We are pleased to introduce the collection Frontiers in Tropical Diseases – Highlights in Major Tropical Diseases 2021/22. This collection will welcome and showcase a selection of high impact articles authored by leaders in the field. The work presented here highlights the broad diversity of research performed across the Major Tropical Diseases section, and aims to put a spotlight on the main areas of interest. This collection aims to further support Frontiers’ strong community by shining a spotlight on our authors' highly impactful research.

Gonçalo Moniz Institute (IGM)

University of São Paulo

Explore broad diversity of research in Tropical diseases and its major areas of interest.

. This research topic focuses on the multi-class classification of arboviral diseases, the adverse drug reactions experienced by patients, the characterization of malaria rapid diagnostic tests, and the prevalence of Nontuberculous mycobacteria in Zambia. <ul> <li>A comparative study of machine learning techniques for multi-class classification of arboviral diseases resulted in a 76.58% recall on the CHIKUNGUNYA class.</li> <li>78.8% of patients experienced at least one adverse drug reaction, most of which were mild/moderate and occurred early in treatment.</li> <li>A series of diagnostic machine learning models were generated from a dataset of 100 P. falciparum PfHRP2 genetic sequences and proved to be effective in characterizing PfHRP2 diversity.</li> <li>Mycobacterium intracellulare was the most common isolated NTM in humans and cattle, while Mycobacterium avium was the most common isolated NTM in water.</li> </ul>

Frontiers in Tropical Diseases

Major Tropical Diseases

Highlights in Major Tropical Diseases 2021/22

Among the neglected tropical diseases (NTDs), arboviral diseases present a significant number of cases worldwide. Their correct classification is a complex process due to the similarity of symptoms and the lack of tests in Brazil countryside is a big challenge to be overcome. Given this context, this paper proposes a comparative study of machine learning techniques for multi-class classification of arboviral diseases, which considers three classes: DENGUE, CHIKUNGUNYA and OTHERS, and uses clinical and socio-demographic data from patients. Feature selection techniques were also used for selecting the best subset of attributes for each model. Gradient boosting machines presented the best result in the metrics and a good subset of attributes for daily usage by the physicians that resulted in a 76.58% recall on the CHIKUNGUNYA class.

Among the neglected tropical diseases (NTDs), arboviral diseases present a significant number of cases worldwide. Their correct classification is a complex process due to the similarity of symptoms and the lack of tests in Brazil countryside is a big challenge to be overcome. Given this context, this paper proposes a comparative study of machine learning techniques for multi-class classification of arboviral diseases, which considers three classes: DENGUE, CHIKUNGUNYA and OTHERS, and uses clinical and socio-demographic data from patients. Feature selection techniques were also used for selecting the best subset of attributes for each model. Gradient boosting machines presented the best result in the metrics and a good subset of attributes for daily usage by the physicians that resulted in a 76.58% recall on the CHIKUNGUNYA class.

A Comparative Study of Machine Learning Techniques for Multi-Class Classification of Arboviral Diseases

Standard anti-tuberculosis treatment is highly effective, but a great challenge is the management of adverse drug reactions (ADR). Our study aimed to characterize ADR according to type, severity and time of occurrence. A prospective tuberculosis (TB) cohort has been followed, from 2010 to 2016, at a reference center in Rio de Janeiro, Brazil. Clinical and laboratory tests information were collected in all visits. ADR were described according to the affected organ/system, classified as clinical and/or laboratory, early (first 2 months) or late. ADR’s causality and intensity were assessed. In our study 552 patients were included, 78.8% presented at least one ADR, 34% were people living with HIV (PLHIV). Most ADR were clinical (53%), early (82.5%), mild/moderate (88.7%) events and of “metabolic annutritional disorders” category. There were no significant differences in type, severity or causality between “early” and “late” groups. However, “early” group presented a higher frequency of “metabolic and nutritional disorders” (27.8%) and “gastrointestinal system disorders” (23.5%), while “skin and appendages disorders” were more frequent in the “late” group. ADR are frequent and occur at any time during treatment, although the majority are early and grade and not severe.

