Download Detection of Single Ring Stage P. falciparum in Human Thin

Detection of Single Ring Stage P. falciparum in Human Thin Film Blood
Smears Using FTIR Microspectroscopy and Differentiation of
Plasmodium Positive from Plasmodium Negative Red Blood Cells
by McKale Santin, Dr. Bryan Holmes, Dr. Adam Hunt, and Kenneth A. Puzey of QuantaSpec, Inc. Contact: [email protected]
ABSTRACT
Currently, rapid diagnostic tests for malaria infection perform poorly at low parasite loads, are
degraded by severe temperatures, and contain reagents, which contribute to their costs. The overall
objective of this study was to perform a preliminary evaluation of the utility of FTIR microspectroscopy
for in vitro diagnosis of thin film blood smears for malaria infection. FTIR microspectroscopy has
potential advantages in detecting low parasite loads, is not affected by temperature, and does not
require any reagents. Giemsa-stained thin film blood smear slides were analyzed in this study. 240
slides with ring stage P. falciparum infected human blood were prepared from culture. P. falciparum
negative controls included 80 clinical P. vivax slides (collected and verified by expert microscopy (EM),
40 slides with Salmonella- infected human blood (prepared from culture), and 40 uninfected human
blood slides. Infrared spectra were measured from a small area of each slide (~13 microns x 13
microns) usually containing only one red blood cell. Algorithms were written to differentiate
Plasmodium positive spectra from Plasmodia negative spectra and tested by cross-validation. The
sensitivity was 98.8% to 100% and the specificity was 95.4% to 100% for Plasmodia positive samples
with a 95% confidence interval. These results suggest that further study of FTIR spectroscopy as an
automated reagent-less diagnostic method with potential for detection of single parasites is warranted.
Infrared spectroscopy could radically lower marginal test costs by eliminating the need for expensive
consumables.
INTRODUCTION
Malarial infection is a major global health problem. A key part of malaria control strategies is early
case detection with in vitro diagnostics. The current gold standard for malaria diagnosis is expert
microscopy of Giemsa-stained blood smears, however this method has many limitations. It is laborintensive, requires consistent, quality staining, and requires diagnosis by a trained, expert
microscopist, which are in short supply. Furthermore, most malaria patients are treated in peripheral
healthcare facilities that do not have access to quality microscopy. Simple rapid diagnostic tests
(RDTs) based on detection of parasite antigens have been introduced to try and provide an alternative
to diagnosis with microscopy, yet these diagnostics also have many limitations. RDTs are poor at
detecting low-level parasitemia, have a limited shelf life, and contain reagents, which contribute to their
cost.
Our present study investigates the feasibility of using infrared (IR) microspectroscopy as an
alternative diagnostic approach that can overcome the limitations inherent to diagnosis based on
analysis of a visible image or by reagent-based assays. Fourier Transform Infrared (FTIR)
microspectroscopy can probe the entire chemistry of an intact biological cell with IR light instead of
reagents. The spectral signatures of biological cells vary depending on the molecular components of
the cell, and the chemical alterations that accompany infection provide the basis for this detection
technology. The goal of this research was to evaluate FTIR microspectroscopy for automatically
differentiating Plasmodium-positive from Plasmodium-negative red blood cells in thinfilm blood smears.
Species/Strain
Control Type
Parasitemia
# of Replicates
P. falciparum 7G8
+
6.75%
40
P. falciparum D6
+
5.08%
40
P. falciparum 3D7
+
10.4-12.3%
40
P. falciparum 1776
+
5.8-6.5%
40
P. falciparum HB3
+
7.3-7.7%
40
P. falciparum Dd2
+
5.7%
40
P. vivax, clinical
+
Variable
40
Salmonella SL1344
-
5 bacteria : 1 RBC
40
Uninfected blood
-
N/A
40
Table 1. Sample Characterization.
Plasmodium-positive controls
contained P. falciparum and P. vivax
infected human blood. Plasmodiumnegative controls contained
Salmonella-infected human blood and
uninfected human blood.
