Non-destructive analysis of museum objects by fibre-optic Raman spectroscopy

Raman spectroscopy is a versatile technique that has frequently been applied for the investigation of art objects. By using mobile Raman instrumentation it is possible to investigate the artworks without the need for sampling. This work evaluates the use of a dedicated mobile spectrometer for the investigation of a range of museum objects in museums in Scotland, including antique Egyptian sarcophagi, a panel painting, painted surfaces on paper and textile, and the painted lid and soundboard of an early keyboard instrument. The investigations of these artefacts illustrate some analytical challenges that arise when analysing museum objects, including fluorescing varnish layers, ambient sunlight, large dimensions of artefacts and the need to handle fragile objects with care. Analysis of the musical instrument (the Mar virginals) was undertaken in the exhibition gallery, while on display, which meant that interaction with the public and health and safety issues had to be taken into account.
Introduction
Advances in analytical technology have led to increasing interest in the scientific examination of precious artefacts and antiquities. These examinations may be for a variety of reasons, most commonly fundamental interest in the materials and techniques used by the artist, or understanding their conservation, condition or degradation processes. Analytical data (e.g. from spectroscopic examinations) can, by material identification, assist in dating or authenticating art objects or works of art. The availability of these techniques is therefore very important and it enables us to solve specific questions concerning the conservation, restoration and history of artworks [1–3].
In conservation science, analytical techniques which are non-destructive or micro-destructive are particularly important. Raman spectroscopy is such a technique, allowing material identification from particles down to 1 μm. This method has already been used for the investigation of different artefacts, including panel paintings [4, 5], glass [6], wall paintings [7, 8], manuscripts [9–11] and historical biomaterials [12, 13]. Although this technique used to be expensive and highly specialised, it is becoming increasingly more accessible to large museums owing to instrumental improvements and lower costs [14–17]. However, regional museums often still do not have access to this kind of analytical facility. Use of mobile equipment is one solution to this problem, since several institutions may benefit from a single instrument. In addition, mobile fibre-optic-based Raman instrumentation may facilitate the analysis of artefacts that are difficult or impossible to bring to the laboratory, such as wall paintings on the vault of a chapel [8].
In this work we evaluate the use of a mobile fibre-optic-based Raman instrument to perform investigations in a museum context. Different types of artefacts were investigated in a variety of locations, testing and demonstrating the flexibility in instrumental set-up. In particular the experimental conditions during the examination of a virginals on exhibition were challenging. Instrument stability after travelling overseas (between Belgium and Edinburgh, UK) was also thoroughly evaluated.
Experimental
Objects were investigated in the conservation and research laboratories, stores and an exhibition gallery of the National Museums of Scotland (NMS), Edinburgh, as well as in the Burrell Collection (Glasgow) (31 October–13 November 2005). For these surveys mobile Raman equipment was used, which has been described extensively elsewhere [19]. The core of the equipment consists of a SpectraPro-150i 150-mm spectrometer and a thermoelectrically cooled charge-coupled device detector (Roper Scientific/Princeton Instruments). The system is also equipped with a 785-nm diode laser for excitation, which has a maximal output power of 300 mW at the source. In order to avoid damaging the artefacts during these investigations, and depending on the object and the colour of the area under investigation, the output power was limited to a maximum of 10 mW (measured at the surface). The accumulation time ranged from 10 s to 1 h for weak Raman scatterers. For these investigations the 600 lines/mm grating was selected. Due to non-ideal focussing conditions, the expected minimal laser spot size of ca. 25-μm diameter (when working with a ×6 infinity-corrected objective lens) could not be reached. In practice we worked with a larger spot size of ca. 50 μm. For each area at least 10 spectra were recorded and evaluated in order to tackle paint inhomogeneity at this scale. The experimental configuration was adapted to suit the artefact under study. For example, the probe head could either be mounted horizontally or vertically on the articulating arm and extension tubes could be used on the probe head to facilitate access to the artworks [8, 18].
Results and discussion
When testing the versatility of a mobile spectrometer in a museum context, the technique needs to be applied to a broad range of artefacts and questions. Throughout this text we will provide several examples, illustrating different facets of this approach. Table 1 gives an overview of the investigated artefacts, together with the analytical challenges that these investigations represent.
Table 1Overview of the different artefacts that were investigated in this work and the analytical challenges they representInvestigated artefactAnalytical challengeEgyptian sarcophagus (NMS) (A.1907.569A)Pigment identification through a thick varnish layerEgyptian sarcophagus (Glasgow) (1895.167.a)Pigment analysis with strong interference from sunlight. Transport to a different museum, analyse, and return in 1 dayDean panel (H.KL 72)Analysis of a varnished paintingKalighat painting (A.1912.122)Analysis of work on paperIndian textile banner (K.2005.234)Analysis of a textile artefact of large dimensions with a thin and friable painted decorationMar virginals (H.LT 122)Investigation in the exhibition gallery open to the public
The examination of Egyptian sarcophagi, both in the National Museums of Scotland (Edinburgh) and in the Burrell Collection (Glasgow Museums & Art Gallery) illustrate the comparative ease of transportation of the instrument. The capability of the technique to analyse pigments on artefacts covered by a thick varnish layer as well as to undertake the investigation with interference from sunlight are shown. During the examination of a Renaissance painted panel (one of the panels from Dean house, Edinburgh), strong inteference from a fluorescing varnish layer was also encountered. The possibilities of the technique for the non-invasive investigation of fragile artefacts on paper are illustrated by the yellow pigment identification on Indian Kalighat paintings and on a painted textile banner. During the latter survey the articulating arm was used to allow analysis of a large artefact displayed horizontally. Finally, working in the galleries represented an interesting experience as it involved undertaking analytical research whilst taking into account ambient conditions and visitors’ curiosity. This approach is illustrated by the study of the Mar virginals.
