We examine the spread of an infectious disease, such as one that is caused by a respiratory virus, with two distinct modes of transmission. To do this, we consider a susceptible--infected--susceptible (SIS) disease on a hypergraph, which allows us to incorporate the effects of both dyadic (i.e., pairwise) and polyadic (i.e., group) interactions on disease propagation. This disease can spread either via large droplets through direct social contacts, which we associate with edges (i.e., hyperedges of size 2), or via infected aerosols in the environment through hyperedges of size at least 3 (i.e., polyadic interactions). We derive mean-field approximations of our model for two types of hypergraphs, and we obtain threshold conditions that characterize whether the disease dies out or becomes endemic. Additionally, we numerically simulate our model and a mean-field approximation of it to examine the impact of various factors, such as hyperedge size (when the size is uniform), hyperedge-size distribution (when the sizes are nonuniform), and hyperedge-recovery rates (when the sizes are nonuniform) on the disease dynamics.
Isqeel Ogunsola, Abosede Akintunde, Kehinde Yusuff
et al.
Lately, a New Transmuted Logistic-exponential (NTLE) distribution was introduced and studied as an extension of the Logistic-Exponential Distribution (LED) with wider applicability in lifetime modelling. However, the maximum likelihood estimates (MLE) of NTLE are not in closed form, and the consistency of the estimates was not examined. Furthermore, some other important properties of NTLE, namely the Shannon entropy, Rényi entropy, stochastic ordering, mode, stress-strength reliability measure, residual life functions (mean and reverse), incomplete moments, Bonferroni and Lorenz curves are yet to be derived. Motivated by this, we derived and studied these important properties and evaluated the performance of ten estimation methods (Maximum Likelihood, Moments, Least Squares, Weighted Least Squares, Maximum product of Spacings, Anderson-Darling, Cramer-von Mises, percentile estimation, and Maximum Goodness-of-Fit methods) for NTLE parameters via Monte Carlo simulation using bias, mean square error, and root mean square error as evaluation criteria. Real-life infectious mortality data fitted to the distributions showed that NTLE has a better fit compared to its base distributions (Exponential and Logistic-Exponential). This finding contributes valuable insights for researchers and practitioners when selecting the appropriate estimation methods, especially for NTLE and some similar distributions in non-closed form.
Abstract Background Acinetobacter baumannii (AB) pneumonia often leads to lung injury and multisystem dysfunction. High Mobility Group Box 1 (HMGB1) is known to play a crucial role in the progression of AB. In this study, we aimed to investigate the association between HMGB1 levels and the severity of AB pneumonia. Methods We conducted a study with a total of 91 participants, divided into three groups: patients with AB infection (n = 44), patients with AB complicated multiple organ disfunctions (MODs) (n = 19), and healthy controls (n = 28). Clinical parameters were assessed, HMGB1 levels were measured, and metabolomic analysis was performed. Validation experiments were conducted using SD rats. Results Patients with AB exhibited significantly elevated levels of HMGB1. In the multi-organ failure group, neutrophils, monocytes, and HMGB1 levels were higher, while lymphocytes were reduced. Metabolomic analysis revealed 28 metabolites correlated with HMGB1, among which 12-HPETE, asparagine, lactic acid, and carbamyl-aspartate showed the most notable changes. These representative metabolites reflect HMGB1-associated shifts in inflammation and energy metabolism. HMGB1 elevation was further confirmed in SD rats, and its blockade alleviated lung inflammation. Consistently, C-reactive protein (CRP) and procalcitonin (PCT) levels were also increased, supporting the link between HMGB1 and systemic inflammation. Conclusion HMGB1 levels rise sharply in AB pneumonia—especially in patients with MODs—and correlate with inflammatory and hypoxic metabolic shifts. Anti-HMGB1 treatment in SD rats reduced lung inflammation and injury, highlighting HMGB1’s potential as a diagnostic biomarker and therapeutic target.
