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Manufacturer
Accuracy compared
to conventional
method (%)
Incubation
time (h)
Incubation
temperature
(°C)
CA
West Sacramento,
CA
68–70 MIDI, Newark, DE
S. Banik
Strip 30 24–48 88–98 bioMerieux
Number and type of tests Method
94 Microtiter tray 30 24–72 48.8 Biolog, Hayward,
Kit
Biolog YT
MicroPlate
Table 1.4 Automatic commercial identication kits
ID 32C 24 carbohydrates, 5 organic
Test panel 37 4 85–96 Dade Behring Inc.,
acid, 1 cycleheximide, 1
esculin
13 aminopeptidases, 3
carbohydrates, 9 glycosidases,
1 phosphatase, 1 urease
MicroScan Yeast
Identication Panel
Fatty acid methyl ester analysis Gas liquid
Sherlock Microbial
chromatography
Identication
System
Adopted and modied from Pincus etal. (2007)
1 Diagnostics of Candida and Candidiasis: Current Methods and Future…
27
should interpret the API system, resulting in conjunction with other clinical and laboratory ndings to ensure accurate diagnosis and appropriate treatment of Candida infections.
1.5.1.2 The VITEK System
The VITEK (bioMerieux Vitek, Inc., Hazelwood, MO, USA) system is an auto­mated microbial identication and antimicrobial susceptibility testing platform that is widely used in clinical microbiology laboratories. In this system, the clinical samples containing Candida are rst cultured and isolated from the samples. A por­tion of the isolated Candida colony is then inoculated into the VITEK ID-GPC (identication Gram-positive cocci) card, which is a specic card designed for the identication of Gram-positive cocci, including yeasts like Candida (Pfaller etal.
1988). The VITEK ID-GPC card is then loaded into the VITEK instrument, which
automatically incubates the inoculated card. The system monitors microbial growth and metabolic activities. The VITEK system analyzes the microbial growth and biochemical reactions in the card. The instrument also measures factors such as color changes, pH changes, and the production of various metabolic byproducts. The VITEK software compares the observed metabolic prole of the Candida iso­late with a comprehensive database of reference proles. The system then provides an identication report, indicating the most likely species of Candida. In addition to species identication, the VITEK system can also perform antifungal susceptibility testing. The VITEK AST-GP card is used for this purpose, providing information on the susceptibility of the Candida isolates to various antifungal agents (el-Zaatari etal. 1990; Fenn etal. 1994).
The Vitek Yeast Biochemical Card (YBC) can perform 26 biochemical tests simultaneously from the same inoculum including 21 carbohydrates, potassium nitrate (KNO3), organic acid, nitrogen, urease, and cycloheximide tests. This Vitek YBC is incubated off-line for 24h at 30°C and then read for colorimetric reactions by the system. It showed correct results between 84.9% and 98% of the time when compared to API 20C or Uni-Yeast-Tek (el-Zaatari etal. 1990; Fenn etal. 1994; Wadlin etal. 1999).
The Vitek 2 YST ID card is used for rapid and accurate identication of 50 patho­genic yeasts including 27 different Candida species. It contains 25 carbohydrates, three glycosidases, nine organic acids, two nitrogen, KNO3, esculin, and urease tests. It is a colorimetric card that is read automatically by the system. Compared to API 20C, it showed 94.8% correct results and is compatible with CHROMagar as an isolation medium (Aubertine etal. 2006).
The VITEK system automates the identication and susceptibility testing pro­cess, provides reliable and accurate results, and hence contributes to the timely and effective management of Candida infections. The system is capable of processing a large number of samples simultaneously, making it suitable for high-throughput laboratories. The VITEK database is regularly updated with new microbial proles, enhancing the system’s ability to identify emerging Candida strains. It is important to note that while the VITEK system is a powerful tool for microbial identication and susceptibility testing, its results should be interpreted alongside clinical
28
S. Banik
information, patient history, and other laboratory ndings for accurate diagnosis and treatment decisions.
