Detection of Citrus tristeza virus by using fluorescence resonance energy transfer-based biosensor

Shojaei, Taha Roodbar; Salleh, Mohamad Amran Mohd; Sijam, Kamaruzaman; Rahim, Raha Abdul; Mohsenifar, Afshin; Safarnejad, Mohammad Reza; Tabatabaei, Meisam

doi:10.1016/j.saa.2016.06.052

Cited by 46 publications

(21 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The platform provided the LOD of 0.03 pg/mL in ultrapure water, and this study demonstrated the versatility of the photonic materials as a nanobiosensor for detecting viruses. In another application, AuNPs and the cadmium-telluride quantum dots (CdTe-QDs) were combined to fabricate a SPR energy transfer-based biosensor for the detection of Citrus tristeza ―an agent of the most detrimental disease named tristeza [ 147 ]. For this purpose, after extraction of viruses from plants, they used and combined AuNPs/CdTe-QDs for the labeling of antibodies and antigens.…”

Section: Smart Materials For Sensor Technologiesmentioning

confidence: 99%

Smart materials-integrated sensor technologies for COVID-19 diagnosis

et al. 2021

View full text Add to dashboard Cite

After the first case has appeared in China, the COVID-19 pandemic continues to pose an omnipresent threat to global health, affecting more than 70 million patients and leading to around 1.6 million deaths. To implement rapid and effective clinical management, early diagnosis is the mainstay. Today, real-time reverse transcriptase (RT)-PCR test is the major diagnostic practice as a gold standard method for accurate diagnosis of this disease. On the other side, serological assays are easy to be implemented for the disease screening. Considering the limitations of today's tests including lengthy assay time, cost, the need for skilled personnel, and specialized infrastructure, both strategies, however, have impediments to be applied to the resource-scarce settings. Therefore, there is an urgent need to democratize all these practices to be applicable across the globe, specifically to the locations comprising of very limited infrastructure. In this regard, sensor systems have been utilized in clinical diagnostics largely, holding great potential to have pivotal roles as an alternative or complementary options to these current tests, providing crucial fashions such as being suitable for point-of-care settings, cost-effective, and having short turnover time. In particular, the integration of smart materials into sensor technologies leverages their analytical performances, including sensitivity, linear dynamic range, and specificity. Herein, we comprehensively review major smart materials such as nanomaterials, photosensitive materials, electrically sensitive materials, their integration with sensor platforms, and applications as wearable tools within the scope of the COVID-19 diagnosis.

show abstract

Section: Smart Materials For Sensor Technologiesmentioning

confidence: 99%

Smart materials-integrated sensor technologies for COVID-19 diagnosis

et al. 2021

View full text Add to dashboard Cite

show abstract

“…The detection limit was found to be 0.27 nM, which is lower than ELISA and other immunoassay techniques for CTV detection. 84,85 In another example of CTV detection, a microfluidic electrochemical device (μFED) was proposed by Freitas et al 86 The μFED included an array of eight screen-printed electrodes fabricated. Poly(diallyldimethylammonium chloride solution) (PDDA) was applied to electrode surface to create a positively charged film.…”

Section: Antibody-based Sensors For Viral Antigen and Particle Detectionmentioning

confidence: 99%

Review—Chemical and Biological Sensors for Viral Detection

Özer

Geiss

Henry

2019

J. Electrochem. Soc.

119

View full text Add to dashboard Cite

Infectious diseases commonly occur in contaminated water, food, and bodily fluids and spread rapidly, resulting in death of humans and animals worldwide. Among infectious agents, viruses pose a serious threat to public health and global economy because they are often difficult to detect and their infections are hard to treat. Since it is crucial to develop rapid, accurate, cost-effective, and in-situ methods for early detection viruses, a variety of sensors have been reported so far. This review provides an overview of the recent developments in electrochemical sensors and biosensors for detecting viruses and use of these sensors on environmental, clinical and food monitoring. Electrochemical biosensors for determining viruses are divided into four main groups including nucleic acid-based, antibody-based, aptamer-based and antigen-based electrochemical biosensors. Finally, the drawbacks and advantages of each type of sensors are identified and discussed.

show abstract

“… Sun et al (2008) discussed the sensor labeled with the 23-mer oligonucleotide derived from the CaMV 35S promoter on a PbS nanoparticle. Investigations into the sensor labeled with the antibody derived from CTV coat protein on CdTe QDs ( Safarnejad et al, 2017 ; Shojaei et al, 2016a ) and the sensor labeled with the GVA antibody derived from GVA coat protein on the ZnO films ( Tereshchenko et al, 2017 ) have followed. The details of QD-based biosensors for the detection of plant viruses are summarized in Table 3 .…”

Section: Detection Of Plant Viruses Using Biosensorsmentioning

confidence: 99%

“…In fact, using antibody derived from CTV coat protein, alternative types of biosensors were reported to enhance the specificity and sensitivity of fluorescence resonance energy transfer (FRET)-based biosensor. Shojaei et al (2016a) reported the complex sensor using the mixture of CdTe quantum dots and gold nanoparticles (AuNPs) labeled with an antibody derived from CTV coat protein. Safarnejad et al (2017) reported on the donor-acceptor complex sensor; QDs conjugated with antibody derived from CTV coat protein and rhodamine 123 conjugated with the recombinant coat protein of the CTV were used as donor and acceptor, respectively ( Fig.…”

Section: Detection Of Plant Viruses Using Biosensorsmentioning

confidence: 99%