The polymorphism of cutaneous leishmaniasis (CL) complicates diagnosis in health care services because lesions may be confused with other dermatoses such as sporotrichosis, paracocidiocomycosis, and venous insufficiency. Automated identification of skin diseases based on deep learning (DL) has been applied to assist diagnosis. In this study, we evaluated the performance of AlexNet, a DL algorithm, to identify pictures of CL lesions in patients from Midwest Brazil. We used a set of 2458 pictures (up to 10 of each lesion) obtained from patients treated between 2015 and 2022 in the Leishmaniasis Clinic at the University Hospital of Brasilia. We divided the picture database into training (80%), internal validation (10%), and testing sets (10%), and trained and tested AlexNet to identify pictures of CL lesions. We performed three simulations and trained AlexNet to differentiate CL from 26 other dermatoses (e.g., chromomycosis, ecthyma, venous insufficiency). We obtained an average accuracy of 95.04% (Confidence Interval 95%: 93.81–96.04), indicating an excellent performance of AlexNet in identifying pictures of CL lesions. We conclude that automated CL identification using AlexNet has the potential to assist clinicians in diagnosing skin lesions. These results contribute to the development of a mobile application to assist in the diagnosis of CL in health care services.

Digital technologies and artificial intelligence (AI) have become integral in many fields, including medicine. Neglected tropical diseases transmitted by vectors, such as arboviral diseases, spotted fever, Chagas disease, and leishmaniasis, pose a significant impact on public health, particularly in the Americas. Strengthening surveillance and control requires the use of digital technology to identify vectors. In this study, we explored how AI can aid in identifying vectors in the Americas and strengthen disease surveillance and control efforts. We reviewed the literature on the automated identification of triatomines, mosquitoes, sand flies, and ticks, focusing on advances in the Americas over the last 10 years, and provided a critical analysis of the automated identification systems for each group. Moreover, we analyzed the development stages of each study: image acquisition, image processing, algorithm training, algorithm testing, app development, app availability, and AI-based devices for vector identification and surveillance. Most studies have applied AI to identify mosquito species. The vector species databases were not diverse, and the most representative group was Triatominae, comprising 65 species (41% of all described species). Currently, approximately 30 algorithms are used for automated vector identification, with the most common being AlexNet, MobileNet, and ResNet. Most studies are in the algorithm training stage, and in the Americas, only one study has progressed to the development of applications or devices. These results highlight the potential of AI for identifying vectors in the Americas, supporting the use of automated visual identification systems as a promising approach to improve vector surveillance, while also promoting citizen science.

Ticks are key ectoparasites for the One Health approach, as they are vectors of pathogens that infect humans, domestic and wild animals. The bacteria Rickettsia rickettsii and R. parkeri are the aetiological agents of tick‐borne spotted fever (SF) in South America, where Amblyomma sculptum , A. aureolatum , A. ovale and A. triste are the main vectors. Studies in the medical and biological fields show that artificial intelligence, through machine learning, has great potential to assist researchers and health professionals in image identification practices. The aim of this study was to evaluate the performance of the Convolutional Neural Networks (CNN) AlexNet, ResNet‐50 and MobileNetV2 for identifying tick species transmitting SF bioagents. We organised an image database with the following groups: females (368), males (458), dorsal (423), ventral (403), low resolution (328), high resolution (498) and all together (sex+position+resolution = 826), to identify the three main vectors of SF bioagents ( Amblyomma aureolatum , A. ovale and A. sculptum ), two other possible vectors ( A. triste and A. dubitatum ) and the species A. cajennense sensu stricto (s.s.), which has similar morphology to A. sculptum but no known vectorial capacity. To evaluate the network's performance, we measured accuracy, sensitivity and specificity. We used Grad‐CAM to highlight the regions of the images most relevant to the predictions. CNNs achieved accuracy rates of ~90% in identifying ticks and showed sensitivities of 59%–100% according to species, sex, position or image resolution. When considering all images, both AlexNet and MobileNetV2 recorded the best sensitivity and specificity values in identifying SF vectors. The most relevant areas for classifying species varied according to algorithms. Our results support the idea of using CNNs for the automated identification of tick species transmitting SF bioagents in South America. Our database could support the development of tick identification apps to aid public health surveillance and contribute to citizen science.

