PI-43Gene expression in human mononuclear cells in response to reactive sulfonamide metabolites
Bibliographic record
Abstract
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
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".