Relevant bibliographies by topics / Leishmaniasis/classification

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Academic literature on the topic 'Leishmaniasis/classification'

Author: Grafiati
Published: 4 June 2021
Last updated: 4 February 2022

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Journal articles on the topic "Leishmaniasis/classification"

1

Sangueza, Omar P., Julio M. Sangueza, Mathew J. Stiller, and Pastor Sangueza. "Mucocutaneous leishmaniasis: A clinicopathologic classification." Journal of the American Academy of Dermatology 28, no. 6 (1993): 927–32. http://dx.doi.org/10.1016/0190-9622(93)70132-d.

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Akilov, Oleg E., Amor Khachemoune, and Tayyaba Hasan. "Clinical manifestations and classification of Old World cutaneous leishmaniasis." International Journal of Dermatology 46, no. 2 (2007): 132–42. http://dx.doi.org/10.1111/j.1365-4632.2007.03154.x.

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3

Paltrinieri, Saverio, Laia Solano-Gallego, Alessandra Fondati, et al. "Guidelines for diagnosis and clinical classification of leishmaniasis in dogs." Journal of the American Veterinary Medical Association 236, no. 11 (2010): 1184–91. http://dx.doi.org/10.2460/javma.236.11.1184.

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4

Arce-Lopera, Carlos Alberto, Javier Diaz-Cely, and Lina Quintero. "Presumptive Diagnosis of Cutaneous Leishmaniasis." Frontiers in Health Informatics 10, no. 1 (2021): 75. http://dx.doi.org/10.30699/fhi.v10i1.278.

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Introduction: Cutaneous Leishmaniasis is a neglected tropical disease caused by a parasite. The most common presumptive diagnostic tool for this disease is the visual examination of the associated skin lesions by medical experts. Here, a mobile application was developed to aid this pre-diagnosis using an automatic image recognition software based on a convolutional neural network model.Material and Methods: A total of 2022 images of cutaneous diseases taken from 2012 to 2018 were used for training. Then, in 2019, machine learning techniques were tested to develop an automatic classification model. Also, a mobile application was developed and tested against specialized human experts to compare its performance.Results: Transfer learning using the VGG19 model resulted in a 93% accuracy of the classification model. Moreover, on average, the automatic model performance on a randomly selected skin image sample revealed a 99% accuracy while, the ensemble prediction of seven human medical expert’s accuracy was 83%.Conclusion: Mobile skin monitoring applications are crucial developments for democratizing health access, especially for neglected tropical diseases. Our results revealed that the image recognition software outperforms human medical experts and can alert possible patients. Future developments of the mobile application will focus on health monitoring of Cutaneous Leishmaniasis patients via community leaders and aiming at the promotion of treatment adherence.
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5

Das Gupta, S., D. K. Ghosh, and H. K. Majumder. "A cloned kinetoplast DNA mini-circle fragment from a Leishmania spp. specific for post-kala-azar dermal leishmaniasis strains." Parasitology 102, no. 2 (1991): 187–91. http://dx.doi.org/10.1017/s0031182000062478.

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SUMMARYDNA–DNA hybridization techniques have been found to be very useful in the classification and identification of Leishmania parasites. We report here the cloning of a mini-circle from Leishmania strain UR6 in a plasmid and subcloning of the mini-circle fragments in M13 mp9. Clone MLURk32, containing a 560 bp fragment of mini-circle, has been found to have unique specificity. Application of this specific probe in identifying different Leishmania isolates reveals that the probe reacted only with strains of post-kala-azar dermal leishmaniasis but not with strains or isolates of visceral leishmaniasis.
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Andrade, Rosilene Viana de, Cesare Massone, Meline Nogueira Barbosa de Lucena, et al. "The use of polymerase chain reaction to confirm diagnosis in skin biopsies consistent with american tegumentary leishmaniasis at histopathology: a study of 90 cases." Anais Brasileiros de Dermatologia 86, no. 5 (2011): 892–96. http://dx.doi.org/10.1590/s0365-05962011000500005.

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BACKGROUND: Cutaneous leishmaniasis is a chronic, infectious disease caused by protozoa of the genus leishmania. The incidence of this disease is high in Brazil, with 19,746 new cases having been detected in 2008. The presence of amastigotes in the cytoplasm of histiocytes constitutes diagnosis of the disease; however, their presence is rarely found in late lesions, making histological diagnosis difficult. Polymerase chain reaction has been shown to represent a highly sensitive and specific technique for the diagnosis of cutaneous leishmaniasis. OBJECTIVES: To use polymerase chain reaction to evaluate paraffin-embedded skin biopsies with histopathological features consistent with cutaneous leishmaniasis. MATERIAL AND METHODS: Polymerase chain reaction amplification of a 120-base-pair fragment of Leishmania kinetoplast DNA (kDNA) minicircles was performed on 90 skin biopsies. The male/female ratio was 75/15. Mean age was 32.36 years, with a median of 31 years, range 4-72 years. Samples were histologically compatible with cutaneous leishmaniasis but a definitive diagnosis could not be made since amastigotes were not found. All cases were histologically classified according to the patterns described by de Magalhães. RESULTS: According to the de Magalhães classification, the most common histological pattern was type IV (exudative granulomatous reaction), which was found in 65.6% of cases (56/90), followed by type I (exudative cellular reaction) in 21.1% of cases (19/90) and type III (exudative and necrotic granulomatous reaction) in 12.2% of cases (11/90). Leishmania DNA was found in 96.7% of the biopsies (87/90). CONCLUSION: Polymerase chain reaction performed by amplifying kDNA is able to confirm a diagnosis of cutaneous leishmaniasis with a high degree of sensitivity in cases in which histopathology is consistent with a diagnosis of cutaneous leishmaniasis but not definitive.
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7

Castillo-Garit, Juan Alberto, Naivi Flores-Balmaseda, Orlando Álvarez, et al. "Computational Identification of Chemical Compounds with Potential Activity against Leishmania amazonensis using Nonlinear Machine Learning Techniques." Current Topics in Medicinal Chemistry 18, no. 27 (2019): 2347–54. http://dx.doi.org/10.2174/1568026619666181130121558.