MATERIALS and METHODS
Positive and Negative Controls. Uninfected negative controls: human blood, 5% Hematocrit.
Salmonella-infected negative controls: uninfected human blood spiked with Salmonella SL1344. P.
falciparum: uninfected blood spiked with strains 7G8, D6 (MR4) and 3D7,1776, HB3, Dd2 (NYU
School of Medicine). P. vivax samples were prepared
ocular
from clinical cases in India Parasite and bacterial counts
MCT
can be found in Table 1. Sample Preparation. 40 thin
detector
film blood smears prepared per control group (Table 1).
detector 2
aperture
All samples prepared on low-e microscope slides
(transparent in the visible region but highly reflective
in the IR). Samples were fixed and stained with a 10%
74x
objective
Giemsa solution. Spectral Data Collection. A Bruker
IR in
sample
Hyperion 1000infrared microscope and a Bruker
Tensor 27 FTIR spectrometer were used to collect the
spectral data (Figures 1 & 2). This system uses a
Figure 1. Hyperion 1000
Figure 2. Bruker Hyperion
glowbar IR source and a MCT detector.
optical beam path.
1000 IR microscope and
The microscope was modified with a high power
Tensor 27 FTIR spectrometer.
reflective objective for an overall magnification of 740X.
Spectra collected from a 13x13 m area of a sing-cell layer of each thin film smear (1-3 RBCs).
Mid-IR spectra collected from 4000cm-1 to 600cm-1 at a spectral resolution of 2cm-1. 100
scans/measurement. Background measurements taken from an adjacent, blank 13x13 m area of
each slide, interactively subtracted from sample spectra using Opus 6.5 software. Data
Processing. Raw spectral data organized into two classification groups based on sample identity:
Plasmodium-positive and Plasmodium-negative. Data imported into Excel, 1st derivative calculated
by taking the slope of the raw data. 1st derivative data imported into JMP software and multivariate
discriminant analysis performed on all spectra. Mahalanobis distances calculated for each
classification group for each replicate. Algorithm Development. Identification algorithm
developed to determine Plasmodium spp. infection based on processed IR spectra. The algorithm
consists of a set of vectors that are multiplied with the first derivative spectra of an unknown sample
to be identified. For algorithm development, the full set of absorbance values (all optical
frequencies) is replaced by a much smaller subset of data containing 350 key optical frequencies
for identification.
Figure 3. Visual Images, 740X magnification. Ring-stage
P. falciparum infected red blood cells (left, red arrow) and
uninfected red blood cells (right). Each sample was visually
located, then the microscope was switched to IR mode to
collect reflectance-absorbance spectra from the center
square.
Figure 4. Absorbance Spectra. 40 absorbance spectra from P. falciparum strain 3D7 infected blood
(left) and 40 absorbance spectra from uninfected human blood (right).
RESULTS
Visual images of uninfected and ring-stage P. falciparum infected red blood cells at 740X
magnification are shown in Figure 3. Spectra were taken from the center square; all other IR light was
blocked off by perpendicular apertures. Spectra in the mid-IR region of Plasmodium-positive (P.
falciparum strain 3D7) and Plasmodium-negative (uninfected blood) are shown in Figure 4. From the
computed Mahalanobis distances, it was found that the longest within-group distances were small
(~103) when compared to the shortest across-group distances (~1012). Cross validation testing was
used to evaluate the accuracy of the developed algorithm for Plasmodium spp. detection. The
algorithm correctly identified 280 out of 280 true positives and 80 out of 80 true negatives. The
sensitivity of the developed algorithm was 98.8-100% (95%CI) and the specificity was 95.4-100%
(95%CI).
CONCLUSION
Initial results indicate FTIR microspectroscopy can be used as a rapid identification tool for the
detection of Plasmodia in human thin film blood smears with high sensitivity and specificity. All 320
replicates were correctly identified as either malaria positive (240) or malaria negative (80), supporting
the hypothesis that FTIR microspectroscopy can be used to detect ring-stage P. falciparum infection.
