Biosensors and Bioelectronics 49 (2013) 536–541

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Biosensors and Bioelectronics

0956-56http://d

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(K. Kho(P.R. Stoedu.au.(

journal homepage: www.elsevier.com/locate/bios

In situ SERS probing of nano-silver coated individual yeast cells

Adam F. Chrimes a,n, Khashayar Khoshmanesh a,b, Shi-Yang Tang a, Bayden R. Wood c,Paul R. Stoddart d, Sean S.E. Collins e, Arnan Mitchell a, Kourosh Kalantar-zadeh a,n

a School of Electrical and Computer Engineering, RMIT University, Melbourne, Victoria, Australiab Department of Mechanical Engineering, Stanford University, Stanford, CA 94305, United Statesc Centre for Biospectroscopy, Monash University, Clayton, Victoria, Australiad Faculty of Engineering and Industrial Science, Swinburne University of Technology, Hawthorn, Victoria, Australiae Bio21 Institute and School of Chemistry, Melbourne University, Parkville, Victoria, Australia

a r t i c l e i n f o

Article history:Received 22 March 2013Received in revised form20 May 2013Accepted 30 May 2013Available online 7 June 2013

Keywords:MicrofluidicsYeast cellSERSPrincipal component analysisDielectrophoresis

63/$ - see front matter & 2013 Elsevier B.V. Ax.doi.org/10.1016/j.bios.2013.05.053

esponding authors. Tel.: +61 39925 3254; faxail addresses: af.chrimes@ieee.org.(A.F. Chrimeshmanesh)bayden.wood@monash.edu.(B.R. Wddart)scol@student.unimelb.edu.au.(S.S.E. ColK. Kalantar-zadeh)

a b s t r a c t

For understanding cells functionalities and their communications, there is a need for highly sensitive cellanalysis platforms capable of assessing non-specific chemicals on the surface and in the vicinity of cells.We report a microfluidic system integrating dielectrophoresis and surface enhanced Raman scattering(SERS) for the trapping and real time monitoring of cell functions in isolated and grouped cell clusters.Yeast cells are coated with silver nanoparticles to enable highly sensitive SERS analysis. The SERSresponses of cells are examined under various conditions: live vs. dead and isolated vs. grouped. Thiswork illustrates the feasibility of the system for in situ cell monitoring and analysis of secreted chemicalsduring their growth, metabolism, proliferation and apoptosis.

& 2013 Elsevier B.V. All rights reserved.

1. Introduction

There are still many unknowns about the functions of livingcells and their communications. Many aspects of cell wall chemicalconstituents and cell metabolisms have been studied using avariety of techniques, including Raman spectroscopy (Piper et al.,2000; Taylor et al., 2009). Raman spectroscopy enables theacquisition of large amounts of information from cells. In parti-cular, it can be efficiently used for identifying molecules inside thecells and on their surface (Chen and Weiss, 2005; Honigberg,2011). The intensity of obtained Raman signals is low for studiesthat focus on only a limited number of cells because of the smallconcentration of the target molecules. Surface-enhanced Ramanscattering (SERS) is frequently used for enhancing the detection ofmolecules in low concentrations (Li et al., 2010; Sayin et al., 2009;Sujith et al., 2009; Xie et al., 2011). Additionally, SERS allows theRaman signals to be obtained at much smaller time intervals andat lower power levels, reducing the chance of harming the cellsunder investigation. For SERS, the surface of cells must be coveredwith SERS inducing nanoparticles such as gold (Guo et al., 2012) orsilver (Sujith et al., 2009). SERS has been successfully utilized forprocesses such as differentiating between various cell types (Sayin

ll rights reserved.

: +61 39925 2007.s)khashaya@stanford.edu.ood)pstoddart@swin.edu.au.lins)kourosh.kalantar@rmit.

et al., 2009) and for cell recognition and diagnostics (Feng et al.,2009). There are also reports showing the inter-cellular transportpathways using gold nanoparticles (Ando et al., 2011). However,despite the obvious capabilities of SERS there are no reports usingSERS for investigating the secretion of chemicals from differenttypes of cells, at different group sizes and under various environ-mental conditions.

