Molecular frontiers in hemodialysis: unraveling the role of membranes in gene expression, epigenetics, and inflammatory pathways
Bibliographic record
Abstract
Hemodialysis (HD) remains a vital treatment for patients with end-stage kidney disease, yet the characteristics of dialysis membranes significantly influence therapeutic outcomes. Membrane fouling, often caused by protein adherence, reduces toxin clearance and may lead to fluid and electrolyte imbalances. Recent studies have focused on modifying membrane chemistry to improve biocompatibility and reduce fouling. Although advancements have been made in understanding the biochemical and inflammatory responses during HD, the underlying molecular mechanisms, especially those involving epigenetic regulation remain incompletely understood. This review explores recent progress in HD treatment, with a specific emphasis on inflammation, gene expression, and epigenetic alterations. We examine how pro-inflammatory cytokines activate transcription factors that regulate gene expression and how genetic variability within inflammatory genes adds complexity to these pathways. In parallel, we assess emerging biocompatible membrane technologies designed to reduce adverse interactions with blood components, thereby enhancing patient safety and treatment efficiency. In addition, we discuss how genetic and epigenetic changes such as DNA mutations, methylation patterns, and histone modifications may influence patient responses to HD and contribute to complications, including mental health disorders such as depression. These insights support the development of personalized HD strategies tailored to the molecular and genetic profiles of individual patients. By integrating current findings from genetics, epigenetics, and gene expression studies, this review provides a comprehensive perspective on HD-related inflammation and molecular dysregulation. Our goal is to highlight key interconnections and identify critical knowledge gaps that must be addressed to improve long-term outcomes and quality of life for HD patients.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".