1.5.2 Automatic Rapid Identification System
1.5.2.1 MALDI-TOF MS
Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry, commonly known as MALDI-TOF MS, is a powerful technique used for the rapid and accurate identication of microorganisms, including Candida species, in clini­cal microbiology laboratories (Spanu et al. 2012). Clinical samples containing Candida isolates are rst cultured on appropriate growth media. Once isolated, a small amount of the microbial colony is selected for analysis. A matrix, often a mixture of an organic acid and a photosensitive molecule, is applied to the microbial colony. This matrix helps in the desorption and ionization of the microbial proteins upon laser irradiation. The prepared sample is then subjected to a laser beam, which causes the molecules in the matrix to ionize. The ionized molecules, including pro­teins from the microbial cells, are then accelerated in an electric eld and enter the time-of-ight mass spectrometer. In the mass spectrometer, the ionized molecules travel through a ight tube, and the time taken for each ion to reach the detector is measured. The time of ight is directly related to the mass-to-charge ratio of the ions. The mass spectrometer generates a mass spectrum based on the mass-to­charge ratios of the ions. This spectrum represents a unique “ngerprint” of the microbial proteins present in the sample. The acquired mass spectrum is compared to a reference database containing mass spectra of known microorganisms, includ­ing Candida species (Timmins etal. 1998). The software uses algorithms to match the observed spectrum with the closest reference spectrum in the database. Based on the comparison, the MALDI-TOF MS system provides a rapid and accurate identi­cation of the Candida species present in the sample. The result is typically reported as a condence score or a probability value (Timmins etal. 1998).
MALDI-TOF MS provides rapid identication, often within minutes, allowing for timely and efcient diagnosis. The technique has high specicity and sensitivity, leading to reliable identication of Candida species. MALDI-TOF MS can identify a wide range of microorganisms, making it a versatile tool in clinical microbiology. It is high throughput as it is capable of handling a high volume of samples, making it suitable for laboratories with varying workloads (Spanu etal. 2012). It is impor­tant to note that while MALDI-TOF MS is highly reliable for the identication of Candida species, it may not provide information on antifungal susceptibility. Therefore, additional susceptibility testing may be necessary for complete patient management. Additionally, databases are regularly updated to improve the accuracy of the method, so staying current with software updates is important.
1.5.2.2 The MALDI Sepsityper IVD Kit
The MALDI Sepsityper IVD kit (Bruker Daltonics, Bremen, Germany) is designed for the identication of microorganisms directly from positive blood cultures using
1 Diagnostics of Candida and Candidiasis: Current Methods and Future…
MALDI-TOF MS analysis. This is particularly important in the context of sepsis, where rapid and accurate identication of the causative agent is crucial for effective treatment. It takes around 20–40min turnaround time and provides 62% reliable identication of all Candida isolates (Bal and McGill 2018).
29
1.5.2.3 The BioFire FilmArray BCID2 Panel
The FilmArray BCID2 Panel (Biomerieux, Marcy I’Etoile, France) enables rapid and accurate identication of 43 pathogens including six Candida species associated with bloodstream infections directly from positive blood cultures. It requires only 2min of hands-on time and has a turnaround time of 1h. It uses nested multiplex PCR and is FDA-cleared. Compared to blood culture, this panel showed a sensitivity and specic­ity of 99.2% and 99.9%, respectively, for Candida species (Salimnia etal. 2016).
1.5.2.4 The Accelerate Pheno BC Panel
The Accelerate Pheno BC Panel (Accelerate Diagnostics, Tucson, AZ, USA) pro­vides the identication of bacteria and fungi including C. albicans and C. glabrata from positive blood cultures using automated FISH.It is FDA-cleared and has a turnaround time of 90min. The sensitivity ranges from 94.6% to 100% for the iden­tication of Candida species (Pancholi etal. 2018).
All these rapid identication systems aim to provide faster and more comprehen­sive results compared to traditional culture-based methods. The ability to detect Candida species quickly is crucial for guiding appropriate antifungal therapy in patients with bloodstream infections or sepsis. Before using any diagnostic plat­form, healthcare professionals should be aware of the specic capabilities, limita­tions, and performance characteristics of the system. Additionally, the availability of these systems may vary by region and healthcare facility. It is advisable to check with local clinical laboratories or manufacturers for the latest software updates on the use of FilmArray, Sepsis Flow Chip, and ePlex systems for Candida diagnosis.

