Leishmaniasis

Thirty-seven dogs with clinical leishmaniosis were enrolled at the time of their diagnosis from January 2014 to May 2015. The dogs were treated at different Catalonian veterinary centers: Fundació Hospital Clínic Veterinari (Bellaterra, Barcelona), Hospital Ars Veterinaria (Barcelona), Hospital Mediterrani Veterinaris (Reus, Tarragona) and Consultori Montsant (Falset, Tarragona). The diagnosis of canine leishmaniosis (day 0) was made based on the results of a physical examination, a complete blood count (System Siemens Advia 120), a biochemical profile including creatinine, urea, total proteins, ALT and total cholesterol (Analyzer Olympus AU 400), serum electrophoresis (Hydrasys), urianalysis with urinary protein creatinine ratio (UPC) and quantitative serology for the detection of L. infantum-specific antibodies by means of an in-house diagnostic ELISA [11 (link)]. All dogs presented medium to high antibody levels [11 (link)]. Cytological or histological evaluations with Leishmania immunohistochemistry of cutaneous or other lesions were also performed when needed [19 (link)]. Dogs were classified into clinical stages at the time of diagnosis as previously described [2 (link)]. Dogs were treated with a daily subcutaneous injection of meglumine antimoniate (80–100 mg/kg) for a month and 10 mg/kg BID of oral allopurinol for 12 months. The dogs were followed up at days 30 (n = 36), 180 (n = 37) and 365 (n = 29) during treatment. A full physical examination and laboratory tests described above were also performed during treatment monitoring visits. A signed informed consent was obtained from all owners. Residual samples from blood EDTA tube and serum were used in this study. Therefore, ethical approval was not needed.

The data were entered using a Microsoft Excel 2007 (Microsoft Corporation, Redmond, WA) spreadsheet and exported into STATA 11.0 for analysis (Stata Corporation, College Station, TX, USA). Point prevalence maps were developed in ArcGIS 10 (ESRI, Redlands, CA) and covariate data extracted for each data point. Multicollinearity between the covariates was initially explored using cross-correlations and where correlation coefficients were >0.7 only non-linearly related covariates were included in the analysis (S1 Text).
Boosted Regression Tree (BRT) modelling[32 (link),33 (link)] was used to identify the environmental factors associated with the occurrence of podoconiosis in Ethiopia. This approach has been effectively used in global mapping of dengue, LF, leishmaniasis and malaria vector mosquitos [34 –37 (link)] and has superior predictive accuracy compared to other distribution models[38 ]. In brief, BRT modelling combines regression or decision trees and boosting in a number of sequential steps [32 (link),33 (link)]. First, the threshold of each input variable that results in either the presence or the absence of podoconiosis is identified, allowing for both continuous and categorical variables and different scales of measurement amongst predictors [32 (link)]. Second, boosting is a machine-learning method that increases a model’s accuracy iteratively, based on the idea that it is easier to find and average many rough ‘rules of thumb’, than to find a single, highly accurate prediction rule.
Boosted Regression Tree utilizes data on both presence and absence of podoconiosis. Presence was defined as an area with at least one case in the two surveys and absence as an area with no cases in either survey. A selection of 16 environmental and climate covariates were included in a single BRT model in order to explore the relative importance of each covariate in explaining the occurrence of podoconiosis in Ethiopia. Four covariates (land cover, soil type, soil texture, urban rural classification) were excluded that showed little explanatory power (<1% of regression trees used the covariate) on the occurrence of podoconiosis. The retained covariates were used to build the final model included annual precipitation, elevation, population density, enhanced vegetation index, terrain slope, distance to water bodies, silt fraction and clay fraction. In order to obtain a measurement of uncertainty for the generated model, we fitted an ensemble of 120 BRT submodels to predict sets of different risk maps (each at 1km x 1km resolution) and these were subsequently combined to produce a single mean ensemble map and the relative importance of predictor variables was quantified. These contributions are scaled to sum 100, with a higher number indicating a greater effect on the response. Marginal effect curves were plotted to visualize dependencies between the probability of podoconiosis occurrence and each of the covariates. To assess the association of covariates and high prevalence podoconiosis, the prevalence estimates were plotted against each environmental variable. This will help to identify the areas with very high prevalence and to prioritize interventions. BRT modelling and model visualization was carried out in R version 3.1.1 [39 ] using the packages raster [40 ]and dismo[41 ].
The resulting predictive map depicts environmental suitability for the occurrence of podoconiosis. In order to convert this continuous map into a binary map outlining the limits of podoconiosis occurrence, a threshold value of suitability was determined, above which the occurrence was assumed to be possible. Using the receiver operating characteristic (ROC) curve, a threshold value of environmental suitability was chosen such that sensitivity, specificity and proportion correctly classified (PCC) values were maximized. Finally, we estimated the number of individuals at risk by overlaying the binary raster dataset displaying the potential suitability for podoconiosis occurrence on a gridded population density map[26 (link),27 (link)] and calculating the population in cells considered to be within the limits of podoconiosis occurrence. The 95% CI of the population at risk were calculated based on binary maps of the lower (2.5%) and upper (97.5%) bounds of the predicted probability of occurrence.
The performance of each sub-model was evaluated using different statistics, including: proportion correctly classified [PCC], sensitivity, specificity, Kappa [κ] and area under the receiver operator characteristics curve (AUC). The mean and confidence intervals for each statistic were used to evaluate the predictive performance of the ensemble BRT model. In addition to ensemble approach to validation, an external validation was performed using data from 96 independent surveys conducted between 1969 and 2012 [6 ,7 (link),9 (link)–12 (link),42 (link)–44 (link)] which we previously identified through structured searches of the published and unpublished literature [14 (link)]. The AUC was used to assess the discriminatory performance of the predictive model, comparing the observed and predicted occurrence of podoconiosis at each historical survey. AUC values of <0.7 indicate poor discriminatory performance, 0.7–0.8 acceptable, 0.8–0.9 excellent and >0.9 outstanding discriminatory performance [45 (link)].