This research study has also demonstrated the potential for infrared microspectroscopy to detect lowlevel parasitemia, as single parasites were detected. We are currently working on collecting a larger
clinical sample set, and increasing the number of red blood cells that can be diagnosed
simultaneously.
ACKNOWLEDGEMENTS
This work is supported by the U.S Army Medical Research and Materiel Command under contract No.W81XWH09-C-0019. The views, opinions and/or findings contained in this report are those of the authors and should not be
construed as an official Department of the Army position, policy or decision unless so designated by other documentation.
“In the conduct of research where humans are the subjects, the investigator(s) adhered to the policies regarding the
protection of human subjects as prescribed by Code of Federal Regulations (CFR) Title 45, Volume 1, Part 46; Title 32,
Chapter 1, Part 219; and Title 21, Chapter 1, Part 50 (Protection of Human Subjects).”
Automated Reagent-less Differentiation of P. falciparum from P. vivax in
Human Thin Film Blood Smears With FTIR Microspectroscopy
by Kenneth A. Puzey, Dr. Bryan Holmes, Dr. Adam Hunt, and McKale Santin of QuantaSpec, Inc. Contact: [email protected]
ABSTRACT
MATERIALS and METHODS
Positive and Negative Controls. Uninfected negative controls: human blood, 5% Hematocrit.
Salmonella-infected negative controls: uninfected blood spiked with Salmonella SL1344. P. falciparum:
uninfected blood spiked with strains 7G8, D6 (MR4) and 3D7,1776, HB3, Dd2 (NYU School of
Medicine). P. vivax samples were prepared from clinical cases in India. Sample Preparation. 40 thinfilm blood smears prepared per control group. All samples prepared on low-e microscope slides
(transparent in the visible region but highly reflective in the IR). Samples were fixed and stained with a
10% Giemsa solution. Spectral Data Collection. A Bruker Hyperion 1000infrared microscope and a
Bruker Tensor 27 FTIR spectrometer were used to collect the spectral data (Figures 1 & 2). This
system uses a glowbar IR source and a liquid nitrogen-cooled MCT detector. The microscope was
modified with a high power reflective objective for an overall magnification of 740X. Spectra collected
from a 13x13 m area of a sing-cell layer of each thin film smear (1-3 RBCs). Mid-IR spectra collected
from 4000cm-1 to 600cm-1 at a spectral resolution of 2cm-1. 100 scans/measurement. Background
measurements taken from an adjacent, blank 13x13 m area of each slide, interactively subtracted
from sample spectra using Opus 6.5 software. Data Processing. Raw spectral data organized into 3
classification groups based on the identity of the spectral sample: P. falciparum-positive, P. vivaxpositive, and Plasmodia-negative. Data imported into Excel, 1st derivative calculated by taking the
slope of the raw data. 1st derivative data imported into JMP software and multivariate discriminant
analysis performed on all spectra. Mahalanobis distances calculated for each classification group for
each replicate. Algorithm Development. Identification algorithms developed to determine P.
INTRODUCTION
falciparum infection, P. vivax infection, or no infection based on processed IR spectra. The algorithms
Over 3 billion people worldwide are at risk of malaria, representing almost half of the world’s
consists of a set of vectors that are multiplied with the first derivative spectra of an unknown sample to
population. Prompt and correct diagnosis of malarial infection is a primary part of malaria control and is be identified. For algorithm development, the full set of absorbance values (all optical frequencies) is
essential for saving patient lives. In regions where both Plasmodium falciparum and Plasmodium
replaced by a much smaller subset of data containing 350 key optical
vivax are present, effective diagnosis requires not only detecting malaria infection but also determining
frequencies for identification.