Methicillin-resistant Staphylococcus aureus (MRSA) represents a considerable challenge to global health owing to its resistance to antibiotics. In our prior research, we identified four ent-kaurane diterpenoids (compounds 1–4) isolated from Siegesbeckia orientalis L., which exhibited anti-MRSA activity. Nevertheless, the precise mechanisms by which these compounds exert their effects remain to be fully elucidated. This study aims to comprehensively evaluate the anti-MRSA properties and explore the biological processes associated with the activity of compounds 1–4. We utilized the minimum broth dilution method, electron microscopy, and membrane integrity assays to demonstrate that these compounds inhibit the growth of MRSA by disrupting cell wall and membrane structures. Additionally, crystal violet staining confirmed their efficacy in disrupting mature biofilms. In a murine model of bacteremia, the tested compounds 1–4 demonstrated a reduction in septic symptoms and exhibited favorable biosafety profiles, with compound 1 showing the most significant antibacterial effects. Transcriptomic analysis indicated that compound 1 disrupts peptidoglycan synthesis and interferes with the metabolism of cell wall precursors. Furthermore, it modulates the expression of genes associated with ion transport and membrane-related metabolic enzymes, thereby compromising the integrity of both the cell wall and the cytoplasmic membrane. In conclusion, this study systematically characterizes the anti-MRSA activity of the diterpenoid components derived from S. orientalis and identifies key biological processes and gene expression changes linked to their effects, and presents a promising new strategy for the development of natural anti-MRSA pharmaceuticals.
We investigate a model of a parasite population invading spatially distributed immobile hosts on a graph, which is a modification of the frog model. Each host has an unbreakable immunity against infection with a certain probability $1-p$ and parasites move as simple symmetric random walks attempting to infect any host they encounter and subsequently reproduce themselves. We show that, on $\mathbb{Z}^d$ with $d\ge 2$ and the $d$-regular tree $\mathbb{T}_d$ with $d\ge 3$, the survival probability of parasites exhibits a phase transition at a critical value of $p_c\in(0,1)$. Also, we show that adding vertices and edges to the underlying graph can, in general, both increase or decrease the value of $p_c$. Finally, we show that on quasi-vertex-transitive graphs, with probability $1$, a fixed vertex is only visited finitely often by a parasite under mild assumptions on the offspring distribution of parasites.
Qiao Yang,1,* Ying He,2,* Yi Zhou,3 Qinzhu Jia,3 Nan Dai,4 Siyuan Ma,5 Xiu Yang,3 Xi Zhang,6 Jianguo Sun3 1Department of Ultrasound, The 941st Hospital of the PLA Joint Logistic Support Force, Xining, 810007, People’s Republic of China; 2Department of Psychiatry, Xinqiao Hospital, Army Medical University, Chongqing, 400037, People’s Republic of China; 3Department of Oncology, Xinqiao Hospital, Army Medical University, Chongqing, 400037, People’s Republic of China; 4Department of Oncology, Daping Hospital, Army Medical University, Chongqing, 400042, People’s Republic of China; 5Institute of Burn Research, State Key Laboratory of Trauma, Burns and Combined Injury, Army Medical University, Chongqing, 400038, People’s Republic of China; 6Department of Hematology, Xinqiao Hospital, Army Medical University, Chongqing, 400037, People’s Republic of China*These authors contributed equally to this workCorrespondence: Jianguo Sun; Xi Zhang, Xinqiao Hospital, Army Medical University, 83 Xinqiao Zhengjie Road, Shapingba District, Chongqing, 400037, People’s Republic of China, Tel +86-23-68774490, Fax +86-23-68774631, Email sunjg09@aliyun.com; zhangxxi@sina.comBackground: This study aimed to investigate the risk factors for persistent viral shedding in cancer patients after Omicron infection.Methods: Patients with asymptomatic or mild Omicron infection (≥ 18 years) who were treated in a makeshift hospital in Shanghai were enrolled from 9 Apr to 11 May, 2022. Deidentified information of all patients were collected retrospectively. Logistic regression model was used to identify risk factors associated with prolonged duration of viral shedding (defined as the time from the day of first positive SARS-CoV-2 RNA test to the first day of two consecutive negative SARS-CoV-2 RNA tests).Results: A total of 1442 Omicron-infected patients were enrolled, including 129 cancer patients and 1313 non-cancer patients. The baseline clinical characteristics of cancer and non-cancer patients were balanced by propensity score matching (1:4). Compared with non-cancer patients, a higher odds ratio ([OR] 1.84, 95% CI 1.24– 2.76, P = 0.003) of lasting viral shedding for ≥ 7 days was found in cancer patients. Further subgroup analyses found that cancer patients were at higher risk for prolonged viral shedding in a subgroup of patients without hypertension (OR 1.89), diabetes (OR 1.80), or other chronic disease (OR 2.13), unvaccinated (OR 1.97), and asymptomatic (OR 2.36). In addition, 29 patients with active cancer and 19 patients with inactive cancer were identified. The median duration of viral shedding in the active cancer group was longer than that in the inactive cancer group (10 vs 6 days, P = 0.002). The risk of persistent viral shedding ≥ 7 days was also increased in the active cancer group (OR 5.33, 95% CI 1.49– 21.51, P = 0.013).Conclusion: Cancer disease is an independent risk factor for prolonged viral shedding in Omicron infected patients, especially in patients with active cancer.Keywords: omicron, cancer, persistent viral shedding, SARS-CoV-2
