Identifying major histocompatibility complex class II-DR molecules in bovine and swine peripheral blood monocyte-derived macrophages using mAb-L243.

Major histocompatibility complex class II (MHC-II) molecules are involved in immune responses against pathogens and vaccine candidates' immunogenicity. Immunopeptidomics for identifying cancer and infection-related antigens and epitopes have benefited from advances in immunopurification methods and mass spectrometry analysis. The mouse anti-MHC-II-DR monoclonal antibody L243 (mAb-L243) has been effective in recognising MHC-II-DR in both human and non-human primates. It has also been shown to cross-react with other animal species, although it has not been tested in livestock. This study used mAb-L243 to identify Staphylococcus aureus and Salmonella enterica serovar Typhimurium peptides binding to cattle and swine macrophage MHC-II-DR molecules using flow cytometry, mass spectrometry and two immunopurification techniques. Antibody cross-reactivity led to identifying expressed MHC-II-DR molecules, together with 10 Staphylococcus aureus peptides in cattle and 13 S. enterica serovar Typhimurium peptides in swine. Such data demonstrates that MHC-II-DR expression and immunocapture approaches using L243 mAb represents a viable strategy for flow cytometry and immunopeptidomics analysis of bovine and swine antigen-presenting cells.

A comparative study of Machine Learning-based classification of Tomato fungal diseases: Application of GLCM texture features.

Globally, agriculture remains an important source of food and economic development. Due to various plant diseases, farmers continue to suffer huge yield losses in both quality and quantity. In this study, we explored the potential of using Artificial Neural Networks, K-Nearest Neighbors, Random Forest, and Support Vector Machine to classify tomato fungal leaf diseases: Alternaria, Curvularia, Helminthosporium, and Lasiodiplodi based on Gray Level Co-occurrence Matrix texture features. Small differences between symptoms of these diseases make it difficult to use the naked eye to obtain better results in detecting and distinguishing these diseases. The Artificial Neural Network outperformed other classifiers with an overall accuracy of 94% and average scores of 93.6% for Precision, 93.8% for Recall, and 93.8% for F1-score. Generally, the models confused samples originally belonging to Helminthosporium with Curvularia. The extracted texture features show great potential to classify the different tomato leaf fungal diseases. The results of this study show that texture characteristics of the Gray Level Co-occurrence Matrix play a critical role in the establishment of tomato leaf disease classification systems and can facilitate the implementation of preventive measures by farmers, resulting in enhanced yield quality and quantity.

Environmental and economic potential of decentralised electrocatalytic ammonia synthesis powered by solar energy.

Intense efforts have been devoted to developing green and blue centralised Haber-Bosch processes (gHB and bHB, respectively), but the feasibility of a decentralised and more sustainable scheme has yet to be assessed. Here we reveal the conditions under which small-scale systems (NH3-leaves) based on the electrocatalytic reduction of nitrogen (eN2R) powered by photovoltaic energy could realise a decentralised scheme competitive in terms of environmental and economic criteria. For this purpose, we calculated energy efficiency targets worldwide, providing clear values that may guide research in the incipient eN2R field. Even at this germinal stage, the NH3-leaf technology would compete favourably in sunny locations for CO2-related Earth-system processes and human health relative to the business-as-usual production scenario. Moreover, a modest 8% gain in energy efficiency would already make them outperform the gHB in terms of climate change-related impacts in the sunniest locations. If no CO2 taxation is enforced, the lowest estimated ammonia production cost would be 3 times the industrial standard, with the potential to match it provided a substantial decrease of investment costs and very high selectivity toward ammonia in eN2R are achieved. The disclosed sustainability potential of NH3-leaf makes it a strong ally of gHB toward defossilised ammonia production.

Comportamiento epidemiológico del Dengue en Ecuador desde su reemergencia. Periodo 1980-2020 / Epidemiological behavior of Dengue in Ecuador since its reemergence. Period 1980-2020

