Gene Expression Analysis in Inflamed and Non-inflamed Hemodialysis (HD) Patients Using a cDNA Microarray
Bibliographic record
Abstract
Systemic inflammation is an independent predictor of mortality in HD patients, and a better understanding of its pathogenetic pathways and identification of targets for intervention are needed. The cDNA microarray technology has recently been used to profile gene expression, and examines simultaneously a broad variety of genes that could determine biological and pathogenetic processes. To investigate the gene expression profile in white blood cells of eight persistently inflamed (CRP > 10 mg/L for 12 months) HD patients compared to eight non-inflamed patients (CRP < 10 mg/L for 12 months), we designed a pilot study using a cytokine expression array (R&D Systems), including 398 different cloned cDNAs, printed as PCR products, on a positively charged nylon membrane. mRNA was extracted from leucocytes and pooled for each group, which were matched for age, gender, primary renal disease, and time on HD. Comparison of the signals from the two samples allowed identification of differentially expressed mRNA. A 10x increase in mRNA expression between the groups was defined as up-regulation. The results showed that 32 genes were up-regulated in the inflamed group compared to the non-inflamed group. Highly up-regulated genes belonged to the following groups: members of the TGF-β superfamily (5 genes), proteolytic enzymes (5), integrin (4), genes involved in nitric oxide metabolism (3), TNF superfamily (3), interleukin receptors (3), cell surface proteins (2), orphan receptors (2), neurothrophic factor (1), fibroblast growth factor family (1), cytokine receptor (1), cytokine (1), and chemokine (1). In conclusion, circulating lymphocytes of inflamed HD patients had markedly up-regulated expression of inflammation-related genes, providing a molecular fingerprint of gene function on the RNA level. These genes should be considered as candidates for genotype analysis or targets for intervention studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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 teacher head, 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".