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Illuminating Malaria: Spectroscopy's Vital Role in Diagnosis and Research

A peer-reviewed version of this preprint was published in:
Spectroscopy Journal 2024, 2(4), 216-263. https://doi.org/10.3390/spectroscj2040015

Submitted:

04 October 2024

Posted:

07 October 2024

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Abstract
Spectroscopic techniques have emerged as crucial tools in the field of malaria research, offering immense potential for improved diagnosis and enhanced understanding of the disease. This re-view article pays tribute to the pioneering contributions of Professor Henry Mantsch in the realm of clinical biospectroscopy, by comprehensively exploring the diverse applications of spectro-scopic methods in malaria research. From the identification of reliable biomarkers to the devel-opment of innovative diagnostic approaches, spectroscopic techniques spanning the ultraviolet to far-infrared regions have played a pivotal role in advancing our knowledge of malaria. This re-view will highlight the multifaceted ways in which spectroscopy has contributed to the field, with a particular emphasis on its impact on diagnostic advancements and drug research. By leverag-ing the minimally invasive and highly accurate nature of spectroscopic techniques, researchers have made significant strides in improving the detection and monitoring of malaria parasites. These advancements hold the promise of enhancing patient outcomes and aiding in the global ef-forts towards the eradication of this devastating disease.
Keywords: 
malaria
;  
hemozoin
;  
spectroscopy
;  
infrared
;  
Raman spectroscopy
;  
near-infrared
;  
SERS
;  
TERS
;  
UV/Visible
;  
photoacoustic
;  
AFM-IR
;  
antimalarial
Subject: 
Biology and Life Sciences  -   Parasitology

1. Introduction

The Malaria remains a formidable global health challenge, with an estimated 249 million cases in 2022, a 55% increase from pre-COVID-19 levels [1]. To effectively combat this disease, there is a critical need for affordable and highly sensitive diagnostic tests that can identify asymptomatic carriers [2]. Nucleic acid amplification tests, such as Polymerase Chain Reaction (PCR) assays, are highly sensitive methods for detecting low-density malaria infections. However, their use is limited to well-equipped laboratory settings due to their complexity [2].
In response to the need for more accessible diagnostic tools, ultrasensitive Rapid Diagnostic Tests (uRDTs) have been developed. These tests, which detect proteins like Histidine-Rich Protein 2 (HRP2), have shown promise in laboratory conditions, following the same principles as conventional Rapid Diagnostic Tests (cRDTs) [3]. However, the sensitivity and specificity of these uRDTs are still lower compared to the gold standard PCR assays. In a recent meta-analysis, the Alere™ Ultra-sensitive Malaria Ag P. falciparum RDT had a sensitivity of 72.1% for symptomatic patients, higher than the 67.4% sensitivity reported for cRDTs in the same field conditions [3]. Yet, these values remain well below the 95% sensitivity achieved by PCR assays [3].
To strengthen the fight against malaria, continued research and development are critical. Improving the performance of malaria diagnostic tests is essential, bringing them closer to the accuracy of laboratory-based molecular tests while maintaining the affordability and portability that are crucial for widespread deployment, especially in resource-limited settings. In this context, spectroscopic approaches offer a viable alternative as point-of-care tests in remote villages and resource-poor clinical settings. These spectroscopic techniques can also play a role in developing new drugs and understanding the mechanisms of drug interaction, along with monitoring the therapeutic effects of drugs by quantifying parasitemia - a capability currently lacking in rapid diagnostic tests (RDTs). By enhancing the accuracy, affordability, and accessibility of malaria diagnostics, we can bolster the tools available to combat this devastating disease, particularly in the most vulnerable communities.
The use of spectroscopic techniques for disease diagnosis was eloquently summarized by Professor Henry Mantsch, who stated that "changes in tissue biochemistry must precede any morphological or symptomatic manifestations, thus allowing spectroscopic diagnosis at an earlier stage of the disease" [4]. This principle has been instrumental in the fight against malaria, where spectroscopic methods have provided a minimally-invasive and accurate approach to detecting and monitoring the disease. These diagnostic tools span a range of spectroscopic modalities, including Fourier transform infrared (FTIR), near-infrared (NIR), Raman, surface enhanced Raman scattering (SERS) tip enhanced Raman scattering (TERS), atomic force microscopy-infrared (AFM-IR), ultraviolet/visible (UV/Vis) and photoacoustic spectroscopy.
Infrared spectroscopy has shown great promise in malaria diagnosis, as it can detect specific biomarkers associated with Plasmodium-infected red blood cells. These biomarkers include lipids, proteins, and hemozoin, a by-product of the malaria parasite's hemoglobin digestion. Raman spectroscopic approaches have relied on detecting hemozoin but other markers including proteins and lipids can also be detected using Raman spectroscopy. Near-infrared spectroscopy, on the other hand, leverages the unique optical properties of hemozoin and lipids to differentiate between infected and uninfected red blood cells using the overtone and combination bands of these biomarkers. UV/Visible spectroscopy has been employed to detect changes in the optical absorption spectra of blood samples, which can be correlated with the presence and density of malaria parasites.
The advantages of spectroscopic approaches for malaria diagnosis are numerous. These techniques are minimally invasive, requiring only a small blood sample, and can provide rapid, objective results without the need for skilled personnel. Moreover, they have the potential to detect asymptomatic infections, which are crucial for interrupting disease transmission. Importantly, spectroscopic methods can also quantify parasitemia levels, enabling healthcare providers to monitor the efficacy of antimalarial drug treatments. The field of spectroscopy-based malaria diagnostics has seen remarkable advancements with the development of miniaturized chip-based spectrometers [5,6]. These compact devices integrate multiple spectroscopic functions onto a single electronic chip, enabling greater affordability and portability compared to traditional bulky spectrometers. The integration of microelectromechanical systems (MEMS) and complementary metal-oxide-semiconductor (CMOS) technology has been pivotal in realizing this compact and versatile design, allowing for the seamless incorporation of spectroscopic capabilities into smaller, more accessible platforms. This technological progress has significantly expanded the applications of clinical spectroscopy, empowering healthcare professionals and researchers with powerful analytical tools that can be readily deployed in various clinical settings.
While spectroscopic techniques have shown promising results in laboratory settings, their translation to the clinic and point-of-care settings is not without limitations. Factors such as sample preparation, environmental interference, and variations in individual physiology can affect the accuracy and reliability of these methods. Addressing these challenges through continued research and development is essential for a broader adoption of spectroscopic approaches in the field. This review will provide an overview of the application of optical spectroscopy techniques in the diagnosis and research of malaria. The review will highlight the key spectroscopic biomarkers that have been identified for the detection of malaria infection before delineating the diverse range of spectroscopic methods that have been utilized in malaria research, including FTIR, Raman, SERS, NIR, AFM/IR, photothermal imaging, TERS photoacoustic and UV/Vis spectroscopy. For each technique, the review will discuss how spectroscopic signatures have contributed to advancing our understanding of malaria, from disease diagnosis to investigating drug interactions and mechanisms of action. Additionally, the review will address the barriers that have hindered the translation of these spectroscopic approaches from the research lab to clinical practice. Finally, the review will speculate on future applications and potential of optical spectroscopy in advancing malaria research and improving disease management. Overall, this review aims to provide a comprehensive survey of the role optical spectroscopy has played, and continues to play, in the fight against the global health challenge of malaria.

2. Life cycle of the parasite

The life cycle of the malaria parasite involves both a sexual and an asexual phase (Figure 1). Most current diagnostic techniques have focused on detecting markers of the erythrocytic (red blood cell) stage of the parasite's life cycle in peripheral blood. However, the future may see the exploration of alternative, non-invasive sample types such as saliva, breath, urine, and stool as potential targets for malaria diagnosis. These non-blood-based approaches could detect markers of infection without the need to identify circulating parasites in the peripheral blood.

3. Spectral biomarkers for malaria

3.1. Hemozoin

3.1.1. Discovery of hemozoin and malaria infection

Hemozoin, also known as malaria pigment, is a dark brown/black molecule that results from the catabolization of hemoglobin by the malaria parasite. For a detailed review on the molecular analysis of hemozoin, the reader is referred to a recent in-depth review by Rathi et al. [8] that comprehensively examines the structure, function, and biosynthesis of this important malaria pigment. Johann Friedrich Meckel, a German pathologist, made early observations of the malaria pigment in 1847. He noticed this dark pigment in the organs of individuals who had died of pernicious fever, often found in the spleen, liver, brain, or kidneys on autopsy. Meckel associated this pigment accumulation with the presence of malaria in the blood. [9] In 1871, Meckel's observations were later confirmed by two scientists - Rudolf Virchow in Germany and Maxime Cornu in France. However, their findings were not widely recognized at the time as they were unpublished. [9] In 1879, Philipp Friedrich Hermann Klencke, a German scientist, was also recognized for his early observations of the malaria parasite. [10] However, due to the lack of publication and differences in his drawings compared to later photomicrographs, his contributions were not widely acknowledged. [9] The ground-breaking discovery of the malaria parasite is credited to Alphonse Lavéran in 1880. [11] Lavéran's understanding of the significance of his discovery and its potential impact on the treatment and transmission of malaria earned him the Nobel Prize in Physiology or Medicine in 1907. While there was some debate over the priority of the discovery, Lavéran's work is considered the most significant in understanding and identifying the malaria parasite. [9]

3.1.1. Organisms producing hemozoin

Human malaria parasites, including P. falciparum, P. vivax, P. malariae, P. ovale, and P. knowlesi, all produce hemozoin during their life cycles. Hemozoin formation has also been documented in the New World monkey malaria parasite P. brasilianum, the rodent malaria parasite P. yoelii, and the avian malaria parasite P. gallinaceum. [12] Beyond the Plasmodium genus, hemozoin has been identified in another protozoan parasite that infects birds, Hemoproteus columbae. Interestingly, the unrelated human parasitic worms Schistosoma mansoni [13] and Echinostoma trivolvis also dispose of heme through hemozoin production, though E. trivolvis only forms the pigment when residing in its intermediate snail host. Even insects, such as kissing bugs of the genus Rhodnius, excrete excess heme as hemozoin in their faeces. [12] So, while hemozoin is closely associated with and vital to the lifecycle of the malaria parasite, trace amounts may occasionally be detected in a few other severe infectious or hematological conditions, though it remains a uniquely defining feature of Plasmodium infections.

