A spatio-temporal agent-based approach for modeling the spread of zoonotic cutaneous leishmaniasis in northeast Iran

Assessment of the antileishmanial activity of diallyl sulfide combined with meglumine antimoniate on Leishmania major: Molecular docking, in vitro, and animal model

Currently, no safe vaccine against leishmaniasis is available. So far, different control strategies against numerous reservoir hosts and biological vectors have not been environment-friendly and feasible. Hence, employing medicinal components and conventional drugs could be a promising approach to developing novel therapeutic alternatives. This study aimed to explore diallyl sulfide (DAS), a dynamic constituent of garlic, alone and in a mixture with meglumine antimoniate (MAT as standard drug) using in vitro and animal model experiments against Leishmania major stages. The binding affinity of DAS and four major defense elements of the immune system (iNOS, IFN-ɣ, IL-12, and TNF-α) was used to predict the predominant binding mode for molecular docking configurations. Herein, we conducted a broad range of experiments to monitor and assess DAS and MAT potential treatment outcomes. DAS, combined with MAT, displayed no cytotoxicity and employed a powerful anti-leishmanial activity, notably against the clinical stage. The function mechanism involved immunomodulation through the induction of Th1 cytokine phenotypes, triggering a high apoptotic profile, reactive oxygen species (ROS) production, and antioxidant enzymes. This combination significantly decreased cutaneous lesion diameter and parasite load in BALB/c mice. The histopathological findings performed the infiltration of inflammatory cells associated with T-lymphocytes, particularly CD4+ phenotypes, as determined by biochemical markers in alleviating the amastigote stage and improving the pathological changes in L. major infected BALB/c mice. Therefore, DAS and MAT deserve further advanced therapeutic development and should be considered as possible candidates for treating volunteer cases with cutaneous leishmaniasis in designing an upcoming clinical trial.

A Geomedical Survey: Is There an Association Between Climatic Conditions and Leishmania Species Distribution in Iran During the Years 1999-2021?

PURPOSE

Iran is among the high-risk leishmaniasis regions in the world. WHO recommends the use of GIS as an ideal tool for healthcare authorities to predict the evolution of a disease, delimit the risk of outbreaks and identify critical areas. The aim of this research is to find the association between the main species of Leishmania (L. major, L. tropica, L. infantum) dispersion and climatic variables in Iran.

METHODS

All molecular-based reports of leishmaniasis from Iran between 1999 and 2021 were gathered from reliable medical sources. Meteorological data (air and soil temperatures, annual rainfall and humidity) of the country along the study period were obtained from the Iranian Climatological Research Centre. The data concerning species distribution and climatic conditions during this period were moved to a base-map through raster layers using ArcGIS 10.4.1 software. The relationship between parasitological and climatic models was examined using ANOVA.

RESULTS

High risk area maps, based on the cut-off thresholds, were generated for Leishmania major, L. tropica and L. infantum. According to the molecular-based reports, the L. major distribution was significantly related to all climatic variables, while L. tropica was merely related to rainfall and humidity, and the L. infantum distribution was significantly associated with rainfall, soil and air temperatures.

CONCLUSION

The association between climatic conditions and Leishmania species distribution in Iran has been confirmed. Consequently, both, the relationship between climatic conditions and the geographical distribution of Leishmania species, and the use of GIS to better understand the spatial epidemiology of leishmaniasis, have been reaffirmed.

Global Dilemma and Needs Assessment Toward Achieving Sustainable Development Goals in Controlling Leishmaniasis

Leishmaniasis is a disease of poverty that imposes a devastating medical, social, and economic burden on over 1 billion people nationwide. To date, no in-depth study to analyze the major global challenges and needs assessment has been carried out. This investigation aimed to explore a comprehensive narrative review of leishmaniasis's main challenges and initially highlight obstacles that might impede the implementation of control measures. Also, we propose a specific list of priorities for needs assessment. The presence of socioeconomic factors, multiple clinical and epidemiological forms, various Leishmania species, the complexity of the life cycle, the absence of effective drugs and vaccines, and the lack of efficient vector and reservoir control make this organism unique and sophisticated in playing a tangled role to react tricky with its surrounding environments, despite extensive efforts and implementation of all-inclusive former control measures. These facts indicate that the previous strategic plans, financial support, and basic infrastructures connected to leishmaniasis surveillance are still insufficient. Strengthening the leishmaniasis framework in a context of accelerated programmatic action and intensification of cross-cutting activities along with other neglected tropical diseases (NTDs) is confidently expected to result in greater effectiveness, cost-benefit, and fruitful management. Sensitive diagnostics, effective therapeutics, and efficacious vaccines are vital to accelerating advancement toward elimination, and reducing morbidity/mortality and program costs. Collective actions devoted by all sectors and policy-makers can hopefully overcome technical and operational barriers to guarantee that effective and coordinated implementation plans are sustained to meet the road map for NTDs 2021- 2030 goals.

