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Point-of-care quantification of blood-borne filarial parasites with a mobile phone microscope

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Science Translational Medicine  06 May 2015:
Vol. 7, Issue 286, pp. 286re4
DOI: 10.1126/scitranslmed.aaa3480
  • Fig. 1. Strategy for quantifying mf in whole blood.

    (A) A single frame from a video of mf resuspended in RPMI medium, as well as a zoomed-in region of a single mf at 1, 2, and 3 s of the video. Scale bars, 500 and 100 μm (zoom). (B) A single frame of a video of mf in whole peripheral blood, as well as zoomed-in region of a single mf at 1, 2, and 3 s of the video. Arrows indicate differences in the images (see movie S1). (C) A single frame of a video taken of mf in whole peripheral blood, as well as difference image calculated by averaging, subtracting, and morphologically filtering subsequent frames of the video, and mf localized within the FOV, which were quantified using a peak-finding algorithm on the processed difference image.

  • Fig. 2. An automated cell phone–based video microscope.

    (A) A 3D-printed lid (i) aligns an isolated iPhone 5s cell phone lens module (ii) with the camera of a removable iPhone 5s (iii). A 3D-printed base (iv) positions a LED array directly beneath a capillary loaded with blood (v), which is mounted on a moveable carriage (vi). The carriage can slide along a single-axis rail driven by a rack coupled to its underside and a servo-mounted gear (vii), moving different regions of the capillary into the microscope FOV. An Arduino microcontroller board (viii) and a Bluetooth controller board (ix) communicate between the iPhone 5s, the servo, and the LED array. (x) A micro-USB (universal serial bus) port powers the device. (B) Simplified optical diagram of the microscope device. White light from the LED array illuminates the blood-loaded capillary. An isolated camera module from an iPhone 5s is inverted and positioned against the capillary. Light from the sample is collected by the inverted (objective) lens and then refocused by the optics of the iPhone 5s lens module onto the camera sensor. The capillary can be translated to position a new FOV into the optical path. (C) A video acquired by the device captures microfilarial motion from a known volume of whole blood within a rectangular glass capillary. A still frame from a video of whole blood is shown (movie S1). Scale bar, 1 mm.

  • Fig. 3. Predicted counting statistics for L. loa diagnostic decisions.

    (A) For a patient with a load of 30,000 mf/ml, the distribution of observed mf/ml sampled in the small blood volume within the capillary obeyed Poisson statistics, such that any measurement of mf/ml load fell either below or above the true mf/ml. A treatment threshold defined the estimated mf/ml above which a patient was excluded from testing. If the true mf/ml was higher than the treatment threshold and the measured mf/ml was lower than the threshold, the measurement was a false negative (purple regions). (B) For a patient with a given true mf/ml, increasing the number of FOVs captured on the device decreased the probability of false negatives. (C) Probability density function of L. loa infection load within the target population around Okola, Cameroon (n = 2000). (D) Impact of increasing the number of captured FOVs on the probability of false negatives within the Okola population. To decrease the risk of false-negative tests, the treatment threshold can be lowered beneath the load at which severe adverse events are triggered (treatment threshold < SAE threshold of 30,000 mf/ml). Using a treatment threshold of 26,000 mf/ml over five observed FOVs yields a false-negative probability below 1 × 10−7 (1 test in 10,000,000). (E) For a patient with a given true mf/ml, increasing the number of FOVs captured on the device decreases the probability of false positives. (F) Impact of increasing the number of captured FOVs on the probability of false positives within the Cameroonian population. To decrease the risk of false-positive tests, the treatment cutoff can be lowered beneath the load at which severe adverse events are triggered (treatment cutoff < SAE cutoff of 30,000 mf/ml). Using a treatment cutoff of 26,000 mf/ml over five observed FOVs yields a false-positive rate below 1 × 10−3 (1 test in 1000).

  • Fig. 4. Results of a pilot study conducted in Cameroon to assess the effectiveness of the device.

    (A) Flowchart describing the test procedure for the device. (B) Blood smear versus CellScope Loa quantifications of mf load. Results from two CellScope Loa readings of the same patient are averaged and scaled by a linear correction factor (Fig. 4C). Results from two thick blood smears are also averaged. r = 0.99. Purple region corresponds to false negatives (that is, patients whom CellScope Loa assessed with 99.99% certainty were under the SAE threshold but were actually over the threshold as assessed by blood smear). Blue-shaded region corresponds to false positives (that is, patients whom CellScope Loa could not confidently guarantee were under the SAE threshold of 30,000 mf/ml but actually were under the SAE threshold as assessed by blood smear). The lower left quadrant corresponds to true negatives (that is, patients whom CellScope Loa assessed with 99.99% certainty were under the SAE threshold and were also under the SAE threshold as assessed by blood smear). The upper right quadrant corresponds to true positives (that is, patients whom CellScope Loa determined were above the SAE threshold of 30,000 mf/ml and were confirmed to be over the SAE threshold as assessed by blood smear). (C) Calculation of the correction factor. A linear fit between CellScope and calibrated thick-smear counts was performed to determine the correction factor m. Results from two CellScope Loa readings were averaged. (D) Comparison of blinded human counts of whole-blood movies to automated CellScope Loa counts. CellScope Loa and human counts from two readings are averaged. (E) Repeatability of CellScope Loa measurements. Two blood samples were taken from each patient (n = 33) and read by separate devices with blinded separate operators using the automated algorithm (r = 0.96).

Supplementary Materials

  • Supplementary Material for:

    Point-of-care quantification of blood-borne filarial parasites with a mobile phone microscope

    Michael V. D'Ambrosio, Matthew Bakalar, Sasisekhar Bennuru, Clay Reber, Arunan Skandarajah, Lina Nilsson, Neil Switz, Joseph Kamgno, Sébastien Pion, Michel Boussinesq, Thomas B. Nutman,* Daniel A. Fletcher*

    *Corresponding author. E-mail: fletch{at}berkeley.edu (D.A.F.); tnutman{at}niaid.nih.gov (T.B.N.)

    Published 6 May 2015, Sci. Transl. Med. 7, 286re4 (2015)
    DOI: 10.1126/scitranslmed.aaa3480

    This PDF file includes:

    • Legends for movies S1 to S3

    [Download PDF]

    Other Supplementary Material for this manuscript includes the following:

    • Movie S1 (.mov format). A video of whole blood taken with the CellScope Loa showing several wriggling L. loa mf.
    • Movie S2 (.mov format). A narrated movie of CellScope Loa operation.
    • Movie S3 (.mp4 format). A demonstration of the motion detection algorithm on CellScope Loa.

    [Download Movie S1]

    [Download Movie S2]

    [Download Movie S3]

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