Since May 2022, human mpox cases have increased unexpectedly in non-endemic countries. The first imported case of human mpox in Hong Kong was reported in September 2022. Here we report the isolation and identification of MPXV from the vesicle swabs of this patient. In this research, the vesicle swabs were inoculated in Vero and Vero E6 cells. In addition to observing cytopathic effects (CPEs) in Vero or Vero E6 cells, the isolated virus was identified as mpox virus (MPXV) using quantitative Real-Time PCR (RT–PCR), transmission electron microscopy, and high-throughput sequencing. The experiment also assessed the cross-protective efficacy of sera from the smallpox vaccinated population and preliminarily assessed the inhibitory effect of anti-smallpox virus drugs against MPXV. CPEs can be observed on Vero E6 cells at 24 h and Vero cells at 48 h. The virus particles could be observed by transmission electron microscope, showing typical orthopoxvirus morphology. In addition, F3L and ATI genes which from MPXV A39R, B2R, HA genes which from orthopoxvirus were confirmed by conventional PCR and Sanger sequencing. The next generation sequencing (NGS) suggests that the MPXV strain belongs to B.1 branch of the West African linage, and has a high identity with the sequence of the 2022 ongoing outbreak. PRNT50 results showed that 26.7% of sera from individuals born before 1981 who had been immunized with smallpox were positive, but no MPXV-neutralizing antibodies were found in sera from individuals born later. All four anti-smallpox virus drugs evaluated demonstrated inhibition of mpox virus.
Asim Khan, Umair Nawaz, Lochan Kshetrimayum
et al.
Tomato leaf diseases pose a significant challenge for tomato farmers, resulting in substantial reductions in crop productivity. The timely and precise identification of tomato leaf diseases is crucial for successfully implementing disease management strategies. This paper introduces a transformer-based model called TomFormer for the purpose of tomato leaf disease detection. The paper's primary contributions include the following: Firstly, we present a novel approach for detecting tomato leaf diseases by employing a fusion model that combines a visual transformer and a convolutional neural network. Secondly, we aim to apply our proposed methodology to the Hello Stretch robot to achieve real-time diagnosis of tomato leaf diseases. Thirdly, we assessed our method by comparing it to models like YOLOS, DETR, ViT, and Swin, demonstrating its ability to achieve state-of-the-art outcomes. For the purpose of the experiment, we used three datasets of tomato leaf diseases, namely KUTomaDATA, PlantDoc, and PlanVillage, where KUTomaDATA is being collected from a greenhouse in Abu Dhabi, UAE. Finally, we present a comprehensive analysis of the performance of our model and thoroughly discuss the limitations inherent in our approach. TomFormer performed well on the KUTomaDATA, PlantDoc, and PlantVillage datasets, with mean average accuracy (mAP) scores of 87%, 81%, and 83%, respectively. The comparative results in terms of mAP demonstrate that our method exhibits robustness, accuracy, efficiency, and scalability. Furthermore, it can be readily adapted to new datasets. We are confident that our work holds the potential to significantly influence the tomato industry by effectively mitigating crop losses and enhancing crop yields.
Jordi Gómez i Prat, Hakima Ouaarab Essadek, Juliana Esperalba
et al.
Abstract Background As a Neglected Tropical Disease associated with Latin America, Chagas Disease (CD) is little known in non-endemic territories of the Americas, Europe and Western Pacific, making its control challenging, with limited detection rates, healthcare access and consequent epidemiological silence. This is reinforced by its biomedical characteristics—it is usually asymptomatic—and the fact that it mostly affects people with low social and financial resources. Because CD is mainly a chronic infection, which principally causes a cardiomyopathy and can also cause a prothrombotic status, it increases the risk of contracting severe COVID-19. Methods In order to get an accurate picture of CD and COVID-19 overlapping and co-infection, this operational research draws on community-based experience and participative-action-research components. It was conducted during the Bolivian elections in Barcelona on a representative sample of that community. Results The results show that 55% of the people interviewed had already undergone a previous T. cruzi infection screening—among which 81% were diagnosed in Catalonia and 19% in Bolivia. The prevalence of T. cruzi infection was 18.3% (with 3.3% of discordant results), the SARS-CoV-2 22.3% and the coinfection rate, 6%. The benefits of an integrated approach for COVID-19 and CD were shown, since it only took an average of 25% of additional time per patient and undoubtedly empowered the patients about the co-infection, its detection and care. Finally, the rapid diagnostic test used for COVID-19 showed a sensitivity of 89.5%. Conclusions This research addresses CD and its co-infection, through an innovative way, an opportunity of systematic integration, during the COVID-19 pandemic.