INTRODUCCIÓN. El dengue es una enfermedad infecciosa de origen viral, transmitida principalmente por el mosquito Aedes aegypti. Es un grave problema de salud pública a nivel mundial, en las Américas y en el Ecuador. OBJETIVOS. Analizar el comportamiento epidemiológico del dengue desde 1980 hasta el 2020, los factores de riesgo que mantienen la transmisión y las acciones que el país ha implementado para su prevención y control. METODOLOGÍA. Revisión bibliográfica narrativa, teniendo como fuente las páginas web de la Organización Mundial de la Salud, de la Organización Panamericana de la Salud, Ministerio de Salud Pública del Ecuador, artículos de revistas de bibliotecas virtuales e informes técnicos publicados en Google académico, Scielo, Biblioteca Virtual en Salud y PubMed. RESULTADOS. Los registros encontrados sobre presencia de casos de dengue datan de 1988 cuando se presentó la gran epidemia de dengue en Guayaquil. Posteriormente, mantiene un comportamiento endemo-epidémico. A partir del año 2000 se presentan casos de dengue grave. Su pico más alto de letalidad fue de 2,44% en el 2010. El serotipo DEN 1 es el más frecuente, pero a partir del 2000 circulan los 4 serotipos. Las acciones de prevención y control no han sido sostenidas. CONCLUSIONES. El dengue en Ecuador en los últimos cuarenta años mantiene una importante trasmisión, caracterizada por años epidémicos. No ha podido ser controlado el vector, el cual ha ido infestando más localidades. Las condicionantes climáticas y ecológicas, explican en parte la persistencia, pero la determinación más importante está dada por las inequidades sociales, falta de servicios básicos, y la poca continuidad e impacto de las medidas de prevención y control.

INTRODUCTION. Dengue is an infectious disease of viral origin, transmitted mainly by the Aedes aegypti mosquito. It is a serious public health problem worldwide, in the Americas and in Ecuador. OBJECTIVES: To analyze the epidemiological behavior of dengue fever from 1980 to 2020, the risk factors that maintain transmission and the actions that the country has implemented for its prevention and control. METHODOLOGY. Narrative bibliographic review, taking as sources the web pages of the World Health Organization, Pan American Health Organization, Ministry of Public Health of Ecuador, journal articles from virtual libraries and technical reports published in Google Scholar, Scielo, Virtual Health Library and PubMed. RESULTS. The records found on the presence of dengue cases date back to 1988 when the great dengue epidemic occurred in Guayaquil. Subsequently, it maintained an endemic-epidemic behavior. Beginning in 2000, severe cases of dengue fever occurred. Its highest lethality peak was 2.44% in 2010. DEN 1 serotype is the most frequent, but since 2000 all 4 serotypes have been circulating. Prevention and control actions have not been sustained. CONCLUSIONS. Dengue in Ecuador over the last forty years has maintained an important transmission, characterized by epidemic years. It has not been possible to control the vector, which has been infesting more localities. Climatic and ecological conditions partly explain its persistence, but the most important determinant is given by social inequalities, lack of basic services, and the lack of continuity and impact of prevention and control measures.

MILimbEEG: A dataset of EEG signals related to upper and lower limb execution of motor and motor imagery tasks.

Biomedical Electroencephalography (EEG) signals are the result of measuring the electric potential difference generated on the scalp surface by neural activity corresponding to each brain area. Accurate and automatic detection of neural activity from the upper and lower limbs using EEG may be helpful in rehabilitating people suffering from mobility limitations or disabilities. This article presents a dataset containing 7440 CSV files from 60 test subjects during motor and motor imagery tasks. The motor and motor imagery tasks performed by the test subjects were: Closing Left Hand (CLH), Closing Right Hand (CRH), Dorsal flexion of Left Foot (DLF), Plantar flexion of Left Foot (PLF), Dorsal flexion of Right Foot (DRF), Plantar flexion of Right Foot (PRF) and Resting in between tasks (Rest). The volunteers were recruited from research colleagues at ESPOL and patients at the Luis Vernaza Hospital in Guayaquil, Ecuador. Each CSV file has 501 rows, of which the first one lists the electrodes from 0 to 15, and the remaining 500 rows correspond to 500 data recorded during the task is performed due to sample rate. In addition, each file has 17 columns, of which the first one indicates the sampling number and the remaining 16 columns represent 16 surface EEG electrodes. As a data recording equipment, the OpenBCI is used in a monopolar setup with a sampling rate of 125 Hz. This work includes statistical measures about the demographic information of all recruited test subjects. Finally, we outline the experimental methodology used to record EEG signals during upper and lower limb task execution. This dataset is called MILimbEEG and contains microvolt (µV) EEG signals acquired during motor and motor imagery tasks. The collected data may facilitate the evaluation of EEG signal detection and classification models dedicated to task recognition.

Adaptive PI Controller Based on a Reinforcement Learning Algorithm for Speed Control of a DC Motor.

