Abstract Background: Real-world, primary data on the treatment of psoriasis are scarce, especially concerning the role of soluble biomarkers as outcome predictors. Objective: The authors evaluated the utility of Th1/Th17 serum cytokines along with clinical characteristics as predictors of drug survival in the treatment of psoriasis. Methods: The authors consecutively included participants with moderate to severe psoriasis who were followed up for 6 years. Baseline interferon-α, tumor necrosis factor-α, and inter-leukin (IL)-2, IL-4, IL-6, IL-10, and IL-17A were measured using a cytometric bead array; clinical data were assessed. The authors calculated hazard ratios (HRs) for drug survival using a Cox proportional hazards model. Results: The authors included 262 patients, most of whom used systemic immunosuppressants or biologics. In the multivariate model, poor quality of life measured by the Dermatology Life Quality Index (HR = 1.04; 95% CI 1.01–1.07; p = 0.012) and elevated baseline IL-6 (HR = 1.99; 95% CI 1.29–3.08; p = 0.002) were associated with treatment interruption. Study limitations: The main limitation of any cohort study is the presence of confounders that could not be detected in clinical evaluation. Conclusions: Poor quality of life and elevated baseline serum IL-6 level predicted treatment interruption in patients with moderate to severe psoriasis. Although IL-6 is not the most important mediator of the inflammatory pathway in the skin environment, it is an interesting biomarker candidate for predicting psoriasis treatment response. Background Realworld, Realworld Real world, world Real-world scarce Objective Th1Th17 ThTh Th1 Th17 Th Th1/Th1 Methods years interferonα, interferonα interferon α, α interferon-α factorα, factorα factor factor-α interleukin inter leukin IL2, IL2 IL 2, 2 (IL)-2 IL4, IL4 4, 4 IL-4 IL6, IL6 6, IL10, IL10 10, 10 IL-10 IL17A ILA 17A A array assessed HRs (HRs model Results 26 biologics HR 1.04 104 1 04 95 1.01–1.07 101107 01 07 0.012 0012 0 012 IL- 1.99 199 99 1.29–3.08 129308 29 3 08 0.002 0002 002 limitations evaluation Conclusions environment response Th1Th1 Th1/Th (IL)- IL1 IL-1 1.0 9 1.01–1.0 10110 0.01 001 1.9 19 1.29–3.0 12930 0.00 000 00 Th1Th (IL) 1. 1.01–1. 1011 0.0 1.29–3. 1293 (IL 1.01–1 101 0. 1.29–3 129 1.01– 1.29– 12 1.01 1.29 1.2

Abstract: Brazil has the second largest number of leprosy cases (a disease with a significant burden) in the world. Despite global and local efforts to eliminate this public health problem, inadequate or late diagnosis contribute to perpetuate its transmission, especially among household contacts. Tests such as the rapid IgM antibody detection (RT) and real-time polymerase chain reaction (RT-PCR) were developed to overcome the challenges of early diagnosis of leprosy. This study aimed to analyze the cost-effectiveness of a new diagnostic algorithm recommended by the Brazilian government to diagnose leprosy in household contacts of confirmed leprosy cases, which includes the RT and RT-PCR tests. A decision tree model was constructed and the perspective of the Brazilian Unified National Health System (SUS) and a 1-year time horizon were adopted. Only direct medical costs related to diagnostic tests were included. Effectiveness was measured as the number of avoided undiagnosed leprosy cases. Different scenarios were analyzed. The sequential use of RT, slit-skin smear (SSS) microscopy, and RT-PCR as recommended by the Brazilian Ministry of Health was compared to a base case (isolated SSS microscopy), yielding an incremental cost-effectiveness ratio of USD 616.46 per avoided undiagnosed leprosy case. Univariate sensitivity analysis showed that the prevalence of leprosy among household contacts was the variable that influenced the model the most. This is the first economic model to analyze a diagnostic algorithm of leprosy. Results may aid managers to define policies and strategies to eradicate leprosy in Brazil.

