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Record W3148525021 · doi:10.1016/j.etap.2021.103651

Pulmonary inflammatory response from co-exposure to LPS and glyphosate

2021· article· en· W3148525021 on OpenAlexafffund
Upkardeep Pandher, Shelley Kirychuk, David Schneberger, Brooke Thompson, Gurpreet Kaur Aulakh, R. S. Sethi, Baljit Singh

Bibliographic record

VenueEnvironmental Toxicology and Pharmacology · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicIndoor Air Quality and Microbial Exposure
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsInflammatory responseGlyphosateAcute exposureChemistryToxicologyMedicineBiologyInflammationImmunologyInternal medicineBiotechnology

Abstract

fetched live from OpenAlex

Agricultural airborne work exposures are complex in nature and workplace exposures are a risk for respiratory outcomes in workers. Endotoxin and glyphosate are two common agents in agricultural exposures. While endotoxin (lipopolysaccaride, LPS) is a potent inflammatory agent it explains only a portion of the respiratory inflammatory response. The inflammatory potential when LPS is presented with another common agricultural respiratory agent, glyphosate, is not known. METHODS: Mice were assigned to four treatment groups: control, LPS alone, glyphosate alone, glyphosate and LPS combined. Treatments were for 1, 5 or 10 days. RESULTS: Five days of repeated exposure to the comintation of LPS and glyphosate resulted in higher neutrophil counts, myloperoxidase, TNF-α, IL-6, KC levels, and ICAM-1 and TLR-2 expression compared to the same length of treatment to LPS or glyphosate alone. After 10-days of exposure, inflammatory responses decreased, however leukocyte infiltration persisted along with increases in IL-4. CONCLUSIONS: Glyphosate exposure modified LPS induced lung inflammatory responses and TLR-2 may be important in the modulated inflammatory response.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

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 integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.008
GPT teacher head0.249
Teacher spread0.241 · 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 source (direct Gemma or distilled Codex), 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

Citations25
Published2021
Admission routes2
Has abstractyes

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