To study cells in different environmental conditions, micro-fluidic systems are ideal platforms which allow the facile manip-ulation of the cells' surroundings. However, SERS imaging requiresthat the cells be kept still during the entire data acquisition period;therefore the cells must be immobilized. Many methods of cellimmobilization are possible in microfluidics (Kayani et al., 2012);however, not all of them are appropriate for SERS (Chrimes et al.,2013). Dielectrophoresis (DEP), the induced motion of dielectricparticles in a non-uniform electric field, has proven to be aneffective method of trapping cells in microfluidics and can effi-ciently allow the control of cell group sizing, and can potentially beused for separating cells prior to Raman studies (Khoshmaneshet al., 2011a, 2011b; Lapizco-Encinas et al., 2004). DEP is especiallycompatible with SERS, as the smooth surface of metallic micro-electrodes does not produce any interfering Raman signals and themicroelectrodes can generate strong localized electric fields to trapboth individual cells and cells in groups of specific sizes.

In this work, DEP is utilized to immobilize a desired number ofcells in either intimate contact or at spacings greater than 10 mmfrom each other. As a proof of concept experiment, we first showthat by enhancing the Raman signals using SERS, it is possible to

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clearly differentiate between live and dead cells. In the next setof experiments, live cells are immobilized into “isolated” and“grouped” cell clusters. This was conducted in order to observethe effect of such conditions on the live cells chemical secretions,and potentially monitor chemical transmissions between cells.

2. Materials and methods

2.1. Mixture details

We use Saccharomyces cerevisiae (yeast) cells that are frequentlyemployed as an ideal model of eukaryotic cells (Chen and Weiss,2005; Culha et al., 2010; Delfino et al., 2005; Honigberg, 2011;Li et al., 2012; Manikantan Syamala et al., 2012; Rosch et al., 2005;Taylor et al., 2009). The yeast cells are purchased from SigmaAldrich and yield 90% active yeast in dried form. The dried yeastcells are suspended in de-ionized water and are mildly sonicated for15 min so as to break up any yeast clusters. For obtaining strongSERS signals, the cells are coated with 90 nm diameter silvernanoparticles, produced using a modified method based on theone described by Panacek et al. (2006) as described in theSupplementary information (Fig. 1a). The silver mixture is addeddirectly to the suspended yeast sample and left for 15 min to allowthe absorption of silver nanoparticles onto the surface of the yeast(Fig. 1b–d).

The image in Fig. 1b clearly shows silver nanoparticles on thesurface of the yeast cells. The distribution of silver over the yeastcells is highly inhomogeneous, which explains why some cellsdemonstrate high intensity SERS peaks, and others show negligi-ble spectral peaks (Fig. S8). This fact is complicated further by thedynamic nature of silver binding to the surface of yeast, as thesignals fluctuate over time (Fig. S9). However, the SERS spectrumof a single yeast cell shows moderate variation in Raman peak

Fig. 1. Details of silver nanoparticle properties and images showing the silver's attachmenanoparticles synthesized using the method described by Panacek et al. (2006). Ins(b) Environmental scanning electron microscope image of yeast cells coated with silverlight source and the consequent generation of SERS signals. (d) Inset schematic of the silout of cell. The chemicals pass near the silver nanoparticles plasmonic SERS enhancem

wavenumbers over its surface (Fig. S10), which is likely due to thevarious chemical interactions between the silver and the yeastcells (Manikantan Syamala et al., 2012).

In order to overcome these issues a statistical analysis approachis used to obtain meaningful results from a large number ofsamples, regardless of the silvers inhomogeneous coverage. Ithas been suggested that more than 10 SERS spectra from a singlecells is sufficient for its identification, due to the dynamic nature ofthe cell wall chemical components (Rosch et al., 2005). In thiswork we use a Raman microscope system with a spot size of∼1.3 mm, which is large enough to average many yeast–silverinteractions. Furthermore, the experiments are performed suchthat each cell is targeted at a random location to remove any cell-specific deficiencies and systematic errors. Before data analysis isperformed the cell SERS signals with poor signal–noise ratio areremoved, and the data is normalized to remove any inconsisten-cies caused by the number of silver–yeast interactions affecting theintensity of the Raman signals.

