We show that the bovid-associated parasite Trypanosoma evansi is endemic in Vietnam and has zoonotic potential. Our study describes the first laboratory-confirmed human case of T. evansi in a previously healthy individual without apolipoprotein L1 deficiency.

Brucellosis is a collective term for infections caused by small Gram-negative coccobacilli belonging to genus Brucella. This genus incorporates the well-described animal pathogens Brucella melitensis, Brucella abortus, Brucella ovis, Brucella suis, and Brucella canis, which are associated with disease in goats, cattle, sheep, pigs, and dogs, respectively. Brucella are facultative intracellular pathogens, and are sequestered by monocytes and macrophages, spreading throughout the body to the liver, spleen, lymph nodes, and bone marrow [1]. These pathogens are synonymous with an aggressive disease syndrome in animals causing abortion, stillbirth, and the delivery of weak offspring. The organisms replicate to high concentrations in the affected tissues and are transmitted through contact with the placenta, foetus, foetal fluids, and vaginal discharge. Notably, goats can shed B. melitensis in vaginal discharge for up to 3 months after abortion and organisms can be shed in milk for the lifetime of an infected animal [2]. Many Brucella species have zoonotic potential and can be transmitted from animals to humans. Brucellosis in humans is typically contracted by contact with infected animals or through the ingestion of animal products prepared from infected animals. In symptomatic cases, disease presentation is highly variable and may arise rapidly or progressively. Classically, brucellosis in humans is a sub-acute, non-specific febrile disease, characterized by high temperatures, headaches, malaise, night sweats, and body aches [3]. Some individuals recover quickly, while others develop more persistent, long-term complications including arthritis, spondylitis, endocarditis, dermatitis, and chronic fatigue, and neurological complications [3]. The disease is treated using antimicrobials; however, relapses are common, even after apparent bacteriological cure. Many middle-high income countries employ successful control programmes to reduce brucellosis in animals and humans. However, such control programmes or surveillance infrastructures are less common in low-middle income countries (LMICs). Consequently, animal brucellosis is endemic in parts of Asia, the Middle East, East and North Africa, Latin America, and some southern and

Background Interpreting point-of-care lung ultrasound (LUS) images from intensive care unit (ICU) patients can be challenging, especially in low- and middle- income countries (LMICs) where there is limited training available. Despite recent advances in the use of Artificial Intelligence (AI) to automate many ultrasound imaging analysis tasks, no AI-enabled LUS solutions have been proven to be clinically useful in ICUs, and specifically in LMICs. Therefore, we developed an AI solution that assists LUS practitioners and assessed its usefulness in  a low resource ICU. Methods This was a three-phase prospective study. In the first phase, the performance of four different clinical user groups in interpreting LUS clips was assessed. In the second phase, the performance of 57 non-expert clinicians with and without the aid of a bespoke AI tool for LUS interpretation was assessed in retrospective offline clips. In the third phase, we conducted a prospective study in the ICU where 14 clinicians were asked to carry out LUS examinations in 7 patients with and without our AI tool and we interviewed the clinicians regarding the usability of the AI tool. Results The average accuracy of beginners’ LUS interpretation was 68.7% [95% CI 66.8–70.7%] compared to 72.2% [95% CI 70.0–75.6%] in intermediate, and 73.4% [95% CI 62.2–87.8%] in advanced users. Experts had an average accuracy of 95.0% [95% CI 88.2–100.0%], which was significantly better than beginners, intermediate and advanced users (p < 0.001). When supported by our AI tool for interpreting retrospectively acquired clips, the non-expert clinicians improved their performance from an average of 68.9% [95% CI 65.6–73.9%] to 82.9% [95% CI 79.1–86.7%], (p < 0.001). In prospective real-time testing, non-expert clinicians improved their baseline performance from 68.1% [95% CI 57.9–78.2%] to 93.4% [95% CI 89.0–97.8%], (p < 0.001) when using our AI tool. The time-to-interpret clips improved from a median of 12.1 s (IQR 8.5–20.6) to 5.0 s (IQR 3.5–8.8), (p < 0.001) and clinicians’ median confidence level improved from 3 out of 4 to 4 out of 4 when using our AI tool. Conclusions AI-assisted LUS can help non-expert clinicians in an LMIC ICU improve their performance in interpreting LUS features more accurately, more quickly and more confidently.