseases
Canadian institutionsnot available
Fundersnot available
KeywordsTremoliteActinoliteMaterials scienceTransmission electron microscopyCrystallographyNanometreSiliconComposite materialNanotechnologyMineralogyChemical engineeringChemistryMetallurgy

Abstract

fetched live from OpenAlex

Tremolite–actinolite (TA) fibers from the lungs of deceased former Quebec mine workers were found to be coated with sub-nanometer particles. Qualitative chemical analyses were performed on the particles indicating that they were composed of silicon and oxygen. The crystal lattice structure of all amphibole minerals, including the TA series, is arranged as pairs of linear chains of SiO 4 tetrahedra that are linked together to form double chains. Our observations of the TA fibers from miner’s lungs, made using a high-resolution transmission electron microscope, indicated that the tetrahedral silica chains were progressively split, forming dispersed sub-nanometer particles. The non-tetrahedral sites were removed at the surface of the TA fibers, presumably by the oxidation process involved in attempted phagocytosis, which also resulted in fragmentation of the tetrahedral chains. It was found that the silicon-rich particles (SRPs) were variable in diameter, consistent with fragments formed from the splitting of the tetrahedral chains. The TA fibers from lungs displayed coatings and linear interior zones of SRP parallel to the planes of longitudinal fiber splitting. The literature on very small nanoparticles is consistent with deep penetration of SRP into cell DNA interiors, oxidative stress, and carcinogenesis.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.049
Threshold uncertainty score0.441

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.057
GPT teacher head0.294
Teacher spread0.237 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2020
Admission routes1
Has abstractyes

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