Resumen: Brasil tiene el segundo mayor número de casos de lepra (enfermedad con carga significativa) del mundo. A pesar de los esfuerzos globales y locales para eliminar ese problema de salud pública, el diagnóstico inadecuado o tardío contribuye a perpetuar su transmisión, sobre todo entre contactos intradomiciliarios. Los exámenes como la prueba rápida de anticuerpos IgM (RT) y la reacción en cadena de la polimerasa en tiempo real (RT-PCR) se desarrollaron para superar las barreras del diagnóstico precoz de la lepra. El objetivo de este estudio fue analizar la relación de costo-efectividad de un nuevo algoritmo de diagnóstico recomendado por el gobierno brasileño para diagnosticar la lepra en contactos domiciliarios de casos confirmados de lepra, que incluye las pruebas RT y RT-PCR. Se construyó un modelo de árbol de decisión y se adoptó la perspectiva del Sistema Único de Salud (SUS) teniendo en cuenta el periodo de un año. Solo se incluyeron los costos médicos directos relacionados con los exámenes diagnósticos. Se midió la efectividad teniendo en cuenta el número de casos de lepra evitados. Se analizaron distintos escenarios. Se comparó el uso secuencial de RT, baciloscopia y RT-PCR, conforme el Ministerio de Salud recomienda, con un caso base (baciloscopia aislada), y se obtuvo un cociente de costo-efectividad incremental de USD 616,46 por cada caso de lepra evitado. El análisis de sensibilidad univariante mostró que la prevalencia de lepra entre contactos intradomiciliarios fue la variable que más influyó el modelo. Este es el primer modelo económico que analiza un algoritmo diagnóstico de lepra. Los resultados podrán ayudar los gestores a definir políticas y estrategias para erradicar la lepra en Brasil.

Resumo: O Brasil tem o segundo maior número de casos de hanseníase (doença com carga significativa) do mundo. Apesar dos esforços globais e locais para eliminar esse problema de saúde pública, o diagnóstico inadequado ou tardio contribui para perpetuar sua transmissão, especialmente entre contatos intradomiciliares. Exames como o teste rápido de anticorpos IgM (RT) e a reação em cadeia da polimerase em tempo real (RT-PCR) foram desenvolvidos para superar as barreiras do diagnóstico precoce da hanseníase. Este estudo teve como objetivo analisar a relação custo-efetividade de um novo algoritmo de diagnóstico recomendado pelo governo brasileiro para diagnosticar a hanseníase em contatos domiciliares de casos confirmados de hanseníase, que inclui os testes RT e RT-PCR. Foi construído um modelo de árvore de decisão e adotada a perspectiva do Sistema Único de Saúde (SUS) considerando o período de um ano. Foram incluídos apenas os custos médicos diretos relacionados aos exames diagnósticos. A efetividade foi medida considerando o número de casos evitados de hanseníase. Diferentes cenários foram analisados. O uso sequencial de RT, baciloscopia e RT-PCR, conforme recomendado pelo Ministério da Saúde, foi comparado a um caso base (baciloscopia isolada), obtendo-se uma razão de custo-efetividade incremental de USD 616,46 por caso evitado de hanseníase. A análise de sensibilidade univariada mostrou que a prevalência de hanseníase entre contatos intradomiciliares foi a variável que mais influenciou o modelo. Este é o primeiro modelo econômico a analisar um algoritmo diagnóstico da hanseníase. Os resultados poderão auxiliar os gestores na definição de políticas e estratégias para a erradicação da hanseníase no Brasil.

Resumo: O Brasil tem o segundo maior número de casos de hanseníase (doença com carga significativa) do mundo. Apesar dos esforços globais e locais para eliminar esse problema de saúde pública, o diagnóstico inadequado ou tardio contribui para perpetuar sua transmissão, especialmente entre contatos intradomiciliares. Exames como o teste rápido de anticorpos IgM (RT) e a reação em cadeia da polimerase em tempo real (RT-PCR) foram desenvolvidos para superar as barreiras do diagnóstico precoce da hanseníase. Este estudo teve como objetivo analisar a relação custo-efetividade de um novo algoritmo de diagnóstico recomendado pelo governo brasileiro para diagnosticar a hanseníase em contatos domiciliares de casos confirmados de hanseníase, que inclui os testes RT e RT-PCR. Foi construído um modelo de árvore de decisão e adotada a perspectiva do Sistema Único de Saúde (SUS) considerando o período de um ano. Foram incluídos apenas os custos médicos diretos relacionados aos exames diagnósticos. A efetividade foi medida considerando o número de casos evitados de hanseníase. Diferentes cenários foram analisados. O uso sequencial de RT, baciloscopia e RT-PCR, conforme recomendado pelo Ministério da Saúde, foi comparado a um caso base (baciloscopia isolada), obtendo-se uma razão de custo-efetividade incremental de USD 616,46 por caso evitado de hanseníase. A análise de sensibilidade univariada mostrou que a prevalência de hanseníase entre contatos intradomiciliares foi a variável que mais influenciou o modelo. Este é o primeiro modelo econômico a analisar um algoritmo diagnóstico da hanseníase. Os resultados poderão auxiliar os gestores na definição de políticas e estratégias para a erradicação da hanseníase no Brasil. Resumo doença significativa mundo pública transmissão (RT RTPCR PCR (RT-PCR custoefetividade custo RTPCR. PCR. RT-PCR SUS (SUS ano diagnósticos analisados RTPCR, PCR, isolada, isolada , isolada) obtendose obtendo se 61646 616 46 616,4 6164 61 4 616, 6