3.1.2. Hemozoin location in humans

For P. falciparum infection, infected red blood cells (RBCs) containing trophozoites and schizonts are often absent from the peripheral circulation especially at low levels of infection. [14] However, at high levels of infection trophozoites have been reported in peripheral blood. [15] The low number of schizonts and trophozoites in peripheral blood is due to a process called sequestration, where the infected RBCs adhere to the endothelium of blood vessels, particularly in the venules. [14] The infected RBCs develop electron-dense structures on their membrane, known as knobs, which facilitate their attachment to the venular endothelium. By sequestering in the vasculature, the mature parasites (trophozoites and schizonts) can evade destruction in the spleen. Importantly, the sequestered parasites can continue to release new merozoites, which can then invade uninfected RBCs, thereby perpetuating the asexual cycle of the parasite. [14] This sequestration of P. falciparum-infected RBCs in the vasculature presents a potential opportunity for non-invasive diagnostic approaches. The stationary, sequestered cells containing hemozoin-rich trophozoites and schizonts could serve as targets for spectroscopic detection, such as NIR or Raman spectroscopy. Late-stage rings and mature stage IV-V gametocytes, which are found in peripheral blood, do contain hemozoin. [16] The female gametocyte is characterized by a centralized accumulation of the hemozoin pigment. The hemozoin granules are condensed and localized within the centre of the female gametocyte. In contrast, the hemozoin pigment in the male gametocyte is dispersed throughout the infected red blood cell, rather than being concentrated in the center. [16] Hemozoin is also found in leukocytes, including neutrophils [15,17] and monocytes. [17] While schizonts and trophozoites are not common in peripheral blood, other cell types including late-stage rings, gametocytes and leukocytes still make hemozoin an attractive marker for spectroscopy-based diagnosis.

3.1.3. Crystal structure

Hemozoin, is a crystalline pigment that has a well-characterized molecular and crystal structure. At the molecular level, hemozoin is a dimer of heme (ferriprotoporphyrin IX) molecules, where each heme molecule consists of a central ferric (Fe3+) iron atom coordinated to a porphyrin ring and various side chains. Synchrotron X-ray fluorescence powder diffraction data indicated that the heme molecules are linked together through a reciprocal coordination bond between the central iron atom of one heme and a carboxylate group of the propionate side chain of the adjacent heme. [18] The Fe–O bond distance converged to a value of 1.886(2) Å, which was found to be consistent with other high spin ferric porphyrins. [18] These hemozoin dimers then crystallize into a unique monoclinic crystal structure, with the heme dimers arranged in stacks and held together through hydrogen bonding and π-π stacking interactions between the porphyrin rings, as well as Van der Waals interactions between the alkyl side chains (Figure 2). The resulting hemozoin crystals typically range in size from 0.2 to 1.0 μm in length and exhibit a characteristic needle-like or rod-shaped morphology with a hexagonal cross-section. This unique crystal structure allows the malaria parasite to sequester the potentially toxic heme molecules, preventing them from causing oxidative damage to the parasite's cellular components, and the presence of hemozoin crystals in the blood of infected individuals serves as a diagnostic marker for malaria.

3.1.4. Raman spectroscopy of hemozoin

Raman spectroscopy is a powerful tool for the analysis of hemozoin, as it can provide detailed information about the molecular structure and vibrational modes of this important biomolecule. When analyzing hemozoin using Raman spectroscopy, several distinct and characteristic bands are observed in the spectrum. Excitation of hemozoin with a laser wavelength in the Soret band region (e.g., 406 nm) results in a significant enhancement of the Raman signal, a phenomenon known as type-A resonance Raman scattering (or Frank-Condon Scattering), due to the π→π* electronic transitions of the porphyrin, [20] which is in resonance with the excitation wavelength, allowing for the detection of very low concentrations of hemozoin and making it a highly sensitive technique for the identification and quantification of this biomolecule. The Raman bands observed in this region are primarily associated with the porphyrin ring vibrations, such as the intense ν4 mode at around 1375 cm−1, which corresponds to the C-N stretching of the porphyrin macrocycle. Excitation of hemozoin in the near-infrared region, such as with a 785 or 830 nm laser, also results in enhanced Raman scattering, attributed to the presence of the central iron atom in the heme group, which can undergo charge-transfer transitions with the porphyrin macrocycle in this wavelength range. Dramatic enhancement of certain Raman modes when irradiating β-hematin and hemin (the precursor to hemozoin) with 780 nm and 830 nm laser excitation wavelengths is observed (Figure 3). Specifically, the A1g modes at 1570, 1371, 795, 677, and 344 cm-1, the ring breathing modes in the 850-650 cm-1 range, and the out-of-plane modes including iron-ligand modes in the 400-200 cm-1 range were significantly enhanced. This enhancement was more pronounced in beta-hematin compared to hemin. The absorbance spectra recorded during the transformation of hemin to beta-hematin showed a red-shift of the Soret and Q (0-1) bands, which has been interpreted as resulting from excitonic coupling due to porphyrin aggregation. Additionally, a small broad electronic transition observed at 867 nm was assigned to a z-polarized charge-transfer transition dxz → eg(π*).
The extraordinary band enhancement observed when exciting beta-hematin with near-infrared wavelengths, compared to hemin, can be explained by the theory of aggregate-enhanced Raman scattering. This occurs due to intermolecular excitonic interactions between the porphyrinic units, leading to a superposition of electronic transitions that result in enhanced Raman scattering. Hemozoin pigment, like other haem pigments, produces intense overtone tones when excited with green laser light. [21] Enhancement with the 514.5 and 532 nm excitation laser lines, which are in close proximity to the vibronic Qv band of the visible spectrum of hemoporphyrins, enables the C–Term enhancement mechanism to dominate, which occurs between forbidden electronic transitions which are prohibited at the equilibrium geometry of the molecule. [21] The enhanced Raman scattering observed in both the Soret band and near-infrared regions is a characteristic feature of hemozoin and allows for its sensitive and selective detection in complex biological samples, such as those obtained from malaria-infected individuals, making Raman spectroscopy a valuable technique for the diagnosis and study of malaria.

3.1.5. FTIR spectroscopy of hemozoin

The FTIR spectra of β-hematin and hemozoin is shown in Figure 4. Slater et al. [22,23] were the first to report the infrared (IR) spectrum of hemozoin and β-hematin. They identified two distinct absorption bands in the hemozoin spectrum at 1664 cm-1 and 1211 cm-1, which were absent in the spectra of free heme (hematin) and heme complexes (hemin). By comparing the hemozoin spectrum to other iron-carboxylate containing compounds, Slater et al. [23] proposed that the heme units in hemozoin are coordinated in a unidentate fashion, where one of the carboxylate C-O bonds exhibits double-bond character, resulting in a C=O stretching vibration between 1700 and 1600 cm-1. The strong absorption at 1211 cm-1 was also attributed to the C-O stretching of an axial carboxylate ligand, based on studies of metalloporphyrin compounds with O-methyl groups, which showed similar C-O stretching bands in the 1270-1080 cm-1 region. [23] These distinct spectroscopic signatures of hemozoin, compared to the heme precursors, provided a valuable IR-based marker that could potentially be exploited for the development of diagnostic tools for malaria infection. The unique IR absorption features highlighted the structural differences between the crystalline hemozoin and the soluble heme species, which have important implications for understanding the biochemistry of malaria parasites.