Carmina Guitart, Sara Bobillo-Perez, Carme Alejandre
et al.
Abstract Background Bronchiolitis is the most common viral infection of the lower respiratory tract in infants under 2 years of age. The aim of this study was to analyze and compare the seasonal bronchiolitis peaks before and during the SARS-CoV-2 pandemic. Methods Descriptive, prospective, and observational study. Patients with severe bronchiolitis admitted to the Pediatric Intensive Care Unit (PICU) of a referral tertiary hospital between September 2010 and June 2021 were included. Demographic data were collected. Viral laboratory-confirmation was carried out. Each season was analyzed and compared. The daily average temperature was collected. Results 1116 patients were recruited, 58.2% of them males. The median age was 49 days. Respiratory syncytial virus (RSV) was isolated in 782 cases (70.1%). In April 2021, the first and only case of bronchiolitis caused by SARS-CoV-2 was identified. The pre- and post-pandemic periods were compared. There were statistically significant differences regarding: age, 47 vs. 73 days (p = 0.006), PICU and hospital length of stay (p = 0.024 and p = 0.001, respectively), and etiology (p = 0.031). The peak for bronchiolitis in 2020 was non-existent before week 52. A delayed peak was seen around week 26/2021. The mean temperature during the epidemic peak was 10ºC for the years of the last decade and is 23ºC for the present season. Conclusion The COVID-19 pandemic outbreak has led to a clearly observable epidemiological change regarding acute bronchiolitis, which should be studied in detail. The influence of the environmental temperature does not seem to determine the viral circulation.
Abstract Background Noroviruses are the leading cause of acute gastroenteritis in all age groups globally. The problem is magnified in developing countries including Africa. These viruses are highly prevalent with high genetic diversity and fast evolution rates. With this dynamicity, there are no recent review in the past five years in Africa. Therefore, this review and meta-analysis aimed to assess the prevalence and genetic diversity of noroviruses in Africa and tried to address the change in the prevalence and genetic diverisity the virus has been observed in Africa and in the world. Methods Twenty-one studies for the pooled prevalence, and 11 out of the 21 studies for genetic characterization of norovirus were included. Studies conducted since 2006, among symptomatic cases of all age groups in Africa, conducted with any study design, used molecular diagnostic methods and reported since 2015, were included and considered for the main meta-analysis. PubMed, Cochrane Library, and Google Scholar were searched to obtain the studies. The quality the studies was assessed using the JBI assessment tool. Data from studies reporting both asymptomatic and symptomatic cases, that did not meet the inclusion criteria were reviewed and included as discussion points. Data was entered to excel and imported to STATA 2011 to compute the prevalence and genetic diversity. Heterogeneity was checked using I2 test statistics followed by subgroup and sensitivity analysis. Publication bias was assessed using a funnel plot and eggers test that was followed by trim and fill analysis. Result The pooled prevalence of norovirus was 20.2% (95% CI: 15.91, 24.4). The highest (36.3%) prevalence was reported in Ghana. Genogroup II noroviruses were dominant and reported as 89.5% (95% CI: 87.8, 96). The highest and lowest prevalence of this genogroup were reported in Ethiopia (98.3%), and in Burkina Faso (72.4%), respectively. Diversified genotypes had been identified with an overall prevalence of GII. 4 NoV (50.8%) which was followed by GII.6, GII.17, GI.3 and GII.2 with a pooled prevalence of 7.7, 5.1, 4.6, and 4.2%, respectively. Conclusion The overall pooled prevalence of norovirus was high in Africa with the dominance of genogroup II and GII.4 genotype. This prevalence is comparable with some reviews done in the same time frame around the world. However, in Africa, an in increasing trained of pooled prevalence had been reported through time. Likewise, a variable distribution of non-GII.4 norovirus genotypes were reported as compared to those studies done in the world of the same time frame, and those previous reviews done in Africa. Therefore, continuous surveillance is required in Africa to support future interventions and vaccine programs.