Automated industrial processes require a controller to obtain an output signal similar to the reference indicated by the user. There are controllers such as PIDs, which are efficient if the system does not change its initial conditions. However, if this is not the case, the controller must be retuned, affecting production times. In this work, an adaptive PID controller is developed for a DC motor speed plant using an artificial intelligence algorithm based on reinforcement learning. This algorithm uses an actor-critic agent, where its objective is to optimize the actor's policy and train a critic for rewards. This will generate the appropriate gains without the need to know the system. The Deep Deterministic Policy Gradient with Twin Delayed (DDPG TD3) was used, with a network composed of 300 neurons for the agent's learning. Finally, the performance of the obtained controller is compared with a classical control one using a cost function.

A 3D Printed, Bionic Hand Powered by EMG Signals and Controlled by an Online Neural Network.

About 8% of the Ecuadorian population suffers some type of amputation of upper or lower limbs. Due to the high cost of a prosthesis and the fact that the salary of an average worker in the country reached 248 USD in August 2021, they experience a great labor disadvantage and only 17% of them are employed. Thanks to advances in 3D printing and the accessibility of bioelectric sensors, it is now possible to create economically accessible proposals. This work proposes the design of a hand prosthesis that uses electromyography (EMG) signals and neural networks for real-time control. The integrated system has a mechanical and electronic design, and the latter integrates artificial intelligence for control. To train the algorithm, an experimental methodology was developed to record muscle activity in upper extremities associated with specific tasks, using three EMG surface sensors. These data were used to train a five-layer neural network. the trained model was compressed and exported using TensorflowLite. The prosthesis consisted of a gripper and a pivot base, which were designed in Fusion 360 considering the movement restrictions and the maximum loads. It was actuated in real time thanks to the design of an electronic circuit that used an ESP32 development board, which was responsible for recording, processing and classifying the EMG signals associated with a motor intention, and to actuate the hand prosthesis. As a result of this work, a database with 60 electromyographic activity records from three tasks was released. The classification algorithm was able to detect the three muscle tasks with an accuracy of 78.67% and a response time of 80 ms. Finally, the 3D printed prosthesis was able to support a weight of 500 g with a safety factor equal to 15.

Combining AI Tools with Non-Destructive Technologies for Crop-Based Food Safety: A Comprehensive Review.

On a global scale, food safety and security aspects entail consideration throughout the farm-to-fork continuum, considering food's supply chain. Generally, the agrifood system is a multiplex network of interconnected features and processes, with a hard predictive rate, where maintaining the food's safety is an indispensable element and is part of the Sustainable Development Goals (SDGs). It has led the scientific community to develop advanced applied analytical methods, such as machine learning (ML) and deep learning (DL) techniques applied for assessing foodborne diseases. The main objective of this paper is to contribute to the development of the consensus version of ongoing research about the application of Artificial Intelligence (AI) tools in the domain of food-crop safety from an analytical point of view. Writing a comprehensive review for a more specific topic can also be challenging, especially when searching within the literature. To our knowledge, this review is the first to address this issue. This work consisted of conducting a unique and exhaustive study of the literature, using our TriScope Keywords-based Synthesis methodology. All available literature related to our topic was investigated according to our criteria of inclusion and exclusion. The final count of data papers was subject to deep reading and analysis to extract the necessary information to answer our research questions. Although many studies have been conducted, limited attention has been paid to outlining the applications of AI tools combined with analytical strategies for crop-based food safety specifically.

Evaluation of the potential use of e-fuels in the European aviation sector: a comprehensive economic and environmental assessment including externalities.

The decarbonisation of the transportation sector is key to meeting the climate goals. Whilst the electrification of road passenger transportation is proving to be a viable low-carbon solution in many contexts, a viable pathway towards a decarbonised aviation sector remains opaque. In this context, so-called e-fuels produced via the combination of H2O, CO2 and renewable energy may have promise owing to their compatibility with existing infrastructure. Most studies on e-fuels focus only on the economic dimension, neglecting their environmental performance and associated costs. Here, we present a techno-economic evaluation and cradle-to-grave life cycle assessment of Fischer-Tropsch (FT) e-jet fuels produced at different locations in Europe from a range of CO2 and green H2 sources to comprehensively assess their potential in aviation, explicitly accounting for externalities. Our results show that e-jet fuel is at present much more expensive (at least 5.4-fold) than its fossil analogue, even when externalities are included (i.e., at least 2.3 fold the current cost of fossil jet fuel). Furthermore, e-jet fuels could exacerbate the damage to human health and ecosystems despite showing lower carbon footprint and resource scarcity impacts than their fossil counterparts. Overall, e-jet fuel could become more economically and environmentally attractive by reducing the cost and impact of CO2 and green H2 and, more specifically, the electricity used in their production processes. In this regard, the production plant's location emerges as a critical factor due to the costs associated with balancing the intermittency of site-specific renewables.