As doenças cardiovasculares estão entre as comorbidades mais complexas associadas ao pior prognóstico da infecção por COVID-19. Os pacientes que possuem fatores de risco cardíacos como, por exemplo, idosos, hipertensivos e diabéticos, parecem apresentar uma probabilidade maior de adquirir a COVID-19, podendo demonstrar uma gravidade aumentada e possíveis sequelas. Este estudo tem por objetivo expor através da análise de artigos científicos a correlação entre a infecção por SARS-CoV-2 e as patologias do sistema cardiovascular. Trata-se de uma revisão bibliográfica de caráter qualitativo sistemático, realizada entre os anos de 2019 a 2021 nos idiomas português, inglês e espanhol. Os resultados deste estudo podem contribuir de forma significativa para atuação dos profissionais de saúde, de forma geral, evidenciando as altas taxas de morbimortalidade da COVID-19 nos indivíduos com algum tipo de problema cardiovascular. Os principais desfechos fatais mostram que as medidas de prevenção devem ser mais bem planejadas, considerando a realidade das cardiopatias no Brasil, e que as situações clínicas sejam conduzidas de forma rigorosa, além de promover dados para atualização de protocolos que diminuam o número de casos que evoluam para o óbito. De forma geral, todos os indivíduos que apresentam a infecção por COVID-19 podem desenvolver comprometimento do coração, em consequência de respostas inflamatórias, antecedentes de outras doenças crônicas e distúrbios bioquímicos. São necessários mais estudos para elucidar, de forma nítida, a correlação entre as cardiopatias e a infecção por SARS-CoV-2, sendo necessário o desenvolvimento de pesquisas mais específicas, e protocolos para o manejo de pacientes com doenças cardiovasculares.

Objective: mapping of the distribution of Aedes spp. in the urban area of the municipality of Picos, Piauí associating the climate variables and identification the dengue virus serotypes circulating in Aedes aegypti mosquito populations. Methods: Thirty larvitraps were installed in 10 neighborhoods in the urban area of the municipality. Larvae were collected weekly during the period from February 2017 to July 2019, and the collected material was taken to laboratory for identification and counting. The number of larvae were associated with meteorological data. In adult stage, the A. aegypti mosquitoes were segregated into pools containing six specimens and were then subjected to RT-PCR and Nested-PCR assay for detection of the DENV serotype. Results: a total of 44,798 Aedes spp. larvae were collected in the urban area of Picos during study period. An increase in precipitation and decrease in temperature in the months of February and March contributed to the increase in infestation of these vectors in April. In the study, two larvae of Haemagogus spegazzinii, a potential vector of yellow fever, were collected. The highest occurrence in the municipality was of the species A. aegypti (96.43%) present in all neighborhoods studied with predominance in domiciliary environment, while A. albopictus (3.57%) occur in the peridomiciliary environment. Molecular analyzes of the A. aegypti were positive for DENV 1 and DENV 3 viruses, demonstrating the existence of transovarial transmission. Conclusion: This study showed the simultaneous occurrence of A. aegypti and A. albopictus in the urban area and the co-circulation of dengue serotypes 1 and 3. Entomological and viral circulation monitoring are important tools to indicate areas of greater infestation of vector mosquitoes and prevent future outbreaks, providing results for the actions aimed at the control and prevention of arboviruses, through the secretary of health and competent bodies in the region.

Background/Objectives: American Tegumentary leishmaniasis typically presents as a single ulcerative lesion, but atypical clinical forms exist with diverse physiopathology and treatment responses. Disseminated cutaneous leishmaniasis is a less common presentation that is often challenging to treat and for which evidence-based clinical decisions are scant. A better understanding of treatment response may support the development of alternative therapeutic strategies. This study reports a retrospective cohort analysis using a time-to-event outcome to compare disseminated leishmaniasis with localized cutaneous leishmaniasis using a multivariate model. Methods: A total of 366 patients were screened for inclusion between 2014 and 2024, of whom 123 met the inclusion criteria. Results: After adjustment for age and sex, patients with disseminated cutaneous leishmaniasis had a significantly longer time to healing than those with localized cutaneous leishmaniasis (HR = 0.29; p = 0.021). Conclusions: Disseminated cutaneous leishmaniasis seems to be an important predictor of poorer clinical outcomes with prolonged healing time. New therapeutic strategies are urgently needed to address this issue.