Pigment analysis of antique Egyptian sarcophagi
An antique Egyptian sarcophagus [A.1907.569 A], dating from the twenty first/second Dynasty (1069–715B.C.), was investigated at the NMS by using mobile Raman spectroscopy (Fig. 1a). Samples from this artefact had been examined before by Fourier transform (FT) Raman spectroscopy [19]. The aim of the present study was firstly to investigate whether non-destructive mobile Raman spectroscopy confirmed the previous findings. Secondly we wished to investigate the thick yellowed varnish layer, to see how it influenced the investigation, to establish when it was most likely to have been applied and what its visual effect was on the underlying colours.
Fig. 1a Experimental set-up for the investigation of an Egyptian sarcophagus at the NMS. b Stack plot of the baseline-corrected Raman spectrum obtained from the yellow areas of this sarcophagus and the reference spectrum of orpiment (As2S3). (Experimental conditions for the spectrum of the artefact: 60 accumulations of 1 s, ×6 objective, 785 nm, ca. 5 mW at the surface)
As expected, the yellow varnish generated serious interference of fluorescence radiation which overwhelmed the weaker Raman signal. Unfortunately it was not possible to identify its composition and therefore determine if it was original or whether it was the result of a previous conservation treatment. Nevertheless different pigments on the artefact could be identified. Figure 1b shows that the baseline-corrected Raman spectrum of the omnipresent yellow pigment on this sarcophagus is orpiment (As2S3). Other pigments that could be identified were hematite (Fe2O3), calcite (CaCO3) and carbon black (C). This is in agreement with the findings by Eremin et al. [19].
Although positive results were obtained, it was impossible to record a satisfactory Raman spectrum of the green and blue painted areas of the sarcophagus. It is very likely that Egyptian blue was used in this artefact; however, when stimulated with a 785-nm laser this pigment does not yield a Raman spectrum but gives rise to serious fluorescence background. Eremin et al. [19] indeed identified Egyptian blue with FT Raman spectroscopy (although the sample did not come from exactly the same area), so this illustrates one of the disadvantages of using mobile equipment. In contrast to many laboratory instruments, mobile instrumentation is usually equipped with a single laser, which makes it impossible to switch to another laser wavelength to overcome fluorescence interference.
A second Egyptian sarcophagus [1895.167.a] was investigated at the Burrell Collection in Glasgow, illustrating one of the advantages of using mobile equipment: several collections can share one instrument. The sarcophagus (Fig. 2a), dating from the ancient Egyptian New Kingdom (1570–1070B.C.), was in the conservation lab, with high levels of natural light. The conservator wished to know the identity of the yellow pigment on the cover of the sarcophagus, since orpiment (As2S3) may degrade when exposed to high levels of light. Figure 2b shows that the yellow pigment on the sculptured head and hands of the sarcophagus lid indeed was indeed orpiment (As2S3), whereas the yellow pigment on the other areas was limonite (FeOOH· nH2O).
Fig. 2a Experimental set-up for the investigation of an Egyptian sarcophagus in the conservation lab of the Burrell Collection, Glasgow. Ambient sunlight interfered with the investigations. b Stack plot of two Raman spectra obtained from the sculptured head and hands of this sarcophagus. (Experimental conditions: ×6 objective, 785 nm, ca. 5 mW at the surface, 120 accumulations of 1 s)
Pigment investigation of a painted panel from Dean House, Edinburgh
Mobile Raman spectroscopy was applied to identify the pigments in one of the Renaissance painted panels from Dean House in Edinburgh. The “Deans Panels” are thought to date from around 1627 [20]. The panel representing the sense of hearing (KL 72) is exhibited on a wall, at a height of about 3 m, which would have required the construction of scaffolding in the gallery. Therefore it was considered more practical to remove the panel temporarily from the exhibition and bring it to the conservation lab for analysis. The painted surface was found to be covered with a varnish layer, which contributed to the fluorescence background, although it still was possible to identify the pigments in the investigated areas of the artefact.
Vermilion (HgS) was observed in the red areas, whereas orange areas merely contained red lead (Pb3O4). Brownish red areas were painted with a hematite (Fe2O3)-containing paint. On several occasions, mixtures of two of these pigments were encountered. Black areas contained carbon black (C), whereas the white regions were painted by using lead white (2PbCO3·Pb(OH)2). Pink zones consisted of a mixture of the latter pigment with vermilion; vermilion could also be identified in the purple regions (the blue component of which could not be identified). Finally, the yellow areas examined were painted by using lead–tin yellow, type I (Pb2SnO4). All these pigments are consistent with the early modern origin of the artwork.