3.1.6. UV/Visible spectroscopy of hemozoin

Hemin exhibits a distinct Soret band (or B band) that is resolved into two bands at 363 nm (S′) and 385 nm (S), along with Q bands at 495 nm, 521 nm, 550 nm (very weak), and 612 nm.In contrast, the Soret band of β-hematin collapses into a single broad band centered at 389 nm, along with a Q band at 649 nm, when observed at low pH. This distinction helps to discriminate β-hematin from hematin and hemin. Other bands observed in the absorbance spectrum of β-hematin include bands at 513 nm and 550 nm. [20] The absorbance spectrum of β-hematin at low pH exhibits characteristics consistent with those reported by Bohle et al.. [24] Their study, using a potassium bromide pellet technique, revealed distinct spectral bands at 406 nm, 510 nm, 538 nm, and 644 nm. [24] These findings align closely with our observed spectrum, providing corroborative evidence for the spectral properties of β-hematin under acidic conditions. [20] They also reported a band at 580 nm that is not observed in the solution spectra reported here. [24] The 649 nm band is characteristic of hemin aggregates, as determined by micro-spectrophotometry [25] and photoacoustic spectroscopy. [26] This band sequentially shifts (640 nm, 645 nm, 649 nm) as the pH decreases. Figure 5 shows representative spectra of the conversion of hemin to β-hematin. The decrease in pH causes broadening and reduced intensity of the Soret band in β-hematin, attributed to aggregation and precipitation. An apparent red-shift occurs from 363 nm to 389 nm, though accurate maxima determination is difficult due to the broad nature of the band at low pH. Red-shifts in both Soret and Q-bands indicate excitonic coupling from porphyrin aggregation. Interestingly, a small, broad band centered around 867 nm is observed in both hemin and β-hematin. [20] This band slightly red-shifts as the reaction progresses and appears less intense in β-hematin compared to hemin, likely due to β-hematin precipitation. This previously unreported band is tentatively assigned to a charge transfer (CT) transition, specifically band I (dxz → eg(π*)). [20] These spectral changes provide insight into the formation and characteristics of β-hematin, offering valuable information about the molecular processes involved in its synthesis and structure.

3.1.8. NIR spectroscopy of hemozoin

The near-infrared (NIR) spectra of hemozoin standard purchased from Invivogen and hemozoin extracted from infected red blood cells, along with synthetic β-hematin, are presented in Figure 6A-C. The spectra do not show much consistency, indicating a lot of impurities. Notably, the spectrum of hemozoin isolated from infected red blood cells (Figure 6B) exhibits slight differences in the 1960 nm - 2500 nm region compared to the hemozoin purchased from Invivogen. Specifically, four intense bands are observed at 1975 nm, 2055 nm, 2133 nm, and 2228 nm in the extracted hemozoin, whereas the synthetic β-hematin (Figure 6C) shows a single intense peak at 2216 nm. [5] These additional bands in the extracted hemozoin sample are tentatively assigned to ν(CH3/CH2) vibrations, possibly originating from residual hemoglobin left behind during the isolation process. The experimental NIR wavelength values are based on the second derivative spectra, providing enhanced spectral resolution and allowing for the identification of these subtle differences between the natural and synthetic forms of the pigment possibly resulting from impurities. [5]

3.2. Lipids

The role of lipids in the formation of hemozoin, a by-product of the malaria parasite's digestion of host hemoglobin, has been the subject of investigation. Bendrat et al. [27] suggested that this process is mediated by lipids, as they observed that an acetonitrile extract of P. falciparum promoted the formation of beta-hematin, a synthetic analogue of hemozoin. Further evidence supporting the involvement of lipids in hemozoin formation comes from the dramatic increase in lipid content within P. falciparum-infected erythrocytes [28,29,30,31] and observations of hemozoin localization in close proximity to neutral lipid bodies (NLBs). [30,32] Specifically, the parasites synthesize and package triacylglycerol (TAG) and diacylglycerol (DAG) into NLBs in a stage-specific manner within the digestive vacuole (DV). [30,33,34] These lipids are barely detectable in uninfected erythrocytes. [28,35] However, the precise function of NLBs during parasite growth remains unclear. It has been hypothesised that NLBs may serve as a depot of lipid intermediates generated during the digestion of phospholipids, [33] or alternatively as a source of lipids that can be mobilized to supply the growing parasite with fatty acids and acylglycerols for membrane generation. [34] Importantly, the specific composition of the P. falciparum NLB lipid blend, identified by mass spectrometry, has been shown to be sufficient for mediating hemozoin formation. [32] This lipid blend consists of a 4:2:1:1:1 ratio of the monoglycerides monostearoylglycerol (MSG) and monopalmitoylglycerol (MPG), and the diglycerides 1,3-dioleoylglycerol (DOG), 1,3-dipalmitoylglycerol (DPG), and 1,3-dilinoleoylglycerol (DLG). [32] Research has revealed some fascinating insights into the formation of β-hematin in the presence of lipids. Egan et al. [36] discovered that β-hematin can rapidly form under conditions that closely mimic the physiological environment, particularly in the presence of interfaces between octanol/water, pentanol/water, and lipid/water. Molecular dynamics simulations have provided further elucidation of this process. These simulations have shown that a precursor to the hemozoin dimer can spontaneously form in the absence of the competing hydrogen bonds that are typically present in water. This suggests that beta-hematin likely self-assembles near a lipid/water interface within the living organism (in vivo) as confirmed by Raman spectroscopy. [36]
The ability of β-hematin to form so readily under these realistic conditions is a significant finding. It sheds light on the mechanisms underlying the production of this important biological substance and highlights the importance of examining these processes in the context of relevant interfaces and environmental factors. Further research in this direction could yield valuable insights into the physiological relevance and potential applications of β-hematin.
FTIR synchrotron spectra recorded of single cells from the different phases of the erythrocytic life cycle show the continuous increase in lipid components as the cells progress from being uninfected to the ring, schizont and trophozoite stages. [31] Figure 7 shows synchrotron FTIR spectra of single uninfected red blood cells compared with ring, trophozoite and schizont stages cells. The ester carbonyl band from triglyceride fatty acids at 1742 cm-1 increases through the different stages and is barely discernible in uninfected cells. Bands at 2922 cm-1, 2852 cm-1, assigned to the νasym(CH2 acyl chain lipids) and νsym(CH2 acyl chain lipids), respectively, increase as the parasite matures from its early ring stage to the trophozoite and finally to the schizont stage. [15] The Principal Component Analysis (PCA) enabled discrimination between uninfected, ring, trophozoite and schizont stages on a PC1 versus PC2 Scores plot. [31] Raman spectroscopy was employed to differentiate between P. falciparum and P. vivax on a PCA 2D scores plot. The PCA performed in the CH stretching region 3100-2800 cm-1 showed a clear clustering of the two species, indicating that the lipidomic component can be used to distinguish species of sp. using Raman spectroscopy. This finding is crucial for informing appropriate drug treatment strategies against different species. [37]

3.3. Nucleic acids

The Plasmodium genome is a circular, AT-rich DNA molecule of approximately 23 megabases, containing around 5,400 genes. [38] Compared to the genomes of free-living eukaryotic microorganisms, the genome of the intracellular malaria parasite Plasmodium encodes a smaller number of enzymes and transporters. However, a substantial proportion of the Plasmodium genome is dedicated to genes involved in immune evasion mechanisms and host-parasite interactions. [38] During the blood stage of the Plasmodium life cycle, the parasite's DNA content or the number of daughter cells can increase dramatically within a single round of replication. On average, studies have documented a 20-fold to 30-fold rise in DNA content or daughter cell number over the course of one proliferative cycle. [39,40] This large amount of Plasmodium DNA inside the host red blood cell can be detected and used for diagnosis of malaria infection. In addition to the DNA within infected red blood cells, Plasmodium parasites also release DNA fragments into the host's bloodstream. These cell-free, circulating Plasmodium DNA molecules can be detected in the modiumma or serum of infected individuals. [40] The presence and quantification of circulating malaria DNA in plasma has emerged as a valuable biomarker for the diagnosis, monitoring, and management of malaria. [41] Circulating Plasmodium DNA can be detected even in low-density infections or in cases where the parasites are sequestered in the deep vasculature, making it a more sensitive method compared to traditional microscopy. Analysis of circulating malaria DNA has also provided insights into parasite dynamics, drug resistance, and the genetic diversity of sp. during infection.
In terms of RNA, malaria parasites exhibit some unique genomic features when compared to other eukaryotic organisms. Most notably, the Plasmodium genome lacks the long, tandemly repeated arrays of ribosomal RNA (rRNA) genes that are characteristic of many other eukaryotes. [38] Instead, Plasmodium parasites contain multiple single 18S-5.8S-28S rRNA units distributed across different chromosomes. [38] Furthermore, the expression of these rRNA units is tightly regulated throughout the various stages of the Plasmodium life cycle. [38] This results in the selective expression of different sets of rRNAs at different points during the parasite's development. This developmental regulation of rRNA gene expression contrasts with the more uniform rRNA profiles seen in many other eukaryotic organisms. [38]
Vibrational spectroscopic techniques, such as Fourier-transform infrared (FTIR) and Raman spectroscopy, have faced challenges in the direct detection of DNA within malaria-infected cells. In an early FTIR spectroscopy study investigating single infected rings, trophozoites, and schizonts, no clear DNA phosphodiester bands were identified at the expected wavenumber values of ~1240 cm-1 and ~1080 cm-1, corresponding to the asymmetric and symmetric PO2- vibrations, respectively. [31] Similarly, Raman spectroscopy studies did not detect the characteristic Raman bands at 813 cm-1 or 840 cm-1 that would be anticipated for RNA or DNA. [42] The apparent lack of detectable DNA/RNA signals in these vibrational spectra suggests that the parasite DNA may be present at levels below the sensitivity threshold of these techniques. Another potential explanation for this observation lies in the conformational dependence of the DNA molar extinction coefficient. When DNA is in the dried, A-DNA conformation, the molar extinction coefficient is significantly lower compared to the hydrated, B-DNA state. This is likely due to the more ordered arrangement of the phosphodiester groups in B-DNA, in contrast to the less organized structure of A-DNA. Specifically, in the case of FTIR spectra of DNA, the symmetric PO2- vibration is approximately 3 times less absorbing in the A-DNA form compared to the B-DNA form, and the asymmetric PO2- band is shifted from ~1240 cm-1 in the A-form to ~1220 cm-1 in the B-form. [43] These conformational-dependent changes in the DNA FTIR spectrum may explain the apparent lack of detectable DNA signals in FTIR analyses of dried, malaria-infected red blood cells, as the less IR-active A-DNA conformation predominates under those conditions.

