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Background: Neglected tropical diseases (NTDs) primarily affect the poorest populations, often living in remote, rural areas, urban slums or conflict zones. Arboviruses are a significant NTD category spread by mosquitoes. Dengue, Chikungunya, and Zika are three arboviruses that affect a large proportion of the population in Latin and South America. The clinical diagnosis of these arboviral diseases is a difficult task due to the concurrent circulation of several arboviruses which present similar symptoms, inaccurate serologic tests resulting from cross-reaction and co-infection with other arboviruses. Objective: The goal of this paper is to present evidence on the state of the art of studies investigating the automatic classification of arboviral diseases to support clinical diagnosis based on Machine Learning (ML) and Deep Learning (DL) models. Method: We carried out a Systematic Literature Review (SLR) in which Google Scholar was searched to identify key papers on the topic. From an initial 963 records (956 from string-based search and seven from a single backward snowballing procedure), only 15 relevant papers were identified. Results: Results show that current research is focused on the binary classification of Dengue, primarily using tree-based ML algorithms. Only one paper was identified using DL. Five papers presented solutions for multi-class problems, covering Dengue (and its variants) and Chikungunya. No papers were identified that investigated models to differentiate between Dengue, Chikungunya, and Zika. Conclusions: The use of an efficient clinical decision support system for arboviral diseases can improve the quality of the entire clinical process, thus increasing the accuracy of the diagnosis and the associated treatment. It should help physicians in their decision-making process and, consequently, improve the use of resources and the patient's quality of life. Author summary: Neglected tropical diseases (NTDs) primarily affect the poorest populations, often living in remote, rural areas, urban slums or conflict zones. Arboviruses are a significant NTD category spread by mosquitoes. Dengue, Chikungunya, and Zika are three arboviruses that affect a large proportion of the population in Latin and South America. The clinical diagnosis of these arboviral diseases is a difficult task due to the concurrent circulation of several arboviruses which present similar symptoms and, sometimes, inaccurate test results. In this paper, we present the state of the art of studies investigating the automatic classification of arboviral diseases based on Machine Learning (ML) and Deep Learning (DL) models. Results show that current research is focused on the classification of Dengue, primarily using tree-based ML algorithms. The use of an efficient clinical decision support system for arboviral diseases can improve the quality of the entire clinical process, thus increasing the accuracy of the diagnosis and the associated treatment. It should help physicians in their decision-making process and, consequently, improve the use of resources and the patient's quality of life. [ABSTRACT FROM AUTHOR]

We describe a new species of Ninia Baird & Girard, 1853 endemic to the cloud forests of northwestern Ecuador. The new species has previously been confused with N. atrata (Hallowell, 1845) and N. teresitae (Angarita-Sierra and Lynch 2017) but is genetically most closely related to a third species of Ninia endemic to the Chocoan--Tumbesian transition area of western Ecuador. We revalidate the name N. schmidti (Jan, 1862), designate a neotype, and provide a diagnosis of the taxon and a description of its hemipenial morphology based on new material. The new and revalidated species can easily be identified from one another and from other trans-Andean South American Ninia based on ventral and subcaudal scale counts, hemipenial morphology, and coloration of the supralabials, throat, and belly. Finally, we remove N. atrata from the herpetofaunal list of Ecuador. [ABSTRACT FROM AUTHOR]

Sporotrichosis, a human and animal disease caused by Sporothrix species, is the most important implantation mycosis worldwide. Sporothrix taxonomy has improved in recent years, allowing important advances in diagnosis, epidemiology, and treatment. Molecular epidemiology reveals that S. brasiliensis remains highly prevalent during the cat-transmitted sporotrichosis outbreaks in South America and that the spread of S. brasiliensis occurs through founder effects. Sporothrix globosa and S. schenckii are cosmopolitan on the move, causing major sapronoses in Asia and the Americas, respectively. In this emerging scenario, one-health approaches are required to develop a creative, effective, and sustainable response to tackle the spread of sporotrichosis. In the 21st century, it has become vital to speciate Sporothrix, and PCR is the main pillar of molecular diagnosis, aiming at the detection of the pathogen DNA from clinical samples through multiplex assays, whose sensitivity reaches remarkably three copies of the target. The treatment of sporotrichosis can be challenging, especially after the emergence of resistance to azoles and polyenes. Alternative drugs arising from discoveries or repositioning have entered the radar of basic research over the last decade and point to several molecules with antifungal potential, especially the hydrazone derivatives with great in vitro and in vivo activities. There are many promising developments for the near future, and in this review, we discuss how these trends can be applied to the Sporothrix-sporotrichosis system to mitigate the advance of an emerging and re-emerging disease. [ABSTRACT FROM AUTHOR]