1.6 Advanced Diagnostics

1.6.1 Artificial Intelligence (AI) andMachine Learning (ML)
Articial intelligence (AI) and machine learning (ML) have shown signicant potential in various elds of medicine, including fungal diagnosis and patient care. Infections with Candida can be challenging to diagnose and treat, and the integra­tion of AI and ML technologies can enhance the accuracy, speed, and efciency of diagnosis and management (Badillo etal. 2020). AI and ML algorithms can analyze microscopic images of clinical specimens, such as skin scrapings or tissue biopsies, to identify fungal structures. This aids in quicker and more accurate diagnosis when compared to traditional manual methods (Nichols etal. 2019). In cases of skin fun­gal infections, AI algorithms can analyze dermatological images to identify charac­teristic patterns associated with different types of fungal infections, helping in early detection and differentiation (Davenport and Kalakota 2019; Zieliński etal. 2020).
30
S. Banik
The analysis of genomic data from NGS related to fungal pathogens can be com­plex. ML algorithms can assist in identifying genetic markers associated with drug resistance or specic species of Candida, aiding in personalized treatment strate­gies. ML algorithms can analyze existing drug databases to identify potential candi­dates for repurposing as antifungal agents. This approach can expedite drug development and reduce costs. ML models can predict the efcacy of antifungal drugs based on patient-specic factors, optimizing treatment plans and minimizing the risk of resistance (Zieliński etal. 2020).
The integration of AI and ML in the diagnosis of candidiasis and patient care holds promise for improving accuracy, efciency, and personalized treatment strate­gies in the management of fungal infections. However, it is crucial to continually validate and rene these technologies through rigorous clinical trials and real-world implementations to ensure their reliability and safety in healthcare settings. AI-based expert systems can provide diagnostic support to healthcare professionals by inte­grating clinical data and guidelines to generate recommendations for the diagnosis and management of candidiasis. ML models can assess the risk factors associated with fungal infections, enabling healthcare providers to prioritize high-risk patients for further diagnostic testing or preventive measures (Davenport and Kalakota 2019).
1.6.2 Biosensor-Based Tests
Biosensor-based fungal diagnostic tests are innovative tools designed to detect and identify fungal infections through the detection of specic biomolecules or the meta­bolic activities associated with fungal pathogens. These biosensors leverage biologi­cal elements, such as enzymes, antibodies, or nucleic acids, combined with transducing elements like electrodes or optical systems, to convert a biochemical signal into a measurable output. The biosensor includes a biological recognition element that selectively interacts with a biomarker along with a transducer element to produce a signal and the signal processor. This biosensor can be an antibody, aptamer, enzyme, or nucleic acid sequence designed to bind to fungal components. The transducer ele­ment converts the biological interaction into a detectable signal. Common transducers include electrochemical, optical, and mass-sensitive sensors. Biosensors can be clas­sied as electrochemical, optical, and mass-sensitive biosensors (Thévenot etal. 2001).
Biosensors can target fungal cell wall components such as β-glucans, chitin, or mannans, which are specic to fungal cells and not present in human cells (Kwasny etal. 2018; Vendele etal. 2020). Detection of fungal metabolites, such as mycotox­ins, can serve as indicators of fungal presence. Biosensors may use nucleic acid probes to target specic DNA or RNA sequences unique to fungal pathogens. Biosensors can be designed to detect multiple fungal species or biomarkers simul­taneously, allowing for comprehensive diagnostic information (Hussain etal. 2020).
Biosensors often provide results in real time or within a short time frame, enabling rapid diagnosis. The use of specic biological recognition elements enhances the sensitivity and specicity of biosensors, reducing the likelihood of false positives or negatives. Many biosensors are designed for easy use at the point of care, making them suitable for decentralized settings and resource-limited
1 Diagnostics of Candida and Candidiasis: Current Methods and Future…
31
environments (Omidfar etal. 2020). Ensuring the reproducibility and standardiza­tion of biosensor-based tests is crucial for their widespread adoption. Rigorous clinical validation is necessary to establish the diagnostic accuracy and reliability of biosensor- based tests in diverse patient populations. While biosensors offer advan­tages, considerations related to cost, scalability, and accessibility need to be addressed for broader implementation, especially in resource-constrained settings (Patel etal. 2016).
