Evidence map›Paper›PMID 25181404›Full record

ArticleThe Brazilian journal of infectious diseases : an official publication of the Brazilian Society of Infectious Diseases

Development of sandwich-form biosensor to detect Mycobacterium tuberculosis complex in clinical sputum specimens.

Taha Roodbar Shojaei, Mohamad Amran Mohd Salleh, Meisam Tabatabaei, Alireza Ekrami, Roya Motallebi, Tavoos Rahmani-Cherati, Abdollah Hajalilou, Raheleh Jorfi

Open access · goldAbstract read
In one paragraph

Article in The Brazilian journal of infectious diseases : an official publication of the Brazilian Society of Infectious Diseases. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
1.4field-weighted citation impact, top 19% of its field
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

7 citing papers in PubMed, 1 synthesis or guideline pooled it, 44 citations in OpenAlex.

  1. Pooled it
  2. Expert review of respiratory medicine · 2026
    Review
  3. Quantum Dot-Based Nanosensors for In Vitro Detection ofNanomaterials (Basel, Switzerland) · 2024
    Review
  4. Review
  5. Article
  6. Review
  7. Review
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

8 authors at 4 institutions in 2 countries.

Taha Roodbar ShojaeiInstitute of Advanced Technology (ITMA), Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia.
Mohamad Amran Mohd SallehInstitute of Advanced Technology (ITMA), Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia. Electronic address: asalleh@upm.edu.my.
Meisam TabatabaeiNanosystems Research Team (NRTeam), Microbial Biotechnology and Biosafety Department, Agricultural Biotechnology Research Institute of Iran (ABRII), Karaj, Iran.
Alireza EkramiInfectious and Tropical Diseases Research Center, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran.
Roya MotallebiDepartment of Plant Breeding and Biotechnology, College of Agriculture, Shahrekord University, P.O. Box 115, Shahrekord, Iran.
Tavoos Rahmani-CheratiResearch and Development Department, Nanozino, 16536-43181 Tehran, Iran.
Abdollah HajalilouInstitute of Advanced Technology (ITMA), Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia.
Raheleh JorfiInstitute of Advanced Technology (ITMA), Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia.
Universiti Putra Malaysia · MYAhvaz Jundishapur University of Medical Sciences · IRBiofuel Research Team · IRShahrekord University · IR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Mycobacterium tuberculosis, the causing agent of tuberculosis, comes second only after HIV on the list of infectious agents slaughtering many worldwide. Due to the limitations behind the conventional detection methods, it is therefore critical to develop new sensitive sensing systems capable of quick detection of the infectious agent. In the present study, the surface modified cadmium-telluride quantum dots and gold nanoparticles conjunct with two specific oligonucleotides against early secretory antigenic target 6 were used to develop a sandwich-form fluorescence resonance energy transfer-based biosensor to detect M. tuberculosis complex and differentiate M. tuberculosis and M. bovis Bacille Calmette-Guerin simultaneously. The sensitivity and specificity of the newly developed biosensor were 94.2% and 86.6%, respectively, while the sensitivity and specificity of polymerase chain reaction and nested polymerase chain reaction were considerably lower, 74.2%, 73.3% and 82.8%, 80%, respectively. The detection limits of the sandwich-form fluorescence resonance energy transfer-based biosensor were far lower (10 fg) than those of the polymerase chain reaction and nested polymerase chain reaction (100 fg). Although the cost of the developed nanobiosensor was slightly higher than those of the polymerase chain reaction-based techniques, its unique advantages in terms of turnaround time, higher sensitivity and specificity, as well as a 10-fold lower detection limit would clearly recommend this test as a more appropriate and cost-effective tool for large scale operations.

Indexed as

Biosensing TechniquesCadmium CompoundsFluorescence Resonance Energy TransferGoldHumansMetal NanoparticlesMycobacterium bovisMycobacterium tuberculosisPolymerase Chain ReactionReproducibility of ResultsSensitivity and SpecificitySputumTelluriumTuberculosis, PulmonaryCadmium Compoundscadmium tellurideGoldTelluriumCadmium-telluride quantum dots (CdTe-QDs)Fluorescence resonance energy transfer (FRET)Gold nanoparticles (AuNPs)Mycobacterium tuberculosis complex (MTBC)Sandwich-form FRET-based biosensor

Identifiers

PMID25181404
PMCPMC9425227
OpenAlexW2015260131

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.