Mixtures involving dead yeast cells are created by adding 50%(v/v) methanol to the mixture. The addition of methanol causesinstantaneous death of cells, without causing cell apoptosis(Khoshmanesh et al., 2010).

2.2. Microfluidic system

The microfluidic channel is fabricated from polydimethylsilox-ane (PDMS) and bonded to a quartz substrate, which contains themicroelectrode pattern (Fig. 2a). Quartz is chosen as the substratematerial because it has a relatively weak Raman response, produ-cing minimal interference with the SERS from the cells by allowingmostly unhindered passage of the Raman laser beam SERS signals.The microfluidic system is driven by a syringe pump in withdrawmode. Once the yeast cells are inside the microchannel DEP isapplied to immobilize the cells onto the quartz substrate

nt to the surface of yeast cells. (a) Transmission electron microscope image of silveret: ultraviolet visible absorbance spectra of the silver nanoparticle suspension.nanoparticles. (c) Schematic of silver bonding to yeast cell wall, the exposure to thevers attachment to the cell wall showing the chemical component exchanges in andent area.

Fig. 2. The microfluidic system, simulation and cell trapping are shown. (a) Schematic of the microfluidic device with microchannel dimensions of 150�70 μm2 and lengthof 12 mm, integrated microelectrodes with 15 μm spacing at the tips, and a 532 nm, 1 mW Raman system. (b) The distribution of net trapping force composed ofdielectrophoretic, hydrodynamic drag and sedimentation forces at a plane 10 μm from the quartz substrate. (c) Formation of “isolated” and “grouped” yeast cells at the tip of amicroelectrode pair. (d) Schematic side view of microchannel showing the expected cell trajectory when exposed to DEP and hydrodynamic forces and the eventual cellimmobilization.

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(Fig. 2b–c), after which the SERS targeted cells are chosen by visualinspection using optical microscopy. Separation of live and deadcells can also be conducted within the microfluidics, with orwithout flow, as has been shown in Cheng et al. (2007) andKhoshmanesh et al. (2011a). However, in our work, the separationof live and dead cells was conducted off-chip.

One drawback to using DEP for yeast manipulation is thatexposure to high electric fields can possibly deteriorate theviability of the cells (Khoshmanesh et al., 2010). This deteriorationis mostly caused by the Joule heating effect due to the presence ofstrong electric fields in the high electrical conductivity mediumsurrounding the cells; moreover, effects such as trans-membranepotential and cell elongation must also be considered whenchoosing appropriate DEP conditions for biological elements suchas yeast cells (Khoshmanesh et al., 2011b, 2010; Morgan andGreen, 2003; Muller et al., 2003; Neek-Amal et al., 2009; Valicet al., 2003). These adverse effects of DEP are minimized byapplying the DEP electrical signals at a relatively high frequencyof 20 MHz (Khoshmanesh et al., 2011b; Morgan and Green, 2003)and using a relatively low voltage of 5 V (See supporting informa-tion document for further discussions). Additionally, the electricfield is only applied for 30–60 s, which is sufficient for efficient celltrapping and permanent immobilization. Once an adequate num-ber of cells are trapped the electric field is turned off to minimizeany issues that could arise from electric field exposure (Sriramet al., 2012).

The yeast cells have spherical to ellipsoidal geometries, withdiameters in the range of 6–10 mm, such that their dielectrophore-tic response is approximated by multi-shell spherical model with acell diameter of 8 mm (Khoshmanesh et al., 2011b, 2010). In orderto characterize the dielectrophoretic response of the system andselect the suitable frequency for the applied electric field, simula-tions of the electric field strength and Clausius–Mossotti (CM)

factor are conducted (Fig. S6). The dielectrophoretic force experi-enced by a cell can be calculated using equation 1:

F!

DEP ¼ 2πr3ε0εmediumRe½f CMðωÞ�∇E2rms ð1Þ

where εo is the permittivity of free space (∼8.854�10−12 F m−1),εmedium is the permittivity of the suspending medium, r is theradius of particles, Re[fCM] is the real part of the CM factor, ω is theangular frequency of the applied signal, and Erms is the root-mean-square value of the induced electric field.