Standard anti-tuberculosis treatment is highly effective, but a great challenge is the management of adverse drug reactions (ADR). Our study aimed to characterize ADR according to type, severity and time of occurrence. A prospective tuberculosis (TB) cohort has been followed, from 2010 to 2016, at a reference center in Rio de Janeiro, Brazil. Clinical and laboratory tests information were collected in all visits. ADR were described according to the affected organ/system, classified as clinical and/or laboratory, early (first 2 months) or late. ADR&#x2019;s causality and intensity were assessed. In our study 552 patients were included, 78.8% presented at least one ADR, 34% were people living with HIV (PLHIV). Most ADR were clinical (53%), early (82.5%), mild/moderate (88.7%) events and of &#x201c;metabolic annutritional disorders&#x201d; category. There were no significant differences in type, severity or causality between &#x201c;early&#x201d; and &#x201c;late&#x201d; groups. However, &#x201c;early&#x201d; group presented a higher frequency of &#x201c;metabolic and nutritional disorders&#x201d; (27.8%) and &#x201c;gastrointestinal system disorders&#x201d; (23.5%), while &#x201c;skin and appendages disorders&#x201d; were more frequent in the &#x201c;late&#x201d; group. ADR are frequent and occur at any time during treatment, although the majority are early and grade and not severe.

Adverse Drug Reactions Related to Treatment of Drug-Susceptible Tuberculosis in Brazil: A Prospective Cohort Study

Malaria, predominantly caused by Plasmodium falciparum, poses one of largest and most durable health threats in the world. Previously, simplistic regression-based models have been created to characterize malaria rapid diagnostic test performance, though these models often only include a couple genetic factors. Specifically, the Baker et al., 2005 model uses two types of particular repeats in histidine-rich protein 2 (PfHRP2) to describe a P. falciparum infection, though the efficacy of this model has waned over recent years due to genetic mutations in the parasite. In this work, we use a dataset of 100 P. falciparum PfHRP2 genetic sequences collected in Ethiopia and derived a larger set of motif repeat matches for use in generating a series of diagnostic machine learning models. Here we show that the usage of additional and different motif repeats in more sophisticated machine learning methods proves effective in characterizing PfHRP2 diversity. Furthermore, we use machine learning model explainability methods to highlight which of the repeat types are most important with regards to rapid diagnostic test sensitivity, thereby showcasing a novel methodology for identifying potential targets for future versions of rapid diagnostic tests.

Malaria, predominantly caused by Plasmodium falciparum, poses one of largest and most durable health threats in the world. Previously, simplistic regression-based models have been created to characterize malaria rapid diagnostic test performance, though these models often only include a couple genetic factors. Specifically, the Baker et al., 2005 model uses two types of particular repeats in histidine-rich protein 2 (PfHRP2) to describe a P. falciparum infection, though the efficacy of this model has waned over recent years due to genetic mutations in the parasite. In this work, we use a dataset of 100 P. falciparum PfHRP2 genetic sequences collected in Ethiopia and derived a larger set of motif repeat matches for use in generating a series of diagnostic machine learning models. Here we show that the usage of additional and different motif repeats in more sophisticated machine learning methods proves effective in characterizing PfHRP2 diversity. Furthermore, we use machine learning model explainability methods to highlight which of the repeat types are most important with regards to rapid diagnostic test sensitivity, thereby showcasing a novel methodology for identifying potential targets for future versions of rapid diagnostic tests.

