Short-Term Erythropoietin Treatment Does Not Substantially Modulate Monocyte Transcriptomes of Patients with Combined Heart and Renal Failure
Bibliographic record
Abstract
BACKGROUND: Combined heart and renal failure is associated with high cardiovascular morbidity and mortality. Anti-oxidant and anti-inflammatory, non-hematopoietic effects of erythropoietin (EPO) treatment have been proposed. Monocytes may act as biosensors of the systemic environment. We hypothesized that monocyte transcriptomes of patients with cardiorenal syndrome (CRS) reflect the pathophysiology of the CRS and respond to short-term EPO treatment at a recommended dose for treatment of renal anemia. METHODS: Patients with CRS and anemia (n = 18) included in the EPOCARES trial were matched to healthy controls (n = 12). Patients were randomized to receive 50 IU/kg/week EPO or not. RNA from CD14(+)-monocytes was subjected to genome wide expression analysis (Illumina) at baseline and 18 days (3 EPO injections) after enrolment. Transcriptomes from patients were compared to healthy controls and effect of EPO treatment was evaluated within patients. RESULTS: In CRS patients, expression of 471 genes, including inflammation and oxidative stress related genes was different from healthy controls. Cluster analysis did not separate patients from healthy controls. The 6 patients with the highest hsCRP levels had more differentially expressed genes than the 6 patients with the lowest hsCRP levels. Analysis of the variation in log(2) ratios of all individual 18 patients indicated that 4 of the 18 patients were different from the controls, whereas the other 14 were quite similar. After short-term EPO treatment, every patient clustered to his or her own baseline transcriptome. Two week EPO administration only marginally affected expression profiles on average, however, individual gene responses were variable. CONCLUSIONS: In stable, treated CRS patients with mild anemia, monocyte transcriptomes were modestly altered, and indicated imprints of inflammation and oxidative stress. EPO treatment with a fixed dose has hematopoietic effects, had no appreciable beneficial actions on monocyte transcription profiles, however, could also not be associated with undesirable transcriptional responses.
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 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) | 0.001 | 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 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".