Neglected disease, neglected populations: the fight against Cryptococcus and cryptococcosis

Rodrigues, Márcio L.

doi:10.1590/0074-02760180111

Cited by 19 publications

(9 citation statements)

References 9 publications

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“…Fungal diseases in developing countries demand special attention. Many of the fungal diseases in Latin America are often overlooked, although they clearly fulfill the criteria to be classified as neglected human diseases (Rodrigues, 2016, 2018). The general impact of fungal pathogens on human health goes beyond the ability of fungi to infect humans, since they destroy a third of all food crops annually (Fisher et al, 2012), causing economical loss and impacting global poverty.…”

Section: The Impact Of Fungal Infections On Food Crops and Disproportmentioning

confidence: 99%

The Still Underestimated Problem of Fungal Diseases Worldwide

2019

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In the past few years, fungal diseases caused estimated over 1.6 million deaths annually and over one billion people suffer from severe fungal diseases ( Brown et al., 2012 ; Anonymous, 2017b ). Public health surveillance of fungal diseases is generally not compulsory, suggesting that most estimates are conservative ( Casadevall, 2017 ; Anonymous, 2017a ). Fungal disease can also damage plants and crops, causing major losses in agricultural activities and food production ( Savary et al., 2012 ). Animal pathogenic fungi are threatening bats, amphibians and reptiles with extinction ( Casadevall, 2017 ). It is estimated that fungi are the highest threat for animal-host and plant-host species, representing the major cause (approximately 65%) of pathogen-driven host loss ( Fisher et al., 2012 ). In this complex scenario, it is now clear that the global warming and accompanying climate changes have resulted in increased incidence of many fungal diseases ( Garcia-Solache and Casadevall, 2010 ). On the basis of all these factors, concerns on the occurrence of a pandemic of fungal origin in a near future have been raised ( Casadevall, 2017 ). In this context, to stop forgetting and underestimating fungal diseases is mandatory.

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Section: The Impact Of Fungal Infections On Food Crops and Disproportmentioning

confidence: 99%

The Still Underestimated Problem of Fungal Diseases Worldwide

2019

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“…However, the poor investment in fungal disease researches contributes to their maintenance as neglected diseases. For instance, researches on cryptococcosis receive only 0.2% of the Global Funding of Innovation for Neglected Diseases (G‐FINDER), although it is the fifth most deadly infectious disease worldwide, 1,2 mainly in developing countries in sub‐Saharan Africa 3 . Among several available methodologies, culture (the gold standard method), direct microscopy and histopathology are the most used techniques for the diagnosis of fungal infections, although serology and molecular methods are also available.…”

Section: Introductionmentioning

confidence: 99%

Isothermal nucleic acid amplification techniques for detection and identification of pathogenic fungi: A review

Zatti

Arantes

Theodoro

2020

Mycoses

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Background Fungal infections have increased during the last years due to the AIDS epidemic and immunosuppressive therapies. The available diagnostic methods, such as culture, histopathology and serology, have several drawbacks regarding sensitivity, specificity and time‐consuming, while molecular methods are still expensive and dependent on many devices. In order to overcome these challenges, isothermal nucleic acid amplification techniques (INAT) arose as promising diagnostic methods for infectious diseases. Objective This review aimed to present and discuss the main contributions of the isothermal nucleic acid amplification techniques applied in medical mycology. Methods Papers containing terms for each INAT (NASBA, RCA, LAMP, CPA, SDA, HAD or PSR) and the terms ‘mycoses’ or ‘disease, fungal’ were obtained from National Center for Biotechnology Information database until August 2019. Results NASBA, RCA, LAMP and PSR are the INAT reported in the literature for detection and identification of pathogenic fungi. Despite the need of a previous conventional PCR, the RCA technique might also be used for genotyping or cryptic species differentiation, which may be important for the treatment of certain mycoses; nevertheless, LAMP is the most used INAT for pathogen detection. Conclusion Among all INATs herein reviewed, LAMP seems to be the most appropriate method for fungal detection, since it is affordable, sensitive, specific, user‐friendly, rapid, robust, equipment‐free and deliverable to end‐users, fulfilling all ASSURED criteria of the World Health Organization for an ideal diagnostic method.

