To comply with the large global need for surgery, surgical equipment that fits the challenging environment in low-and middle-income countries (LMICs) should be designed. The aim of this study is to present a context-specific design of an electrosurgical unit (ESU) and a monopolar handheld to improve global access to surgery. This paper presents both a detailed description of electrosurgery in clinical practice in LMICs and the design of an ESU generator and monopolar handheld for this specific setting. Extensive fieldwork (by means of surveys, interviews, observations, and collection of maintenance records) was done by authors RO, KO, and LH. Feedback from users working in Kenya on the first demonstrator designs was obtained, after which the designs were adapted into conceptual prototypes. These were further evaluated by surveying respondents who attended the annual meeting of the College of Surgeons of East, Central, and Southern Africa (COSECSA) in Kigali, Rwanda in December 2018. Conceptual prototypes were developed for (a) an affordable ESU that is compact and battery powered and (b) a robust reusable monopolar handheld that can be cleaned in the autoclave and by chemicals (e.g., glutaraldehyde solution). The conceptual prototypes were positively received by the 51 respondents of the survey. The findings from the field work and the feedback from users during the design phase have led to a clear understanding of the specific needs and potential solutions. The presented conceptual prototypes need to be further developed into functional prototypes, which could be implemented in Kenya and other settings for further evaluation.

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We present a simple method for the diagnosis of urinary schistosomiasis using an in-line lensless holographic microscope combined with flow cytometry technique. Using simple image processing algorithms and binary image classifier, our system provides automated detection of Schistosoma haematobium eggs in infected urine samples. Registered hologram is reconstructed by applying backpropagation from sensor to sample plane and reconstructed image is automatically analysed for the presence of S. haematobium eggs. Designed for use in a resource-poor laboratory setting, our proposed method has been implemented using a Raspberry Pi computer. From pre-clinical test performed with human urine samples spiked with S. haematobium eggs (approximately 200 eggs per 12 ml of urine), we achieved a sensitivity and specificity of 50.6% and 98.6% respectively. Our proposed method requires no complex sample preparation methods making the system simple to operate and useable in point-of-care diagnosis of urinary schistosomiasis.This method can be optimized to complement existing diagnostic procedures for the detection of S. haematobium eggs and can be deployed to inaccessible remote areas.@en