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Record W2003273470 · doi:10.1177/1091581811413831

Immunotoxicity of 3 Chemical Forms of Beryllium Following Inhalation Exposure

2011· article· en· W2003273470 on OpenAlexafffund
Caroline Müller, Fariba Salehi, Bruce Mazer, Michèle Bouchard, Ariane Adam-Poupart, Gaston Chevalier, Ginette Truchon, Jean Lambert, Joseph Zayed

Bibliographic record

VenueInternational Journal of Toxicology · 2011
Typearticle
Languageen
FieldMedicine
TopicSarcoidosis and Beryllium Toxicity Research
Canadian institutionsInstitut de recherche Robert-Sauvé en santé et en sécurité du travailMcGill UniversityUniversité du Québec à MontréalChristie (Canada)Université de Montréal
FundersInstitut de Recherche Robert-Sauvé en Santé et en Sécurité du Travail
KeywordsInhalationToxicityBeryllium oxideChemistryTumor necrosis factor alphaInhalation exposureImmunologyInternal medicineMedicineToxicologyPathologyPhysiologyBerylliumAnesthesiaBiologyOrganic chemistry

Abstract

fetched live from OpenAlex

The toxicity of 3 chemical forms of beryllium (Be) was compared in this study. A total of 160 mice equally divided into 4 groups were exposed by inhalation (nose only) for 3 consecutive weeks, 5 d/week, 6 h/d. One group was used as control, while the 3 others were exposed to fine particles of Be metal, Be oxide (BeO), or Be aluminum (BeAl). Except for the controls, the target level of exposure was 250 μg/m(3). In all, 35 mice/group were sacrificed 1 week postexposure and another 5 mice 3 weeks postexposure. The BeO group showed the highest lung Be concentration with higher interleukin 12 (IL-12) and interferon-γ (IFN-γ) levels, while the Be group produced the most severe lung inflammation and higher tumor necrosis factor-α (TNF-α) and CD4+ T cells levels. Data suggested that Be and BeO apparently produced more pulmonary toxicity than BeAl. However, this conclusion is not definitive, because of different confounding factors such as particle sizes, specific surface area, and solubility.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.458

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.046
GPT teacher head0.332
Teacher spread0.287 · 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 designBench or experimental
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

Citations11
Published2011
Admission routes2
Has abstractyes

Explore more

Same venueInternational Journal of ToxicologySame topicSarcoidosis and Beryllium Toxicity ResearchFrench-language works237,207