Abstract: Brazil has the second largest number of leprosy cases (a disease with a significant burden) in the world. Despite global and local efforts to eliminate this public health problem, inadequate or late diagnosis contribute to perpetuate its transmission, especially among household contacts. Tests such as the rapid IgM antibody detection (RT) and real-time polymerase chain reaction (RT-PCR) were developed to overcome the challenges of early diagnosis of leprosy. This study aimed to analyze the cost-effectiveness of a new diagnostic algorithm recommended by the Brazilian government to diagnose leprosy in household contacts of confirmed leprosy cases, which includes the RT and RT-PCR tests. A decision tree model was constructed and the perspective of the Brazilian Unified National Health System (SUS) and a 1-year time horizon were adopted. Only direct medical costs related to diagnostic tests were included. Effectiveness was measured as the number of avoided undiagnosed leprosy cases. Different scenarios were analyzed. The sequential use of RT, slit-skin smear (SSS) microscopy, and RT-PCR as recommended by the Brazilian Ministry of Health was compared to a base case (isolated SSS microscopy), yielding an incremental cost-effectiveness ratio of USD 616.46 per avoided undiagnosed leprosy case. Univariate sensitivity analysis showed that the prevalence of leprosy among household contacts was the variable that influenced the model the most. This is the first economic model to analyze a diagnostic algorithm of leprosy. Results may aid managers to define policies and strategies to eradicate leprosy in Brazil. Abstract burden world problem transmission (RT realtime real RTPCR PCR (RT-PCR costeffectiveness cost effectiveness SUS (SUS 1year year 1 adopted included analyzed slitskin slit skin (SSS microscopy isolated , microscopy) 61646 616 46 616.4 most 6164 61 4 616. 6

Resumen: Brasil tiene el segundo mayor número de casos de lepra (enfermedad con carga significativa) del mundo. A pesar de los esfuerzos globales y locales para eliminar ese problema de salud pública, el diagnóstico inadecuado o tardío contribuye a perpetuar su transmisión, sobre todo entre contactos intradomiciliarios. Los exámenes como la prueba rápida de anticuerpos IgM (RT) y la reacción en cadena de la polimerasa en tiempo real (RT-PCR) se desarrollaron para superar las barreras del diagnóstico precoz de la lepra. El objetivo de este estudio fue analizar la relación de costo-efectividad de un nuevo algoritmo de diagnóstico recomendado por el gobierno brasileño para diagnosticar la lepra en contactos domiciliarios de casos confirmados de lepra, que incluye las pruebas RT y RT-PCR. Se construyó un modelo de árbol de decisión y se adoptó la perspectiva del Sistema Único de Salud (SUS) teniendo en cuenta el periodo de un año. Solo se incluyeron los costos médicos directos relacionados con los exámenes diagnósticos. Se midió la efectividad teniendo en cuenta el número de casos de lepra evitados. Se analizaron distintos escenarios. Se comparó el uso secuencial de RT, baciloscopia y RT-PCR, conforme el Ministerio de Salud recomienda, con un caso base (baciloscopia aislada), y se obtuvo un cociente de costo-efectividad incremental de USD 616,46 por cada caso de lepra evitado. El análisis de sensibilidad univariante mostró que la prevalencia de lepra entre contactos intradomiciliarios fue la variable que más influyó el modelo. Este es el primer modelo económico que analiza un algoritmo diagnóstico de lepra. Los resultados podrán ayudar los gestores a definir políticas y estrategias para erradicar la lepra en Brasil. Resumen enfermedad significativa mundo pública transmisión (RT RTPCR PCR (RT-PCR costoefectividad costo RTPCR. PCR. RT-PCR SUS (SUS año diagnósticos evitados escenarios RTPCR, PCR, recomienda aislada, aislada , aislada) 61646 616 46 616,4 evitado 6164 61 4 616, 6

