MétaCan
Menu
Back to cohort
Record W4411627008 · doi:10.1007/s11255-025-04613-z

Molecular frontiers in hemodialysis: unraveling the role of membranes in gene expression, epigenetics, and inflammatory pathways

2025· review· en· W4411627008 on OpenAlexaff
Hira Syeda, Victoria Doan, Ahmed Shoker, Amira Abdelrasoul

Bibliographic record

VenueInternational Urology and Nephrology · 2025
Typereview
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsSt. Paul's HospitalUniversity of Saskatchewan
Fundersnot available
KeywordsEpigeneticsMedicineGene expressionNephrologyHemodialysisGeneBioinformaticsComputational biologyInternal medicineGeneticsBiology

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.008
GPT teacher head0.251
Teacher spread0.243 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueInternational Urology and NephrologySame topicDialysis and Renal Disease ManagementFrench-language works237,207