3.4. Proteins

3.4.1. FTIR spectroscopy of proteins

The FTIR spectra of uninfected red blood cells is dominated by the alpha-helical protein hemoglobin. Red blood cells contain a remarkably high concentration of hemoglobin, comprising approximately 95% of the total cytosolic proteins within these cells. [55] This hemoglobin is present at a concentration of 5 millimolar (mM) inside the red blood cell. [55] The percentage of hemoglobin in different stages of malaria parasites (rings, trophozoites, and schizonts) varies as the parasite develops within the red blood cell. In the early ring stage, the parasite has consumed relatively little hemoglobin, with approximately 0-20% of the host cell hemoglobin digested. The trophozoite stage is the most active feeding stage, during which about 60-80% of the host cell hemoglobin is typically digested. [55] By the late schizont stage, most of the hemoglobin has been consumed, with approximately 80-100% of the host cell hemoglobin digested. These percentages are approximate and can vary depending on the specific sp. and individual parasites.
FTIR spectra of both infected and uninfected red blood cells at all stages are dominated by strong amide bands appearing at 1650 cm-1 and 1544 cm-1 assigned to the amide I mode (ν(C=O) + ν(C-N) + δ(NH2)) and amide II mode (ν(C-N) + δ(NH2) + ν(C-C) + ν(C=O)), respectively, and a weaker band at ~1300 cm-1 assigned to the amide III mode ((ν(C-N) + δ(CH2) + δ(NH2) +δ(C-C-N) + δ(C-O)). Other bands including the band at 1450 cm-1 and ~3300 cm-1 are assigned to the carboxylate group of amino acid side chains and the amide A mode (N-H stretching) from the peptide functional group. The amide I mode is very sensitive to protein conformational change and the profile can be used to predict the relative contribution of alpha helical, β-pleated sheet, random coil and other protein conformational motifs. [56] The amide I and II band profile has been shown to change radically between the different stages of the malaria parasite especially after performing a second derivative. [31] However, in our field trial using ATR-FTIR technology the amide I and II region was found not to be particularly useful in diagnosing infected from uninfected patients and instead the region between 3000-2700 cm-1 and 1200-900 cm-1 proved to better in terms of sensitivity and specificity for diagnosing patients with malaria infection. [57]

3.4.2. Raman spectroscopy of proteins

Raman spectroscopy provides valuable information about protein structure and composition through characteristic vibrational bands. Besides the amide modes mentioned above in the context of FTIR, Raman also shows bands from aromatic amino acid side chains which result in distinct bands from phenylalanine, which shows a sharp peak near 1000 cm⁻¹, tyrosine exhibits a doublet at approximately 830 and 850 cm⁻¹, and tryptophan displays bands at about 760 and 1340 cm⁻¹. The S-S stretching vibration of disulfide bonds appears around 500-550 cm⁻¹. C-H stretching vibrations from aliphatic amino acids are observed in the 2800-3000 cm⁻¹ region. The band near 1450 cm⁻¹ is attributed to CH₂ and CH₃ deformations. Additionally, the region between 500-800 cm⁻¹ contains various skeletal vibrations that can provide information about protein conformation. These Raman bands collectively offer insights into protein secondary structure, side chain environments, and overall conformation, making Raman spectroscopy a powerful tool for protein analysis.

3.4.3. UV/Visible spectroscopy of proteins

The UV/Visible spectrum of proteins is characterized by several key absorption bands that provide valuable information about their structure and composition. The primary absorption band in the UV region, known as the peptide bond absorption, occurs around 190-230 nm and is due to the n→π* and π→π* transitions of the peptide bond. This band is present in all proteins and is sensitive to secondary structure. The far-UV region (180-240 nm) is particularly useful for analyzing protein secondary structure, with α-helices showing a characteristic double minimum at 208 and 222 nm, and β-sheets displaying a single minimum near 215 nm. In the near-UV region (250-300 nm), absorption is primarily due to aromatic amino acid side chains: phenylalanine absorbs weakly near 257 nm, tyrosine shows a peak around 274 nm, and tryptophan exhibits the strongest absorption with a maximum near 280 nm. The exact positions and intensities of these aromatic peaks can provide information about the local environment and tertiary structure of the protein. Disulfide bonds (cystine) contribute a weak absorption band around 260 nm. Some proteins containing metal ions or other chromophores may show additional absorption bands in the visible region, such as the heme group in hemoglobin and myoglobin, which gives rise to the Soret band around 400 nm and Q-bands between 500-600 nm. Flavoproteins typically show absorption bands in the 350-500 nm range. The overall shape and intensity of the UV/Visible spectrum can be used to estimate protein concentration, purity, and to monitor conformational changes. It is worth noting that the exact positions and intensities of these bands can vary depending on the specific protein, its environment, and any modifications or ligands present.

3.4.4. Near infrared spectroscopy of proteins

NIR spectroscopy has been widely used to investigate protein structures, folding patterns in polypeptides, and amino acid composition. [58] The primary functional groups for Near-Infrared (NIR) spectroscopic analysis in protein studies are the amides and C-H modes. NIR absorption bands related to proteins, particularly amides, are thoroughly detailed in the review by Salzer. [59] The 1500-1530 nm region includes the NH stretching first overtone, while the 2050-2060 nm region pertains to the NH-stretching combination bands. Additionally, absorption bands within the 2148 to 2200 nm wavelength range are valuable for constructing calibration and prediction models in protein research. [59,60]
Around the early 2000s, more compact, rapid, and user-friendly spectrometers equipped with state-of-the-art software began to emerge. Miniaturization of NIR devices has significantly reduced the high capital costs associated with traditional large NIR instruments. Over the past decade, there have been substantial advancements in instrument miniaturization, including those designed for use in non-traditional environments. [61,62] Miniaturized NIR instruments are now being utilized by the military for security surveillance, farmers for rapid analysis of agricultural produce and pest control, and in pharmacies for drug screening. Additionally, NIR handheld instruments have shown strong potential for disease diagnosis, though this area has not yet been fully explored. [63]

