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Record W2119356879 · doi:10.1002/ps.449

The effects of phytochemical pesticides on the growth of cultured invertebrate and vertebrate cells

2002· article· en· W2119356879 on OpenAlexaff
A. Salehzadeh, Abdul Jabbar, Lynn Jennens, Steven V. Ley, Ramasamy S Annadurai, Roger Adams, Robin H.C. Strang

Bibliographic record

VenuePest Management Science · 2002
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicInsect Pest Control Strategies
Canadian institutionsSurrey Memorial Hospital
Fundersnot available
KeywordsAzadirachtinRotenoneBiologyPhytochemicalTerpenoidPesticideCell growthCell cultureInsect growth regulatorInvertebrateGrowth inhibitionIn vitroToxicityInsectToxicologyBotanyBiochemistryChemistryEcologyMitochondrion

Abstract

fetched live from OpenAlex

A range of cultured cells of invertebrate and vertebrate origin was grown in the presence of a number of phytochemical pesticides to test the effect of the latter on cell proliferation. The main observation was that azadirachtin was a potent inhibitor of insect cell replication, with an EC50 of 1.5 x 10(10) M against Spodoptera cells and of 6.3 x 10(9) M against Aedes albopictus cells, whilst affecting mammalian cells only at high concentrations (> 10(-4) M). As expected, the other phytochemical pesticides, except for rotenone, had little effect on the growth of the cultured cells. Rotenone was highly effective in inhibiting the growth of insect cells (EC50:10(-8) M) but slightly less toxic towards mammalian cells (EC50:2 x 10(-7) M). Neem terpenoids other than azadirachtin and those very similar in structure significantly inhibited growth of the cell cultures, but to a lesser degree. The major neem seed terpenoids, nimbin and salannin, for example, inhibited insect cell growth by 23% and 15%, respectively.

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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.009
GPT teacher head0.180
Teacher spread0.172 · 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

Citations48
Published2002
Admission routes1
Has abstractyes

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