The influence of ML Flow test in leprosy classification

Algorithm design for a cytokine release assay of antigen-specific in vitro stimuli of circulating leukocytes to classify leprosy patients and household contacts

Background

Immunological biomarkers have often been used as a complementary approach to support clinical diagnosis in several infectious diseases. The lack of commercially available laboratory tests for conclusive early diagnosis of leprosy has motivated the search for novel methods for accurate diagnosis. In the present study, we describe an integrated analysis of a cytokine release assay using a machine learning approach to create a decision tree algorithm. This algorithm was used to classify leprosy clinical forms and monitor household contacts.

Methods

A model of Mycobacterium leprae antigen-specific in vitro assay with subsequent cytokine measurements by enzyme-linked immunosorbent assay was employed to measure the levels of tumor necrosis factor (TNF), interferon-γ, interleukin 4, and interleukin 10 (IL-10) in culture supernatants of peripheral blood mononuclear cells from patients with leprosy, healthy controls, and household contacts. Receiver operating characteristic curve analysis was carried out to define each cytokine’s global accuracy and performance indices to identify clinical subgroups.

Results

Data demonstrated that TNF (control culture [CC]: AUC = 0.72; antigen-stimulated culture [Ml]: AUC = 0.80) and IL-10 (CC: AUC = 0.77; Ml: AUC = 0.71) were the most accurate biomarkers to classify subgroups of household contacts and patients with leprosy, respectively. Decision tree classifier algorithms for TNF analysis categorized subgroups of household contacts according to the operational classification with moderate accuracy (CC: 79% [48/61]; Ml: 84% [51/61]). Additionally, IL-10 analysis categorized leprosy patients’ subgroups with moderate accuracy (CC: 73% [22/30] and Ml: 70% [21/30]).

Conclusions

Together, our findings demonstrated that a cytokine release assay is a promising method to complement clinical diagnosis, ultimately contributing to effective control of the disease.

Laboratory diagnosis of leprosy: Two staining methods from bacilloscopy and rapid ml flow test

Background

The diagnosis of leprosy is based on the characteristic signs and symptoms of the disease, subsidized by laboratory tests. When positive, the bacilloscopy closes the diagnosis for leprosy. Phenolic glycolipid-I, or PGL-I, is a molecule in the bacillus cell wall that confers a greater immune response. The ML Flow test is an immunochromatographic test for the detection of anti-PGL-I IgM in human blood or serum.

Methods

A prospective study with data collection and biological materials in patients with suspected leprosy from August 2020 to May 2021. For microscopy, intradermal smears were stained with Auramine O, and after reading under a fluorescence microscope, reviewed by Ziehl-Neelsen. The ML flow test was performed according to the Bührer-Sékula protocol. To assess the agreement between the methods, the Kappa index was estimated.

Results

Of the 94 suspected leprosy patients, 31 (32.9%) were diagnosed with leprosy. There was moderate agreement between the results of the ML Flow and Auramine O tests (Kappa = 0.58) and substantial agreement between the ML Flow and Ziehl-Neelsen microscopy (Kappa = 0.72). In paucibacillary cases, serology was positive in 100% of patients.

Conclusions

This study concluded that the Ziehl-Neelsen technique remains the best option for standard leprosy staining, and the ML flow test is more positive among the three techniques evaluated and can be an effective tool in the early diagnosis of leprosy cases.

Leprosy Screening Based on Artificial Intelligence: Development of a Cross-Platform App

Background

According to the World Health Organization, achieving targets for control of leprosy by 2030 will require disease elimination and interruption of transmission at the national or regional level. India and Brazil have reported the highest leprosy burden in the last few decades, revealing the need for strategies and tools to help health professionals correctly manage and control the disease.

Objective

The main objective of this study was to develop a cross-platform app for leprosy screening based on artificial intelligence (AI) with the goal of increasing accessibility of an accurate method of classifying leprosy treatment for health professionals, especially for communities further away from major diagnostic centers. Toward this end, we analyzed the quality of leprosy data in Brazil on the National Notifiable Diseases Information System (SINAN).

Methods

Leprosy data were extracted from the SINAN database, carefully cleaned, and used to build AI decision models based on the random forest algorithm to predict operational classification in paucibacillary or multibacillary leprosy. We used Python programming language to extract and clean the data, and R programming language to train and test the AI model via cross-validation. To allow broad access, we deployed the final random forest classification model in a web app via shinyApp using data available from the Brazilian Institute of Geography and Statistics and the Department of Informatics of the Unified Health System.

Results

We mapped the dispersion of leprosy incidence in Brazil from 2014 to 2018, and found a particularly high number of cases in central Brazil in 2014 that further increased in 2018 in the state of Mato Grosso. For some municipalities, up to 80% of cases showed some data discrepancy. Of a total of 21,047 discrepancies detected, the most common was “operational classification does not match the clinical form.” After data processing, we identified a total of 77,628 cases with missing data. The sensitivity and specificity of the AI model applied for the operational classification of leprosy was 93.97% and 87.09%, respectively.

Conclusions

The proposed app was able to recognize patterns in leprosy cases registered in the SINAN database and to classify new patients with paucibacillary or multibacillary leprosy, thereby reducing the probability of incorrect assignment by health centers. The collection and notification of data on leprosy in Brazil seem to lack specific validation to increase the quality of the data for implementations via AI. The AI models implemented in this work had satisfactory accuracy across Brazilian states and could be a complementary diagnosis tool, especially in remote areas with few specialist physicians.