Modelling of Proton Exchange Membrane Fuel Cells with Sinusoidal Approach.

This paper validates a sinusoidal approach for the proton-exchange membrane fuel cell (PEMFC) model as a supplement to experimental studies. An FC simulation or hardware emulation is necessary for prototype design, testing, and fault diagnosis to reduce the overall cost. For this objective, a sinusoidal model that is capable of accurately estimating the voltage behavior from the operating current value of the DC was developed. The model was tested using experimental data from the Ballard Nexa 1.2 kW fuel cell (FC). This methodology offers a promising approach for static and current-voltage, characteristic of the three regions of operation. A study was carried out to evaluate the effectiveness and superiority of the proposed FC Sinusoidal model compared with the Diffusive Global model and the Evolution Strategy.

Hydrogen Production and Its Applications to Mobility.

Hydrogen has been identified as one of the key elements to bolster longer-term climate neutrality and strategic autonomy for several major countries. Multiple road maps emphasize the need to accelerate deployment across its supply chain and utilization. Being one of the major contributors to global CO2 emissions, the transportation sector finds in hydrogen an appealing alternative to reach sustainable development through either its direct use in fuel cells or further transformation to sustainable fuels. This review summarizes the latest developments in hydrogen use across the major energy-consuming transportation sectors. Rooted in a systems engineering perspective, we present an analysis of the entire hydrogen supply chain across its economic, environmental, and social dimensions. Providing an outlook on the sector, we discuss the challenges hydrogen faces in penetrating the different transportation markets.

Identification of Lower-Limb Motor Tasks via Brain-Computer Interfaces: A Topical Overview.

Recent engineering and neuroscience applications have led to the development of brain-computer interface (BCI) systems that improve the quality of life of people with motor disabilities. In the same area, a significant number of studies have been conducted in identifying or classifying upper-limb movement intentions. On the contrary, few works have been concerned with movement intention identification for lower limbs. Notwithstanding, lower-limb neurorehabilitation is a major topic in medical settings, as some people suffer from mobility problems in their lower limbs, such as those diagnosed with neurodegenerative disorders, such as multiple sclerosis, and people with hemiplegia or quadriplegia. Particularly, the conventional pattern recognition (PR) systems are one of the most suitable computational tools for electroencephalography (EEG) signal analysis as the explicit knowledge of the features involved in the PR process itself is crucial for both improving signal classification performance and providing more interpretability. In this regard, there is a real need for outline and comparative studies gathering benchmark and state-of-art PR techniques that allow for a deeper understanding thereof and a proper selection of a specific technique. This study conducted a topical overview of specialized papers covering lower-limb motor task identification through PR-based BCI/EEG signal analysis systems. To do so, we first established search terms and inclusion and exclusion criteria to find the most relevant papers on the subject. As a result, we identified the 22 most relevant papers. Next, we reviewed their experimental methodologies for recording EEG signals during the execution of lower limb tasks. In addition, we review the algorithms used in the preprocessing, feature extraction, and classification stages. Finally, we compared all the algorithms and determined which of them are the most suitable in terms of accuracy.

Comparing Class II MHC DRB3 Diversity in Colombian Simmental and Simbrah Cattle Across Worldwide Bovine Populations.

The major histocompatibility complex (MHC) exerts great influence on responses to infectious diseases and vaccination due to its fundamental role in the adaptive immune system. Knowledge about MHC polymorphism distribution among breeds can provide insights into cattle evolution and diversification as well as population-based immune response variability, thus guiding further studies. Colombian Simmental and Simbrah cattle's BoLA-DRB3 genetic diversity was compared to that of taurine and zebuine breeds worldwide to estimate functional diversity. High allele richness was observed for Simmental and Simbrah cattle; nevertheless, high homozygosity was associated with individual low sequence variability in both the ß1 domain and the peptide binding region (PBR), thereby implying reduced MHC-presented peptide repertoire size. There were strong signals of positive selection acting on BoLA-DRB3 in all populations, some of which were poorly structured and displayed common alleles accounting for their high genetic similarity. PBR sequence correlation analysis suggested that, except for a few populations exhibiting some divergence at PBR, global diversity regarding potential MHC-presented peptide repertoire could be similar for the cattle populations analyzed here, which points to the retention of functional diversity in spite of the selective pressures imposed by breeding.