ABSTRACT Introduction: Antimicrobial resistance in leprosy is an emerging problem, and the quantitative impact of low bacilloscopic indexes (BIs) on the sensitivity of molecular tests is unknown. We aimed to evaluate the sensitivity of gene sequencing for the detection of mutations related to antimicrobial resistance in Mycobacterium leprae in patients with low BIs using an analytical model. Methods: Patients with leprosy were included and divided into two groups depending on their BIs (≥ 2+ and < 2+). The sensitivities of the two DNA extraction methods were compared after amplifying and sequencing the repetitive element (RLEP), folP1, rpoB and gyrA in M. leprae. Results: We included 56 patients with leprosy: 35 had BIs less than 2+ (22 had negative slit-skin smear [SSS] results) and 21 patients had BIs greater than or equal to 2+. The sensitivity of the amplification of the RLEP target and the gene sequencing of folP1, rpoB and gyrA was 50 to 70% lower in patients with a BI less than 2+ and was significantly reduced in patients with lower BIs for all targets (p < 0.001). One patient had a mutation in the folP1 gene, and 14 patients had mutations in the gyrA gene, but no mutations related to antimicrobial resistance were found. Conclusions: We can conclude that the sensitivity of molecular tests is directly related to the BI, but these tests can still detect up to 20% of the targets in patients with BIs < 2+. New strategies to improve the sensitivity for detecting antimicrobial resistance in leprosy patients and reasonable clinical criteria for follow-up and the introduction of alternative treatments must be developed.

Abstract Non-tuberculous mycobacteriosis, previously known as atypical, anonymous, opportunistic, or unclassified mycobacteriosis, refers to pathogenic mycobacterioses other than those caused by Mycobacterium tuberculosis and Mycobacterium leprae. These mycobacteria are known for their environmental distribution, mainly in water and soil. The incidence of non-tuberculous mycobacteriosis has been increasing in all countries and skin infections are being increasingly studied, mainly with the increase in immunosuppressive conditions and the development of new medications that affect immunological function. In the present article, a detailed narrative review of the literature is carried out to study the main non-tuberculous mycobacteriosis that cause diseases of the skin and appendages. The article also aims to present a historical context, followed by epidemiological, microbiological, and clinical characteristics of these diseases. Practical considerations about the diagnosis and treatment of non-tuberculous mycobacteriosis are detailed.

Abstract A 39-year-old woman was diagnosed with relapsed multibacillary leprosy and refractory neuritis. Here, we describe an evident loss of therapeutic effectiveness after the third pulse of corticosteroids, which may be attributed to tachyphylaxis and the posterior modulation of interferon- γ (IFN-γ), tumor necrosis factor- α (TNF-α,) interleukin-17A (IL-17A), and IL-12/23p40 after the induction phase of secukinumab. In this case, plasma cytokine analysis showed that secukinumab induced a reduction in IL-17 concomitant with impressive clinical improvements in the patient’s neural function. Interestingly, secukinumab induced reductions in cytokines related to Th1 responses and earlier stages of the Th17 response, including IL-23/12.

Abstract INTRODUCTION: Although supervised doses are essential for reducing leprosy treatment failure, the impact of specific drug interactions has rarely been assessed. This study aimed to estimate the risk of leprosy treatment suspension in patients receiving polypharmacy. METHODS We performed this case-control study in which the primary outcome was defined as the need to discontinue multibacillary leprosy treatment for at least one supervised dose, and the main risk factor was the detection of polypharmacy. Multivariate analysis by logistic regression was used for calculating odds ratio (OR). RESULTS: This study included 103 patients, of whom 43 needed to discontinue leprosy treatment (hemolysis = 26, hepatitis = 2, hemolysis associated with hepatitis = 6, and suspected treatment resistance = 9) and the rest did not. The severity of drug interactions had no effect on treatment discontinuation. Patients who used five or more drugs in addition to leprosy treatment had almost a 4-fold greater risk of treatment suspension (OR, 3.88; 95% confidence interval: 1.79-9.12; p < 0.001). The number of drugs used also positively influenced the occurrence of hemolysis (p < 0.001). No patient presented evidence of molecular resistance to rifampicin, dapsone, or ofloxacin treatment, as evidenced by genetic sequencing detection of rpoB, folp1, and gyrA mutations. CONCLUSIONS: Polypharmacy has deleterious effects on the already difficult-to-adhere-to treatment of leprosy and polypharmacy induces hemolysis. Additional measures must be taken to avoid the undesirable effects of inadequate polypharmacy.