5. Clinical field trials of ATR-FTIR spectroscopy

Henry Mantsch was vociferous in promoting biospectroscopy into medicine with development of a widely implemented point-of-care (PoC) spectroscopic disease diagnostic being a major aim. Malaria diagnosis by IR spectroscopy shows great promise is this respect. Laboratory experiments with mid-IR spectroscopy using spiked red blood cells [7,91] had demonstrated potential for detection of parasitemia at levels somewhere between the limits of light microscopy and PCR detection, as well as being sensitive to different stages in the malaria parasite life cycle. [31]Indeed, the need for a cheap, rapid and accurate spectroscopic test with the potential to detect parasitemia at levels necessary to detect asymptomatic carriers in mass screening efforts in low-income settings has made IR spectroscopy-based testing very attractive. This has led to date to two pilot field trials designed to test this notion, published concurrently in the Malaria Journal. [57,92]
Both trials were similar employing portable FTIR ATR spectrometers to measure extracted red blood cell (RBC) fractions isolated from clinical samples obtained using venipuncture. The studies differed in terms of sampling strategy. The Heraud et al. [57] study obtained samples at regional hospitals in Northeast Thailand from patient presenting with symptoms consistent with malaria infection. Whereas, in the Mwanga et al. [92] samples were obtained in a cross-sectional malaria parasite prevalence survey in south-eastern Tanzania, with subjects included regardless of the perceived state of their health. The patient sample size was similar in both studies with Mwanga et al. [92] considering 296 and Heraud et al. [57] 318 patients. Neither of the studies was point-of-care, with both measuring stored samples. In the Heraud et al. [57] study methanol-fixed RBCs were stored and measured subsequently by placing an aliquot of the fixed cell slurry on the ATR crystal that was then dried by air stream and measured. Mwanga et al. [92] placed a drop of the RBC extract on Whatman filter paper with the dried sample spot pressed down onto the ATR crystal with the instrument anvil.
Analysis was similar in overall approach for both studies with the use of PCR as the gold standard, however details differed. Heraud et al. [57] employed quality testing of spectra using water vapour and signal to noise ratio as testing criteria as well as examining similarity of individual spectra to the model as a basis for accepting or rejecting spectra, whereas Mwanga et al. [92] appeared to not quality test spectra at all. Heraud et al. [57] tested the effects of spectral pre-processing and spectral regions on classification model performance, emphasising the need for the incorporation of pre-processing into a spectroscopy-based malaria diagnostic, conceiving a system where quality testing, pre-processing and classification would utilise “cloud-based” algorithms and demonstrated the ability of their measurement system to receive and send data from a remote computer. By contrast, Mwanga et al. [92] used an arbitrarily chosen pre-processing scheme.
Classification modelling approaches were similar in both studies with data split 70:30 and 80:20 into calibration and validation sets, for Heraud et al. [57] and Mwanga et al. [92], respectively. Prospective validation of calibration models was not done in either study. Heraud et al. [57] compared Support Vector Machine (SVM) and Partial Least Squares Discriminant Analysis (PLS-DA) classification modelling, finding SVM performed slightly better with 92% sensitivity (3 false negatives out of 39 true positives) and 97% specificity (2 false positives out of 57 true negatives) and an area under the receiver operation curve (AUROC) of 0.98. Whereas Mwanga et al. [92] compared k-nearest neighbours (KNN), logistic regression (LR), (SVM), naïve Bayes (NB), XGBoost (XGB), random forest (RF), and Multilayer perceptron (MLP), finding LR performed slightly better than the other approaches with a sensitivity of 92% (2 false negatives out of set of 28 true positives) and specificity of 93% (2 false positives out of a set of 24 true negatives). Mwanga et al. [92] did not state AUROC performance. One reason for the much smaller validation set in the Mwanga et al. [92] study was that it only included patients with monospecific infection (P. falciparum). When mixed infections (P. falciparum plus P. ovale) were included, the sensitivity dropped to 82% and specificity to 91%. Unlike the study of Mwanga et al. [92], Heraud et al. [57] employed positive malaria samples infected by both P. falciparum, P. vivax and mixed infections (P. falciparum, n=58; P. vivax, n=77; P. falciparum/ P. vivax, n=16) in both calibration and validation sets. Comparison of classification performance between the two studies are summarised in Table 4.
The advantage of PLS-DA modelling as described by Heraud et al. [57] was the ability to examine regression loadings explicitly and hence determine the spectral regions most heavily weighted for classifying malaria positive and negative spectra. This is shown in Figure 21.
Analysis of the Partial Least Squares Discriminant Analysis (PLS-DA) loadings reveals a striking pattern: the most significant positive weightings for malaria-infected samples are predominantly associated with lipid-related spectral bands. Specifically, the CH3/CH2 bending modes (~1370 cm-1 and 1450 cm-1) and CH2 stretching modes (~2850 cm-1 and 2920 cm-1) show particularly strong correlations with malarial infection (Figure 21). This pronounced lipid signature in the spectral data suggests a potential biochemical marker for malaria infection, likely reflecting the parasite's impact on host cell lipid metabolism or its own lipid-rich structures. This is consistent with the increase in lipid absorbance observed in malaria spiked RBC samples in all life stages compared with controls. [31] By contrast, the spectral regions most heavily weighted for malaria positive and negative samples with the Logistic Regression modelling employed by Mwanga et al. [92] are harder to determine with any precision with only single wavenumber values influencing model prediction provided . Contrary to what one would presume to be logical, and remaining unexplained, wavenumbers 1729 cm-1 and 1730 cm-1, presumably relating to lipid ester carbonyl absorbance, were those most strongly determining the classification of non-infected control samples in the LR modelling.
Little information could be gained about the differences between infected and non-infected samples from the average spectra presented by Mwanga et al. [92] which were dominated by intense bands from cellulose. The spectral contamination from the cellulose background was intense across the entire fingerprint region and potentially could obscure bands related to malaria parasitemia. It is likely to be difficult to fully account for the spectral contamination by spectral pre-processing, a less than perfect situation in terms of modelling accuracy, limiting the usefulness of this sampling approach, compared to the Heraud et al. [57] study where fixed aliquots of RBCs were measured directly. The very small sample volume probed by ATR FTIR spectroscopy may also be a factor limiting detection sensitivity in both these studies. Other techniques that by their nature measure larger sample volumes such as IR transflection [161]and NIR spectroscopy [5] would be expected to have lower detection limits by probing larger cell volumes, thus proving superior to ATR methods for the development of a reliable malaria field diagnostic. These alternative approaches also have the potential to use smaller handheld devices better suited to point-of-care measurements in the field than the still rather cumbersome ATR spectrometers used in these studies. The ease of use of these handheld devices for use by minimally trained users would certainly be enhanced by employing remote “Cloud-computing” to handle spectral quality control, pre-processing, modelling and return of the diagnostic result to the user as advocated by Heraud et al. [57].

6. Pathway and obstacles to translation

In addition to improvements in measurement technologies, further scope for improvement of the diagnostic concept may lie in reducing the blood sample volume for the measurement. The two field studies relied on venepuncture for sampling however only a small quantity of the blood sample was used for spectroscopy. Development of less invasive methods are required, especially to compete with existing technology such as rapid antigen testing used as a comparison diagnostic in both studies. Given the very small measurement volumes that are probed by spectroscopic methods it should be possible to engineer a more refined sample processing technology that could work with finger-prick volumes of blood and provide direct presentation of the processed sample to the measurement device. This development is essential for making the diagnostic more rapid, easier to use and e improving measurement consistency. A potentially ground-breaking approach in malaria diagnostics would be the in vivo detection of parasitemia using Near-Infrared (NIR) spectroscopy, where NIR light is transmitted directly through the patient's skin. While this non-invasive method is theoretically feasible, it currently faces significant practical challenges. The primary hurdle is the high detection limit, which surpasses the sensitivity required for accurate diagnosis of low-level parasitemia. This limitation stems from factors such as signal attenuation by skin and other tissues, the complex optical properties of blood, and the relatively low concentration of parasites in peripheral circulation during early stages of infection. Despite these current constraints, ongoing advancements in NIR technology, signal processing algorithms, and our understanding of malaria pathophysiology may eventually overcome these barriers, making this non-invasive approach a reality in future malaria diagnostics. How many patient spectra are needed to develop classification models powerful enough to accurately diagnose malaria prospectively?
Heraud et al. [57] attempted to estimate empirically the size of the calibration set necessary to achieve optimal modelling accuracy by using field data from their trial with classification performance being monitored by cross validation using successive PLS-DA models with increasing number of samples in the calibration data sets (up to n = 200; using 20 replicates in each case). Classification error was observed to decrease exponentially with sample size (n), with extrapolation of the trend line predicting very low error rate at n = 500. A “cloud” based diagnostic would enhance the opportunity to improve classification power in real time by enabling the updating of models on a continuous basis as new spectra were acquired.
Apart from having adequate sample size in the calibration set, the need to control for environmental variables is paramount for developing a reliable spectroscopy-based diagnostics. Unlike genetic testing such as PCR, it can be argued that phenotypic testing such as spectroscopy-based diagnostics is more vulnerable to perturbation by environmental variables. For example, the study by Martin et al. [90] demonstrated that the use of different anticoagulant tubes could affect the detection and quantification of malaria parasitemia in human red blood cells by ATR-FTIR spectroscopy. This underscores the need to develop standardised methods and protocols that attempt to minimise and control for as many of these environmental variables as possible. An advantage of a spectroscopy-based diagnostic compared to PCR testing, for example, is that other important diagnostic information apart from the presence of parasitemia might be gleaned from the single sample spectrum using multiple classification models applied in parallel. Examples are the determination of blood chemistry such as glucose, urea and haemoglobin levels [91] or diagnosis of other key indicators such as Glucose-6 Phosphate Dehydrogenase deficiency, [162] in addition to the malaria diagnosis, making the test more valuable to the practitioner in deciding the best course of treatment.