1.6.3 Next-Generation Sequencing (NGS)
Next-generation sequencing (NGS), also known as high-throughput sequencing, has revolutionized the eld of microbiology, including the diagnosis of fungal infec­tions (Deurenberg et al. 2017). NGS technologies enable the high-throughput sequencing of nucleic acids, allowing for the rapid and comprehensive analysis of microbial genomes (Slatko etal. 2018). In the context of Candida diagnosis, NGS has been applied to various aspects, including identifying the species, determining antifungal resistance proles, and understanding the genetic diversity within Candida populations (Song etal. 2021). A schematic diagram depicting the work­ow of NGS for application in the diagnosis of fungal pathogen is illustrated in Fig.1.2.
Fig. 1.2 Schematic diagram depicting workow of NGS for application in fungal pathogen diag­nosis. [The gure has been created in Biorender.com] (reprinted from (Fang etal. 2023)) under permission of Creative Commons Attribution 4.0 International License (https://creativecommons.
org/licenses/by/4.0/)
32
S. Banik
NGS allows for the sequencing of the entire genome or specic regions of Candida DNA.By comparing the obtained sequences to reference genomes, the species of Candida causing the infection can be accurately identied (Biswas etal.
2017). The ITS region is a commonly targeted region for Candida identication.
NGS can rapidly and accurately sequence the ITS region, enabling the discrimina­tion of closely related Candida species. NGS can be applied to analyze the entire genome of Candida isolates, providing insights into the presence of mutations asso­ciated with antifungal resistance (Huseyin etal. 2017). This is crucial for guiding appropriate antifungal therapy. Specic genes associated with antifungal resistance, such as those encoding ergosterol biosynthesis enzymes (e.g., ERG11) or drug transporters, can be sequenced to identify resistance-conferring mutations (Song etal. 2021).
NGS enables the identication of single nucleotide variations within Candida genomes. A single-nucleotide polymorphism (SNP) analysis can be used for epi­demiological studies, tracking the spread of specic Candida strains within healthcare settings (Sherry et al. 2013). Comparing the genomes of different Candida isolates can provide insights into their genetic diversity, evolution, and adaptation to different environments. NGS is highly sensitive and can detect low levels of Candida DNA, even in samples with a low fungal burden. This is particu­larly valuable for early detection and in cases where traditional diagnostic meth­ods may yield false- negative results. NGS allows for the simultaneous detection of multiple Candida species and other fungal pathogens, providing a more com­prehensive view of the fungal composition in clinical samples (Wain and Mavrogiorgou 2013).
NGS generates large volumes of data, and sophisticated bioinformatics tools are required for accurate interpretation. This includes assembling and aligning sequences, identifying variants, and predicting antifungal resistance. Establishing standardized protocols for sample preparation, sequencing, and data analysis is essential for ensuring reproducibility and comparability of results across different laboratories. While the cost of NGS has decreased, it may still be higher than some traditional diagnostic methods. Additionally, the infrastructure and expertise required for the NGS analysis may pose challenges, especially in resource-limited settings (McCombie and McPherson 2019).