Apple diseases, if not diagnosed early, can lead to massive resource loss and pose a serious threat to humans and animals who consume the infected apples. Hence, it is critical to diagnose these diseases early in order to manage plant health and minimize the risks associated with them. However, the conventional approach of monitoring plant diseases entails manual scouting and analyzing the features, texture, color, and shape of the plant leaves, resulting in delayed diagnosis and misjudgments. Our work proposes an ensembled system of Xception, InceptionResNet, and MobileNet architectures to detect 5 different types of apple plant diseases. The model has been trained on the publicly available Plant Pathology 2021 dataset and can classify multiple diseases in a given plant leaf. The system has achieved outstanding results in multi-class and multi-label classification and can be used in a real-time setting to monitor large apple plantations to aid the farmers manage their yields effectively.
With the development of natural language processing techniques(NLP), automatic diagnosis of eye diseases using ophthalmology electronic medical records (OEMR) has become possible. It aims to evaluate the condition of both eyes of a patient respectively, and we formulate it as a particular multi-label classification task in this paper. Although there are a few related studies in other diseases, automatic diagnosis of eye diseases exhibits unique characteristics. First, descriptions of both eyes are mixed up in OEMR documents, with both free text and templated asymptomatic descriptions, resulting in sparsity and clutter of information. Second, OEMR documents contain multiple parts of descriptions and have long document lengths. Third, it is critical to provide explainability to the disease diagnosis model. To overcome those challenges, we present an effective automatic eye disease diagnosis framework, NEEDED. In this framework, a preprocessing module is integrated to improve the density and quality of information. Then, we design a hierarchical transformer structure for learning the contextualized representations of each sentence in the OEMR document. For the diagnosis part, we propose an attention-based predictor that enables traceable diagnosis by obtaining disease-specific information. Experiments on the real dataset and comparison with several baseline models show the advantage and explainability of our framework.
Abstract Background Cystic echinococcosis (CE), caused by the larval stage of the complex Echinococcus granulosus sensu lato (s.l.), is a zoonotic parasitic disease with a high social burden in China. E. ortleppi is a species (formerly genotype 5 of E. granulosus s.l.) with unique epidemic areas (tropical areas), transmission patterns (mainly cattle origin), and pathological characteristics (large and small hook lengths) compared to other species that cause CE. A 19-year-old female patient in an area with no history of echinococcosis in Guizhou Province, China, was diagnosed with E. ortleppi infection in 2019. This study is to understand the source of this human E.ortleppi infection. Methods We performed computer tomography (CT) scans, surgical operation, morphological sectioning, molecular diagnosis, phylogenetic analyses, and epidemiological investigation in Anshun City, Guizhou Province, China in 2019. Results The patient presented with intermittent distension and pain in the upper abdomen without other abnormal symptoms. Routine blood examination results were normal. However, abdominal CT revealed a fertile cyst with a diameter of approximately 8 cm, uniform density, and a clear boundary, but without an evident cyst wall in the right lobe of the liver. The cyst was fertile, and phylogenetic analyses revealed that the isolates represented a new E. ortleppi genus haplotype. A result of 10‒14 years incubation period with indigenous infection was considered available for the case through the epidemiological survey. Conclusions CE due to E. ortleppi infection can be confused with other diseases causing liver cysts, resulting in misdiagnosis. A transmission chain of E. ortleppi may exist or existed in the past in the previously considered non-endemic areas of echinococcosis in southwestern China. Graphic abstract
Zsuzsa Kalmár, Violeta Briciu, Mircea Coroian
et al.