7. Application of spectroscopy to monitor drug interactions

Vibrational spectroscopy including techniques like FTIR and Raman spectroscopy, offers a powerful tool for monitoring drug interactions in malaria-infected red blood cells. These non-destructive methods allow for real-time analysis of biochemical changes at the molecular level, enabling researchers to observe how antimalarial drugs affect the parasite's metabolism and the host cell environment. This approach provides insights into the mode of action of drugs, parasite resistance mechanisms, and the overall biochemical impact of the treatment. Furthermore, combining these spectroscopic techniques with advanced chemometric modelling enhances the ability to discriminate between healthy and infected cells, facilitating high-throughput drug screening and personalized therapeutic strategies against malaria.
Many antimalarial drugs exhibit strong Raman scattering, making this technique highly suitable for (i) conducting structural studies both in the presence and absence of metabolites, and (ii) detecting and quantifying these drugs under various conditions. [163] Many studies have investigated the significance of complex formation between antimalarial drugs such as chloroquine and ferriprotoporphyrin IX Fe(III)PPIX in solution. [164] For instance, Frosch et al. [165] reported the presence of a non-covalent interaction in the electronic ground state of the drug–target complex using polarization-resolved resonance Raman spectroscopy. They suggested that the non-covalent interaction of chloroquine with hematin induces a change in the excited-state geometry along specific ground-state normal coordinates. In another study by the same team, the Density Functional Theory (DFT) calculations were used to perform the mode assignment, which indicated that the protonation states of CQ significantly affect its molecular geometry, vibrational modes, and molecular orbitals. These alterations are crucial for its π–π interactions with haemozoin. [166] Furthermore, the team has performed a number of investigations to elucidate the structure of antimalarial drugs such as halafantine, [167] the antiplasmodial naphthylisoquinoline alkaloid dioncophylline A, [168,169] quinine in cinchona bark, [166] and mefloquine [170] using Raman spectroscopy and DFT calculations.
Webster et al. [31] utilized resonance Raman spectroscopy to monitor the effects of chloroquine (CQ) treatment on cultures of falciparum trophozoites. This vibrational spectroscopic study is the first of its kind to investigate the effect of drug treatment on single P. falciparum-infected red blood cells. PCA reveals that the intensity of the A1g modes; 1570, 1376, 796, 678 cm−1 and the B1g modes: 1552, 751 cm−1 is reduced in CQ treated cells compared to the untreated controls. The exact mechanism by which CQ exerts its antimalarial effects remains not fully elucidated. Theories have suggested that the CQ binds to ferriprotoporphyrin IX (FPIX) to form the FPIX–CQ complex and can lead to parasite cell autodigestion. Kozicki et al. [171] investigated and compared the CQ-treated and untreated cultured falciparum-infected human red blood cells (iRBCs) using attenuated total reflection (ATR-FTIR) and Raman spectroscopy. The intensities of bands correspond to biochemical moieties such as proteins, lipids, nucleic acids and carbohydrates were changed in response to CQ treatment. The ATR-FTIR analysis reveals an increase in the CH stretching bands within the 3100–2800 cm⁻¹ range in CQ-treated iRBCs, indicating a higher concentration of saturated lipids. The PCA showed a characteristic of ferric heme band at 1379 cm−1 due to high oxygenated hemoglobin concentration in the CQ-treated iRBCs. Recently, Wolf et al. [172] manifested the significance of quinoline chloroquine – hematin interaction in solution using an advanced, highly parallelized Raman difference spectroscopy setup. A shift of (-1.12 ± 0.05) cm⁻¹ was observed in the core-size marker band ν(CαCm)asym peak position of the 1:1 chloroquine-hematin mixture compared to pure hematin. Additionally, the oxidation-state marker band ν(pyrrole half-ring)sym showed a shift of (+0.93 ± 0.13) cm⁻¹. This is consistent with the results from DFT calculations. The study has provided significant insights to the antimalarial action of CQ.
These findings demonstrate the capability of vibrational spectroscopy to capture molecular-level changes induced by drug binding and metabolism. However, a multimodal spectroscopic approach, combining both Raman and FTIR techniques, would provide more comprehensive insights by offering complementary information on the molecular structure, biochemical environment, and interaction dynamics. Furthermore, integrating computational chemistry tools such as DFT and molecular dynamics simulations can enhance the interpretation of spectral data, helping to elucidate the underlying mechanisms of drug action and resistance at the atomic level. This holistic approach holds promise for optimizing antimalarial drug design and improving our understanding of their interactions with both the parasite and the host cell.

8. Conclusions

The application of spectroscopic techniques in malaria research represents a paradigm shift in our approach to disease diagnosis, management, and drug development. As we stand at the crossroads of technological innovation and global health challenges, these advanced spectroscopic methods offer a glimpse into a future where rapid, accurate, and minimally invasive diagnostics could revolutionize malaria control efforts. Moreover, spectroscopy plays a crucial role in the development and evaluation of new antimalarial drugs. By providing detailed molecular insights into drug-parasite interactions, it enables researchers to design more effective compounds and optimize their efficacy against resistant strains.
The work pioneered by Professor Henry Mantsch and furthered by numerous researchers has laid a foundation for translating laboratory successes into real-world applications. Spectroscopic techniques, such as Raman and infrared spectroscopy, offer powerful tools for assessing the effectiveness of antimalarials. These methods can monitor drug uptake, metabolism, and distribution within infected cells, providing valuable information on drug action mechanisms and potential resistance development.
However, the journey from bench to bedside is fraught with challenges. The variability in field conditions, the need for standardization, and the complexities of biological systems all pose significant hurdles. Yet, these very challenges present opportunities for interdisciplinary collaboration and innovation. The integration of spectroscopic techniques with emerging technologies such as artificial intelligence and cloud computing could potentially overcome current limitations and usher in a new era of personalized malaria management and tailored drug therapies.
As we look to the future, we must ask ourselves: How can we harness the full potential of spectroscopic techniques to not only diagnose but also predict and prevent malaria outbreaks? How can we leverage these technologies to accelerate the discovery and development of novel antimalarial compounds? What role will these technologies play in the broader context of global health equity and disease eradication efforts? The answers to these questions may well determine the course of malaria control and treatment in the coming decades.
Ultimately, the true measure of success for these spectroscopic approaches will not be in their technical sophistication, but in their ability to make a tangible difference in the lives of those most affected by malaria. This includes not only improving diagnostics but also facilitating the development of more effective and accessible antimalarial drugs. As we continue to push the boundaries of what is possible with spectroscopy, we must remain focused on the end goal: a world free from the burden of malaria. The path forward requires not only scientific ingenuity but also a commitment to translating these promising technologies into accessible, affordable, and effective tools for communities around the globe, encompassing both diagnostic capabilities and enhanced therapeutic interventions.

