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Leptospirosis is a widespread disease throughout the world, presenting in severe clinical forms in dogs. The pathogenicity of the different serovars in field infections is not fully documented, and clinical diagnosis is often limited to a combination of serological tests and molecular analyses. The latter, although a fundamental tool, cannot identify the infecting strain without further analysis.

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Little is known about the epidemiology of leptospirosis in the Dominican Republic, the second most populous country in the Caribbean. We report on findings from a multi-stage household survey across two regions in the country that reveals a previously under-estimated burden of human Leptospira infection. Our findings, based on the reference-standard microscopic agglutination test, indicate a complex picture of serogroup diversity, spatial heterogeneity in infection and risk, and a marked discrepancy between reported cases and serologically estimated infections.

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Rationale: The mortality rate associated with pulmonary hemorrhage induced by leptospirosis is notably high. Available treatment modalities are limited, and their efficacy has not been fully demonstrated. Here, we present the case report of a patient with leptospirosis-induced pulmonary hemorrhagic syndrome.

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This case report describes the unusual presentation of a 32-year-old male from Guayaquil, Ecuador, who was diagnosed with a rare triple infection caused by , , and . The patient presented with persistent high fever, severe gastrointestinal symptoms, abdominal pain, and jaundice, following the consumption of street food in a resource-limited area. Important clinical findings included hepatosplenomegaly and elevated liver enzymes, which initially complicated the differential diagnosis.

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Introduction Leptospirosis is a zoonotic infection caused by bacteria, which is reemerging in various regions and often poses a diagnostic challenge due to its nonspecific symptoms. While most infections are mild, severe cases occur in 5-10% of patients and are associated with high mortality, especially in areas with poor sanitation and urbanization. This study aims to investigate the association of specific parameters with leptospirosis diagnosis using a machine learning model and geographic mapping tools to identify spatial patterns and high-risk areas for the disease.

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