ArticleNature communications2019
Synthetic molecular recognition nanosensor paint for microalbuminuria.
Article in Nature communications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
What it found
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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.
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Who cites it
22 citing papers in PubMed, 84 citations in OpenAlex.
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- Albumin Encapsulation of Cyanine Dye for High-Performance NIR-II Imaging-Guided Photodynamic Therapy.Chemical & biomedical imaging · 2025Article
- Effect of Polyelectrolytes on the Photoluminescence of Single-Walled Carbon Nanotubes-Experimental and Simulation Studies.ACS omega · 2025Article
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- Noninvasive Injectable Optical Nanosensor-Hydrogel Hybrids Detect Doxorubicin in Living Mice.Advanced optical materials · 2024Article
- Optical Nanosensor Passivation Enables Highly Sensitive Detection of the Inflammatory Cytokine Interleukin-6.ACS applied materials & interfaces · 2024Article
- Carbon Nanomaterial Fluorescent Probes and Their Biological Applications.Chemical reviews · 2024Review
- Development of Single Molecule Techniques for Sensing and Manipulation of CRISPR and Polymerase Enzymes.Small (Weinheim an der Bergstrasse, Germany) · 2023Review
- Impedimetric immunosensor for microalbuminuria based on a WSMikrochimica acta · 2023Article
- Clickable Polysaccharides for Biomedical Applications: A Comprehensive Review.Progress in polymer science · 2022Article
- Advancements in nanomedicines for the detection and treatment of diabetic kidney disease.Biomaterials and biosystems · 2022Review
- Biosensing with Fluorescent Carbon Nanotubes.Angewandte Chemie (International ed. in English) · 2022Review
- Reimagining cancer research with art.Nature reviews. Cancer · 2022Article
- Molecular Probes, Chemosensors, and Nanosensors for Optical Detection of Biorelevant Molecules and Ions in Aqueous Media and Biofluids.Chemical reviews · 2022Review
- Glycopolymer-Wrapped Carbon Nanotubes Show Distinct Interaction of Carbohydrates With Lectins.Frontiers in chemistry · 2022Article
- Beyond Color: The New Carbon Ink.Advanced materials (Deerfield Beach, Fla.) · 2021Review
- En route to single-step, two-phase purification of carbon nanotubes facilitated by high-throughput spectroscopy.Scientific reports · 2021Article
Corrections and comments
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Authors and funding
11 authors at 1 institution in 1 country.
Funding
Abstract
Microalbuminuria is an important clinical marker of several cardiovascular, metabolic, and other diseases such as diabetes, hypertension, atherosclerosis, and cancer. The accurate detection of microalbuminuria relies on albumin quantification in the urine, usually via an immunoturbidity assay; however, like many antibody-based assessments, this method may not be robust enough to function in global health applications, point-of-care assays, or wearable devices. Here, we develop an antibody-free approach using synthetic molecular recognition by constructing a polymer to mimic fatty acid binding to the albumin, informed by the albumin crystal structure. A single-walled carbon nanotube, encapsulated by the polymer, as the transduction element produces a hypsochromic (blue) shift in photoluminescence upon the binding of albumin in clinical urine samples. This complex, incorporated into an acrylic material, results in a nanosensor paint that enables the detection of microalbuminuria in patient samples and comprises a rapid point-of-care sensor robust enough to be deployed in resource-limited settings.
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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.