1.7 Conclusion

The prevalence of candidiasis is underscored by its ability to affect diverse popula­tions, from healthy individuals to those with compromised immune systems. A comprehensive understanding of the epidemiology, risk factors, and clinical presen­tations of candidiasis is fundamental for healthcare practitioners to facilitate timely diagnosis and effective management, ultimately improving patient outcomes in the face of this prevalent fungal infection. Conventional methods, such as blood cul­tures, remain foundational in identifying Candida species and guiding treatment decisions. However, limitations in sensitivity and the extended time required for
1 Diagnostics of Candida and Candidiasis: Current Methods and Future…
33
results have prompted the development and adoption of novel diagnostic strategies (Pasqualotto and Denning 2005). Molecular techniques, including polymerase chain reaction (PCR) and NGS, have ushered in a new era of precision and rapidity in Candida diagnosis. These methods not only enable accurate species identica­tion but also provide insights into antifungal resistance proles, aiding clinicians in tailoring therapeutic interventions. Point-of-care (POC) tests have emerged as valu­able tools, offering quick and on-the-spot results for timely decision- making. Various POC tests, including antigen detection assays and T2MR technology, have demonstrated their efcacy in expediting the diagnosis of candidiasis, particularly in critical scenarios like candidemia.
In the realm of candidiasis diagnosis of candidiasis, a holistic and integrated approach that combines traditional methods with state-of-the-art technologies is key. The goal is to not only identify the presence of Candida but also to character­ize its species, determine antifungal susceptibility, and provide clinically relevant information swiftly. With these advancements, the medical community is better equipped to diagnose candidiasis accurately, enabling timely and targeted inter­ventions that can signicantly impact patient outcomes. The evolving diagnostic strategies underscore the dynamic nature of the eld and pave the way for contin­ued improvements in the detection and management of Candida infections in diverse clinical settings.
As the eld progresses, challenges such as standardization, cost considerations, and accessibility persist. Researchers and clinicians must work collaboratively to address these issues and ensure the widespread implementation of advanced diag­nostic techniques (Neppelenbroek etal. 2014). Moreover, ongoing research into host biomarkers, immune response patterns, and innovative imaging technologies holds promise for further rening the diagnostic landscape. In conclusion, the diag­nosis of candidiasis has undergone signicant advancements with the integration of various cutting-edge technologies and methodologies. From traditional culture­based methods to the latest molecular techniques, the landscape of diagnostic approaches has evolved to meet the challenges posed by Candida infections, par­ticularly in the context of invasive candidiasis.

References

Adams ED Jr, Cooper BH (1974) Evaluation of a modied Wickerham medium for identifying
medically important yeasts. Am J Med Technol 40(9):377–388 Ahearn DG, Roth F, Fell JW, Meyers SP (1960) Use of shaken cultures in the assimilation test for
yeast identication. J Bacteriol 79(3):369–371 Ahmad S, Khan Z, Mustafa AS, Khan ZU (2002) Seminested PCR for diagnosis of candidemia:
comparison with culture, antigen detection, and biochemical methods for species identica-
tion. J Clin Microbiol 40(7):2483–2489 Ahmad S, Khan Z, Mustafa AS, Khan ZU (2003) Epidemiology of Candida colonization in an
intensive care unit of a teaching hospital in Kuwait. Med Mycol 41(6):487–493 Akamatsu N, Sugawara Y, Kaneko J, Tamura S, Makuuchi M (2007) Preemptive treatment of
fungal infection based on plasma (1 --> 3)beta-D-glucan levels after liver transplantation.