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“…open Scientific RepoRtS | (2020) 10:2362 | https://doi.org/10.1038/s41598-020-59276-w www.nature.com/scientificreports www.nature.com/scientificreports/ yeast kill 180,000 people around the world every year 13 . In addition, the treatment of cryptococcosis is unaffordable in most in developing countries 14,15 .An important trait for cryptococcal virulence is the presence of a polysaccharide capsule, which has been well described at the molecular and functional levels 16,17 . Capsular morphology includes a huge heterogeneity among distinct isolates and even in clonal isolates.…”

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Scanning electron microscopy and machine learning reveal heterogeneity in capsular morphotypes of the human pathogen Cryptococcus spp.

Lopes

Cruz²,

Rodrigues

et al. 2020

Sci Rep

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phenotypic heterogeneity is an important trait for the development and survival of many microorganisms including the yeast Cryptococcus spp., a deadly pathogen spread worldwide. Here, we have applied scanning electron microscopy (SEM) to define four Cryptococcus spp. capsule morphotypes, namely Regular, Spiky, Bald, and Phantom. These morphotypes were persistently observed in varying proportions among yeast isolates. to assess the distribution of such morphotypes we implemented an automated pipeline capable of (1) identifying potentially cell-associated objects in the SEM-derived images; (2) computing object-level features; and (3) classifying these objects into their corresponding classes. The machine learning approach used a Random Forest (RF) classifier whose overall accuracy reached 85% on the test dataset, with per-class specificity above 90%, and sensitivity between 66 and 94%. Additionally, the RF model indicates that structural and texture features, e.g., object area, eccentricity, and contrast, are most relevant for classification. The RF results agree with the observed variation in these features, consistently also with visual inspection of SEM images. Finally, our work introduces morphological variants of Cryptococcus spp. capsule. these can be promptly identified and characterized using computational models so that future work may unveil morphological associations with yeast virulence. open Scientific RepoRtS | (2020) 10:2362 | https://doi.org/10.1038/s41598-020-59276-w www.nature.com/scientificreports www.nature.com/scientificreports/ yeast kill 180,000 people around the world every year 13 . In addition, the treatment of cryptococcosis is unaffordable in most in developing countries 14,15 .An important trait for cryptococcal virulence is the presence of a polysaccharide capsule, which has been well described at the molecular and functional levels 16,17 . Capsular morphology includes a huge heterogeneity among distinct isolates and even in clonal isolates. This cellular diversity has been exploited in terms of drug resistance and pathogenic potential [18][19][20] .The study of phenotype heterogeneity is hampered by the lack of proper detection methods and statistical analyses. In order to further explore this morphotype diversity, we implemented an automated image analysis pipeline. This machine learning approach is capable of detecting and classifying capsular morphotypes, being applicable to cell type quantification in microscopy-based experiments. While not a high-throughput technique per se, SEM does yield vast amounts of complex data. Here, we describe the adaptation of one algorithmic implementation for the analysis and classification of Cryptococcus spp. capsular morphotypes captured using scanning electron microscopy (SEM). Our model substantially increases data analysis efficiency and provides a template for future machine learning applications within microbiology. ResultsCryptococcus spp. exhibit distinct capsule morphotypes under scanning electron microscopy. The analysis of Cryptococcus s...

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Neglected disease, neglected populations: the fight against Cryptococcus and cryptococcosis

Cited by 19 publications

References 9 publications

The Still Underestimated Problem of Fungal Diseases Worldwide

The Still Underestimated Problem of Fungal Diseases Worldwide

Isothermal nucleic acid amplification techniques for detection and identification of pathogenic fungi: A review

Scanning electron microscopy and machine learning reveal heterogeneity in capsular morphotypes of the human pathogen Cryptococcus spp.

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