Fig. 2b depicts the distribution of net trapping force composedof dielectrophoretic, hydrodynamic drag and sedimentation forcesat a plane 10 μm from the quartz substrate. The density of trappedcells is proportional to the magnitude of the net trapping force, asshown in Fig. 2c. The details of numerical simulations arepresented in the Supplementary information. Fig. 2c also depictsthe formation of “isolated” and “grouped” cell clusters at micro-electrode tips. “Isolated cell” refers to cells that have beenimmobilized but are not in the vicinity of other cells (i.e. 41 celldistance). “Grouped cells” refer to cells that are in the intimatecontact with one another.

2.3. Raman microscopy system

The Raman microscopy system uses a Horiba TRIAX 320spectrometer, with a CCD resolution of 0.06 nm. The spectrometeris fiber coupled to an Olympus BX41 confocal microscope, fittedwith a 50� , 0.5 numerical aperture objective lens. The lasersystem operates at 532 nm with 1 mW optical power output,creating a final spot size of ∼1.3 mm in diameter with a spatialresolution of 0.65 mm.

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3. Results and discussion

3.1. Control experiments

Examples of SERS of nanoparticle-yeast (silver and yeast mixedtogether) Raman spectra for both live and dead cells are presentedin Fig. 3a and b. As a benchmark, Raman spectrum of silvernanoparticles and pristine yeast cells (no added silver) are alsodemonstrated (Fig. 3c and d, respectively). All data acquisitionsare background corrected and obtained inside the microfluidicchannel.

The Raman spectrum of a typical pristine yeast cell (Fig. 3d)shows only three low intensity peaks near the 600 cm−1 range;and it is determined that those peaks belong to the PDMS frommicrochannel structure (Chrimes et al., 2011, 2012). These PDMSpeaks always appear in spectra taken with long acquisition times(120 s). For comparison the Raman spectrum of PDMS is shown inFig. 3e. A short acquisition time (20 s) is possible for the yeastsilver nanoparticles covered cells, causing minimal degradation ofthe cells by the Raman laser source and eliminating the PDMSsignals. The SERS pattern from pristine silver nanoparticles(Fig. 3c) shows strong peaks of D-glucose, as it is present in theliquid surrounding the silver nanoparticles as a by-product in thesynthesis process (Panacek et al., 2006). Both live (Fig. 3a) anddead (Fig. 3b) nanoparticle-yeast cell show strong SERS signals

Fig. 3. Raman spectra for control experiments showing: (a) two examples of liveyeast cell SERS (acquisition time of 20 s), (b) two examples of dead yeast cell SERS(acquisition time of 20 s), (c) pristine suspended silver nanoparticles Ramanspectrum (acquisition time of 10 s) (d) an example of live pristine yeast Ramanspectrum (without nanoparticle coating—acquisition time of 120 s) and (e) PDMSRaman spectrum (acquisition time of 10 s).

even at small acquisition durations of 20 s. These signals do notappear to come from pure D-glucose, confirming the validity of theSERS measurements for identifying cells' Raman peak shifts.

3.2. Dead and live yeast cell characterization

We conduct a series of experiments to assess the capability ofour system and show the outcomes from comparison of dead vs.live and grouped vs. isolated SERS datasets. Although the identi-fication of live and dead cells can be quite readily achieved usingfluorescent dyes, the approach taken for this experiment isdesigned to prove-the-concept of using SERS for identification.Principal component analysis (PCA) is used to visualize andexamine hidden patterns and trends in the SERS dataset (Woodet al., 2011), which is pre-processed for PCA using the methoddetailed in the Supplementary information.

For this experimental procedure, live cells were introduced intothe channel and trapped using DEP (Fig. 2c). Isolated cells weretargeted with the Raman system, and the resulting spectra weresaved. The channel was then replaced and dead cells wereintroduced. Again, DEP was used for trapping the cells and theSERS spectra from isolated dead cells were obtained. Finally, inorder to confirm that the cells in the dead SERS test were actuallydead, the cells are removed from the microchannel and stainedwith Trypan blue. The image in Fig. 4a shows a mixture of deadand live cells spread out onto a glass slide, proving that the deadcell mixture does indeed contain dead yeast cells (Khoshmaneshet al., 2010). Fig. 4b depicts the average SERS patterns of liveand dead cells. It is clear that there is a significant differencebetween the two spectra. The live cell SERS average demonstratesmany characteristic peaks for yeast cells walls such as shiftpeaks from COO− wagging (∼620 cm−1 and ∼1520 cm−1), amide I(∼1630 cm−1), δCH2 (∼1446 cm−1) and amide II (∼1550 cm−1)(Downes and Elfick, 2010; Honigberg, 2011; Sujith et al., 2009).Alternatively, the average of the spectra from dead yeast cellsshows very different Raman shift peaks at 530, 783 and 883 and1503 cm−1.