A scoping review on the field validation and implementation of rapid diagnostic tests for vector-borne and other infectious diseases of poverty in urban areas

BACKGROUND

Health personnel face challenges in diagnosing vector-borne and other diseases of poverty in urban settings. There is a need to know what rapid diagnostic technologies are available, have been properly assessed, and are being implemented to improve control of these diseases in the urban context. This paper characterizes evidence on the field validation and implementation in urban areas of rapid diagnostics for vector-borne diseases and other diseases of poverty.

MAIN BODY

A scoping review was conducted. Peer-reviewed and grey literature were searched using terms describing the targeted infectious diseases, diagnostics evaluations, rapid tests, and urban setting. The review was limited to studies published between 2000 and 2016 in English, Spanish, French, and Portuguese. Inclusion and exclusion criteria were refined post hoc to identify relevant literature regardless of study design and geography. A total of 179 documents of the 7806 initially screened were included in the analysis. Malaria (n = 100) and tuberculosis (n = 47) accounted for the majority of studies that reported diagnostics performance, impact, and implementation outcomes. Fewer studies, assessing mainly performance, were identified for visceral leishmaniasis (n = 9), filariasis and leptospirosis (each n = 5), enteric fever and schistosomiasis (each n = 3), dengue and leprosy (each n = 2), and Chagas disease, human African trypanosomiasis, and cholera (each n = 1). Reported sensitivity of rapid tests was variable depending on several factors. Overall, specificities were high (> 80%), except for schistosomiasis and cholera. Impact and implementation outcomes, mainly acceptability and cost, followed by adoption, feasibility, and sustainability of rapid tests are being evaluated in the field. Challenges to implementing rapid tests range from cultural to technical and administrative issues.

CONCLUSIONS

Rapid diagnostic tests for vector-borne and other diseases of poverty are being used in the urban context with demonstrated impact on case detection. However, most evidence comes from malaria rapid diagnostics, with variable results. While rapid tests for tuberculosis and visceral leishmaniasis require further implementation studies, more evidence on performance of current tests or development of new alternatives is needed for dengue, Chagas disease, filariasis, leptospirosis, enteric fever, human African trypanosomiasis, schistosomiasis and cholera.

Specific antigen serologic tests in leprosy: implications for epidemiological surveillance of leprosy cases and household contacts

BACKGROUND

There is a lack of straightforward tests for field application and known biomarkers for predicting leprosy progression in infected individuals.

OBJECTIVE

The aim was to analyse the response to infection by Mycobacterium leprae based on the reactivity of specific antigens: natural disaccharide linked to human serum albumin via an octyl (NDOHSA), a semisynthetic phenolic glycolipid-I (PGL-I); Leprosy Infectious Disease Research Institute Diagnostic-1 (LID-1) and natural disaccharide octyl - Leprosy Infectious Disease Research Institute Diagnostic-1 (NDOLID).

METHODS

The study population consisted of 130 leprosy cases diagnosed between 2010 and 2015 and 277 household contacts. An enzyme-linked immunosorbent assay (ELISA) was used to analyse the reactivity of antibodies against NDOHSA, LID-1 and NDOLID. The samples and controls were tested in duplicate, and the antibody titer was expressed as an ELISA index. Data collection was made by home visits with application of questionnaire and dermatological evaluation of all household contacts to identify signs and symptoms of leprosy.

FINDINGS

Significant differences in the median ELISA results were observed among leprosy cases in treatment, leprosy cases that had completed treatment and household contacts. Higher proportions of seropositivity were observed in leprosy cases in treatment. Seropositivity was also higher in multibacillary in relation to paucibacillary, with the difference reaching statistical significance. Lower titers were observed among cases with a longer treatment time or discharge. For household contacts, the differences according to the clinical characteristics of the leprosy index case were less pronounced than expected. Other factors, such as the endemicity of leprosy, exposure outside the residence and genetic characteristics, appeared to have a greater influence on the seropositivity.

MAIN CONCLUSIONS

Serologic tests could be used as auxiliary tools for determining the operational classification, in addition to identifying infected individuals and as a strategy for surveillance of household contacts.

Epidemiological situation of leprosy in Salvador from 2001 to 2009

Mycobacterium leprae was first described as the bacillus that causes leprosy, a chronic granulomatous infectious disease, in 1873 by Amauer Hansen. Leprosy is part of a group of 10 neglected diseases and Bahia has endemic levels of this illness, varying between high and very high. The detection of 52 new cases of leprosy in children under 15 years old in Salvador in 2006 is alarming, and suggests an early contact with the disease. The aim of this review is to analyze the epidemiological situation, the detection rate and evaluate the clinical and epidemiological profile of leprosy in Salvador, in the period 2001-2009. A retrospective cross-sectional study was performed using secondary data collected at Notifiable Diseases Information System Database (SINAN) through the notification of patients with leprosy. Over these nine years 3,226 patients were reported, with a predominance of: females (51.5%), and clinical multibacillary forms in the general population (51.7%), but when we analyze those under 15 years old, paucibacillary forms (tuberculoid + indeterminate) prevailed. The tuberculoid form was the most diagnosed type of presentation. The annual detection rate in Salvador remained at a very high level of endemicity during the studied period and for those under 15 years old it ranged between high and very high. Grade 2 disabilities both at the time of diagnosis and at discharge after cure, varied between low and medium. Based on these data we conclude that the high levels of leprosy detection rates in the general population, plus the variation between high and very high levels in those under 15 years old, associated with the medium level of grade 2 disabilities at the time of diagnosis and discharge, demonstrate the need for improvement on the existing services, investment in active case finding and training of the healthcare professionals in Salvador.