Evidence map›Paper›PMID 31817932›Full record

ReviewSensors (Basel, Switzerland)2019

Fluorescent Single-Walled Carbon Nanotubes for Protein Detection.

Adi Hendler-Neumark, Gili Bisker

Abstract readReview
In one paragraph

Review in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.

0numbers the graph read from it
0cells of the map it votes in
31citing 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

31 citing papers in PubMed.

  1. Article
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  5. High-throughput SWCNT NIR-II screening enables cell andbioRxiv : the preprint server for biology · 2026
    Article
  6. Article
  7. Review
  8. Near-Infrared Fluorescent Single-Walled Carbon Nanotubes for Biosensing.Small (Weinheim an der Bergstrasse, Germany) · 2025
    Review
  9. Article
  10. Article
  11. Review
  12. Review
  13. Article
  14. Review
  15. Article
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  17. Article
  18. Review
  19. Review
  20. 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

2 authors.

Adi Hendler-NeumarkDepartment of Biomedical Engineering, Faculty of Engineering, Tel-Aviv University, Tel Aviv 6997801, Israel.
Gili BiskerDepartment of Biomedical Engineering, Faculty of Engineering, Tel-Aviv University, Tel Aviv 6997801, Israel.

Funding

Zuckerman STEM Leadership Program and the Israeli Science Foundation 456/18
6 · The paper itself

Abstract

Nanosensors have a central role in recent approaches to molecular recognition in applications like imaging, drug delivery systems, and phototherapy. Fluorescent nanoparticles are particularly attractive for such tasks owing to their emission signal that can serve as optical reporter for location or environmental properties. Single-walled carbon nanotubes (SWCNTs) fluoresce in the near-infrared part of the spectrum, where biological samples are relatively transparent, and they do not photobleach or blink. These unique optical properties and their biocompatibility make SWCNTs attractive for a variety of biomedical applications. Here, we review recent advancements in protein recognition using SWCNTs functionalized with either natural recognition moieties or synthetic heteropolymers. We emphasize the benefits of the versatile applicability of the SWCNT sensors in different systems ranging from single-molecule level to in-vivo sensing in whole animal models. Finally, we discuss challenges, opportunities, and future perspectives.

Indexed as

AnimalsBiosensing TechniquesChelating AgentsIndocyanine GreenNanotubes, CarbonNitrilotriacetic AcidProteinsSpectrometry, FluorescenceChelating AgentsIndocyanine GreenNanotubes, CarbonNitrilotriacetic AcidProteinsfluorescent nanoparticlesmolecular recognitionnanosensorsprotein detectionsingle-walled carbon nanotubes

Identifiers

PMID31817932
PMCPMC6960995

What OpenQuestion holds

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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.