the species of infection, as different species respond to different chemotherapeutic treatments. Expert
microscopy remains the gold standard for distinguishing different species of malarial infection, but
Figure 3. Visual Images, 740X
unfortunately high-quality expert microscopy is difficult to maintain in resource-poor settings where the
magnification. P. vivax infected
majority of malaria diagnosis is being performed. Rapid diagnostic tests (RDTs) based on detection of
red blood cells (left, blue arrow)
species-specific antigens such as pLDH (parasite lactate dehydrogenase) have been introduced to
and P. falciparum infected red
provide an alternative to diagnosis with microscopy. However, RDTs have many limitations. They are
blood cells (right, red arrow).
poor at detecting low-level parasitemia, have a limited shelf life, and contain reagents, which contribute
Each sample was visually located,
to their cost. Our present study investigates the feasibility of using infrared (IR) microspectroscopy as
then the microscope was switched
an alternative diagnostic approach that can overcome the limitations inherent to diagnosis based on
to IR mode to collect reflectanceanalysis of a visible image or by reagent-based assays. Fourier Transform Infrared (FTIR)
absorbance spectra from the
microspectroscopy can probe the entire chemistry of an intact biological cell with IR light instead of
center 13x13 m square.
reagents. The spectral signatures of biological cells vary depending on the molecular components of
the cell, and the chemical alterations that accompany infection provide the basis for this detection
RESULTS
technology. The goal of this research was to evaluate FTIR microspectroscopy for automatically
Visual images of P. vivax and P. falciparum infected red blood cells at 740X magnification are
differentiating Plasmodium falciparum from Plasmodium vivax infected red blood cells
shown in Figure 3. Spectra were taken from the center square; all other IR light was blocked off by
in thin- film human blood smears.
perpendicular apertures. From the FTIR absorbance spectra, P. vivax and P. falciparum cannot be
visually distinguished. From multivariate analysis, Mahalanobis distances were calculated for each
replicate to every other replicate. It was found that the longest within-group distances are small (~103)
Ocular
 Camera

when compared to the shortest across-group distances (~1012). Cross validation testing was used to
evaluate the accuracy of the developed algorithm for Plasmodium spp. detection. The algorithm
correctly identified 240 out of 240 true positives and 80 out of 80 true negatives. The sensitivity of
the P.f. identification algorithm was 98.4-100% (95%CI) and the specificity was 97.7-100%
 MCT detector
(95%CI). The sensitivity of the P.v. identification algorithm was 95.4%-100% (95%CI) and the
specificity was 98.8%-100%(95%CI).
In malaria cases species of infection affects course of treatment. Differentiation of P. falciparum
from P. vivax by RDTs requires multiple antibodies, which increases test costs. Furthermore, RDTs are
subject to reader error. Speciation by visual microscopy is dependent on the skill and availability of an
expert microscopist. The objective of this study was to evaluate the utility of FTIR microspectroscopy
for automatic reagent-less differentiation of P. falciparum from P. vivax infected human red blood cells.
Geimsa-stained thin film blood smear slides were analyzed in this study. For P. falciparum positive
controls, 240 slides with ring stage P. falciparum were prepared from culture. For P. vivax positive
controls, 40 clinical P. vivax slides were collected and verified by expert microscopy (EM). For
negative controls, 40 slides with Salmonella-infected blood (prepared from culture) and 40 uninfected
blood slides were prepared. Infrared spectra were measured from a small area of each slide (~13
microns x13 microns) typically containing only one red blood cell. Algorithms were written to
differentiate red blood cells infected with P. falciparum, red blood cells infected with P. vivax, red blood
cells infected with Salmonella and uninfected red blood cells based on their infrared spectra.
Algorithms were tested by cross-validation. For P. falciparum sensitivity was 98.4 to 100% and
specificity was 97.7% to 100% (95% CI). For P. vivax the sensitivity was 95.4% to 100% and the
specificity was 98.8% to 100% (95% CI). These results suggest that FTIR spectroscopy may be useful
for automated reagent-less differentiation of malaria infection. In high throughput settings
spectroscopy testing may be lower cost because it does not require consumables.
