ABSTRACT

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Atomic force microscopy (AFM) is a powerful method for topographic imaging of surfaces with nanometer resolution. AFM offers significant advantages over scanning electron microscopy (SEM) including the acquisition of quantitative 3D-images and biomechanical information. More importantly, for in-vivo biological imaging, AFM does not require sample dehydration/labeling. We show for the first time high-resolution topographical images of the cuticle of the model organism C. elegans under physiological conditions using AFM. C. elegans is used extensively for drug screening and to study pathogen adherence in innate immunity; both applications highly depend on the integrity of the nematode's cuticle. Mutations affecting both drug adsorption and pathogen clearance have been proposed to relate to changes in the cuticle structure, but never visually examined in high resolution. In this study we use AFM to visualize the topography of wild-type adult C. elegans as well as several cuticle collagen mutants and describe previously unseen anatomical differences.

Over 200 million malaria cases globally lead to half a million deaths annually. Accurate malaria diagnosis remains a challenge. Automated imaging processing approaches to analyze Thick Blood Films (TBF) could provide scalable solutions, for urban healthcare providers in the holoendemic malaria sub‐Saharan region. Although several approaches have been attempted to identify malaria parasites in TBF, none have achieved negative and positive predictive performance suitable for clinical use in the west sub‐Saharan region. While malaria parasite object detection remains an intermediary step in achieving automatic patient diagnosis, training state‐of‐the‐art deep‐learning object detectors requires the human‐expert labor‐intensive process of labeling a large dataset of digitized TBF. To overcome these challenges and to achieve a clinically usable system, we show a novel approach. It leverages routine clinical‐microscopy labels from our quality‐controlled malaria clinics, to train a Deep Malaria Convolutional Neural Network classifier (DeepMCNN) for automated malaria diagnosis. Our system also provides total Malaria Parasite (MP) and White Blood Cell (WBC) counts allowing parasitemia estimation in MP/μL, as recommended by the WHO. Prospective validation of the DeepMCNN achieves sensitivity/specificity of 0.92/0.90 against expert‐level malaria diagnosis. Our approach PPV/NPV performance is of 0.92/0.90, which is clinically usable in our holoendemic settings in the densely populated metropolis of Ibadan. It is located within the most populous African country (Nigeria) and with one of the largest burdens of Plasmodium falciparum malaria. Our openly available method is of importance for strategies aimed to scale malaria diagnosis in urban regions where daily assessment of thousands of specimens is required.