Funding

This research received no external funding.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Asexual and sexual phases of the malaria parasite in RBC. After sporozoites enter the bloodstream, they travel to the liver, where they invade hepatocytes and develop into schizonts, each containing thousands of merozoites. These merozoites are then released and invade erythrocytes, initiating the intraerythrocytic asexual phase. During this phase, the parasites grow and divide within the food vacuole, progressing through three distinct morphological stages: ring, trophozoite, and schizont. When schizonts rupture, they release merozoites, continuing the erythrocytic cycle. Some merozoites, instead of replicating, differentiate into male and female gametocytes capable of transmission to mosquitoes. The digestion of hemoglobin by the parasite leads to the accumulation of Hz. In the circulation, only ring-stage parasites and late-stage gametocytes are observed. Reproduced with permission from the Royal Society of Chemistry. [7]
Figure 1. Asexual and sexual phases of the malaria parasite in RBC. After sporozoites enter the bloodstream, they travel to the liver, where they invade hepatocytes and develop into schizonts, each containing thousands of merozoites. These merozoites are then released and invade erythrocytes, initiating the intraerythrocytic asexual phase. During this phase, the parasites grow and divide within the food vacuole, progressing through three distinct morphological stages: ring, trophozoite, and schizont. When schizonts rupture, they release merozoites, continuing the erythrocytic cycle. Some merozoites, instead of replicating, differentiate into male and female gametocytes capable of transmission to mosquitoes. The digestion of hemoglobin by the parasite leads to the accumulation of Hz. In the circulation, only ring-stage parasites and late-stage gametocytes are observed. Reproduced with permission from the Royal Society of Chemistry. [7]
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Figure 2. Hematin and β-hematin structure. (A) Schematic representation of hematin, the monomeric precursor of β-hematin. (B) Structure and packing arrangement of β-hematin (synthetic malaria pigment) viewed along the c-axis. Some (h,k,l) planes are indicated. Reprinted with permission from the American Chemical Society. [19]
Figure 2. Hematin and β-hematin structure. (A) Schematic representation of hematin, the monomeric precursor of β-hematin. (B) Structure and packing arrangement of β-hematin (synthetic malaria pigment) viewed along the c-axis. Some (h,k,l) planes are indicated. Reprinted with permission from the American Chemical Society. [19]
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Figure 3. Raman excitation wavelength measurements recorded of β-hematin. The asterisks highlight the bands enhanced relative to the other excitation wavelengths at 830 nm. Reproduced with permission from the American Chemical Society. [20]
Figure 3. Raman excitation wavelength measurements recorded of β-hematin. The asterisks highlight the bands enhanced relative to the other excitation wavelengths at 830 nm. Reproduced with permission from the American Chemical Society. [20]
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Figure 4. FTIR spectrum of β-hematin. B. FTIR spectrum of hemozoin extracted from malaria trophozoites. Reproduced with permission from the American Chemical Society. [20].
Figure 4. FTIR spectrum of β-hematin. B. FTIR spectrum of hemozoin extracted from malaria trophozoites. Reproduced with permission from the American Chemical Society. [20].
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Figure 5. Absorbance spectra recorded during the acidification of hemin to form β-hematin. Reproduced with permission from the American Chemical Society. [20].
Figure 5. Absorbance spectra recorded during the acidification of hemin to form β-hematin. Reproduced with permission from the American Chemical Society. [20].
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Figure 6. Representative second derivative spectra, a) β-hematin, b) dry hemozoin isolated from infected red blood cells, c) dry crystalline hemozoin purchased from Invivogen. Reproduced with permission from the American Chemical Society. [5].
Figure 6. Representative second derivative spectra, a) β-hematin, b) dry hemozoin isolated from infected red blood cells, c) dry crystalline hemozoin purchased from Invivogen. Reproduced with permission from the American Chemical Society. [5].
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Figure 7. FTIR averaged normalized spectra of the C-H stretching region and fingerprint region from the Australian Synchrotron of RBCs (control) and the three stages of the parasitic life cycle (ring, trophozoite, and schizont) within a fixed RBC. Standard deviation spectra are shown below each spectrum for both spectral regions. Reproduced with permission from the American Chemical Society. [31].
Figure 7. FTIR averaged normalized spectra of the C-H stretching region and fingerprint region from the Australian Synchrotron of RBCs (control) and the three stages of the parasitic life cycle (ring, trophozoite, and schizont) within a fixed RBC. Standard deviation spectra are shown below each spectrum for both spectral regions. Reproduced with permission from the American Chemical Society. [31].
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Figure 8. (A) Visible image of the thick film of malaria infected RBCs. (B) Partial dark-field effect visible micrograph highlighting the trophozoites. (C) Chemical map of the area outlined by the red square (ROI) in (B), generated by integrating the region between 1680 and 1620 cm−1, with lighter colors indicating hemozoin deposits within the trophozoites. (D) UHCA of ROI using D-values algorithm in the range of 1700-1300 cm-1 revealing two clusters: Blue cluster- hemozoin and red cluster-hemoglobin. (E) UHCA of ROI showing five clusters where the pink cluster spectrum is like hemozoin in the late-stage trophozoites, while green and grey clusters represent a mix of haemoglobin and hemozoin. The light blue cluster corresponds well with the haemoglobin present within RBCs along with red cluster present as submicron dots (300 nm) corresponding to the hemozoin throughout the stages of P. falciparum life cycle. (F) Mean spectra corresponding to each cluster shown in (E). Reproduced with permission from the Royal Society of Chemistry. [68].
Figure 8. (A) Visible image of the thick film of malaria infected RBCs. (B) Partial dark-field effect visible micrograph highlighting the trophozoites. (C) Chemical map of the area outlined by the red square (ROI) in (B), generated by integrating the region between 1680 and 1620 cm−1, with lighter colors indicating hemozoin deposits within the trophozoites. (D) UHCA of ROI using D-values algorithm in the range of 1700-1300 cm-1 revealing two clusters: Blue cluster- hemozoin and red cluster-hemoglobin. (E) UHCA of ROI showing five clusters where the pink cluster spectrum is like hemozoin in the late-stage trophozoites, while green and grey clusters represent a mix of haemoglobin and hemozoin. The light blue cluster corresponds well with the haemoglobin present within RBCs along with red cluster present as submicron dots (300 nm) corresponding to the hemozoin throughout the stages of P. falciparum life cycle. (F) Mean spectra corresponding to each cluster shown in (E). Reproduced with permission from the Royal Society of Chemistry. [68].
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Figure 9. Raman Acoustic Levitation Spectroscopy (RALS) A. A droplet of isolated red blood cells levitated using a piezo electric transducer and reflective plate. B. Schematic showing acoustic levitator coupled to a Raman microscope using a right-angled adaptor. C. Spectra of trophozoite lysate from lysed red blood cells (top) and micro-Raman spectrum of hemozoin. Reproduced with permission from the Royal Society of Chemistry. [69].
Figure 9. Raman Acoustic Levitation Spectroscopy (RALS) A. A droplet of isolated red blood cells levitated using a piezo electric transducer and reflective plate. B. Schematic showing acoustic levitator coupled to a Raman microscope using a right-angled adaptor. C. Spectra of trophozoite lysate from lysed red blood cells (top) and micro-Raman spectrum of hemozoin. Reproduced with permission from the Royal Society of Chemistry. [69].
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Figure 10. A.Graphium weiskei butterfly wings. B. Schematic cross-sectional view of a gold-coated wing showing typical chitinous conical protrusion dimensions and spacings based on SEM images. C-F. SEM images of chitinous nano-structured conical arrays found on the wings of the G. weiskei butterfly. (C and D) SEM images acquired after deposition with P. falciparum-infected RBC lysate. E and F Control butterfly wings without lysate deposition. G, H, I SERS spectra of 0.0005%, 0.005% and 0% (control) malarial-infected RBC lysate, respectively. J. Conventional Raman spectrum of hemozoin at 785 nm. Reproduced with permission from the Royal Society of Chemistry. [81].
Figure 10. A.Graphium weiskei butterfly wings. B. Schematic cross-sectional view of a gold-coated wing showing typical chitinous conical protrusion dimensions and spacings based on SEM images. C-F. SEM images of chitinous nano-structured conical arrays found on the wings of the G. weiskei butterfly. (C and D) SEM images acquired after deposition with P. falciparum-infected RBC lysate. E and F Control butterfly wings without lysate deposition. G, H, I SERS spectra of 0.0005%, 0.005% and 0% (control) malarial-infected RBC lysate, respectively. J. Conventional Raman spectrum of hemozoin at 785 nm. Reproduced with permission from the Royal Society of Chemistry. [81].
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Figure 12. A. Diagram of the instrumentation and operation of an O-PTIR microscope. This Figure is from an open access article distributed under the terms of the Creative Commons CC-BY license. [109].
Figure 12. A. Diagram of the instrumentation and operation of an O-PTIR microscope. This Figure is from an open access article distributed under the terms of the Creative Commons CC-BY license. [109].
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Figure 13. Trends in miniaturization of near infrared spectrometers. This article containing this Figure is distributed under the terms of the Creative Commons Attribution-Non-commercial 4.0 License. [119].
Figure 13. Trends in miniaturization of near infrared spectrometers. This article containing this Figure is distributed under the terms of the Creative Commons Attribution-Non-commercial 4.0 License. [119].
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Figure 14. Scanning of mice and blood spots using NIRS. Panel A and B illustrate how mice were held and non-invasively scanned. Panel C illustrates scanning of dry blood spots on the slides. Panel D shows the resultant raw spectral signatures from various body parts of a mouse and spectral signatures from blood spots. The Figure is from an open access article distributed under the terms of the Creative Commons Attribution License. [131].
Figure 14. Scanning of mice and blood spots using NIRS. Panel A and B illustrate how mice were held and non-invasively scanned. Panel C illustrates scanning of dry blood spots on the slides. Panel D shows the resultant raw spectral signatures from various body parts of a mouse and spectral signatures from blood spots. The Figure is from an open access article distributed under the terms of the Creative Commons Attribution License. [131].
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Figure 15. Partial least-squares regression plots for malaria-diluted samples ranging from A. 6% to 0.00001%. B. 0.1% to 0.00001% where the actual parasitemia plotted on the x-axis and the predicted parasitemia on the y-axis. C. PCA Score plots (PC1 vs. PC2) for 6% to 0.00001% range. D. Comparison between control samples and those with the lowest parasitemia (0.000001%). Reproduced with permission from the American Chemical Society. [5].
Figure 15. Partial least-squares regression plots for malaria-diluted samples ranging from A. 6% to 0.00001%. B. 0.1% to 0.00001% where the actual parasitemia plotted on the x-axis and the predicted parasitemia on the y-axis. C. PCA Score plots (PC1 vs. PC2) for 6% to 0.00001% range. D. Comparison between control samples and those with the lowest parasitemia (0.000001%). Reproduced with permission from the American Chemical Society. [5].