Abstract Background The Borrelia burgdorferi sensu lato (s.l.) genogroup is the causative agent responsible for Lyme borreliosis, a common tick-borne infectious disease in some temperate regions of the Northern Hemisphere. In humans, the clinical manifestations of Lyme borreliosis vary from dermatological infection to severe systemic manifestations. In Romania, data on the seroprevalence of Lyme borreliosis and associated risk factors are scarce and outdated, as the only seroprevalence study with a large dataset was published more than 20 years ago. Therefore, the aim of the present study was to evaluate the seroprevalence for Borrelia burgdorferi s.l. in healthy blood donors from six Romanian counties and identify the associated risk factors. Methods The study was conducted among 1200 healthy blood donors aged between 18 and 65 years during November 2019 and September 2020 from six counties in the northwestern and central parts of Romania. A two-tiered testing strategy was applied. Positive and equivocal immunoenzymatic test results for IgG and IgM antibodies were further confirmed by Western blot. Results Serum samples from 20% of the blood donors had positive or equivocal IgG and IgM ELISA index values. In total, 2.3% of the serum samples for IgG and 1.8% for IgM were positive by Western blot. The seroprevalence for both antibodies varied between 1.5% (Satu-Mare) and 6.5% (Bistrița-Năsăud) in the six counties investigated. The highest seroprevalence was observed in men (4.7%), in blood donors performing their professional activities outdoors (4.2%), and in those aged ≥ 56 years (8%). Conclusions These findings confirm the presence of specific IgG and IgM antibodies to B. burgdorferi s.l. among healthy blood donors from Romania. Furthermore, potential risk factors, such as gender, age, and behavior, associated with the presence of positive B. burgdorferi s.l. antibodies among healthy blood donors were identified. Graphical Abstract
Mathematical models of infectious diseases exhibit robust dynamics such as stable endemic or a disease-free equilibrium, or convergence of the solutions to periodic epidemic waves. The present work shows that the accuracy of such dynamics can be significantly improved by incorporating both local and global dynamics of the infection in disease models. To demonstrate improved accuracies, we extended a standard Susceptible-Infected-Recovered (SIR) model by incorporating global dynamics of the COVID-19 pandemic. The extended SIR model assumes three possibilities for the susceptible individuals traveling outside of their community: They can return to the community without any exposure to the infection, they can be exposed and develop symptoms after returning to the community, or they can be tested positive during the trip and remain quarantined until fully recovered. To examine the predictive accuracies of the extended SIR model, we studied the prevalence of the COVID-19 infection in Kansas City, Missouri influenced by the COVID-19 global pandemic. Using a two-step model-fitting algorithm, the extended SIR model was parameterized using the Kansas City, Missouri COVID-19 data during March to October 2020. The extended SIR model significantly outperformed the standard SIR model and revealed oscillatory behaviors with an increasing trend of infected individuals. In conclusion, the analytics and predictive accuracies of disease models can be significantly improved by incorporating the global dynamics of the infection in the models.
Samira Salari, Iraj Sharifi, Ali Reza Keyhani
et al.
Abstract Background Leishmaniasis is a serious health problem in some parts of the world. In spite of the many known leishmaniasis control measures, the disease has continued to increase in endemic areas, and no effective vaccine has been discovered. Methods In this study, Leishmania tarentulae was used as a living factory for the production of two LACK and KMP11 immunogenic antigens in the mice body, and safety profiles were investigated. The sequences of the KMP11 and LACK L. major antigens were synthesized in the pLEXSY-neo 2.1 plasmid and cloned into E. coli strain Top10, and after being linearized with the SwaI enzyme, they were transfected into the genome of L. tarentolae. The L. tarentolae-LACK/KMP11/EGFP in the stationary phase with CpG ODN as an adjuvant was used for vaccination in BALB/c mice. Vaccination was performed into the left footpad. Three weeks later, the booster was injected in the same manner. To examine the effectiveness of the injected vaccine, pathogenic L. major (MRHO/IR/75/ER) was injected into the right footpad of all mice three weeks following the booster vaccination. In order to assess humoral immunity, the levels of IgG1, and IgG2a antibodies before and 6 weeks after the challenge were studied in the groups. In addition, in order to investigate cellular immunity in the groups, the study measured IFN-γ, IL-5, TNF-α, IL-6 and IL-17 cytokines before, 3 weeks and 8 weeks after the challenge, and also the parasite load in the lymph node with real-time PCR. Results The lowest level of the parasitic load was observed in the G1 group (mice vaccinated with L. tarentolae-LACK/KMP11/EGFP with CpG) in comparison with other groups (L. tarentolae-LACK/KMP11/EGFP +non-CpG (G2); L. tarentolae-EGFP + CpG (G3, control); L. tarentolae-EGFP + non-CpG (G4, control); and mice injected with PBS (G5, control). Moreover, the evaluation of immune response showed a delayed-type hypersensitivity towards Th1. Conclusions According to the results of this study, the live recombinant vaccine of L. tarentolae-LACK/KMP11/EGFP with the CpG adjuvant reduced the parasitic load and footpad induration in infected mice. The long-term effects of this vaccine can be evaluated in volunteers as a clinical trial in future planning.