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Open AccessArticle

Mobile Diagnostics Based on Motion? A Close Look at Motility Patterns in the Schistosome Life Cycle

Department of Microbiology, Tumor and Cell Biuology, Karolinska Institutet, SE-17177 Stockholm, Sweden
Center for Machine Vision and Signal Analysis, University of Oulu, FI-90014 Oulu, Finland
Public Health Agency of Sweden, SE-17182 Solna, Sweden
Author to whom correspondence should be addressed.
Academic Editor: Aydogan Ozcan
Diagnostics 2016, 6(2), 24;
Received: 2 February 2016 / Revised: 8 April 2016 / Accepted: 23 May 2016 / Published: 17 June 2016
(This article belongs to the Special Issue Mobile Diagnosis)
Imaging at high resolution and subsequent image analysis with modified mobile phones have the potential to solve problems related to microscopy-based diagnostics of parasitic infections in many endemic regions. Diagnostics using the computing power of “smartphones” is not restricted by limited expertise or limitations set by visual perception of a microscopist. Thus diagnostics currently almost exclusively dependent on recognition of morphological features of pathogenic organisms could be based on additional properties, such as motility characteristics recognizable by computer vision. Of special interest are infectious larval stages and “micro swimmers” of e.g., the schistosome life cycle, which infect the intermediate and definitive hosts, respectively. The ciliated miracidium, emerges from the excreted egg upon its contact with water. This means that for diagnostics, recognition of a swimming miracidium is equivalent to recognition of an egg. The motility pattern of miracidia could be defined by computer vision and used as a diagnostic criterion. To develop motility pattern-based diagnostics of schistosomiasis using simple imaging devices, we analyzed Paramecium as a model for the schistosome miracidium. As a model for invasive nematodes, such as strongyloids and filaria, we examined a different type of motility in the apathogenic nematode Turbatrix, the “vinegar eel.” The results of motion time and frequency analysis suggest that target motility may be expressed as specific spectrograms serving as “diagnostic fingerprints.” View Full-Text
Keywords: POC diagnostics; schistosomiasis; motility patterns; mini-microscopes; image analysis; computer vision; telemedicine; neglected diseases; remote sensing; spectrogram POC diagnostics; schistosomiasis; motility patterns; mini-microscopes; image analysis; computer vision; telemedicine; neglected diseases; remote sensing; spectrogram
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Linder, E.; Varjo, S.; Thors, C. Mobile Diagnostics Based on Motion? A Close Look at Motility Patterns in the Schistosome Life Cycle. Diagnostics 2016, 6, 24.

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  • Supplementary File 1:

    Video S3 (MOV, 6829 KB)

    Schistosoma mansoni cercaria in water. The different motility behavior of the tail and the head parts is seen. The tail will be lost upon penetration of host skin. The frontal part will invade the skin of host and transform into schistosomulum. The recording frame rate 15.63 FPS is too slow to allow for adequate signal processing.

  • Supplementary File 2:

    Video S4 (MOV, 726 KB)

    Trichobilharzia cercaria attaches to skin-lipid coated microscope slide and attempts penetration. The wiggling motility of Trichobilharzia cercaria was recorded at a frame rate of 15.63 FPS. Original magnification of video recordings was 100×.

  • Supplementary File 3:

    Video S10 (MOV, 1788 KB)

    Swimming protozoa: Paramecium organisms swimming in water were captured with iPhone 4S equipped with 4× objective; 100-mL sample in glass jar. Dark field illumination using flashlight. The dark field imaging facilitates the segmentation of object candidates.

  • Supplementary File 4:

    Video S11 (MPG, 1060 KB)

    Individual trajectories of Paramecium have a waveform apparently due to the spiral/helical motion of the organism. The waveforms seen apparently represent a helical path and the rotational speed is deductible from the time interval between two maximal readings.

  • Supplementary File 5:

    Video S1 (MP4, 2942 KB)

    Hatching of Schistosoma mansoni egg: Miracidium rotating inside schistosome egg exposed to water. Rotational speed ~14.1 rpm (0.235 rps). Upon bursting of the egg shell (“hatching”), the miracidium rapidly accelerates to achieve a linear speed of about 0.3 mm/s (see Table 1). Digitized VHS-video recording at 15.63 FPS using microscope video camera Sony CCD-IRIS. Original magnification 200×.

  • Supplementary File 6:

    Video S2 (MP4, 480 KB)

    Miracidia released from isolated Schistosoma mansoni eggs suspended in water swim in droplet on microscope slide under a coverslip. Digitized VHS-video recording as described in the text of Figure 1. Eleven miracidia were identified by computer vision and trajectories of 7 were analyzed in detail (video recording with trajectories).

  • Supplementary File 7:

    Video S5 (MP4, 3759 KB)

    Ingestion of erythrocytes present in the medium.

  • Supplementary File 8:

    Video S6 (MP4, 15253 KB)

    Transport of erythrocytes within the gut of female worm; regurgitation within the intestine. In the female worm, egg maturation within the ootype is seen as pulsating, rhythmic contractions at a rate of 156 contractions/min.

  • Supplementary File 9:

    Video S7 (MP4, 5333 KB)

    Egg transportation through the oviduct can be seen as it ends with a conspicuous dorsal nick of the anterior end of the worm as the egg is ejected. Video recordings at 40× magnificatiion at a speed of 15.63 FPS obtained with microscope fitted with 4× objective and video camera Sony CCD-IRIS.

  • Supplementary File 10:

    Video S8 (MP4, 17008 KB)

    Video recording of marker beads 5–200 microns in diameter suspended in water. Imaging with iPhone 4S and screw cap-mounted lens.

  • Supplementary File 11:

    Video S9 (MP4, 931 KB)

    Turbatrix aceti at the water/air interphase. Illumination using external UV/blue light emitting diode (LED). Computer vision: The worm was extracted using Maximally Stable Extremal region, its ends were located, and the worm displacement from the line connecting the ends was monitored over time. The obtained oscillating signal was analyzed using Fourier transformation. The work flow is similar to what has been used for analysis of human motion.

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