Identification of the yellow pigment from an Indian Kalighat painting
The yellow pigment from different areas from an Indian Kalighat painting was examined by using mobile Raman spectroscopy. These are nineteenth century Indian watercolour and opaque media paintings on industrial paper. Since paper artefacts are fragile, special care has to be taken not to damage them during handling and positioning of the spectrometer. Gloves were worn and positioning of the probe head was performed with great care. On all the yellow painted areas of the Kalighat paintings that were examined, the same yellow pigment was encountered. Figure 3 shows the Raman spectrum obtained, along with the reference spectrum of chrome yellow (PbCrO4). It is clear that the yellow pigment in the Kalighat paintings is chrome yellow.
Fig. 3Stack plot of the Raman spectra obtained from a yellow area from an Indian Kalighat painting and the reference spectrum of chrome yellow (PbCrO4). (Experimental conditions for the spectrum of the artefact: 8 accumulations of 150 s, ×6 objective, 785 nm, ca. 3 mW at the surface)
Analysis of a painted banner
The National Museums of Scotland has a large (1,080×2,030 mm) textile banner of uncertain provenance in its collection. Stylistic interpretations of the polychrome design suggest an Indian origin. The banner depicts, on a green background, colourful imagery of the different stages on the way to heaven (Fig. 4b, detail). Because of its large dimensions and the absence of appropriate non-destructive analytical methods, no previous investigation had been made and little was known of the materials that have been applied. The mobile Raman spectrometer was therefore used to identify the pigments.
Fig. 4a Experimental set-up for the investigation of a large textile banner. b Lower part of the investigated textile banner. c Raman spectrum of an orange area of the textile banner, together with the reference spectra of massicot (PbO) and red lead (Pb3O4). (Experimental conditions for the spectrum of the artefact: 1 accumulation of 150 s, ×6 objective, 785 nm, ca. 10 mW at the surface). d Raman spectrum of a red area of the textile banner and reference spectrum of vermilion. (Experimental conditions: 1 accumulation of 150 s, ×6 objective, 785 nm, ca. 5 mW at the surface)
In order to be able to examine a range of positions on the surface of the banner, the articulating arm was used for macro-positioning of the probe head, while the artefact was placed horizontally on a table (Fig. 4a). All the paint surfaces were thin, and in several areas the paint was friable or had already been lost. Red areas gave rise to the Raman spectrum of vermilion (HgS, Fig. 4d), whereas the orange areas yielded a more complex spectrum, which consists of a combination of massicot (PbO) and red lead (Pb3O4) (Fig. 4c). Red lead is an orange pigment, whereas massicot has a more yellowish shade. It is not clear whether massicot was intentionally added to the paint or whether the latter pigment arose as a side-product of the synthesis of red lead. Other pigments that were positively identified in the banner were calcite (CaCO3), carbon black (C), lead white (2PbCO3·2 H2O), azurite (2CuCO3·Cu(OH)2) and anatase (TiO2). Under the ambient conditions of the conservation laboratory, which did not have full light exclusion, and using the 785-nm laser, it was impossible to obtain a spectrum of sufficient quality to identify the green areas on the artefact. One solution to overcome this problem would be to use a Raman instrument with a different laser, with a smaller absorption cross section for the green pigments.
Investigation of an early keyboard instrument, the Mar virginals
The Mar virginals is an early keyboard instrument, which, although probably made in the Low Countries, has a long association with Lady Mary Stewart, countess of Mar and could have been made in Scotland. It probably dates from between A.D. 1560 and 1660, and its lid and soundboard contain three separate painted panels, the central one depicting Orpheus playing to the animals. We wished to compare the paintings on the lid to those on the soundboard, since similar or different pigment compositions may support or disprove the idea that the lid is contemporary to the virginals.
This artefact had to be analysed while on display in the museum gallery, which enabled us to evaluate the feasibility of undertaking the analytical work in a public space. It was necessary to ensure that there were no health and safety risks, especially from direct or scattered light from the laser beam, as well as keeping the electronics and power supply cables and units away from enquiring fingers. The visiting public were clearly intrigued with what was going on and to help explain this, a poster informed the visitors about the experiments.
Working in the gallery also meant that the ambient conditions were far from ideal. During this study, two issues severely hampered the Raman investigations: background light and spatial limitations. It was not possible to reduce the intensity of background light to avoid interference which tended to overwhelm the spectrum, nor on this occasion to undertake the work overnight. And since the Mar virginals was not taken out of its display case, little space was available to align the probe head with the painted surface, the cramped conditions meaning that great care had to be taken not to touch the artefact. To maximise flexibility the probe head was mounted on an articulating arm and extension tubes [8] were used to approach the painted surface (Fig. 5).
Fig. 5Experimental set-up for the investigation of the Mar virginals
Nevertheless, despite these non-ideal conditions, the pigments could be identified. White lead (2PbCO3·Pb(OH)2) is omnipresent: almost all the recorded Raman spectra contain the intense Raman band at 1,056 cm−1, whereas the less intense bands of white lead at 260, 203 and 154 cm−1 are only observed in a few spectra. Apparently, this pigment was applied to modify the hue of the coloured areas, making them appear brighter. From the intense Raman bands at 254, 283 and 343 cm−1, it is clear that the red areas were painted with the red pigment vermilion (HgS), whereas the bright yellow areas were painted by using orpiment (As2S3). Carbon black (C) is the black pigment which was used in the painted decoration.