Infection 35(5):346–351
34
Anttila VJ, Ruutu P, Bondestam S, Jansson SE, Nordling S, Färkkilä M etal (1994) Hepatosplenic
yeast infection in patients with acute leukemia: a diagnostic problem. Clin Infect Dis
18(6):979–981 Arafa SH, Elbanna K, Osman GEH, Abulreesh HH (2023) Candida diagnostic techniques: a
review. J Umm Al-Qura Univ Appl Sci 9(3):360–377 Arastehfar A, Fang W, Daneshnia F, Al-Hatmi AM, Liao W, Pan W etal (2019) Novel multiplex
real-time quantitative PCR detecting system approach for direct detection of Candida auris and
its relatives in spiked serum samples. Future Microbiol 14:33–45 Arendrup MC, Bergmann OJ, Larsson L, Nielsen HV, Jarløv JO, Christensson B (2010) Detection
of candidaemia in patients with and without underlying haematological disease. Clin Microbiol
Infect 16(7):855–862 Arendrup MC, Sulim S, Holm A, Nielsen L, Nielsen SD, Knudsen JD etal (2011) Diagnostic
issues, clinical characteristics, and outcomes for patients with fungemia. J Clin Microbiol
49(9):3300–3308 Arya M, Shergill IS, Williamson M, Gommersall L, Arya N, Patel HR (2005) Basic principles of
real-time quantitative PCR.Expert Rev Mol Diagn 5(2):209–219 Ascioglu S, Rex JH, de Pauw B, Bennett JE, Bille J, Crokaert F etal (2002) Dening opportunis-
tic invasive fungal infections in immunocompromised patients with cancer and hematopoietic
stem cell transplants: an international consensus. Clin Infect Dis 34(1):7–14 Aubertine CL, Rivera M, Rohan SM, Larone DH (2006) Comparative study of the new colorimet-
ric VITEK 2 yeast identication card versus the older uorometric card and of CHROMagar
Candida as a source medium with the new card. J Clin Microbiol 44(1):227–228 Avni T, Leibovici L, Paul M (2011) PCR diagnosis of invasive candidiasis: systematic review and
meta-analysis. J Clin Microbiol 49(2):665–670 Badillo S, Banfai B, Birzele F, Davydov II, Hutchinson L, Kam-Thong T etal (2020) An introduc-
tion to machine learning. Clin Pharmacol Ther 107(4):871–885 Bal AM, McGill M (2018) Rapid species identication of Candida directly from blood culture
broths by Sepsityper-MALDI-TOF mass spectrometry: impact on antifungal therapy. J R Coll
Physicians Edinb 48(2):114–119 Ball LM, Bes MA, Theelen B, Boekhout T, Egeler RM, Kuijper EJ (2004) Signicance of ampli-
ed fragment length polymorphism in identication and epidemiological examination of
Candida species colonization in children undergoing allogeneic stem cell transplantation. J
Clin Microbiol 42(4):1673–1679 Berardinelli S, Opheim DJ (1985) New germ tube induction medium for the identication of
Candida albicans. J Clin Microbiol 22(5):861–862 Biesbroek JM, Verduyn Lunel FM, Kragt JJ, Amelink GJ, Frijns CJ (2013) Culture-negative
Candida meningitis diagnosed by detection of Candida mannan antigen in CSF.Neurology
81(17):1555–1556 Biswas C, Chen SC, Halliday C, Martinez E, Rockett RJ, Wang Q et al (2017) Whole genome
sequencing of Candida glabrata for detection of markers of antifungal drug resistance. J Vis
Exp 28(130):56714 Borman AM, Johnson EM (2013) Genomics and proteomics as compared to conventional pheno-
typic approaches for the identication of the agents of invasive fungal infections. Curr Fungal
Infect Rep 7(3):235–243 Borst A, Leverstein-Van Hall MA, Verhoef J, Fluit AC (2001) Detection of Candida spp. in blood
cultures using nucleic acid sequence-based amplication (NASBA). Diagn Microbiol Infect
Dis 39(3):155–160 Bowman SM, Free SJ (2006) The structure and synthesis of the fungal cell wall. BioEssays
28(8):799–808 Buesching WJ, Kurek K, Roberts GD (1979) Evaluation of the modied API 20C system for iden-
tication of clinically important yeasts. J Clin Microbiol 9(5):565–569 Bump CM, Kunz LJ (1968) Routine identication of yeasts with the aid of molybdate-agar
medium. Appl Microbiol 16(10):1503–1506
S. Banik
1 Diagnostics of Candida and Candidiasis: Current Methods and Future…
Campbell CK, Holmes AD, Davey KG, Szekely A, Warnock DW (1998) Comparison of a new
chromogenic agar with the germ tube method for presumptive identication of Candida albi-
cans. Eur J Clin Microbiol Infect Dis. Ofcial publication of the European Society of Clinical
Microbiology 17(5):367–368 Chen XL, Zheng H, Li WG, Zhong YH, Chen XP, Lu JX (2020) Direct blood culturing of Candida
spp. on solid medium by a rapid enrichment method with magnetic beads coated with recombi-
nant human mannan-binding lectin. J Clin Microbiol 58(4):e00057–e00020 Chen B, Xie Y, Zhang N, Li W, Liu C, Li D etal (2021) Evaluation of droplet digital PCR assay for
the diagnosis of candidemia in blood samples. Front Microbiol 12:700008 Christensen WB (1946) Urea decomposition as a means of differentiating proteus and paracolon
cultures from each other and from Salmonella and Shigella types. J Bacteriol 52(4):461–466 Clancy CJ, Nguyen MH (2013) Finding the “missing 50%” of invasive candidiasis: how noncul-
ture diagnostics will improve understanding of disease spectrum and transform patient care.
