Nano-Synthetic Devices in Leishmaniasis: A Bioinformatics Approach

Mol, Milsee; Kosey, Dipali; Singh, Shailza

doi:10.3389/fimmu.2015.00323

Cited by 8 publications

(7 citation statements)

References 38 publications

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“…New drug targets and the designing of novel compounds against the newly identified drug targets are necessary for clinical trials and toxicity studies (Table 2) (Hefnawy et al 2017). Unfortunately, there has been little progress in developing alternative methods for managing leishmaniasis (Flórez et al 2010;Pawar et al 2014;Mol et al 2015). Bioinformatic analysis can significantly reduce costs associated with the expensive clinical trials by identifying and analyzing drug candidates to the existing drug targets and also help us to identify new targets in silico.…”

Section: Natural Products: Source Of Antileishmanial Agentmentioning

confidence: 99%

Leishmaniasis: Plants as a source of antileishmanial agents

et al. 2022

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Leishmania infection causes a group of tropical diseases and has remained neglected for decades. It spreads by sandfly vector and is one of the most fatal protozoan diseases after malaria. Leishmaniases are a group of diseases caused by the infection of different Leishmania species and display clinically different forms like “Visceral leishmaniasis” (VL), “mucocutaneous leishmaniasis” and “cutaneous leishmaniasis” (CL). Approximately one billion people living in an endemic area are at high risk. Three hundred thousand cases of VL are reported annually and around twenty thousand people die every year, proving it as one of the most lethal forms of leishmaniasis. Until now, no effective vaccine could be made. There is an increase in drug resistance in the case of conventional drugs. New synthetic drugs are either too costly or have side effects. Requirements of new drugs are of utmost importance to control this situation. Plants provide a source of unlimited chemical diversity, which can be screened for antileishmanial activities. Moreover, their low cost and less or no side effects make them idle candidates in the search of new antileishmanial drugs.

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Section: Natural Products: Source Of Antileishmanial Agentmentioning

confidence: 99%

Leishmaniasis: Plants as a source of antileishmanial agents

et al. 2022

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“…With research advancements, several diagnostic and therapeutic lines have been developed against leishmaniasis. Among all forms of leishmaniasis, VL could be fatal as it affects the organs if not treated properly within two years [29,31]. The diagnosis of leishmaniasis is usually done by examination of the tissue of lesions under microscope.…”

Section: Conventional Treatment Strategies Against Leishmaniasis and mentioning

confidence: 99%

“…A diverse group of nanocarriers also serve as a method of enhancing efficacy, regulating pharmacokinetics, and reduced drug toxicity with sustained release of the drug. Nanotechnology has enabled advancements in pharmacology by providing treatment to various forms of leishmaniasis by targeted delivery of drugs [28,29]. Various nanotherapeutics have been approved by the Food and Drug Administration (FDA) and are currently available for clinical use [30].…”

Section: Introductionmentioning

confidence: 99%

Applications of Nanomaterials in Leishmaniasis: A Focus on Recent Advances and Challenges

et al. 2019

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Leishmaniasis is a widely distributed protozoan vector-born disease affecting almost 350 million people. Initially, chemotherapeutic drugs were employed for leishmania treatment but they had toxic side effects. Various nanotechnology-based techniques and products have emerged as anti-leishmanial drugs, including liposomes, lipid nano-capsules, metal and metallic oxide nanoparticles, polymeric nanoparticles, nanotubes and nanovaccines, due to their unique properties, such as bioavailability, lowered toxicity, targeted drug delivery, and biodegradability. Many new studies have emerged with nanoparticles serving as promising therapeutic agent for anti-leishmanial disease treatment. Liposomal Amphotericin B (AmB) is one of the successful nano-based drugs with high efficacy and negligible toxicity. A new nanovaccine concept has been studied as a carrier for targeted delivery. This review discusses different nanotechnology-based techniques, materials, and their efficacies in leishmaniasis treatment and their futuristic improvements.

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“…Leishmanial parasites are capable of modulating the human immune system for their survival. They also modify the self-healing cutaneous lesions to fatal conditions 5,6 . And some species of leishmaniasis are becoming virulent and drug resistant.…”

Section: Introductionmentioning

confidence: 99%

Construction of Feed Forward MultiLayer Perceptron Model For Genetic Dataset in Leishmaniasis Using Cognitive Computing

Mahalingam

Kabra

Singh

2018

Preprint

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Leishmaniasis is an endemic parasitic disease, predominantly found in the poor locality of Africa, Asia and Latin America. It is associated with malnutrition, weak immune system of people and their housing locality. At present, it is diagnosed by microscopic identification, molecular and biochemical characterisation or serum analysis for parasitic compounds. In this study, we present a new approach for diagnosing Leishmaniasis using cognitive computing. The Genetic datasets of leishmaniasis are collected from Gene Expression Omnibus database and it's then processed. The algorithm for training and developing a model, based on the data is prepared and coded using python. The algorithm and their corresponding datasets are integrated using TensorFlow dataframe. A feed forward Artificial Neural Network trained model with multi-layer perceptron is developed as a diagnosing model for Leishmaniasis, using genetic dataset. It is developed using recurrent neural network. The cognitive model of the trained network is interpreted using the maps and mathematical formula of the influencing parameters. The credit of the system is measured using the accuracy, loss and error of the system. This integrated system of the leishmaniasis genetic dataset and neural network proved to be the good choice for diagnosis with higher accuracy and lower error. Through this approach, all records of the data are effectively incorporated into the system. The experimental results of feed forward multilayer perceptron model after normalization; mean square error (219.84), loss function (1.94) and accuracy (85.71%) of the model, shows good fit of model with the process and it could possibly serve as a better solution for diagnosing Leishmaniasis in future, using genetic datasets.The code is available in Github repository: https://github.com/shailzasingh/Machine-Learning-code-for-analyzing-genetic-dataset-in-Leishmaniasis

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Nano-Synthetic Devices in Leishmaniasis: A Bioinformatics Approach

Cited by 8 publications

References 38 publications

Leishmaniasis: Plants as a source of antileishmanial agents

Leishmaniasis: Plants as a source of antileishmanial agents

Applications of Nanomaterials in Leishmaniasis: A Focus on Recent Advances and Challenges

Construction of Feed Forward MultiLayer Perceptron Model For Genetic Dataset in Leishmaniasis Using Cognitive Computing

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