CONCLUSION
aperture
Detector 2
Initial results indicate that RBCs infected with P.f. can be differentiated from RBCs infected with P.v.
in thin film blood smears using FTIR microspectroscopy with high sensitivity and specificity. This
method is reagent-less and automated (results provided by computer) and is capable of detecting a
single Plasmodia parasite. Further study with slides from both clinical P.f. and clinical P.v. from a larger
number of cases will be needed to determine the clinical utility of FTIR microspectroscopy for
diagnosis and such a study is underway. Equipment modifications to examine a large number of RBCs
in parallel are also underway to improve diagnostic throughput.
74x Objective 
Sample

 IR in
Figure 1. Hyperion 1000™ optical beam path.
ACKNOWLEDGEMENTS
Figure 2. Bruker Hyperion 1000™ IR microscope
and Tensor™ 27 FTIR spectrometer.
This work is supported by the U.S Army Medical Research and Materiel Command under contract No.W81XWH09-C-0019. The views, opinions and/or findings contained in this report are those of the authors and should not be
construed as an official Department of the Army position, policy or decision unless so designated by other documentatio
“In the conduct of research where humans are the subjects, the investigator(s) adhered to the policies regarding the
protection of human subjects as prescribed by Code of Federal Regulations (CFR) Title 45, Volume 1, Part 46; Title 32,
Chapter 1, Part 219; and Title 21, Chapter 1, Part 50 (Protection of Human Subjects).”
Automated Reagent-less Differentiation of Three Drug Susceptible Strains of
P. falciparum from Three Drug Resistant Strains of P. falciparum in Human
Thin Film Blood Smears Using FTIR Microspectroscopy
by Kenneth A. Puzey, Dr. Bryan Holmes, Dr. Adam Hunt, and McKale Santin of QuantaSpec, Inc. Contact: [email protected]
ABSTRACT
In some regions of the world malaria parasite drug resistance is present in 50% of cases.
Unfortunately, tests to determine drug resistance are not clinically available forcing health ministries
and doctors to make difficult choices. An economical clinical test for drug resistance would enable
doctors to administer less expensive chloroquine to susceptible cases, lowering health costs and
slowing the spread of resistance to newer drugs. The objective of this study was a preliminary
evaluation of the utility of FTIR microspectroscopy for differentiating red blood cells infected with drug
resistant strains and drug susceptible strains of P. falciparum. 120 Geimsa-stained thin film blood
smear slides were prepared with drug-susceptible ring stage P. falciparum from culture (40 slides
strain 3D7, 40 slides strain 1776, 40 slides D6), and 120 Geimsa-stained thin film blood smear slides
were prepare with drug-resistant ring stage P. falciparum from culture (40 slides strain HB3, 40 slides
strain Dd2, 40 slides strain 7G8). Negative controls included 40 Geimsa-stained thin film blood smear
slides of uninfected human blood as well as human blood infected with Salmonella from culture (40
slides). Additional P. falciparum negative controls included 40 clinical Geimsa-stained P. vivax slides
collected and verified by expert microscopy (EM). Infrared spectra were measured from a small area
of each slide (~13 microns x13 microns) typically containing only one red blood cell. Algorithms were
written to differentiate red blood cells infected with P. falciparum, red blood cells infected with P. vivax,
red blood cells infected with Salmonella and uninfected red blood cells based on their infrared
spectrum. Algorithms were tested by cross-validation. For drug susceptible strains, sensitivity was
97% to 100% and specificity was 98.7% to 100% (95% CI). For drug resistant strains sensitivity was
97% to 100% and specificity was 98.7% to 100% (95% CI). These results suggest that FTIR
spectroscopy may be useful for automated reagent-less differentiation of drug resistant and drug
susceptible strains of P. falciparum in thin film blood smears. This capability could enable more cost
effective case management and reduce the spread of drug resistance to newer drugs.
microscope slides (transparent in the visible region but highly reflective in the IR). Samples were
fixed and stained with a 10% Giemsa solution. Spectral Data Collection. A Bruker Hyperion
1000infrared microscope and a Bruker Tensor 27 FTIR spectrometer were used to collect the
spectral data (glowbar IR source/ liquid nitrogen-cooled MCT detector). 74X reflective objective used
to collect spectra from a 13x13 m area of a sing-cell layer of each thin film smear (1-3 RBCs). Mid-IR
spectra collected from 4000cm-1 to 600cm-1 at a spectral resolution of 2cm-1. 100 scans/measurement.