Abstract INTRODUCTION: As highly specific molecular biology-based techniques may not be sensitive enough for the diagnosis of American tegumentary leishmaniasis (ATL), clinicians frequently rely on immunological tests before treatment initiation. Hence, the correct combination of diagnostic tests is imperative for ATL diagnosis. We aimed to evaluate the accuracy of the Montenegro (Leishmanin) skin test (MST) in polymerase chain reaction (PCR)-negative patients to accurately detect ATL. METHODS: Patients with a clinical picture compatible with ATL were divided into ATL (confirmed by lesion smear, culture indirect immunofluorescence, and/or histopathology) and no-ATL (diseases that can mimic leishmaniasis) groups. Conventional PCR for the minicircle kDNA of Leishmania was performed, and the MST was carried out for PCR-negative patients. RESULTS: Ninety-nine patients were included in this study, including 79 diagnosed with ATL (6 with mucocutaneous leishmaniasis) and 20 without ATL (no-ATL group). The MST showed a high sensitivity of 90.0% (95% confidence interval [CI] = 69.90-97.21) in PCR-negative patients that was 10% higher than the sensitivity reported in PCR-positive population (79.66%; 95% CI = 67.73-87.96). CONCLUSIONS: One of the most important reasons for PCR negativity among patients with active ATL is the presence of a strong cellular immunological response, especially in chronic and mucocutaneous leishmaniasis. This reinforces the considerable utility of the tests that detect cellular responses against Leishmania antigens such as the MST in PCR-negative patients when the performance in screening situations is questionable.

ABSTRACT Immunosuppression is an important risk factor for leishmaniasis. We assessed the clinical profile, geographic distribution and prevalence of leishmaniasis in patients undergoing immunosuppressive therapy for dermatological, rheumatological or gastroenterological autoimmune diseases. We identified relevant studies in PubMed, EMBASE, Scopus, Web of Science and LILACS on July 3rd, 2018. We included articles that reported at least one case of leishmaniasis in patients undergoing immunosuppressive treatment for dermatological, rheumatological or gastroenterological diseases. Our protocol was registered in PROSPERO (CRD42018103050). We assessed the quality of the included studies with the Joanna Briggs Institute Critical Appraisal Tool. After the removal of duplicates, 5,431 articles were collected and screened. We included 138 articles; the prevalence of leishmaniasis in six methodologically similar studies varied from three to 1,282 cases per 100,000 patients using anti-TNFα drugs, but the results were significantly heterogeneous . Leishmaniasis in patients treated with immunosuppressive drugs is a health problem mostly reported in European countries bordering the Mediterranean Sea; sporadic activities, such as travelling, seem not to be associated with a significant risk of leishmaniasis, although effective control measures must always be observed.

Abstract Coronavirus disease 2019 (COVID-19) was first officially described in Brazil on February 26th, 2020. The accumulation of reports of concomitant infections with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and pathogens that cause diseases endemic to tropical countries, such as dengue and chikungunya fever, has started to draw attention. Chagas disease and leprosy remain public health problems in many developing countries, such as Brazil. In this manuscript, we describe a case of concomitant leprosy, Chagas disease, and COVID-19, highlighting the cutaneous manifestations of SARS-CoV-2 infection and the clinical behavior of household contacts who previously received prophylactic Bacillus Calmette-Guérin vaccines.