In general, the blue areas of the painting did not yield Raman spectra of sufficient quality to allow identification. The reasons for this are multiple. In general, blue and green pigments absorb the red (785 nm) laser light which hinders the recording of a spectrum. In addition there was a large amount of background radiation, both from the gallery lights and from fluorescence, which was probably caused by the varnish layer. It is very likely that the blue pigment azurite (2CuCO3·Cu(OH)2) is used in the artwork, since this pigment was very commonly used in Renaissance period: however, unfortunately azurite happens to be a weak Raman scatterer when using a 785-nm laser, which means that small amounts of background radiation easily overwhelm the Raman spectrum.
Although only a few areas on the soundboard could be examined, the pigments on the lid were in good agreement with those on the soundboard. Despite the fact that this research project should be considered as a pilot study, this tends to support the thesis that the lid and the soundboard originate from the same instrument, or at least were painted at a similar period. However other approaches, such as stylistic studies, or taking samples to matching the spore-element compositions of the paint from the lid and the soundboard [9], are needed to be more definite. Since the investigation of this rare musical instrument was performed in one day, time was confined and only a limited number of areas could be examined. In fact, except for the absence of lead–tin yellow, none of the pigments were rare for an artwork of this period.
Conclusions
This work reports on the application of mobile Raman spectroscopy to the non-invasive investigation of a range of object of arts in a museum context. The different cases presented here illustrate different obstacles and analytical challenges that are often encountered during direct Raman investigations of museum objects. The investigation of two antique Egyptian sarcophagi shows the possibilities of the technique to deal with artefacts covered by a thick varnish layer as well as the investigation with the interference of ambient sunlight in the conservation laboratory. The investigation of the Dean House panel illustrates the possibilities of pigment investigation although a fluorescing varnish layer is present; the suitability of the method to analyse fragile artefacts was demonstrated by the investigation of Indian Kalighat paintings on paper; the textile banner was both fragile and large. Finally, the Mar virginals has been examined for the first time directly in the exhibition gallery with normal lighting conditions and with visitors present. All these examples illustrate that non-destructive, mobile Raman spectroscopy is able to cover a broad field of research questions on artistic and historical objects, in a range of different experimental conditions, encountered in a museum context.

raman spectroscopy
conservation science
mobile raman equipment
art analysis
non-destructive investigation


The Effects of Time Varying Curvature on Species Transport in Coronary Arteries

Alterations in mass transport patterns of low-density lipoproteins (LDL) and oxygen are known to cause atherosclerosis in larger arteries. We hypothesise that the species transport processes in coronary arteries may be affected by their physiological motion, a factor which has not been considered widely in mass transfer studies. Hence, we numerically simulated the mass transport of LDL and oxygen in an idealized moving coronary artery model under both steady and pulsatile flow conditions. A physiological inlet velocity and a sinusoidal curvature waveform were specified as velocity and wall motion boundary conditions. The results predicted elevation of LDL flux, impaired oxygen flux and low wall shear stress (WSS) along the inner wall of curvature, a predilection site for atherosclerosis. The wall motion induced changes in the velocity and WSS patterns were only secondary to the pulsatile flow effects. The temporal variations in flow and WSS due to the flow pulsation and wall motion did not affect temporal changes in the species wall flux. However, the wall motion did alter the time-averaged oxygen and LDL flux in the order of 26% and 12% respectively. Taken together, these results suggest that the wall motion may play an important role in coronary arterial transport processes and emphasise the need for further investigation.
Introduction
The formation of an atherosclerotic plaque is dependent upon a range of factors including local variations in haemodynamics, the transport of chemical species and cells from the blood to the arterial wall and their accumulation within the arterial wall.3,21,38The local susceptibility of coronary arteries to atherosclerosis has been putatively linked to the physiological motion arising out of their anatomical attachment to the epicardial surface of the beating heart.7,8Coronary arteries exhibit highly complex wall motion patterns such as bending, stretching, twisting, vessel torsion and vessel displacement due to the cardiac and respiratory motion of the heart.7,22 Despite their seemingly important role, to date, only a handful of studies have looked at the effects of wall motion on atherogenesis.
Early studies investigated the axial and lateral movement of a straight tube model of the coronary arteries and showed that the wall shear stress (WSS) patterns were significantly affected by the axial movement and not so by the lateral movement.4,23Other studies examined the effects of time dependent curvature on the flow field and demonstrated that the flow patterns were affected by the wall motion due to instantaneous changes in curvature and that the WSS patterns were strongly dependent on the frequency of curvature variation.18,24,32,33Another study considered the cyclic flexion of coronary arteries and reported that the plaque advancement was proportional to the flexion angle.37
More recently, studies have concluded that the dynamic curvature effects could play an important role in the flow and WSS patterns in curved and in bifurcating arteries.28,40,41Nevertheless, these studies reported that the temporal WSS variations produced by the wall motion was only secondary to that caused by the pulsatility of blood flow.28,41So far studies have only paid attention to the haemodynamic effects of wall motion. However, it has now come to light that the transport of atherogenic macromolecules such as LDL and solutes such as oxygen from the blood to the arterial wall play an important role in atherogenesis.38 Interestingly, with the exception of a single study, no other past study has investigated the effects of species transport to the moving walls of coronary arteries.17 Even in that study only the effects of diameter variation (<6% of mean diameter) on oxygen transport was considered.