Background measurements taken from an adjacent, blank 13x13 m area of each slide, subtracted
using Opus 6.5 software. Data Processing. Raw spectral data organized into 3 classification groups
based on the identity of the spectral sample: drug susceptible P.f.-positive, drug-resistant P.f.-positive,
and P.f.-negative. Data imported into Excel, 1st derivative calculated by taking the slope of the raw
data. 1st derivative data imported into JMP software and multivariate discriminant analysis performed
on all spectra. Mahalanobis distances calculated for each classification group for each replicate.
Algorithm Development. Identification algorithms developed to differentiate drug-susceptible P.f.positive from drug- resistant P.f.- positive from P.f.-negative infection based on processed IR spectra.
The algorithm consists of a set of vectors that are multiplied with the first derivative spectra of an
unknown sample to be identified. For algorithm development, the full set of absorbance values (all
optical frequencies) is replaced by a much smaller subset of data containing 350 key optical
frequencies for identification.
Figure 1. Absorbance Spectra. 40
absorbance spectra from drug
susceptible P. falciparum D6 (left) and
40 absorbance spectra from drug
resistant P. falciparum Hb3 (right).
INTRODUCTION
Each year, there are an estimated 250 million malaria cases and approximately 1 million malariarelated deaths. Fundamental to reducing the burden of malaria infection and improving patient
outcome is rapid and accurate diagnosis. Field diagnosis and treatment of malarial infection in
malaria-endemic regions remains a problem, and is becoming increasingly difficult due to malaria
parasite drug resistance. Major methods for malaria diagnosis (expert microscopy and rapid
diagnostic tests) are unable to detect drug resistance prior to treatment, and instead are used to
monitor for treatment failure. This method is time consuming, with prolonged periods of patient followup, and is costly due to the need for multiple tests.
Regardless of the species of malaria, all drug resistance mechanisms involve genetic and
chemical differences in the parasite. Our present study investigates the feasibility of using infrared (IR)
microspectroscopy as an alternative diagnostic approach that can overcome the limitations inherent to
diagnosis based on analysis of a visible image or by reagent-based assays. Fourier Transform Infrared
(FTIR) microspectroscopy can probe the entire chemistry of an intact biological cell with IR light
instead of reagents. The chemical differences between strains provides the basis for this detection
technology. The goal of this research was to evaluate FTIR microspectroscopy for automatically
differentiating drug susceptible P. falciparum from drug resistant P. falciparum in thin-film blood
smears.
Mahalanobis Distances
Actual
3D7
7G8
1776
D6
Dd2
HB3
P. vivax
Salmonella
UIB
3D7
1.8e4
7.8e12
3.5e13
1.9e13
5.7e13
3.8e13
2.3e14
3.1e13
1.8e13
7G8
7.8e12
5.5e3
1.1e13
55e12
2.6e13
1.3e13
1.6e14
1.1e13
3.8e12
1776
3.5e13
1.1e13
1.1e4
4.7e12
3.4e12
2.8e11
8.7e13
7.4e11
3.7e12
D6
1.9e13
5.5e12
4.7e12
1.6e4
1.2e13
6.3e12
1.2e14
2.9e12
1.6e12
Dd2
5.7e13
2.6e13
3.4e12
1.2e13
7.3e3
2.5e12
6.0e13
3.9e12
1.2e13
HB3
3.8e13
1.3e13
2.8e11
6.3e12
2.5e12
3.4e3
8.2e13
1.1e12
4.9e12
P. vivax
2.3e14
1.6e14
8.7e13
1.2e14
6.0e13
8.2e13
7.2e3
9.3e13
1.2e14
Salmonella
3.1e13
1.1e13
7.4e11
2.9e12
3.9e12
1.1e12
9.3e13
1.2e4
2.5e12
UIB
1.8E13
3.8e12
3.7e12
1.6e12
1.2e13
4.9e12
1.2e14
2.5e12
2.4e4
Figure 2. 1st Derivative of Absorbance
Spectra. 40 1st derivatives from drug
susceptible P. falciparum D6 spectra
(left) and 40 1st derivatives from drug
resistant P. falciparum Hb3 spectra
(right).