Field Efficacy of Topical Nano-Liposomal Amphotericin B (Sina Ampholeish®) Alone or in Combination with Glucantime® and Cryotherapy on Human Cutaneous Leishmaniasis

Background

Cutaneous leishmaniasis (CL) is a parasitic disease that presents a broad spectrum of clinical features. Treatment of CL is problematic. We aimed to compare the field therapeutic efficacy of topical nanoliposomes containing 0.4% amphotericin B (Nano Lip-AmB) alone and in combination with cryotherapy and/or Glucantime® on human CL in the endemic areas of Iran.

Methods

This retrospective study was performed based on the results of using Nano Lip-AmB alone or with Glucantime® and/or cryotherapy in the treatment of zoonotic cutaneous leishmaniasis (ZCL) in patients referred to health centers of Isfahan, Golestan and Ilam Provinces of Iran as endemic foci of ZCL caused by Leishmania major besides Mashhad and Bam cities as endemic foci of anthroponotic cutaneous leishmaniasis (ACL) caused by with L. tropica.

Results

Two hundred and seventy-eight patients with CL were included in the current study. All of the patients (100%) who received Nano Lip-AmB alone or in combination with Glucantime® and/or cryotherapy based on guideline of Iranian national committee for the treatment of CL. Two patients with 7 skin lesions, who was resident in ACL endemic area and received Nano Lip-AmB plus Glucantime® and another patient was a resident of ZCL endemic area and received Nano Lip-AmB plus cryotherapy showed clinical relapses after treatment.

Conclusion

Sina Ampholeish® in combination with other standard protocols of treatment of CL is well tolerated and with acceptable clinical efficacy rate.

In silico and in vitro potentials of crocin and amphotericin B on Leishmania major: Multiple synergistic mechanisms of actions

A significant barrier to optimal antileishmanial treatment is low efficacy and the emergence of drug resistance. Multiple approaches were used to monitor and assess crocin (a central component of saffron) mixed with amphotericin B (AmpB) potential in silico and in vitro consequences. The binding behavior of crocin and iNOS was the purpose of molecular docking. The results showed that crocin coupled with AmpB demonstrated a safe combination, extremely antileishmanial, suppressed Leishmania arginase absorption, and increased parasite death. This natural flower component is a robust antioxidant, significantly promoting the expression of the Th1-connected cytokines (IL12p40, IFN-γ, and TNF- α), iNOS, and transcription factors (Elk-1, c-Fos, and STAT-1). In comparison, the expression of the Th2-associated phenotypes (IL-10, IL-4, and TGF-β) was significantly reduced. The leishmanicidal effect of this combination was also mediated through programmed cell death (PCD), as confirmed by the manifestation of phosphatidylserine and cell cycle detention at the sub-GO/G1 phase. In conclusion, crocin with AmpB synergistically exerted in vitro antileishmanial action, generated nitric oxide and reactive oxygen species, modulated Th1, and Th2 phenotypes and transfer factors, enhanced PCD profile and arrested the cell cycle of Leishmania major promastigotes. The main action of crocin and AmpB involved wide-ranging mechanistic insights for conducting other clinical settings as promising drug candidates for cutaneous leishmaniasis. Therefore, this combination could be esteemed as a basis for a potential bioactive component and a logical source for leishmanicidal drug development against CL in future advanced clinical settings.

Cutaneous leishmaniasis situation analysis in the Islamic Republic of Iran in preparation for an elimination plan

Iran has invariably been under the growing public health threat of cutaneous leishmaniasis (CL), a significant barrier to local development that hinders the prevention and control efforts toward eliminating the disease. So far, no comprehensive and in-depth epidemiological analysis of the CL situation has been carried out nationwide. This study aimed to employ advanced statistical models to analyze the data collected through the Center for Diseases Control and Prevention of Communicable Diseases during 1989-2020. However, we emphasized the current trends, 2013-2020, to study temporal and spatial CL patterns. In the country, the epidemiology of CL is incredibly intricate due to various factors. This fact indicates that the basic infrastructure, the preceding supports, and the implementation plan related to preventive and therapeutic measures need crucial support. The leishmaniasis situation analysis is consistent with desperate requirements for efficient information on the control program in the area. This review provides evidence of temporally regressive and spatially expanding incidence of CL with characteristic geographical patterns and disease hotspots, signifying an urgent need for comprehensive control strategies. This information could be a suitable model and practical experience in the Eastern Mediterranean Region, where over 80% of CL is reported.

