Evidence map›Paper›PMID 24967678›Full record

ArticlePLoS computational biology2014

Optimal reaction coordinate as a biomarker for the dynamics of recovery from kidney transplant.

Sergei V Krivov, Hayley Fenton, Paul J Goldsmith, Rajendra K Prasad, Julie Fisher, Emanuele Paci

Open access · goldAbstract read
In one paragraph

Article in PLoS computational biology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.9field-weighted citation impact, top 28% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 9 citations in OpenAlex.

  1. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 2 institutions in 1 country.

Sergei V KrivovSchool of Molecular and Cellular Biology, University of Leeds, Leeds, United Kingdom; Astbury Centre for Structural Molecular Biology, University of Leeds, Leeds, United Kingdom.
Hayley FentonSchool of Chemistry, University of Leeds, Leeds, United Kingdom.
Paul J GoldsmithHepatopancreatobiliary Transplant Unit, St. James's University Hospital, Leeds, United Kingdom.
Rajendra K PrasadHepatopancreatobiliary Transplant Unit, St. James's University Hospital, Leeds, United Kingdom.
Julie FisherAstbury Centre for Structural Molecular Biology, University of Leeds, Leeds, United Kingdom; School of Chemistry, University of Leeds, Leeds, United Kingdom.
Emanuele PaciSchool of Molecular and Cellular Biology, University of Leeds, Leeds, United Kingdom; Astbury Centre for Structural Molecular Biology, University of Leeds, Leeds, United Kingdom.
University of Leeds · GBSt James's University Hospital · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The evolution of disease or the progress of recovery of a patient is a complex process, which depends on many factors. A quantitative description of this process in real-time by a single, clinically measurable parameter (biomarker) would be helpful for early, informed and targeted treatment. Organ transplantation is an eminent case in which the evolution of the post-operative clinical condition is highly dependent on the individual case. The quality of management and monitoring of patients after kidney transplant often determines the long-term outcome of the graft. Using NMR spectra of blood samples, taken at different time points from just before to a week after surgery, we have shown that a biomarker can be found that quantitatively monitors the evolution of a clinical condition. We demonstrate that this is possible if the dynamics of the process is considered explicitly: the biomarker is defined and determined as an optimal reaction coordinate that provides a quantitatively accurate description of the stochastic recovery dynamics. The method, originally developed for the analysis of protein folding dynamics, is rigorous, robust and general, i.e., it can be applied in principle to analyze any type of biological dynamics. Such predictive biomarkers will promote improvement of long-term graft survival after renal transplantation, and have potentially unlimited applications as diagnostic tools.

Indexed as

Models, BiologicalBiomarkersComputational BiologyHumansKidneyKidney TransplantationMagnetic Resonance SpectroscopyRecovery of FunctionBiomarkers

Identifiers

PMID24967678
PMCPMC4072519
OpenAlexW2013006372

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.