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Leishmaniasis is a poverty-related disease endemic in 98 countries worldwide, with morbidity and mortality increasing daily. All currently used first-line and second-line drugs for the treatment of leishmaniasis exhibit several drawbacks including toxicity, high costs and route of administration. Consequently, the development of new treatments for leishmaniasis is a priority in the field of neglected tropical diseases. The aim of this work is to develop computational models those allow the identification of new chemical compounds with potential anti-leishmanial activity. A data set of 116 organic chemicals, assayed against promastigotes of Leishmania amazonensis, is used to develop the theoretical models. The cutoff value to consider a compound as active one was IC50≤1.5μM. For this study, we employed Dragon software to calculate the molecular descriptors and WEKA to obtain machine learning (ML) models. All ML models showed accuracy values between 82% and 91%, for the training set. The models developed with k-nearest neighbors and classification trees showed sensitivity values of 97% and 100%, respectively; while the models developed with artificial neural networks and support vector machine showed specificity values of 94% and 92%, respectively. In order to validate our models, an external test-set was evaluated with good behavior for all models. A virtual screening was performed and 156 compounds were identified as potential anti-leishmanial by all the ML models. This investigation highlights the merits of ML-based techniques as an alternative to other more traditional methods to find new chemical compounds with anti-leishmanial activity.
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8

Sakyi, Patrick O., Richard K. Amewu, Robert N. O. A. Devine, Emahi Ismaila, Whelton A. Miller, and Samuel K. Kwofie. "The Search for Putative Hits in Combating Leishmaniasis: The Contributions of Natural Products Over the Last Decade." Natural Products and Bioprospecting 11, no. 5 (2021): 489–544. http://dx.doi.org/10.1007/s13659-021-00311-2.

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Abstract Despite advancements in the areas of omics and chemoinformatics, potent novel biotherapeutic molecules with new modes of actions are needed for leishmaniasis. The socioeconomic burden of leishmaniasis remains alarming in endemic regions. Currently, reports from existing endemic areas such as Nepal, Iran, Brazil, India, Sudan and Afghanistan, as well as newly affected countries such as Peru, Bolivia and Somalia indicate concerns of chemoresistance to the classical antimonial treatment. As a result, effective antileishmanial agents which are safe and affordable are urgently needed. Natural products from both flora and fauna have contributed immensely to chemotherapeutics and serve as vital sources of new chemical agents. This review focuses on a systematic cross-sectional view of all characterized anti-leishmanial compounds from natural sources over the last decade. Furthermore, IC50/EC50, cytotoxicity and suggested mechanisms of action of some of these natural products are provided. The natural product classification includes alkaloids, terpenes, terpenoids, and phenolics. The plethora of reported mechanisms involve calcium channel inhibition, immunomodulation and apoptosis. Making available enriched data pertaining to bioactivity and mechanisms of natural products complement current efforts geared towards unraveling potent leishmanicides of therapeutic relevance. Graphic Abstract
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9

Botelho, A. C. C., W. L. Tafuri, O. Genaro, and W. Mayrink. "Histopathology of human American cutaneous leishmaniasis before and after treatment." Revista da Sociedade Brasileira de Medicina Tropical 31, no. 1 (1998): 11–18. http://dx.doi.org/10.1590/s0037-86821998000100002.

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Chemical therapy for the treatment of leishmaniasis is still inadequate, and a number of drugs and therapeutic programs are being tested. Besides treatment, the ultimate goal is an effective cure, and histopathological analyses of the lesion cicatrices constitute an important measure of treatment success, or otherwise, in this respect. In this paper, we describe histopathological patterns in cases of American cutaneous leishmaniasis in 32 patients from the municipality of Caratinga, Minas Gerais, Brazil, before and after treatment with the following therapeutic methodos: l) leishvacin + glucantime; 2) leishvacin + BCG associated with glucantime; 3) glucantime; 4) leishvacin + BCG. Lesion fragments were collected from all patients by biopsy prior to, and approximately 30 days after, each treatment which resulted in a clinical diagnosis of cure. Following the analysis of slides, the preparations were described from a histopathological point of view and grouped taking into account the prevalence or significance of the characteristic elements. This process resulted in the following classification: 1. exsudative reaction (ER); 2. exsudative giant cell reaction (EGCR); 3. exsudative productive reaction (EPR); 4. exsudative productive giant cell reaction (EPGCR); 5. exsudative productive necrotic reaction (EPNR); 6. necrotic exsudative reaction (NER); 7. productive exsudative reaction (PER), 8. productive giant cell reaction (PGCR); 9. productive exsudative giant cell reaction (PEGCR); 10. productive exsudative giant cell granulomatous reaction (PEGCGR); 11. productive reaction (PR) and 12. productive cicatricial (cure) reaction (PCR). After this analysis, it was noted that clinical cure did not always coincide with histopathological cure.
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10

Fakiola, Michaela, Anshuman Mishra, Madhukar Rai, et al. "Classification and Regression Tree and Spatial Analyses Reveal Geographic Heterogeneity in Genome Wide Linkage Study of Indian Visceral Leishmaniasis." PLoS ONE 5, no. 12 (2010): e15807. http://dx.doi.org/10.1371/journal.pone.0015807.

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