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Figure 17. Analysis of IR and Raman spectra from a single isolated red blood cell (RBC). For the IR analysis: A. An Unsupervised Hierarchical Cluster Analysis (UHCA) cluster image was generated, and B. shows a visible image of a Giemsa-stained cell. For the Raman analysis: C. presents the UHCA cluster image, and (D-E) the average spectra for each cluster were displayed for the IR and Raman analysis respectfully while F. displays the integration map of the Raman band for hemozoin, in the range of 1629–1599 cm⁻¹, using baselines set at 1585–1575 cm⁻¹ and 1652–1643 cm⁻¹. The Raman spectra provided precise localization of hemozoin bands, which was not possible to identify directly in the IR spectra of the RBC. Reproduced with permission from the American Chemical Society. [94].
Figure 17. Analysis of IR and Raman spectra from a single isolated red blood cell (RBC). For the IR analysis: A. An Unsupervised Hierarchical Cluster Analysis (UHCA) cluster image was generated, and B. shows a visible image of a Giemsa-stained cell. For the Raman analysis: C. presents the UHCA cluster image, and (D-E) the average spectra for each cluster were displayed for the IR and Raman analysis respectfully while F. displays the integration map of the Raman band for hemozoin, in the range of 1629–1599 cm⁻¹, using baselines set at 1585–1575 cm⁻¹ and 1652–1643 cm⁻¹. The Raman spectra provided precise localization of hemozoin bands, which was not possible to identify directly in the IR spectra of the RBC. Reproduced with permission from the American Chemical Society. [94].
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Figure 18. A-C. PLS-R results for WB samples spiked with R-staged parasites. PLS-R scores plots for the higher parasitemia models (1–0.25%) are shown for: A. the lower wavelength range (200–700 nm), B. the higher wavelength range (1000–2500 nm), and C. the entire wavelength range (200–2500 nm). D. The PLS regression vector for the lower parasitemia models highlights key marker bands associated with both infected and control aqueous blood. Reproduced with permission from the American Chemical Society. [132].
Figure 18. A-C. PLS-R results for WB samples spiked with R-staged parasites. PLS-R scores plots for the higher parasitemia models (1–0.25%) are shown for: A. the lower wavelength range (200–700 nm), B. the higher wavelength range (1000–2500 nm), and C. the entire wavelength range (200–2500 nm). D. The PLS regression vector for the lower parasitemia models highlights key marker bands associated with both infected and control aqueous blood. Reproduced with permission from the American Chemical Society. [132].
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Figure 19. AFM-IR imaging of a P. falciparum trophozoite inside a red blood cell. (a) AFM topography. (b) AFM deflection map showing the location of the points where spectra were measured, inside (blue) and outside (red) of the protrusion. (c, d) Spectra measured from the signal of the IR intensity peak (V) showing different bands for the red and blue spots in the 1450−950 and 1800−1450 cm−1 regions, respectively. (e, f) IR peak maps obtained at 1207 and 1660 cm−1, respectively. (g, h) Score and loading plots from the PCA applied to the 3100−2800 cm−1 region.Replicated from. [156].
Figure 19. AFM-IR imaging of a P. falciparum trophozoite inside a red blood cell. (a) AFM topography. (b) AFM deflection map showing the location of the points where spectra were measured, inside (blue) and outside (red) of the protrusion. (c, d) Spectra measured from the signal of the IR intensity peak (V) showing different bands for the red and blue spots in the 1450−950 and 1800−1450 cm−1 regions, respectively. (e, f) IR peak maps obtained at 1207 and 1660 cm−1, respectively. (g, h) Score and loading plots from the PCA applied to the 3100−2800 cm−1 region.Replicated from. [156].
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Figure 20. A-C. AFM images recorded of sectioned cells prior to TERS acquisition. A. A 30 x 30 μm AFM image recorded of a population of infected red blood cells showing a potential cell target highlighted by the blue square. B. A high-resolution image of the cell highlighted in (A) showing hemozoin crystals aligned in the digestive vacuole. C. An even higher resolution AFM image of the digestive vacuole of the parasite showing single crystals of hemozoin that can be selectively targeted with the TERS active tip. D. TERS spectrum recorded of the edge of a hemozoin crystal. The spectrum was recorded with a laser power of 600 μW and exposure time of 5 s. E. After recording a spectrum, the tip was retracted by several micrometers and a further spectrum recorded to ensure the tip had not been contaminated by the sample. F. Surface-enhanced Raman spectrum recorded of β-hematin prepared using SERS active Ag-particles. Spectra were recorded using a 532 nm laser and 10 s acquisition time. G. Resonance Raman spectrum of β-hematin recorded at 532 nm with 10 s acquisition time. Reproduced with permission from the American Chemical Society [157].
Figure 20. A-C. AFM images recorded of sectioned cells prior to TERS acquisition. A. A 30 x 30 μm AFM image recorded of a population of infected red blood cells showing a potential cell target highlighted by the blue square. B. A high-resolution image of the cell highlighted in (A) showing hemozoin crystals aligned in the digestive vacuole. C. An even higher resolution AFM image of the digestive vacuole of the parasite showing single crystals of hemozoin that can be selectively targeted with the TERS active tip. D. TERS spectrum recorded of the edge of a hemozoin crystal. The spectrum was recorded with a laser power of 600 μW and exposure time of 5 s. E. After recording a spectrum, the tip was retracted by several micrometers and a further spectrum recorded to ensure the tip had not been contaminated by the sample. F. Surface-enhanced Raman spectrum recorded of β-hematin prepared using SERS active Ag-particles. Spectra were recorded using a 532 nm laser and 10 s acquisition time. G. Resonance Raman spectrum of β-hematin recorded at 532 nm with 10 s acquisition time. Reproduced with permission from the American Chemical Society [157].
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Figure 16. The image illustrates PCA applied to visible spectra of single cells. A. A 3D Scores plot for various RBC samples (control, rings, trophozoites, schizonts), demonstrating clear separation of control RBCs from infected cells along PC 2. B. The PC1 loadings plot, highlighting significant positive and negative loadings. C. The PC2 loadings plot. D. A 3D Scores plot comparing RBCs infected with rings (R) and trophozoites (T). E. Schizonts (S) and trophozoites (T). F. Rings (R) and schizonts (S). (G-J). presents the computed confusion matrices (CM) that illustrate the accuracy of the SVM models developed in this study. G. shows multiclass models including datasets from control, rings, schizonts, and trophozoites. Panels (B–D) display binary classifications comparing infected classes with the control: (B) Control vs. trophozoites, (C) Control vs. rings, and (D) Control vs. schizonts. The numbers in each class indicate the spectra count used for testing. Reproduced with permission from the American Chemical Society. [6].
Figure 16. The image illustrates PCA applied to visible spectra of single cells. A. A 3D Scores plot for various RBC samples (control, rings, trophozoites, schizonts), demonstrating clear separation of control RBCs from infected cells along PC 2. B. The PC1 loadings plot, highlighting significant positive and negative loadings. C. The PC2 loadings plot. D. A 3D Scores plot comparing RBCs infected with rings (R) and trophozoites (T). E. Schizonts (S) and trophozoites (T). F. Rings (R) and schizonts (S). (G-J). presents the computed confusion matrices (CM) that illustrate the accuracy of the SVM models developed in this study. G. shows multiclass models including datasets from control, rings, schizonts, and trophozoites. Panels (B–D) display binary classifications comparing infected classes with the control: (B) Control vs. trophozoites, (C) Control vs. rings, and (D) Control vs. schizonts. The numbers in each class indicate the spectra count used for testing. Reproduced with permission from the American Chemical Society. [6].
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Figure 21. A Partial least squares discriminant analysis (PLS-DA) prediction plot showing the classification of either malaria positive (< 0.5) or negative (> 0.5); spectra colour-coded malaria positive (red) or negative (green) by PCR. b Same as in a) except support vector machine (SVM) learning is used for the classification. c Receiver operating characteristic (ROC) curves showing the diagnostic of the PLS-DA and SVM classification. d ROC curve for data where samples were assigned positive- and negative, based on PCR versus randomized models. e Average spectra over the three spectral ranges used for PLS-DA classification. Superimposed is a colour code showing the regression loadings for malaria positive (“warm colours”) or negative (“cool colours”) classification for each absorbance value. This Figure is from an open access article published by Biomedical Central (BMC). [57].
Figure 21. A Partial least squares discriminant analysis (PLS-DA) prediction plot showing the classification of either malaria positive (< 0.5) or negative (> 0.5); spectra colour-coded malaria positive (red) or negative (green) by PCR. b Same as in a) except support vector machine (SVM) learning is used for the classification. c Receiver operating characteristic (ROC) curves showing the diagnostic of the PLS-DA and SVM classification. d ROC curve for data where samples were assigned positive- and negative, based on PCR versus randomized models. e Average spectra over the three spectral ranges used for PLS-DA classification. Superimposed is a colour code showing the regression loadings for malaria positive (“warm colours”) or negative (“cool colours”) classification for each absorbance value. This Figure is from an open access article published by Biomedical Central (BMC). [57].
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Table 1. Summary of SERS based methods to detect malarial parasites.
Table 1. Summary of SERS based methods to detect malarial parasites.
Mode of detection sensitivity Methods Citation
SERS – AgNP 500 parasites/ µL
(0.01% parasitemia level)		 silver nanoparticles were synthesized separately and then mixed with lysed blood [85]
SERS – AgNP 2.5 parasites/ µL
(0.00005% parasitemia level)
 silver nanoparticles were synthesized directly inside the parasites of P. falciparum [85]
SERS - AgNP 1 parasite AgNPs were synthesized inside P. falciparum. Giemsa stain was used to identify single parasite. [89]
SERS -AgNP 125 parasites/ µL
(0.0025 % parasitemia)		 SERS-active silver nanoparticles are synthesized on site near hemozoin on a chip platform [85]
Table 3. Absorption bands of DNA and protein macromolecules in the ultraviolet-visible region. [133,134].
Table 3. Absorption bands of DNA and protein macromolecules in the ultraviolet-visible region. [133,134].
Molecules Absorption Bands (nm)
Protein Macromolecules (Amino acids)
Tryptophan 280
Tyrosine 275
Phenylalanine 258
Haem group from RBCs
Oxyhemoglobin 416
Oxyhemoglobin doublet 540
Oxyhemoglobin doublet 575
DNA nucleobases and chromophoric groups
Guanine 253
Cytidine 271
Polymeric DNA and RNA 260
Table 4. Comparison of the classification performance of spectroscopy-based models in the studies of Heraud et al. [57] and Mwanga et al. [92].
Table 4. Comparison of the classification performance of spectroscopy-based models in the studies of Heraud et al. [57] and Mwanga et al. [92].
STUDY SENSITIVITY SPECIFICITY CALIBRATION SET VALIDATION SET
Heraud et al. , 2019 92 97 Ntotal=232
Npositive=112
Nnegative=110		 Ntotal=96
Npositive=39
Nnegatve=57
Mwanga et al. , 2019
P. falciparum only		 92a 93a Ntotal=208
Npositive=86
Nnegative=122		 Ntotal=52
Npositive=28
Nnegative=24
Mwanga et al. , 2019
including P. falciparum and P. ovale mixed infection 		82b 91b Not Stated Not stated
1 For the study of Mwanga et al. the superscript a refers to modelling where only monospecific P. falciparum infections were included.; the superscript b refers to modelling where 9 samples with mixed P.falciparum/P. ovale infections were included in the modelling. In the study of Heraud et. al. samples with P. falciparum, P. vivax and mixed P. falciparum/P. vivax were used in the modelling (see text for breakdown of numbers).
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