Conectar
by John Efromson, Giuliano Ferrero, Aurélien Bègue, Thomas Jedidiah Jenks Doman, Clay Dugo, Andi Barker, Veton Saliu, Paul Reamey, Kanghyun Kim, Mark Harfouche, Jeffrey A. Yoder
Normal development of the immune system is essential for overall health and disease resistance. Bony fish, such as the zebrafish (Danio rerio), possess all the major immune cell lineages as mammals and can be employed to model human host response to immune challenge. Zebrafish neutrophils, for example, are present in the transparent larvae as early as 48 hours post fertilization and have been examined in numerous infection and immunotoxicology reports. One significant advantage of the zebrafish model is the ability to affordably generate high numbers of individual larvae that can be arrayed in multi-well plates for high throughput genetic and chemical exposure screens. However, traditional workflows for imaging individual larvae have been limited to low-throughput studies using traditional microscopes and manual analyses. Using a newly developed, parallelized microscope, the Multi-Camera Array Microscope (MCAM™), we have optimized a rapid, high-resolution algorithmic method to count fluorescently labeled cells in zebrafish larvae in vivo. Using transgenic zebrafish larvae, in which neutrophils express EGFP, we captured 18 gigapixels of images across a full 96-well plate, in 75 seconds, and processed the resulting datastream, counting individual fluorescent neutrophils in all individual larvae in 5 minutes. This automation is facilitated by a machine learning segmentation algorithm that defines the most in-focus view of each larva in each well after which pixel intensity thresholding and blob detection are employed to locate and count fluorescent cells. We validated this method by comparing algorithmic neutrophil counts to manual counts in larvae subjected to changes in neutrophil numbers, demonstrating the utility of this approach for high-throughput genetic and chemical screens where a change in neutrophil number is an endpoint metric. Using the MCAM™ we have been able to, within minutes, acquire both enough data to create an automated algorithm and execute a biological experiment with statistical significance. Finally, we present this open-source software package which allows the user to train and evaluate a custom machine learning segmentation model and use it to localize zebrafish and analyze cell counts within the segmented region of interest. This software can be modified as needed for studies involving other zebrafish cell lineages using different transgenic reporter lines and can also be adapted for studies using other amenable model species.In this study, we used the information generated by community members during an intervention design process to understand the features needed for a successful community participatory intervention to improve child health.
We conducted a concurrent mixed-methods study (November 2019–March 2020) to inform the design and evaluation of a community–facility linkage participatory intervention.
Kiyawa Local Government Area (Jigawa State, Nigeria)—population of 230 000 (n=425 villages).
Qualitative data included 12 community conversations with caregivers of children under-5 (men, older and younger women; n=9 per group), 3 focus group discussions (n=10) with ward development committee members and interviews with facility heads (n=3). Quantitative data comprised household surveys (n=3464) with compound heads (n=1803) and women (n=1661).
We analysed qualitative data with thematic network analysis and the surveys with linear regression—results were triangulated in the interpretation phase. Participants identified the following areas of focus: community health education; facility infrastructure, equipment and staff improvements; raising funds to make these changes. Community involvement, cooperation and empowerment were recognised as a strategy to improve child health, and the presence of intermediate bodies (development committees) was deemed important to improve communication and solve problems between community and facility members. The survey showed functional community relations’ dynamics, with high levels of internal cohesion (78%), efficacy in solving problems together (79%) and fairness of the local leaders (82%).
Combining the results from this study and critical theories on successful participation identified community-informed features for a contextually tailored community–facility link intervention. The need to promote a more inclusive approach to future child health interventions was highlighted. In addition to health education campaigns, the relationship between community and healthcare providers needs strengthening, and development committees were identified as an essential feature for successfully linking communities and facilities for child health.
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