Clin Infect Dis 56(9):1284–1292 Clancy CJ, Nguyen MH (2018) Diagnosing invasive candidiasis. J Clin Microbiol 56(5). https://
doi.org/10.1128/jcm.01909- 17
Coleman DC, Bennett DE, Sullivan DJ, Gallagher PJ, Henman MC, Shanley DB etal (1993) Oral
Candida in HIV infection and AIDS: new perspectives/new approaches. Crit Rev Microbiol
19(2):61–82 Costa SO, Brancocde L (1964) Evaluation of a molybdenum culture medium as selective and dif-
ferential for yeasts. J Pathol Bacteriol 87:428–431 Davenport T, Kalakota R (2019) The potential for articial intelligence in healthcare. Future
Healthc J 6(2):94–98 De Carolis E, Marchionni F, Torelli R, Angela MG, Pagano L, Murri R etal (2020) Comparative
performance evaluation of Wako β-glucan test and Fungitell assay for the diagnosis of invasive
fungal diseases. PLoS One 15(7):e0236095 del Negro GMB, Delgado AF, Manuli ER, Yamamoto L, Okay TS (2010) Dual candidemia detected
by nested polymerase chain reaction in two critically ill children. Med Mycol 48(8):1116–1120 Deorukhkar SC, Saini S (eds) (2014) Laboratory approach for diagnosis of candidiasis through
ages, pp206–218 Deorukhkar SC, Saini S, Jadhav PA (2012) Evaluation of different media for germ tube production
of Candida albicans and Candida dubliniensis. Int J Biomed Adv Res 3:704–707 Deurenberg RH, Bathoorn E, Chlebowicz MA, Couto N, Ferdous M, García-Cobos S etal (2017)
Application of next generation sequencing in clinical microbiology and infection prevention.
J Biotechnol 243:16–24 Donnelly JP, Chen SC, Kauffman CA, Steinbach WJ, Baddley JW, Verweij PE et al (2020)
Revision and update of the consensus denitions of invasive fungal disease from the European
Organization for Research and Treatment of Cancer and the Mycoses Study Group Education
and Research Consortium. Clin Infect Dis. An ofcial publication of the Infectious Diseases
Society of America 71(6):1367–1376 Eggimann P, Garbino J, Pittet D (2003) Epidemiology of Candida species infections in critically ill
non-immunosuppressed patients. Lancet Infect Dis 3(11):685–702 el-Zaatari M, Pasarell L, McGinnis MR, Buckner J, Land GA, Salkin IF (1990) Evaluation of the
updated Vitek yeast identication data base. J Clin Microbiol 28(9):1938–1941 Ericson EL, Klingspor L, Ullberg M, Ozenci V (2012) Clinical comparison of the Bactec Mycosis
IC/F, BacT/Alert FA, and BacT/Alert FN blood culture vials for the detection of candidemia.
Diagn Microbiol Infect Dis 73(2):153–156 Fang W, Wu J, Cheng M, Zhu X, Du M, Chen C etal (2023) Diagnosis of invasive fungal infec-
tions: challenges and recent developments. J Biomed Sci 30(1):42 Fenn JP, Segal H, Barland B, Denton D, Whisenant J, Chun H etal (1994) Comparison of updated
Vitek Yeast Biochemical Card and API 20C yeast identication systems. J Clin Microbiol
32(5):1184–1187 Fontecha G, Montes K, Ortiz B, Galindo C, Braham S (2019) Identication of cryptic species of
four Candida complexes in a culture collection. J Fungi (Basel, Switzerland) 5(4):117
35