Dynamic Modeling of a Proton-Exchange Membrane Fuel Cell Using a Gaussian Approach.

This paper proposes a Gaussian approach for the proton-exchange membrane fuel cell (PEMFC) model that estimates its voltage behavior from the operating current value. A multi-parametric Gaussian model and an unconstrained optimization formulation based on a conventional non-linear least squares optimizer is mainly considered. The model is tested using experimental data from the Ballard Nexa 1.2 kW fuel cell (FC). This methodology offers a promising approach for static and current-voltage, characteristic of the three regions of operation. A statistical study is developed to evaluate the effectiveness and superiority of the proposed FC Gaussian model compared with the Diffusive Global model and the Evolution Strategy. In addition, an approximation to the exponential function for a Gaussian model simplification can be used in systems that require real-time emulators or complex long-time simulations.

Addressing the Data Acquisition Paradigm in the Early Detection of Pediatric Foot Deformities.

The analysis of plantar pressure through podometry has allowed analyzing and detecting different types of disorders and treatments in child patients. Early detection of an inadequate distribution of the patient's weight can prevent serious injuries to the knees and lower spine. In this paper, an embedded system capable of detecting the presence of normal, flat, or arched footprints using resistive pressure sensors was proposed. For this purpose, both hardware- and software-related criteria were studied for an improved data acquisition through signal coupling and filtering processes. Subsequently, learning algorithms allowed us to estimate the type of footprint biomechanics in preschool and school children volunteers. As a result, the proposed algorithm achieved an overall classification accuracy of 97.2%. A flat feet share of 60% was encountered in a sample of 1000 preschool children. Similarly, flat feet were observed in 52% of a sample of 600 school children.

Cattle seroprevalence and risk factors associated with bovine viral diarrhea in the northeastern of Colombia.

Bovine viral diarrhea (BVD) is one of the most common and economically significant viral infections in cattle. Different risk factors have been associated with viral exposure and transmission, generating endemic regions with different biotype circulations. To find the BVD status in the northeastern region of Colombia, this study aimed to determine the seroprevalence and risk factors associated with the disease in non-vaccinated farms. For this purpose, a two-time point sampling strategy was developed, obtaining serum from 1157 animals housed in 25 farms distributed in 46 townships, thus for antibody detection against non-structural protein 3 (NS3, p80) of bovine viral diarrhea virus (BVDV) using an indirect ELISA test. For the first time in Colombia, the presence of serological persistent cattle was evaluated in 1047 animals 3 weeks later of first sampling. The information on a standardized questionnaire with closed and dichotomic answers was used to calculate the putative risk factors. The association analysis by univariate and multivariate logistic regression reported odds ratios (OR) with a 95% of confidence interval (C.I). The overall seroprevalence was 65% (95% CI 62.5-72.9%), with variations according to age ranges. The prevalence of persistent serological positive corresponded to 37.7% (95% CI 34.2-41.5). The risk factors found by multivariate analyses were the lease of pastures (OR = 2.071 CI 1.485-3.690), the use of the same needle (OR = 2.249 CI 1.354-3.736), the molasses supplementation (OR = 2.742 CI 1.156-5.807), and the native Creole breed (OR = 1.895 CI 1.416-2.804). The results of this study confirmed the endemism and higher common exposure to BVDV, as well as the presence of serological persistent cattle in Valledupar, Colombia.

Babesia Bovis Ligand-Receptor Interaction: AMA-1 Contains Small Regions Governing Bovine Erythrocyte Binding.

Apical membrane antigen 1 is a microneme protein which plays an indispensable role during Apicomplexa parasite invasion. The detailed mechanism of AMA-1 molecular interaction with its receptor on bovine erythrocytes has not been completely defined in Babesia bovis. This study was focused on identifying the minimum B. bovis AMA-1-derived regions governing specific and high-affinity binding to its target cells. Different approaches were used for detecting ama-1 locus genetic variability and natural selection signatures. The binding properties of twelve highly conserved 20-residue-long peptides were evaluated using a sensitive and specific binding assay based on radio-iodination. B. bovis AMA-1 ectodomain structure was modelled and refined using molecular modelling software. NetMHCIIpan software was used for calculating B- and T-cell epitopes. The B. bovis ama-1 gene had regions under functional constraint, having the highest negative selective pressure intensity in the Domain I encoding region. Interestingly, B. bovis AMA-1-DI (100YMQKFDIPRNHGSGIYVDLG119 and 120GYESVGSKSYRMPVGKCPVV139) and DII (302CPMHPVRDAIFGKWSGGSCV321)-derived peptides had high specificity interaction with erythrocytes and bound to a chymotrypsin and neuraminidase-treatment sensitive receptor. DI-derived peptides appear to be exposed on the protein's surface and contain predicted B- and T-cell epitopes. These findings provide data (for the first-time) concerning B. bovis AMA-1 functional subunits which are important for establishing receptor-ligand interactions which could be used in synthetic vaccine development.

