RESUMEN
Leishmaniases are a group of neglected vector-borne infectious diseases that are among the six priority endemic diseases worldwide. Visceral leishmaniasis (VL) is the most severe clinical manifestation, characterized by systemic and chronic visceral involvement and high mortality in immunosuppressed and untreated patients. VL can be complicated into post-kala-azar dermal leishmaniasis (PKDL), and when dermatologic disorders occur simultaneously with active VL, an intermediate clinical form called para-kala-azar dermal leishmaniasis (para-KDL) occurs. This clinical form is of great epidemiological relevance, as humans act as a source of infection for vectorial transmission. In the Americas, Brazil is among the seven countries responsible for more than 90% of VL cases, though reports of PKDL and para-KDL are rare. This paper presents three cases of VL-HIV co-infection with Leishmania-containing skin lesions resembling para-kala-azar dermal leishmaniasis. The cases were investigated by the team from the Infectious Diseases Department of University Hospital (HUMAP/UFMS) in Mato Grosso do Sul, Brazil. The three patients exhibited skin lesions where amastigote forms of L. (L.) infantum were identified. All cases exhibited similar clinical manifestations of para-KDL, including fever, hepatosplenomegaly, pancytopenia, and disseminated skin lesions. The study described the prevalence of comorbidities, the incidence of VL relapse, and the therapeutic regimen in relation to the outcomes. The study underscores the importance of follow-up and secondary prophylaxis in patients with VL, which are essential for the efficacy of the treatment. Furthermore, the study provides insight into the potential epidemiological profile of para-KDL cases in Brazil, which contributes to the development of more efficient clinical management strategies for patients.
Asunto(s)
Coinfección , Infecciones por VIH , Leishmaniasis Cutánea , Leishmaniasis Visceral , Humanos , Leishmaniasis Visceral/complicaciones , Leishmaniasis Visceral/epidemiología , Leishmaniasis Visceral/tratamiento farmacológico , Masculino , Infecciones por VIH/complicaciones , Adulto , Coinfección/parasitología , Coinfección/epidemiología , Brasil/epidemiología , Leishmaniasis Cutánea/epidemiología , Leishmaniasis Cutánea/complicaciones , Femenino , Leishmania infantum/aislamiento & purificación , Piel/patología , Piel/parasitología , Persona de Mediana EdadRESUMEN
Cutaneous leishmaniasis (CL) is a polymorphic and spectral skin disease caused by Leishmania spp. protozoan parasites. CL is difficult to diagnose because conventional methods are time-consuming, expensive, and low-sensitive. Fourier transform infrared spectroscopy (FTIR) with machine learning (ML) algorithms has been explored as an alternative to achieve fast and accurate results for many disease diagnoses. Besides the high accuracy demonstrated in numerous studies, the spectral variations between infected and noninfected groups are too subtle to be noticed. Since variability in sample set characteristics (such as sex, age, and diet) often leads to significant data variance and limits the comprehensive understanding of spectral characteristics and immune responses, we investigate a novel methodology for diagnosing CL in an animal model study. Blood serum, skin lesions, and draining popliteal lymph node samples were collected from Leishmania (Leishmania) amazonensis-infected BALB/C mice under experimental conditions. The FTIR method and ML algorithms accurately differentiated between infected (CL group) and noninfected (control group) samples. The best overall accuracy (â¼72%) was obtained in an external validation test using principal component analysis and support vector machine algorithms in the 1800-700 cm-1 range for blood serum samples. The accuracy achieved in analyzing skin lesions and popliteal lymph node samples was satisfactory; however, notable disparities emerged in the validation tests compared to results obtained from blood samples. This discrepancy is likely attributed to the elevated sample variability resulting from molecular compositional differences. According to the findings, the successful functioning of prediction models is mainly related to data analysis rather than the differences in the molecular composition of the samples.
Asunto(s)
Leishmania , Leishmaniasis Cutánea , Animales , Ratones , Espectroscopía Infrarroja por Transformada de Fourier , Ratones Endogámicos BALB C , Leishmaniasis Cutánea/diagnóstico , Leishmaniasis Cutánea/parasitología , Modelos Animales , Aprendizaje AutomáticoRESUMEN
Lutzomyia longipalpis and Lutzomyia cruzi are the main sandflies species involved in the transmission of Leishmania infantum protozoan in Brazil. The morphological characteristics can be used for species identification of males specimens, while females are indistinguishable. Although, sandflies identification is essential to understand vectorial capacity, and susceptibility to infectious agents or insecticides, there is a lack of new strategies for specimen identification. In this study, Fourier transform infrared photoacoustic spectroscopy combined with multivariate analysis identified intraspecific differences between Lutzomyia populations. Successfully group clustering was achieved by principal component analysis. The main differences observed can be related to the protein content of the specimens. A classification with 100% accuracy was obtained using machine learning approach, allowing the identification of sandflies specimens.