Hence, the aim of our work was to determine the effects of wall motion on mass transport patterns in coronary arteries with physiological flow. For our investigation, we chose a geometric model of the left anterior descending coronary artery (LAD) because of its high predilection to atherosclerosis among the major coronary arteries.12 Computer aided design (CAD) models of the LAD were employed in this study as opposed to realistic geometries to simplify the complexity of the problem definition, solution and analysis of results so as to gain a fundamental understanding of the haemodynamic effects of wall motion in coronary arteries. The species of interest in this study were low-density lipoprotein (LDL) and oxygen.
Methods
Computational Model
A 3-D computational model of the LAD (Fig. 1) was constructed based on the average LAD dimensions (diameter (d) = 0.36 cm and mean radius of curvature (Rmean) = 4 cm),9,15,16using the CFD pre-processor Gambit (v2.0.4 – Fluent Inc., Lebanon, NH). The total length (l) of the LAD considered in this study was 4.68 cm which was equivalent to 13 tube diameters. In order to make the simulation as simple as possible, the model was assumed to be rigid with a circular cross section of constant radius and a constant uniplanar curvature over its entire length at any instant in time. An unstructured grid composed of hexahedral mesh elements with a thin boundary layer was generated using the Cooper meshing scheme2 with strict adherence to mesh quality metrics.
Figure 1.A schematic model of the left anterior descending coronary artery (LAD).
Blood Flow Boundary Conditions
The flow boundary conditions controlling the model included no slip at the tube wall, velocity inlet at the inlet, symmetry condition at the plane of symmetry and a traction free outflow condition. The LAD inlet velocity profile was assumed to be parabolic. A pulsatile physiological LAD inlet velocity waveform (Fig. 2) derived from a previous publication by Marcus et al.22 was applied at the inlet boundary through a user defined function. The time-averaged mean velocity (ν), mean flow rate (qo) Reynolds number (Re) and Dean number (κ) corresponding to this flow waveform were 9.715 cm/s, 0.99 ml/s, 105 and 22.3. The Dean number is defined as below
Figure 2.The LAD inlet flow and curvature waveform.
The Womersley parameter (α) of the flow was 2.78 and the period of a cardiac cycle (T) was 0.8 s. Blood was considered to be an incompressible, homogeneous, Newtonian fluid with a viscosity (μ) of 0.0035 Pa s and a density (ρ) of 1050 kg/m3. Past arterial flow dynamic studies have shown that these assumptions are reasonable enough to use in haemodynamic studies.16,29
Wall Motion Boundary Conditions
The wall motion boundary condition used in this study was based on the model presented by Santamarina et al.32 The main components of coronary artery motion are solid body motion and deformation. The deformation parameter (ε) was calculated as below  where R is the radius of curvature of the LAD. The present study used a deformation parameter of 0.6 which falls within the physiological range of LAD deformations.15 A sinusoidal function as given below was used to specify the time dependent variation of curvature of the LAD (Fig. 2)  where ω is the angular frequency of deformation. A user-defined function was written to apply this curvature waveform as a grid motion boundary condition. The tube inlet was fixed constant in the simulations at all times. This allowed the centre of curvature to vary sinusoidally in a cardiac cycle.
Mass Transfer Boundary Conditions
The mass transport of species in the arterial lumen is mathematically described using the three dimensional convection-diffusion equation  where C is the concentration of species, u is the velocity vector and D is the diffusivity of species in the blood.
In this study, the transport of LDL and oxygen from the blood to the endothelium (fluid phase), and across the endothelium (transendothelial) were modelled. The fluid phase transport of both LDL and oxygen takes place via convection and diffusion. However, the transendothelial transport of LDL and oxygen are governed by different mechanisms due to their relative size and the endothelial function. The semi permeable endothelial membrane, acts as a barrier for the entry of macromolecules such as LDL into the arterial wall, but allows blood plasma and small solutes such as oxygen to pass through it.10 This is reflected in the permeability of LDL (K = 2 × 10−8cm/s) which is five orders of magnitude smaller than that of oxygen (K = 2 × 10−03cm/s).36 This indicates the need for modelling LDL and oxygen transport differently through appropriate mass transport boundary conditions.
LDL transport was modelled as a concentration polarisation effect, a phenomenon which has been demonstrated to occur in large and medium sized arteries.5,11,39Concentration polarisation is the accumulation and elevation of endothelial surface concentration of LDL due to plasma filtration through the semi permeable endothelium. LDL mass transport boundary conditions applied in this study were uniform inlet concentration, Co (=1.2 mg/ml of blood),36 a zero gradient in concentration at the outlet and mass conservation of LDL at the luminal surface of the endothelium39 described as  where Vw is the plasma filtration velocity normal to the wall (=4 × 10−6cm/s),36Cw is the endothelial surface concentration of LDL, n is the unit vector normal to the vessel wall, D is the physiological LDL diffusivity (=5 × 10−8cm2/s)39 and K is the overall mass transfer coefficient of LDL which was considered equivalent to the physiological endothelial permeability of LDL. Equation (5) was rearranged as below to implement a wall reaction boundary condition in the CFD solver.