Modeling Plasmodium falciparum Diagnostic Test Sensitivity Using Machine Learning With Histidine-Rich Protein 2 Variants

Nontuberculous mycobacteria (NTM) are environmental opportunistic pathogens of humans and animals that are emerging with a serious public health impact particularly in individuals with Acquired Immunodeficiency Syndromes. Recent scientific evidence is shifting from NTMs being known as traditional environmental organisms to serious pathogenic organisms in both animals and humans. In humans, factors attributable to this rise have been linked mainly to Human Immunodeficiency Virus and Acquired Immunodeficiency Syndrome pandemic. In recent years there has been an increase in multidrug resistant Tuberculosis in Zambia and it is thought that NTMs could possibly be the cause. This study was therefore formulated to review available information on the prevalence of NTM in humans, animals and the environment, species distribution, zoonotic potential and public health importance in Zambia. This review was conducted in accordance with PRISMA guidelines. A literature search was done in PubMed and Google scholar using predefined search terms such as ‘nontuberculous mycobacteria’, ‘atypical mycobacteria’, ‘mycobacteria other than mycobacterium tuberculosis’ and ‘Zambia’, in combination with Boolean operators (AND, OR). This particular systematic review draws findings based on literature search between 2000 and 2020. Through literature search 243 papers were identified, 23 duplicates were identified and removed and 206 articles were excluded as they did not meet the inclusion criteria. The full text of the remaining 14 articles were considered for this review. The overall prevalence of NTM in humans was 24.39%, in water 21.5%, in animals 16.05% of which the prevalence in cattle was 14.81% and Kafue Lechwe 1.23%. Mycobacterium intracellulare was the most common isolated nontuberculous mycobacteria in humans and cattle while Mycobacterium gordonae was the most common in water, and Mycobacterium stomatepiae sp Nov in Kafue Lechwe. Nontuberculous mycobacteria are an emerging public health threat in Zambia both in humans and animals and this calls for the need for molecular information on the zoonotic transmission of nontuberculous mycobacteria. Increased awareness of nontuberculous mycobacteria diseases among clinicians and laboratory personnel is crucial for patient management and an essential step for facilitating the identification of nontuberculous mycobacteria species in laboratories.

Nontuberculous mycobacteria (NTM) are environmental opportunistic pathogens of humans and animals that are emerging with a serious public health impact particularly in individuals with Acquired Immunodeficiency Syndromes. Recent scientific evidence is shifting from NTMs being known as traditional environmental organisms to serious pathogenic organisms in both animals and humans. In humans, factors attributable to this rise have been linked mainly to Human Immunodeficiency Virus and Acquired Immunodeficiency Syndrome pandemic. In recent years there has been an increase in multidrug resistant Tuberculosis in Zambia and it is thought that NTMs could possibly be the cause. This study was therefore formulated to review available information on the prevalence of NTM in humans, animals and the environment, species distribution, zoonotic potential and public health importance in Zambia. This review was conducted in accordance with PRISMA guidelines. A literature search was done in PubMed and Google scholar using predefined search terms such as ‘nontuberculous mycobacteria’, ‘atypical mycobacteria’, ‘mycobacteria other than mycobacterium tuberculosis’ and ‘Zambia’, in combination with Boolean operators (AND, OR). This particular systematic review draws findings based on literature search between 2000 and 2020. Through literature search 243 papers were identified, 23 duplicates were identified and removed and 206 articles were excluded as they did not meet the inclusion criteria. The full text of the remaining 14 articles were considered for this review. The overall prevalence of NTM in humans was 24.39%, in water 21.5%, in animals 16.05% of which the prevalence in cattle was 14.81% and Kafue Lechwe 1.23%. Mycobacterium intracellulare was the most common isolated nontuberculous mycobacteria in humans and cattle while Mycobacterium gordonae was the most common in water, and Mycobacterium stomatepiae sp Nov in Kafue Lechwe. Nontuberculous mycobacteria are an emerging public health threat in Zambia both in humans and animals and this calls for the need for molecular information on the zoonotic transmission of nontuberculous mycobacteria. Increased awareness of nontuberculous mycobacteria diseases among clinicians and laboratory personnel is crucial for patient management and an essential step for facilitating the identification of nontuberculous mycobacteria species in laboratories.