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Record W1987453352 · doi:10.1080/03602530701468268

Relevance of Cytochrome P450s in Plants: Also One of Ron Estabrook's Research Interests

2007· review· en· W1987453352 on OpenAlexaff
Manjunath S. Shet

Bibliographic record

VenueDrug Metabolism Reviews · 2007
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant tissue culture and regeneration
Canadian institutionsPurdue Pharma (Canada)
Fundersnot available
KeywordsReductaseMung beanCytochrome P450BiochemistryVignaEnzymeMicrosomeBiosynthesisIsolation (microbiology)BiologyChemistryBotanyMicrobiologyHorticulture

Abstract

fetched live from OpenAlex

I worked with Dr. Ronald Estabrook for nearly 10 years at The University of Texas Southwestern Medical Center in Dallas, Texas. In Ron's lab, when I joined I was initially involved in the isolation, purification, and characterization of cytochrome P450s and NADPH-P450(c) reductase(s) from plants, which was his new exploratory project at the time. We developed methods for the isolation, solubilization, and purification of P450s and NADPH-P450(c) reductase from plant tissue microsomes. We carried out number of in vitro experiments to study the involvement P450s and NADPH-P450(c) reductase in the biosynthesis of number of phytoalexins. We successfully isolated, purified, and cloned NADPH-P450(c) reductase from etiolated mung bean (Vigna radiate) seedlings. In addition, a series of studies were undertaken to show that purified mung bean NADPH-P450(c) reductase was able to catalyze P450-supported reactions for mammalian and bacterial P450s. My stay in Ron's lab was very educational and productive. He provided the necessary support and led the way through the maze in different research projects in the lab, which allowed me to understand the roles of P450s in humans, animals, plants, and microorganisms. He liked to teach and discover new things everyday in the lab. He is a great scientist, as well as loving and caring mentor.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.966
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.134
GPT teacher head0.416
Teacher spread0.282 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations1
Published2007
Admission routes1
Has abstractyes

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