The performance of two recombinants, rK18 and rKR95, was evaluated in an ELISA format and compared to the known recombinant proteins rK28 and rK39 at the Instituto de Medicina Tropical, Faculdade de Medicina, Universidade de São Paulo (USP) (IMT/FMUSP) between 2021 and 2022. We employed stored samples from healthy asymptomatic individuals, VL patients, VL/AIDS co-infected patients, and patients with other diseases that may cross-react with VL antigens. Except for the other disease samples, all were collected in different Brazilian leishmaniasis endemic areas from the Northeast, Midwest, and Southeast.

This study focuses on dengue, malaria, and leishmaniasis in Punjab, Pakistan. Punjab is a province of Pakistan. It has a population of approximately 110 million, according to the 2017 Pakistan Census [38 ]. It is the most populous province and second largest province by area after Balochistan. Punjab is located at 30°2 ${}^{\prime }$ 42.8856 ${}^{″}$ N and 72°20 ${}^{\prime }$ 55.9284 ${}^{″}$ E latitude and longitude, respectively. Its total area is 205,344 km ${}^2$ [38 ]. The province is divided into nine divisions, 36 districts, and 145 tehsils [39 ]. Lahore is the capital and largest city of Punjab. From west to east, five rivers flow through Pakistan’s Punjab province. Punjab’s region temperature ranges from −2 to 45 ${}^{\circ }$ C; in winter, the temperature can go down to −10 ${}^{\circ }$ C, and in summer, it can rise to 50 ${}^{\circ }$ C. Figure 3 shows the Punjab map. The Punjab region has three main seasons: (i) hot weather season from April to June when the temperature rises to 51 ${}^{\circ }$ C; (ii) the rainy season from July to September; and (iii) cold weather from October to March, and the temperature goes down to 2 ${}^{\circ }$ C [38 ]. Figure 3 was created on a tool named ArcGIS by using a shapefile of Pakistan with districts’ boundaries; then, we created a new layer for the province of Punjab with districts’ boundaries from the previous layer. ArcGIS is a powerful analysis tool that can perform the most fundamental GIS operations.