The Endocrine Society of Australia Proceedings 2006/The New Zealand Society of Endocrinology Proceedings 2004 and 2005-4
Bibliographic record
Abstract
There is strong evidence that growth hormone (GH) activates the immune system both directly, and indirectly through IGF-1 and the cytokine network.Little is known, about what genes are regulated by GH in immune cells in vivo.The aim of this study is to investigate the effects of GH on gene expression in leukocytes.Healthy male subjects, recruited as part of a intervention study aimed at developing a GH doping test, were administered 2mg/day GH for eight weeks followed by a 6 week washout period.Total RNA was extracted from white blood cells collected at baseline (week 0), weeks 4 and 8 (GH treatment) and week 14 (GH washout).Gene expression analysis was performed using Affymetrix HG-133 Plus 2.0 human genome arrays, which consist of 54925 probe sets, and the data analysed by GeneSpring software.Differential expression was analysed by one-way ANOVA.In preliminary analysis of data from 4 subjects, GH induced significant change in 1049, 1463 and 690 probe sets at weeks 4, 8 and 14, respectively, compared to baseline (p < 0.05).Of these, 11, 16 and 1 corresponding genes were up or down-regulated by greater than 2-fold at weeks 4, 8 and 14, respectively.Consistent changes in six of these genes were present at both weeks 4 and 8 and these genes are involved in biological process-metabolism (n=3), cellular componentgolgi stack (n=2) and molecular function-lipid binding (n=1) and -catalytic activity (n=2), using Gene Ontology.This data indicates that GH induces the expression of genes in peripheral leukocytes during GH treatment.Since peripheral blood is easily accessible, identification of a gene expression fingerprint in leukocytes could lead to the development of a GH doping test.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.259 | 0.114 |
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".