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Record W4396992715 · doi:10.1681/asn.20213210s1317a

High-Throughput Analysis of Changes in Protein Biomarkers During Hemodialysis

2021· article· en· W4396992715 on OpenAlexaff
Matthew B. Lanktree, David Collister, Guillaume Paré, Michael Walsh

Bibliographic record

VenueJournal of the American Society of Nephrology · 2021
Typearticle
Languageen
FieldChemistry
TopicAdvanced Proteomics Techniques and Applications
Canadian institutionsUniversity of ManitobaMcMaster University
Fundersnot available
KeywordsHemodialysisThroughputMedicineInternal medicineIntensive care medicineComputational biologyComputer scienceBiology

Abstract

fetched live from OpenAlex

Background: The impact of hemodialysis on the concentration of circulating protein biomarkers remains unclear. Biomarkers may decrease in concentration due to filter adsorption, diffusive clearance, or convective clearance, while others may increase in concentration due to production and secretion or intracellular release. Ultrafiltration of water is expected to also increase biomarker concentration. We sought to evaluate the impact of hemodialysis on 1,163 protein biomarkers in a high-throughput fashion. Methods: A nested cohort of 44 patients (25 male, 19 female) including 29 with intradialytic hypotension and 15 without were selected from the prospective Hemodialysis Outcomes and Symptoms assessment (HOST) cohort. Intradialytic hypotension was stringently defined as a 60 mmHg drop in systolic blood pressure or a nadir systolic blood pressure of less than 70 mmHg during hemodialysis treatment. All hemodialysis treatments were done using the same hemodialysis filter type. 1,163 unique biomarkers were measured in each patient before and after a hemodialysis session using the Olink proximity extension assay (www.olink.com). Paired sample t-tests were used to compare pre- and post-dialysis concentrations with a Bonferroni-corrected significance threshold (P < 5 × 10-5). Results: 54 biomarkers (5%) significantly increased during hemodialysis treatment, while 243 (24%) significantly decreased. Change in biomarker concentration was significantly associated with biomarker molecular weight (r = 0.37, P = 2.8 × 10-16), isoelectric point (r = -0.26, P = 6.4 × 10-14), and pre-dialysis concentration (r = -0.21, P = 3.0 × 10-9). There was a significant enrichment of cardiovascular biomarkers in the top 20 biomarkers associated with drop in systolic blood pressure (P = 2.8 × 10-10), including Kidney Injury Molecule 1 (KIM1, P = 0.005). Conclusions: Hemodialysis is associated with significant changes in protein biomarker concentrations related to protein properties and clinical events during treatment. These changes are measurable on a high-throughput platform. Further highthroughput biomarker studies could assess dialysis adequacy, test biomarker-symptom associations, and improve risk prognostication. Funding: Private Foundation Support

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.010
GPT teacher head0.269
Teacher spread0.259 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueJournal of the American Society of NephrologySame topicAdvanced Proteomics Techniques and ApplicationsFrench-language works237,207