Spatial modeling of zoonotic cutaneous leishmaniasis with regard to potential environmental factors using ANFIS and PCA-ANFIS methods

This study compares two adaptive neuro-fuzzy inference system (ANFIS) and principal component analysis (PCA)-ANFIS techniques for spatial modeling and forecasting of zoonotic cutaneous leishmaniasis (ZCL) cases in rural districts of Golestan province, Iran. We collected and prepared data on ZCL cases and climatic, topographic, vegetation, and human population factors. By applying the PCA algorithm, the parameters affecting the ZCL incidence were decomposed into principal components (PCs), and their dimensions were reduced. Then, PCs were used to train the ANFIS model. To evaluate the proposed approaches in model assessment phase, we used test data in 2016. In this phase, we showed that PCA-ANFIS model with values ​​of R² = 0.791, MAE = 0.681, RMSE = 0.904 compared to ANFIS model with values ​​of R² = 0.705, MAE = 0.827, RMSE = 1.073 has better performance in prediction of the ZCL cases. Actual and predicted maps of ZCL cases in 2016 by both models demonstrated that the high-risk regions of the disease are located in the northeastern, northern parts, and some central rural districts of Golestan province. Sensitivity analysis of the ANFIS model showed that population, vegetation, average wind speed, elevation, and average soil temperature, respectively, are the most significant factors in predicting the ZCL cases. The findings indicated the importance of machine learning (ML) techniques (ANFIS and PCA-ANFIS) in medical geography studies. By using these approaches, with less cost and shorter time, high-risk areas of diseases can be predicted, and the most effective factors on the spatial prediction of diseases can be identified. Public health policymakers can use these useful tools to control and prevent the disease and to allocate resources to disease-prone areas.

Spatio-temporal visualisation of cutaneous leishmaniasis in an endemic, urban area in Iran

INTRODUCTION

Cutaneous Leishmaniasis (CL) is a significant public health concern worldwide. Iran is among the most CL-affected countries, being one of the six most endemic countries in the world. This study aimed to provide a spatio-temporal visualisation of CL cases in an endemic urban area in north-eastern Iran identifying high-risk and low-risk areas during the period 2016-2019.

METHODS

This ecological study was conducted in the city of Mashhad, north-eastern Iran. All cases (n=2425) were diagnosed based on clinical findings and parasitological tests. The patient data were aggregated at the census tract level (the highest resolution available). CL incidence rates were subjected to Empirical Bayesian smoothing across the census tracts followed by spatial autocorrelation analysis to identify clusters and outliers. Spatial scan statistic was used to explore the purely temporal, purely spatial and spatio-temporal trend of the disease. In all instances, the null hypothesis of no clusters was rejected at p ≤0.05.

RESULTS

The overall crude incidence rate decreased from 34.6 per 100,000 individuals in 2016 to 19.9 per 100,000 in 2019. Cluster analysis identified high-risk areas in south-western Mashhad and low-risk areas in the north-eastern areas. Purely time scan statistics identified March to July as the time period with highest risk for CL occurrence. One most likely purely high-risk spatial cluster and six secondary purely high-risk spatial clusters were identified. Further, two spatio-temporal high-risk clusters, one in the north of the city from April to August and a second in the south-western part from March to September were observed.

CONCLUSIONS

Significant spatial, temporal and spatio-temporal patterns of CL distribution were observed in the study area, which should be considered when designing tailored interventions, such as effective resource allocation models, informed control plans and implementation of efficient surveillance systems. Furthermore, this study generated new hypotheses to test potential relationships between socio-economic and environmental risk factors and incidence of CL in high-risk areas.

Spatial prediction of human brucellosis (HB) using a GIS-based adaptive neuro-fuzzy inference system (ANFIS)

OBJECTIVE

This study pursues three main objectives: 1) exploring the spatial distribution patterns of human brucellosis (HB); 2) identifying parameters affecting the disease spread; and 3) modeling and predicting the spatial distribution of HB cases in 2012-2016 and 2017-2018, respectively, in rural districts of Mazandaran province, Iran.