In this study, oxygen transport was modelled by applying a passive transport law for oxygen flux at the wall.31 It is known from previous estimates that the diffusion velocity of oxygen is orders of magnitude greater than the filtration velocity.1 Hence, the flux of oxygen to the wall is essentially the diffusive flux. To satisfy flux continuity at the wall the diffusive flux of oxygen to the wall should be equal to the wall flux of oxygen which can be modelled with a constant permeability wall boundary condition.31 Oxygen mass transport boundary conditions employed in this study were uniform inlet oxygen mass fraction (=0.005),20 a zero gradient in outlet concentration and a constant permeability wall boundary condition31 which is given as  where the oxygen diffusivity was 1 × 10−5cm2/s1.
Numerical Method
The commercial CFD solver Fluent (v6.1.22 – Fluent Inc., Lebanon, NH) was used to simulate species transport in the moving LAD model. Fluent uses a finite volume technique to solve the three-dimensional (3-D) unsteady equations of momentum, mass and species conservation. QUICK scheme was employed to solve the flow and species transport equations. The time discretization and pressure–velocity coupling were achieved through second order time implicit, and PISO schemes respectively. The dynamic mesh model feature of the Fluent was used to implement the deformation of the model according to Eq. (3). The dynamic mesh model uses an arbitrary Lagrangian–Eulerian approach to move the mesh nodes. At each time step, the volume mesh is updated automatically by the solver based on the new co-ordinates of the boundary zones that were calculated by the user-defined function. At the beginning of each simulation, the radius of curvature of the model was at its maximum (=6.4 cm). At t = 0.2 s and 0.6 s the radius of curvature of the model was equivalent to its mean value.
Modelling Approach
To separate the effects of blood flow pulsatility and wall motion on mass transport patterns in coronary arteries so as to determine their relative importance, we undertook the following four different numerical simulations:Case 1: Steady flow inlet (qo = 0.99 ml/s) in the static geometry with the mean radius of curvature (R = 4 cm).Case 2: Pulsatile flow inlet in the static geometry with the mean radius of curvature (R = 4 cm), to isolate the haemodynamics effects of flow pulsatility.Case 3: Steady flow inlet (qo = 0.99 ml/s) with dynamic geometry, to isolate the haemodynamics effects of wall motion.Case 4: Pulsatile flow inlet with dynamic geometry, to study the combined effects of pulsatility and wall motion.
Model Validation
The computational method was validated by simulating Santamarina et al.’s32 coronary artery motion study in a curved model of exactly similar dimensions under identical flow and wall motion conditions. The flow and wall shear stress (WSS) patterns agreed well with that of Santamarina et al’s study and the maximum difference in the wall shear rate was less than 2%. The mesh and time step independence of the solutions were investigated by successively refining the grid and the time step size by a factor of two. Computational solutions obtained with three different mesh densities (80,310, 156,861 and 310,464 nodes) and time step sizes (0.025 s, 0.0125 s and 0.00625 s) were then compared against each other (Fig. 3). Mesh and time step independence were considered to have been achieved when the maximum difference between the time-averaged normalised outer and inner wall flux of LDL (Cw/Co) between the successive simulations was less than 2% at any axial location. The outer and inner walls were defined as the wall regions farthest and shortest from the centre of curvature to represent epicardial and myocardial surface of the curved coronary arteries. This study was only carried out for LDL transport in the dynamic model with pulsatile flow because this was the computationally most challenging problem (Schmidt number, μ/ρD = 6.7 × 105 & Peclet number, dv/D = 7 × 107) encountered in this study. Based on these criteria, a mesh containing 156,861 nodes with a time step size of 0.0125 s was chosen as the optimum case for simulation.
Figure 3.Comparison of the time-averaged normalised LDL wall flux predicted with three different meshes and time step sizes to demonstrate mesh (top panel) and time step (bottom panel) independence of the computational solutions.
Results
Numerical simulations of oxygen and LDL transport in a 3-D curved tube model of the LAD were carried out under physiological flow and mass transport conditions. The computational solutions concerned the prediction of flow, WSS and mass transport patterns. The results will be presented only for the first 10 diameters length as this length is more relevant to realistic coronary flow situation as discussed elsewhere.32
Velocity Patterns
The temporal velocity magnitude (coloured contour plots) and secondary flow patterns (vector plots) in the static and dynamic models of the LAD are shown in Fig. 4. The fundamental flow patterns in a curved tube flow situation with a characteristic velocity skewing towards the outer wall of bend and the formation of Dean vortices were clearly seen in all the models (Fig. 4). Although both the pulsatility (Fig. 4b) and wall motion (Fig. 4c) produced time dependent velocity skewing, the flow pulsation had a greater effect on velocity skewing than did wall motion. The secondary flow was also affected by both the pulsatility and wall motion (Fig. 4e and f). However, the wall motion induced time dependent curvature (Fig. 4f) seemed to have a major influence on the secondary flow patterns. Adding the dynamic motion to the pulsatile flow reduced skewing over the entire cycle (Fig. 4d). Moreover, this combination led to a reduction in secondary flow when the time dependent vessel curvature was small and vice versa during the peak curvature phases (Fig. 4g). In general, both the velocity skewing and secondary flow increased with increase in curvature and the flow rate (Fig. 4).