Further assessment of the live and dead yeast cell spectra iscarried out using PCA. The output from the analysis is shown inFig. 4c,d. The PCA scores plot for PC2 vs. PC3 shows a cleardistinction between dead (red score dots) and live (blue scoresquares), which depicts the majority of the variation between thetwo datasets is along the PC2. The corresponding PC2 loadings plotshows a shift in the SERS peak around 1590 cm−1. More specifi-cally, for live cells, which have positive PC2 scores, the prominentRaman peak shift is at ∼1550 cm−1 (mainly from amide II) (Sujithet al., 2008). This peak shifts toward ∼1630 cm−1 (toward amide I)(Stewart and Fredericks, 1999) for the dead cells, which havenegative PC2 scores.

3.3. Behavior of yeast when isolated or grouped

To investigate a targeted cell functions, and possible commu-nications between cells, further testing on live yeast cells areconducted. Live yeast cells are sorted into isolated and groupedcell clusters using DEP, as indicated in Fig. 2c,d, and SERS signalsare obtained from the two types of cell configurations. A particu-larly interesting observation is that isolated cells exhibit budding(after 1 h) whereas for cells in groups budding is uncommon (asseen in Fig. 5a). The SERS patterns of the isolated cells are testedagainst the SERS patterns from yeast cells in groups of two ormore. The plot in Fig. 5b depicts four randomly chosen SERSpatterns, two from each dataset. There is a clear differencebetween the two cell groupings, with isolated cells demonstratingmore “active” SERS patterns than the grouped cells. As with theprevious experiment, the PCA score plot shows a significant

Fig. 4. Results from SERS of dead and live yeast cells. (a) Image of live and dead cells; dead cells have been stained to highlight them in the image. (b) SERS patterns of liveand dead yeast cells covered with nanoparticles. (c) PCA score plot for live (blue squares) and dead (red dots) yeast cells, showing live cells with positive PC2 scores, and deadcells with negative PC2 scores (d) loadings plot for PC2 which shows that live cells with positive PC2 scores contain peaks from the positive loading plot, and dead cells SERSwith negative PC2 scores contain peaks from the negative side of the PC2 loading plot. (For interpretation of the references to color in this figure legend, the reader is referredto the web version of this article.)

Fig. 5. Results from SERS of yeast cells either isolated from one another, or in intimate contact. (a) Image of isolated yeast cells budding, and grouped yeast cells being ratherstagnant. (b) SERS patterns of isolated yeast cells, grouped yeast cells and pristine glycine. (c) PCA scores plot for isolated and grouped cells showing that isolated cells havenegative PC3 scores, while cells in groups have positive PC3 scores. (d) Loadings plot for PC3, one cell SERS contain peaks from the negative side of the PC3 loading, whileSERS from grouped cells contain peaks from the positive side of the PC3 loading.

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systematic difference between the two isolated and grouped SERSspectra. This difference cannot come from the random SERSvariations due to the inhomogeneous silver coverage of the cells,nor is it a side effect of temporal SERS variations (Fig. S9).

