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Biodetection Strategies for Selective Identification of Candidiasis

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Abstract

Fungi are among the predominant pathogens seen in a greater proportion of infections acquired in healthcare settings. A common fungus that causes infections in medical settings is Candida species. Hospitalized patients who suffer from fungal diseases such as candidiasis and candidemia often have elevated rates of mortality and morbidity. It is evident that longer hospital stays have the possibility of bacterial and fungal recurrence and also have a negative economic impact. If left untreated, a Candida infection can spread to other organs and cause a systemic infection that can result in sepsis. Clinicians can treat patients quickly when fungal infections are timely detected, this enhances the results of clinical trials. Developing novel, sensitive, and quick methods for detecting Candida species is imperative. Conventional detection techniques are unsuitable for clinical settings and point-of-care systems as they require expensive equipment and take a longer detection time. This review examines a few of the most widely used biosensor systems for the detection of Candida species, their sensitivity, and the limit of detection. It focuses on various biorecognition elements used and follows utilization and advances in nanotechnology in the context of sensing. In addition to enabling general analysis and quick real-time analysis, crucial for detecting Candida species, biosensors provide an intriguing alternative to more conventional techniques.

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Abbreviations

ISFET:

Ion-sensitive field-effect transistor

NAC:

N-acetylcysteine

BMT:

Bone marrow transplantation

OC:

Oral candidiasis

BSA:

Broad spectrum antibiotic

PNA-FISH:

Peptide nucleic acid fluorescent in situ hybridization

SWCNTs:

Single-walled carbon nanotubes

SPR:

Surface plasmon resonance

FET:

Field-effect transistor

SAM:

Self-assemble monolayer

SERS:

Surface-enhanced Raman spectral

NP:

Nanoparticle

LOD:

Limit of detection

AuNP:

Gold nanoparticle

C. albicans :

Candida albicans

NMR:

Nuclear magnetic resonance

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Acknowledgements

The authors would like to acknowledge Shodh Grant (UPES/R&D/SHODH/202314) supported by UPES R&D, Dehradun to Nidhi Chauhan and Riya Verma.

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Authors and Affiliations

  1. School of Health Sciences and Technology, UPES, Dehradun, Uttarakhand, 248007, India

    Riya Verma, Smriti Gaba, Nidhi Chauhan & Utkarsh Jain

  2. Department of Chemistry, University of Delhi, Delhi, India

    Ramesh Chandra

  3. Institute of Nanomedical Sciences (INMS), University of Delhi, Delhi, India

    Ramesh Chandra

  4. Dr. B.R. Ambedkar Centre for Biomedical Research, University of Delhi, Delhi, India

    Ramesh Chandra

  5. Maharaja Surajmal Brij University, Kumher, Bharatpur, 321201, India

    Ramesh Chandra

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  1. Riya Verma
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Contributions

Riya Verma: Writing original draft, Review—writing and editing; Smriti Gaba: Writing original draft, Review—writing and editing, Nidhi Chauhan: Supervision, Review—writing and editing; Ramesh Chandra: Supervision, Review—writing and editing, Utkarsh Jain: Supervision, Conceptualization, Review—writing and editing.

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Correspondence to Utkarsh Jain.

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Verma, R., Gaba, S., Chauhan, N. et al. Biodetection Strategies for Selective Identification of Candidiasis. Indian J Microbiol (2024). https://doi.org/10.1007/s12088-024-01288-5

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