Evidence map›Paper›PMID 41656511›Full record

ArticleNMR in biomedicine2026

Quantifying Transmembrane Water Exchange by Diffusion NMR Methods: From Yeast Cells to Optic Nerve Ex Vivo.

Yuval Scher, Shlomi Reuveni, Yoram Cohen

Abstract read
In one paragraph

Article in NMR in biomedicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
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

3 citing papers in PubMed.

  1. Localization-driven exchange contrast in diffusion exchange spectroscopy.Journal of magnetic resonance (San Diego, Calif. : 1997) · 2026
    Article
  2. Article
  3. Article
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

3 authors.

Yuval ScherSchool of Chemistry, Raymond and Beverly Sackler Faculty of Exact Sciences, Tel Aviv, Israel.ORCID https://orcid.org/0000-0002-8020-2352
Shlomi ReuveniSchool of Chemistry, Raymond and Beverly Sackler Faculty of Exact Sciences, Tel Aviv, Israel.ORCID https://orcid.org/0000-0003-2292-8005
Yoram CohenSchool of Chemistry, Raymond and Beverly Sackler Faculty of Exact Sciences, Tel Aviv, Israel.ORCID https://orcid.org/0000-0002-9442-8547

Funding

European Research Council (ERC) 947731Israel Science Foundation (ISF, Jerusalem, Israel)U.S.-Israel Binational Science Foundation (BSF) 2019-25
6 · The paper itself

Abstract

Noninvasive measurement of exchange is paramount in different fields, ranging from material to biological sciences. Unaccounted exchange may even blur microstructural or other characteristics of multicompartmental systems studied by MR methods. Despite the growing interest in diffusion-exchange studies of complex systems, comparative studies remain scarce. Most existing investigations have applied different diffusion MR methods to different biological samples under varying experimental conditions, making direct comparisons difficult. Moreover, the lack of a gold standard for exchange rate measurements further complicates efforts to validate and interpret results. To address these challenges, we employed two diffusion NMR-based methods-the constant-gradient pulsed-field gradient (CG-PFG) and the recently introduced filter-exchange NMR spectroscopy (FEXSY)-to investigate apparent water exchange in yeast cells and optic nerves, both before and after fixation. We first evaluated the effect of the

Indexed as

Cell MembraneOptic NerveSaccharomyces cerevisiaeWaterDiffusionMagnetic Resonance SpectroscopyReproducibility of ResultsWaterCG‐PFGCG‐PGSTEdiffusion MRFEXIFEXSYfixationKärger modelMRINMRoptic nervepermeabilityspin echotricompartmental modelwater exchangewhite matteryeast cells

Identifiers

PMID41656511
PMCPMC12883707

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.