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Record W2317752050 · doi:10.1016/j.clpt.2005.12.064

PI-43Gene expression in human mononuclear cells in response to reactive sulfonamide metabolites

2006· article· en· W2317752050 on OpenAlexaff
Michael Rieder, M TUCKER, D Carter, Simon Rieder

Bibliographic record

VenueClinical Pharmacology & Therapeutics · 2006
Typearticle
Languageen
FieldMedicine
TopicMedical Imaging and Pathology Studies
Canadian institutionsWestern University
Fundersnot available
KeywordsPeripheral blood mononuclear cellGene expressionApoptosisMolecular biologyGeneJurkat cellsRNAMetaboliteChemistryBiologyPharmacologyImmune systemBiochemistryImmunologyT cellIn vitro

Abstract

fetched live from OpenAlex

BACKGROUND/AIMS To identify genes differentially expressed in sulfamethoxazole (SMX)-sensitive and resistant individuals. METHODS Peripheral blood mononuclear cells (PBMCs) were isolated from 2 volunteers: one sensitive to SMX, and one not. The cells were incubated at 37°C in a humidified CO2 atmosphere for 0, 4, or 8 hours with 100μM SMX-NO, the presumed toxic nitroso-metabolite of the parent SMX. At the designated times, RNA was isolated from the cells using Trizol followed by an Rneasy column. RNA quality was analyzed by an Agilent 2100 Bioanalyzer. Each sample was biotinylated and hybridized to an Affymatrix HGU133 plus 2.0 gene chip and scanned for the expression of 38,500 well characterized human genes. RESULTS Initially 19 genes implicated in stress, apoptosis or immune activation were identified that are expressed five-fold higher in the control, but not in the sensitized individual. These include 5 heat shock proteins, TNF-α, apoptosis-inducing kinase, MAPKKK1, ras, CD58, and a calcium membrane channel protein. CONCLUSIONS Study of the differential gene expression by individuals exposed to toxic drug metabolites will help us to understand the mechanisms of cell death involved in adverse drug reactions. Clinical Pharmacology & Therapeutics (2005) 79, P18–P18; doi: 10.1016/j.clpt.2005.12.064

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.721
Threshold uncertainty score0.821

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.084
GPT teacher head0.448
Teacher spread0.364 · 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

Citations0
Published2006
Admission routes1
Has abstractyes

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