Environment Monitoring of Rose Crops Greenhouse Based on Autonomous Vehicles with a WSN and Data Analysis.

This work presents a monitoring system for the environmental conditions of rose flower-cultivation in greenhouses. Its main objective is to improve the quality of the crops while regulating the production time. To this end, a system consisting of autonomous quadruped vehicles connected with a wireless sensor network (WSN) is developed, which supports the decision-making on type of action to be carried out in a greenhouse to maintain the appropriate environmental conditions for rose cultivation. A data analysis process was carried out, aimed at designing an in-situ intelligent system able to make proper decisions regarding the cultivation process. This process involves stages for balancing data, prototype selection, and supervised classification. The proposed system produces a significant reduction of data in the training set obtained by the WSN while reaching a high classification performance in real conditions-amounting to 90 % and 97.5%, respectively. As a remarkable outcome, it is also provided an approach to ensure correct planning and selection of routes for the autonomous vehicle through the global positioning system.

Evaluating the immunogenicity of chemically-synthesised peptides derived from foot-and-mouth disease VP1, VP2 and VP3 proteins as vaccine candidates.

Foot-and-mouth disease (FMD) is one of the most contagious veterinary viral diseases known, having economic, social and potentially devastating environmental impacts. The vaccines currently being marketed/sold around the world for disease control and prevention in bovines do not stimulate the production of antibodies having crossed reactions to different serotypes. This means that if an animal becomes infected by a serotype which has not been included in a vaccine then it will develop the disease. Synthetic peptide vaccines represent a safer option and (depending on the design) can stimulate antibodies protecting against different variants. Based on the forgoing, this work was aimed at evaluating FMDV VP1, VP2 and VP3 protein-derived, modified and chemically-synthesised peptides' ability to induce an immune response for developing a vaccine contributing towards controlling the disease. VP1, VP2 and VP3 proteins' conserved regions were selected for this. Peptides from these regions were chemically synthesised; binding assays were then carried out for ascertaining whether they were involved in BHK-21 cell binding. Selected peptides' structure and location were studied. Peptides which did bind were modified and formulated with Montanide ISA 70 adjuvant; 17 animals were immunised twice with the formulation. The animals were genotyped by amplifying the BoLA-DRB3.2 gene. Blood samples were taken from 17 cattle on day 43 post-first immunisation for studying the formulation's immunogenicity. The sera were used in ELISA, immunofluorescence, flow cytometry, immunoadsorption and seroneutralisation assays. The A24 Cruzeiro and O1 Campos virus serotypes were used for these assays. The results revealed that even though protein exposure and 3D structure might be different amongst serotypes, the antibodies so produced could inhibit virus entry to cells, thereby showing the selected peptides' in vitro protection-inducing ability.

A Trypanosoma cruzi Genome Tandem Repetitive Satellite DNA Sequence as a Molecular Marker for a LAMP Assay for Diagnosing Chagas' Disease.

Chagas' disease is a neglected tropical disease caused by Trypanosoma cruzi which is endemic throughout Latin America and is spread by worldwide migration. Diagnosis is currently limited to serological and molecular techniques having variations regarding their sensitivity and specificity. This work was aimed at developing a new sensitive, applicable, and cost-effective molecular diagnosis technique for loop-mediated isothermal amplification-based detection of T. cruzi (Tc-LAMP). The results led to determining a highly homologous satellite repeat region (231 bp) among parasite strains as a molecular marker for diagnosing the disease. Tc-LAMP was performed correctly for detecting parasite DNA (5 fg for the CL Brener strain and 50 fg for the DM28, TcVI, and TcI strains). Assay results proved negative for DNA from 16 helminth species and 7 protozoa, including Leishmania spp. Tc-LAMP based on the highly repeated T. cruzi satellite region is thus proposed as an important alternative for diagnosing T. cruzi infection, overcoming other methods' limitations such as their analytic capability, speed, and requiring specialized equipment or highly trained personnel. Tc-LAMP could be easily adapted for point-of-care testing in areas having limited resources.