ArticleACS sensors2024
Diagnostics of Tuberculosis with Single-Walled Carbon Nanotube-Based Field-Effect Transistors.
Article in ACS sensors, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed.
- Monitoring Vaccine-Induced Antibody Levels Using Carbon Nanotube-Based Field-Effect Transistors.Analytical chemistry · 2025Article
- Global disease and economic burden of main mycobacterial infections in the working-age population from 1990 to 2021 with a forecast to 2045.Infectious medicine · 2025Article
- Advanced Materials for Biological Field-Effect Transistors (Bio-FETs) in Precision Healthcare and Biosensing.Advanced healthcare materials · 2025Review
- Recent advances in nanomaterials for the detection of mycobacterium tuberculosis (Review).International journal of molecular medicine · 2025Review
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Tuberculosis (TB) is still threatening millions of people's lives, especially in developing countries. One of the major factors contributing to the ongoing epidemic of TB is the lack of a fast, efficient, and inexpensive diagnostic strategy. In this work, we developed a semiconducting single-walled carbon nanotube (SWCNT)-based field-effect transistor (FET) device functionalized with anti-
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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.