A machine learning-based system for detecting leishmaniasis in microscopic images

Background Leishmaniasis, a disease caused by a protozoan, causes numerous deaths in humans each year. After malaria, leishmaniasis is known to be the deadliest parasitic disease globally. Direct visual detection of leishmania parasite through microscopy is the frequent method for diagnosis of this disease. However, this method is time-consuming and subject to errors. This study was aimed to develop an artificial intelligence-based algorithm for automatic diagnosis of leishmaniasis. Methods We used the Viola-Jones algorithm to develop a leishmania parasite detection system. The algorithm includes three procedures: feature extraction, integral image creation, and classification. Haar-like features are used as features. An integral image was used to represent an abstract of the image that significantly speeds up the algorithm. The adaBoost technique was used to select the discriminate features and to train the classifier. Results A 65% recall and 50% precision was concluded in the detection of macrophages infected with the leishmania parasite. Also, these numbers were 52% and 71%, respectively, related to amastigotes outside of macrophages. Conclusion The developed system is accurate, fast, easy to use, and cost-effective. Therefore, artificial intelligence might be used as an alternative for the current leishmanial diagnosis methods.

Protective and Pathogenic Immune Responses to Cutaneous Leishmaniasis

Leishmania (Kinetoplastida: Trypanosomatidae) parasites are known to cause a broad spectrum of clinical diseases in humans, collectively known as the leishmaniases. Cutaneous leishmaniasis is the most common clinical presentation with varying degrees of severity largely driven by host immune responses, specifically the interplay between innate and adaptive immune response. The establishment of a T lymphocyte driven cell-mediated immune response, leading to activated phagocytic cells, leading to Leishmania parasite killing and control of infection. Alternatively, the Leishmania parasite manipulates the host immune system, enabling parasite proliferation and clinical disease. Here we review how the cumulative interactions of different aspects of the host immune response determines disease outcome, severity, and immunity to re-infection.

Silver Nanoparticles AgNPs Preparation by Chemical Method to Obtain Optimal Characteristics of The Pharmaceutical Form and Study Their Effect on The Leishmania Parasite in Laboratory: تحضير جسيمات الفضة النانوية AgNPs بالطريقة الكيميائية للحصول على الخصائص المثلى للشكل الصيدلاني المعتمد ودراسة تأثيرها على طفيلي الليشمانيا مختبريا

This study aimed first to prepare silver nanoparticles AgNPs in a chemical reduction, The silver nanoparticles AgNPs properties were characterized using spectroscopy of Vision and UV- Visible (UV- Vis), and atomic force microscopy (AFM) and a ZetaSizer device. This study also aimed to study the results effect of preparing Silver nanoparticles on the Leishmania parasite using an artificial environment. The results of the spectrophotometer of the secondary silver solution with yellow color, showed maximum absorption at a wavelength of 400 mm, while the image of atomic force microscope (AFM) showed the hierarchical morphology shape of the prepared Silver nanoparticles with an average size of (10- 30 nm). Silver secondary particles were estimated with a diameter ranging from (12- 60 nm) and with good dispersion in water by ZetaSizer device. The results illustrated that the number of Promastigotes of the Leishmania parasite decreased after adding the Silver nanoparticles AgNPs in varying proportions, as the concentration of 500 μg/ml had the highest effect after 72 hours from incubation, and the percentage of remaining parasites was 5.2% comparing with the control percentage of 14.99%, while the value of IC50 was 253.075 μg/ml, The value of R2 = 0.8809, slope = 0.0478 after 24 hours from incubation.

A Machine Learning-Based System for Detecting Leishmaniasis in Microscopic Images

Background: Leishmaniasis, a disease caused by a protozoan, causes numerous deaths in humans each year. After malaria, Leishmaniasis is known to be the deadliest parasitic disease globally. Currently, direct visual detection of Leishmania parasite through microscopy is the “gold standard” for the diagnosis of this disease. However, this method is time-consuming and subject to errors. This study was aimed to develop an artificial intelligence-based algorithm and image processing algorithms for the automatic diagnosis of Leishmaniasis.Methods: The Viola-Jones algorithm was used in this study due to its high recognition speed. This algorithm performs in four stages: detection of Haar-like features, integral image creation, Adaboost training, cascade architecture.Results: A 65% recall and 83% precision was concluded in the detection of macrophages infected with the Leishmania parasite. Also, these numbers were 52% and 35%, respectively, related to amastigotes outside of macrophages.Conclusion: The results contain a fairly high sensitivity, with the specificity being less satisfactory. High processing speed, ease of work, and low expenses are advantages of the presented method compared to other procedures. By adding a few adjustments, this method could be considered a viable option.

Investigation of Leishmania cutanenous Antigens in blood Specimens

The study was conducted on 444 patients suffering from skin lesions of different ages and genders that were diagnosed by dermatologists as cutaneous leishmaniasis for people reviewing and throughout the days of the week for the department of epidemiology and transitional diseases who attended to hospitals in the city of Baghdad, namely (Al-Shaheed Dari Al Fayyad Teaching Hospital , Al-Shaheed Al Sadr Hospital , Al-Numan Teaching Hospital, Al-Karama Teaching Hospital) for the study period from October 1, 2019 until the end of March 2020. The methods of molecular diagnostics showed that multiplex pcr examined40 random samples of the parasite that were collected throughthis technique from blood samples taken from the ulcers of infectedpeople to reveal the DNA parts of the Leishmania parasite. Specialprefixes for the Leishmania parasite species showed that 28 positive samplescontain the parasite's DNA and 12 negative samples, of which 11 are forthe Leishmania tropica type (27.5%) and 17 samples for the Leishmania major type(42.5%) at the molecular weight 1150 base pair. 30 serum samples were taken from patients diagnosed with cutaneous leishmaniasis for serological tests. The current study showed a significant decrease in immunoglobulin G for patients with cutaneous leishmaniasis by 1205.100 ± 210.155 , The current results also showed a significant decrease in the level of immunoglobulin M in patients with cutaneous leishmaniasis by 90.366 ± 26.181