RESULTS
Spectra in the mid-IR region of drug susceptible P. falciparum D6 and drug resistant P. falciparum Hb3
are shown in Figure 1. From the FTIR absorbance spectra, drug susceptible and drug resistant
species cannot be visually distinguished. Figure 2 shows the 1st derivative of the absorbance data for
drug susceptible P.f D6 and drug resistant P.f Hb3. Table 1 shows the computed Mahalanobis
distances using all optical frequencies. The chart shows within-group maximum distances (green,
diagonal values) and between-group minimum distances (red values). It was found that the longest
within-group distances are small (~103) when compared to the across-group shortest distances
(~1012). Cross validation testing was used to evaluate the accuracy of the developed algorithms for
drug susceptible P.f positive, drug resistant P.f positive, and P.f negative. The algorithm correctly
identified 120/120 drug susceptible P.f replicates, 120/120 drug resistant P.f replicates and 120/120 P.f
negatives. The sensitivity of the developed algorithm for drug-susceptible P.f. was 97-100%
(95%CI) and the specificity was 98.7-100% (95%CI). The sensitivity of the developed algorithm
for drug-resistant P.f. was 97-100%(95%CI) and the specificity was 98.7-100% (95%CI).
CONCLUSION
Table 1. Calculated Mahalanobis Distances. The vertical column represents the actual identity of the sample and
the horizontal row represents the comparison group. Values were calculated using all optical frequencies measured
from 4000-600cm-1. Values in green represent the furthest distance between samples in the same identity group, and
values in red represent the closest distance between sample from different identity groups.
Initial results indicate that FTIR IR microscopy can differentiate drug-susceptible and drug-resistant
strains of P.f. (for the strains that were studied). Additional studies with additional strains would be of
interest to determine if the initial results are valid for a wider variety of strains. In addition, it was found
that IR spectra could be used to accurately differentiate all six strains from each other as well as from
P.v. and negative controls with sensitivity of 91.19-100%(95%CI) and with a specificity of 98.98100%(95%CI). Therefore, further study of FTIR microspectroscopy as an alternative diagnostic
method is warranted.
MATERIALS and METHODS
ACKNOWLEDGEMENTS
Positive and Negative Controls. Uninfected negative controls: human blood. Salmonella/ P. vivax
infected negative controls: uninfected blood spiked with Salmonella SL1344, P. vivax clinical cases
from India. P. falciparum drug susceptible: uninfected blood spiked with strains 3D7, or 1776, or D6. P.
falciparum drug resistant: uninfected blood spiked with strains HB3, or Dd2, or 7G8. Sample
Preparation. 40 thin-film Blood smears prepared per group. All samples prepared on low-e
This work is supported by the U.S Army Medical Research and Materiel Command under contract No.W81XWH09-C-0019. The views, opinions and/or findings contained in this report are those of the authors and should not be
construed as an official Department of the Army position, policy or decision unless so designated by other documentation.
“In the conduct of research where humans are the subjects, the investigator(s) adhered to the policies regarding the
protection of human subjects as prescribed by Code of Federal Regulations (CFR) Title 45, Volume 1, Part 46; Title 32,
Chapter 1, Part 219; and Title 21, Chapter 1, Part 50 (Protection of Human Subjects).”