The PCA output is displayed in Fig. 5c,d; it depicts the scoresplot for PC3 vs. PC2; indicating that the distinguishing variationbetween the two cell groupings (isolated and grouped) is mostlydue to the PC3 component. The loadings for PC3 are also shown in

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Fig. 5d, which demonstrate that the spectra from cells in groups oftwo or more contain a Raman shift peak at 1532 cm−1 (COO−)(Stewart and Fredericks, 1999). Alternatively, when cells areisolated they demonstrate many peaks such as those at ∼724(COO- deformation), ∼1155 (NH3

+ deformation) and ∼1322 cm−1

(CH2 wagging) (Sujith et al., 2009).The peaks present for isolated cells can be attributed to either

N-acetyl-D-glucosamine (NAG) (Downes and Elfick, 2010; Jarvisand Goodacre, 2004), cyclic adenosine-5′-monophosphate (cAMP)(Ervin et al., 1980), Ser/Thr protein (Zhang et al., 2005) or glycine(Stewart and Fredericks, 1999). However, careful study of the SERSpeaks generated by NAG, cAMP and Ser/Thr rule them out aspossible candidates, as they all contain phosphate groups whoseSERS peaks are not present in the PC3 loading. Instead, glycine is apossible perfect fit for the variance of the SERS patterns betweenthe isolated and grouped cell clusters (Fig. 5b). Additionally, inmost of the isolated cell signals the dissolved CO2 vu band alsoappears at ∼1385 cm−1 (Liu et al., 2012). The presence of dissolvedCO2 makes sense as yeast produce CO2 during the budding process(Lumsden et al., 1987; Novak and Mitchison, 1986). Furthermore,the sharp peak shift of amide II around ∼1550 cm−1 is much largerin grouped cells than the isolated cells (Sujith et al., 2008). Thiscan be due to the budding process disrupting the cell membrane inisolated cells, hence reducing this peak intensity. Interestingly, aslightly shifted and broadened peaks shift around ∼1580 cm−1 isalways seen for the isolated cells. This peak, and the broad peak inthe region of 900–100 cm−1 only seen in the isolated cell signals,can be a combination of several Raman peak shifts that yet to beinvestigated and identified.

Specifically, the sharp peaks at 724, 1155 and 1323 cm−1

(Fig. 5b) are all present in isolated cell spectra, and are likelycaused by the different components of the glycine structure(NH2CH2COOH) (Stewart and Fredericks, 1999). It is known thatglycine, as an amino acid used in cells for one-carbon metabolism,can play an important role in the synthesis of metabolites neededfor cell growth and cell division (Flynn et al., 2010; Piper et al.,2000; Wu, 2009). One carbon metabolism is also important for cellwall synthesis, since a metabolite from the pathway, L-serine,makes up a significant proportion of many cell wall proteins (Tsoiet al., 2009). Additionally, yeast cells have very active chemicaltransporters that take up or release glycine (Piper et al., 2000).Recently, similar observations have also been reported regardingthe presence of glycine during cancer cell slow proliferation (Jainet al., 2012) and that the concentration of glycine outside suchcancer cells is high. It seems that, similar to cancer cells with a lowrate of proliferation, yeast cell glycine production may exceedendogenous cell requirement and the excess is released to theenvironment. The other possible candidate that would explainthe presence of glycine peaks is the secretion of proteins thatcontain this amino acid unit as a functional group, which shouldbe investigated further.

4. Conclusion

We demonstrate that in situ SERS measurements can be utilizedto investigate the targeted functions of cells covered with silvernanoparticles. The integration of silver nanoparticles increases theintensity of SERS signals, making it possible to keep the DEPimmobilized cells under chemical surveillance in the microfluidicenvironment. PCA is able to define and classify a large number ofSERS patterns into distinct groups based on selected physiologicalproperties (i.e., live/dead, isolated/grouped). The analysis of resultsallow us to draw conclusions about the cells' biological behavior,signifying the presence of chemicals and proteins around isolated

cells before the budding process, a condition that is not observedfor grouped cells. Our work demonstrates the feasibility of themicrofluidics-SERS-DEP system for in situ cell monitoring ofsecreted chemicals during their different functions.

Acknowledgments

Dr. Khoshmanesh is the recipient of the Discovery Early CareerResearcher Award (DECRA) under project DE120101402. Dr. Woodis supported by an ARC QEII Fellowship grant, DP0878464. Theauthors acknowledge the facilities, and the scientific and technicalassistance, of the Australian Microscopy & Microanalysis ResearchFacility at RMIT University.

Appendix A. Supporting information

Supplementary data associated with this article can be found inthe online version at http://dx.doi.org/10.1016/j.bios.2013.05.053.

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