Figure 4.Temporal axial and secondary flow patterns in a plane located at 9 diameters from the inlet in the (a) steady flow static model (R = 4 cm, qo = 0.99 ml/s), (b and e) pulsatile flow static model (R = 4 cm), (c and f) steady flow dynamic model (qo = 0.99 ml/s), and (d and g) pulsatile flow dynamic model. The outer and inner walls are located at the top right corner and top left corner of individual figures respectively.
WSS Patterns
The effects of pulsatility and wall motion on the instantaneous WSS distribution in a cardiac cycle at two axial locations (5D & 9D) are plotted in Fig. 5. The effect of motion in steady flow at the outer wall was to increase the WSS when the curvature was maximum. Along the inner wall the opposite effects were seen with a drop in the WSS at the maximum curvature. The wall motion also produced asymmetric WSS distribution over time in the steady flow dynamic model despite the imposed symmetric wall motion curve.
Figure 5.Temporal axial inner and outer WSS distribution at 5 diameters (top panel) and 9 diameters (bottom panel) from the LAD inlet.
The pulsatility of blood flow caused time varying WSS distribution along the outer and inner walls. The WSS increased with the pulsatile flow rate and overall this increase was greater along the outer wall. Hence peak WSS was noted along the outer wall at maximum flow rate. Together, wall motion and flow pulsatility produced instantaneous variations in the outer and inner WSS which were somewhat similar to the WSS distribution in the pulsatile flow static model. Although the gross distribution of WSS primarily followed the flow rate curve, the dynamic curvature effects were evident in the central section where the location and magnitude of peak WSS were different from that of the pulsatile flow static model.
The time-averaged axial WSS distribution at the outer and inner walls in all the models is shown in Fig. 6. In general, the spatial dependence of time averaged WSS was evident in all the models. In the steady flow static model the outer WSS increased to a peak and thereafter was found to decrease very gradually. In contrast, the inner WSS decreased to a minimum and was thereafter found to increase slowly. In comparison, the flow pulsatility caused a big increase in the outer WSS in the pulsatile flow static model. However, the wall motion only produced a little change in the outer WSS in the steady flow dynamic model.
Figure 6.Time-averaged axial WSS distribution along the inner and outer walls of the LAD.
The combined effects of wall motion and pulsatility caused an increase in the outer WSS up to its peak value and a subsequent decrease in the WSS in comparison with that of the pulsatile flow static model, and an overall increase in the outer WSS when compared to the steady flow models. Along the inner wall the flow pulsatility and the wall motion produced contrasting effects whereby the former decreased the WSS and the later increased the WSS. Due to this opposing effect of wall motion and flow pulsatility their combination had only caused smaller variations in the WSS distribution.
Oxygen Transport
The time dependent non-dimensional oxygen wall flux (KCw/KCo) distribution at 5D and 9D is presented in Fig. 7. The oxygen wall flux did not vary appreciably over time in a cardiac cycle. At any instance the outer wall oxygen flux was higher than that at the inner wall. These patterns were similar in all the models studied. When compared to the oxygen wall flux in the steady flow static model, pulsatility caused a reduction and wall motion an increase in the oxygen wall flux along the inner wall. Along the outer wall the effects of pulsatility and wall motion were opposite to this. In total, the net effect of pulsatility and wall motion was an increase in oxygen flux at the inner wall and decrease at the outer wall when compared to the steady flow static model. This effect was more prominent in the distal region (Fig. 7c and d).
Figure 7.Temporal normalised oxygen flux to the inner and outer walls at 5 diameters (top panel) and 9 diameters (bottom panel) from the LAD inlet.
The time-averaged non-dimensional oxygen wall flux to the outer and inner walls of all the four models are shown in Fig. 8. The overall effects of pulsatility and wall motion were similar to that predicted with the time dependent oxygen wall flux distribution. The effect of flow pulsation was to decrease the oxygen flux to the inner wall. In contrast, the wall motion contributed to an increase in oxygen flux in the distal region of the inner wall. Hence the combined effects of wall motion and flow pulsatility were to decrease oxygen flux in the proximal and medial regions of the inner wall and a very gradual increase distally.
Figure 8.Time-averaged normalised oxygen wall flux distribution along the inner and outer walls of the LAD.
Along the outer wall after an initial sharp drop the flow pulsation caused a gradual increase and then a gradual decrease in the oxygen flux. The wall motion produced a net reduction in the oxygen flux to the outer wall from the inlet. When both the wall motion and flow pulsatility were included the oxygen flux decreased sharply closer to the inlet followed by a gradual increase in the medial region and a gradual decrease thereafter. Nevertheless, in all the models the time-averaged outer wall oxygen flux was more than that to the inner wall.
LDL Transport
The time dependent non-dimensional LDL wall flux (KCw/KCo) distribution at 5D and 9D is plotted in Fig. 9. Like oxygen wall flux the LDL wall flux did not exhibit notable variations over time. The flow pulsatility caused an increase in LDL flux to the inner wall and a decrease in LDL flux to the outer wall when compared to the LDL flux distribution in the steady flow static model. In contrast, the wall motion induced LDL wall flux was lower along the inner wall. However, along the outer wall it produced a differential distribution whereby the LDL wall flux was lower than that in the steady flow static model at 5D and vice versa at 9D (Fig. 9c and d). At 9D the outer wall LDL flux was greater than that along the inner wall. Also the outer wall flux in the steady flow dynamic model was higher than that in the pulsatile flow static model. Hence the net effect of wall motion and pulsatility produced LDL wall flux distribution in the pulsatile flow dynamic model such that it was between the range of LDL wall fluxes caused by the pulsatility and wall motion separately.