RESUMO Deep Learning é uma técnica de aprendizado de máquina na qual o programa computacional aprende padrões diretamente a partir de imagens classificadas previamente. O presente ensaio objetivou apresentar essa técnica e algumas de suas aplicações para diagnóstico de doenças e identificação de insetos vetores para incentivar profissionais da saúde que não tenham conhecimento aprofundado em informática e que desejem utilizar a ferramenta para realizar análises automatizadas. Deep Learning tem sido aplicado para diagnóstico de câncer, fibrose cardíaca, tuberculose, detecção de parasitos como Plasmodium e Leishmania e ainda para identificação de insetos vetores. Na Universidade de Brasília, a técnica tem sido aplicada para desenvolver uma ferramenta para identificar lesões ulceradas de leishmaniose em diagnóstico diferencial e para detectar Leishmania em lâminas de estudos histopatológicos. Além disso, Deep Learning tem sido usado para identificar as espécies de vetores da doença de Chagas – o que é importante para auxiliar na vigilância epidemiológica. O uso da tecnologia envolve desafios éticos e procedimentais que são discutidos no presente ensaio. O ensaio aponta perspectivas de desenvolvimento de aplicativos que auxiliem os profissionais de saúde no diagnóstico de Leishmaniose e de vetores da doença de Chagas, o que vai ao encontro dos objetivos da pesquisa translacional.

ABSTRACT Deep Learning is a machine learning technique in which the computational algorithm learns patterns directly from images previously classified. The present essay aims to show some of its applications for clinical diagnosis and identification of insect vectors to encourage health professionals who do not have deep knowledge of computer science and who wish to use the tool to perform automated analyzes. Deep Learning has been applied to the diagnosis of cancer, cardiac fibrosis, tuberculosis, detection of parasites such as Plasmodium and Leishmania, and to identify insect vectors. At the University of Brasília, Deep Learning has been used to develop a tool to identify ulcers caused by leishmaniasis, as well as to detect Leishmania parasites. Moreover, Deep Learning was applied to identify the species of vectors of Chagas disease, an important contribution to the epidemiological surveillance of the disease. The use of Deep Learning involves some ethical and procedural issues that are discussed in this paper. Finally, the essay points out perspectives of development of apps that assist health professionals in the diagnosis of Leishmaniasis and Chagas disease vectors, which meets the goals of translational research.

RESUMO Deep Learning é uma técnica de aprendizado de máquina na qual o programa computacional aprende padrões diretamente a partir de imagens classificadas previamente. O presente ensaio objetivou apresentar essa técnica e algumas de suas aplicações para diagnóstico de doenças e identificação de insetos vetores para incentivar profissionais da saúde que não tenham conhecimento aprofundado em informática e que desejem utilizar a ferramenta para realizar análises automatizadas. Deep Learning tem sido aplicado para diagnóstico de câncer, fibrose cardíaca, tuberculose, detecção de parasitos como Plasmodium e Leishmania e ainda para identificação de insetos vetores. Na Universidade de Brasília, a técnica tem sido aplicada para desenvolver uma ferramenta para identificar lesões ulceradas de leishmaniose em diagnóstico diferencial e para detectar Leishmania em lâminas de estudos histopatológicos. Além disso, Deep Learning tem sido usado para identificar as espécies de vetores da doença de Chagas – o que é importante para auxiliar na vigilância epidemiológica. O uso da tecnologia envolve desafios éticos e procedimentais que são discutidos no presente ensaio. O ensaio aponta perspectivas de desenvolvimento de aplicativos que auxiliem os profissionais de saúde no diagnóstico de Leishmaniose e de vetores da doença de Chagas, o que vai ao encontro dos objetivos da pesquisa translacional.

ABSTRACT Deep Learning is a machine learning technique in which the computational algorithm learns patterns directly from images previously classified. The present essay aims to show some of its applications for clinical diagnosis and identification of insect vectors to encourage health professionals who do not have deep knowledge of computer science and who wish to use the tool to perform automated analyzes. Deep Learning has been applied to the diagnosis of cancer, cardiac fibrosis, tuberculosis, detection of parasites such as Plasmodium and Leishmania, and to identify insect vectors. At the University of Brasília, Deep Learning has been used to develop a tool to identify ulcers caused by leishmaniasis, as well as to detect Leishmania parasites. Moreover, Deep Learning was applied to identify the species of vectors of Chagas disease, an important contribution to the epidemiological surveillance of the disease. The use of Deep Learning involves some ethical and procedural issues that are discussed in this paper. Finally, the essay points out perspectives of development of apps that assist health professionals in the diagnosis of Leishmaniasis and Chagas disease vectors, which meets the goals of translational research.