ArticleFrontiers in physiology2025
A novel kinetic model estimating the urea concentration in plasma during non-invasive sweat-based monitoring in hemodialysis.
Article in Frontiers in physiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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.
Who cites it
1 citing paper in PubMed.
- Non-Invasive Blood Cortisol Estimation from Sweat Analysis by Kinetic Modeling of Cortisol Transport Dynamics.Sensors (Basel, Switzerland) · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: The adequacy of hemodialysis (HD) in patients with end-stage renal disease is evaluated frequently by monitoring changes in blood urea concentrations multiple times between treatments. As monitoring of urea concentrations typically requires blood sampling, the development of sweat-sensing technology offers a possible less-invasive alternative to repeated venipuncture. Moreover, this innovative technology could enable personalized treatment in a home-based setting. However, the clinical interpretation of sweat monitoring is hampered by the limited literature on the correlation between urea concentrations in sweat and blood. This study introduces a pioneering approach to estimate blood urea concentrations using sweat urea concentration values as input. Methods: To simulate the complex transport mechanisms of urea from blood to sweat, a novel pharmacokinetic transport model is proposed. Such a transport model, together with a double-loop optimization strategy from our previous work, was employed for patient-specific estimation of blood urea concentration. 32 patient samples of paired sweat and blood urea concentrations, collected both before and after HD, were used to validate the model. Results: This resulted in an excellent Pearson correlation coefficient (0.98, 95%CI: 0.95-0.99) and a clinically irrelevant bias (-0.181 mmol/L before and -0.005 mmol/L after HD). Discussion: This model enabled the accurate estimation of blood urea concentrations from sweat measurements. By accurately estimating blood urea concentrations from sweat measurements, our model enables non-invasive and more frequent assessments of dialysis adequacy in ESRD patients. This approach could facilitate home-based and patient-friendly dialysis management, enhancing patient comfort while enabling more personalized treatment across diverse clinical settings.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.