METHODS

We collected data on the disease incidence, demography, ecology, climate, topography, and vegetation. Using the Global Moran's I statistic, we measured spatial autocorrelation between log (number of HB cases). We applied the Getis-Ord G_i^* statistic to identify areas with high and low risk of the disease. To investigate the relationships between the factors affecting the incidence of HB as input variables together and the factors with the log (number of HB cases) as an output variable, we used the statistical linear regression model and the Pearson correlation coefficient. Then, we implemented a GIS-based adaptive neuro-fuzzy inference system (ANFIS) with two subtractive clustering and fuzzy c-means (FCM) clustering methods to model and predict the spatial distribution of HB.

RESULTS

Global Moran's I spatial autocorrelation analysis indicated that the type of HB distribution is clustered in all years except 2014 and 2017, which are random. According to the Getis-Ord G_i^* analysis, the location of the hot spots varied during 2012-2018. In 2012 and 2013, most of the hot spots were seen in the west of the province. While in 2018, they were mostly concentrated in the eastern regions of the province. The linear regression model indicated that the parameters affecting the incidence of HB are independent of each other and can explain only 25.3% of the total changes in the log (number of HB cases). The results of the Pearson correlation coefficient showed that there were positive relationships between vegetation, log (population), and the number of sheep and cattle (p-value < 0.05). The above-mentioned factors had the strongest positive correlation with the log (number of HB cases) (p-value < 0.01). These results may be due to the fact that vegetation regions are suitable for livestock grazing, attracting large crowds of people. Therefore, this will increase HB cases. We compared the results of subtractive clustering and FCM clustering methods by evaluation criteria (e.g., linear correlation coefficient (LCC) and mean absolute error (MAE)) in two phases of development and assessment of the ANFIS model. In the assessment phase, we predicted the spatial distribution of log (number of HB cases) in 2017 and 2018 by subtractive clustering (R² = 0.699, LCC or R = 0.692, MAE = 0.509, MSE = 0.455) and by FCM clustering (R² = 0.704, LCC or R = 0.697, MAE = 0.512, MSE = 0.448) that showed FCM clustering outperformed the subtractive clustering.

CONCLUSION

The findings may have important implications for public health. The emergence of the hot spots in the east of the province can be a warning to the health system. Health authorities can use the findings of this study to predict the spread of HB and perform HB prevention programs. They can also investigate the factors affecting the prevalence of the disease, identify high-risk areas, and ultimately allocate resources to high-risk regions.

Spatiotemporal distribution of cutaneous leishmaniasis in Sri Lanka and future case burden estimates

BACKGROUND

Leishmaniasis is a neglected tropical vector-borne disease, which is on the rise in Sri Lanka. Spatiotemporal and risk factor analyses are useful for understanding transmission dynamics, spatial clustering and predicting future disease distribution and trends to facilitate effective infection control.

METHODS

The nationwide clinically confirmed cutaneous leishmaniasis and climatic data were collected from 2001 to 2019. Hierarchical clustering and spatiotemporal cross-correlation analysis were used to measure the region-wide and local (between neighboring districts) synchrony of transmission. A mixed spatiotemporal regression-autoregression model was built to study the effects of climatic, neighboring-district dispersal, and infection carryover variables on leishmaniasis dynamics and spatial distribution. Same model without climatic variables was used to predict the future distribution and trends of leishmaniasis cases in Sri Lanka.

RESULTS

A total of 19,361 clinically confirmed leishmaniasis cases have been reported in Sri Lanka from 2001-2019. There were three phases identified: low-transmission phase (2001-2010), parasite population buildup phase (2011-2017), and outbreak phase (2018-2019). Spatially, the districts were divided into three groups based on similarity in temporal dynamics. The global mean correlation among district incidence dynamics was 0.30 (95% CI 0.25-0.35), and the localized mean correlation between neighboring districts was 0.58 (95% CI 0.42-0.73). Risk analysis for the seven districts with the highest incidence rates indicated that precipitation, neighboring-district effect, and infection carryover effect exhibited significant correlation with district-level incidence dynamics. Model-predicted incidence dynamics and case distribution matched well with observed results, except for the outbreak in 2018. The model-predicted 2020 case number is about 5,400 cases, with intensified transmission and expansion of high-transmission area. The predicted case number will be 9115 in 2022 and 19212 in 2025.

CONCLUSIONS

The drastic upsurge in leishmaniasis cases in Sri Lanka in the last few year was unprecedented and it was strongly linked to precipitation, high burden of localized infections and inter-district dispersal. Targeted interventions are urgently needed to arrest an uncontrollable disease spread.