Figure 9.Temporal normalised LDL flux to the inner and outer walls at 5 diameters (top panel) and 9 diameters (bottom panel) from the LAD inlet.
The time-averaged non-dimensional LDL wall flux to the outer and inner walls of all the four models are shown in Fig. 10. In all the models the time-averaged LDL wall flux distribution was seen to vary appreciably with respect to the axial distance from the inlet. The flow pulsatility caused an increase and decrease in the flux along the inner and outer walls respectively when compared to the steady flow static model. In contrary, the wall motion caused a reduction in the inner wall LDL flux when compared to that of the steady flow static model. Whereas the outer wall LDL flux was lower and higher than that in the steady flow static model in the medial and distal regions respectively. The combined effects of pulsatility and wall motion led to a substantial increase in the outer wall LDL flux in the distal regions. In the proximal and medial regions the outer wall LDL flux was lower than that to the inner wall. However, the inner wall LDL flux increased to a maximum and was then seen to drop off to a value lower than that along the outer wall in the distal part.
Figure 10.Time-averaged normalised LDL flux distribution along the inner and outer walls of the LAD.
Discussion
To determine the effects of wall motion, a factor which has received less attention in the previous mass transport studies in coronary arteries, mass transport patterns were computed in an idealised moving model of the LAD. In this study, LDL and oxygen transport were simulated because of their important role in atherogenesis. The flow patterns reported in the current study (Fig. 4) qualitatively agree with the findings of past experimental and computational studies in that they all demonstrate a general trend of velocity skewing towards the outer wall of curvature and the appearance of in-plane secondary flow.16,27,29,32The centripetal acceleration of the fluid due to the arterial curvature was primarily responsible for these flow patterns (Fig. 4a). This basic flow feature was further altered by the flow pulsatility (Fig. 4b and e) and the dynamic curvature (Fig. 4c and f). Their relative role on secondary flow can be characterised using the non-dimensional Dean number (κ).
From the definition of the Dean number (Eq. 1), it is clear that higher the flow rate or smaller the radius of curvature the stronger the Dean vortices will be. This was evident in the figures as the magnitude of skewing and the strength of Dean vortices increased with increasing flow rate and curvature and were maximum at maximum flow rate in the pulsatile flow static model and at maximum curvature in the steady flow dynamic model. Therefore, the combination of flow pulsatility and wall motion were responsible for the appearance of time dependent flow patterns in the pulsatile flow dynamic model (Fig. 4d and g). The flow patterns seen in this study are qualitatively similar to that of Santamarina et al’s work which considered the effects of dynamic curvature.32
The flow patterns at t = 0.2 s and 0.6 s in the steady flow dynamic model where the time dependent curvature was equal to the mean curvature appeared to be different from that in the steady flow static model (Fig. 4c and f). This suggests that a quasi-static model cannot predict the flow patterns accurately and that the dynamic wall motion could be as important as the instantaneous geometry. Similar findings were reported by Ethier et al. who compared the flow patterns in frozen geometries and in a moving model of the right coronary artery segment.41
The WSS patterns calculated in the pulsatile flow static models are similar to those reported by other studies on coronary artery flow.16,27,29Since the WSS is directly proportional to the gradient of velocity the WSS patterns followed the trends of velocity distribution. The velocity skewing towards the outer wall subjected the outer and inner walls to higher and lower WSS respectively except up to a distance of 1D where the entrance effects were dominant (Fig. 6). The instantaneous and mean WSS distribution in the pulsatile flow dynamic model closely followed that of the pulsatile flow static model except in the central section where the curvature was high. These results suggest that the effects of wall motion on temporal WSS patterns are only secondary to the pulsatile flow effects and agree well with the findings of other dynamic model studies.28,41
Concentration polarisation of LDL has been demonstrated to occur in large arteries by several past experimental and numerical studies.5,11,39The build-up of LDL concentration at the apical surface of the endothelium due to concentration polarisation could increase the influx of LDL into the arterial wall.5,35 This, in turn, could initiate a sequence of biological and biophysical events that are associated with atherogenesis.5,25 Therefore, LDL transport was modelled as a concentration polarisation effect and the results were presented as normalised LDL wall flux (KCw/KCo).
The present study also considered oxygen transport because hypoxia or low oxygen tension in the arterial wall has long been considered to be a factor responsible for atherogenesis.1,17It induces endothelial cell apoptosis, causes breakdown of endothelial barrier and forms interendothelial gaps which may cause lipid infiltration into the arterial wall. Recent evidences suggest that hypoxia may also increase vascular permeability to macromolecules by up regulating vascular endothelial growth factor (VEGF) release by vascular cells.38 Hence oxygen transport was modelled with a passive transport law for oxygen flux at the wall.31 To describe oxygen transport patterns normalised oxygen wall flux (KCw/KCo) was calculated.
In general, the mass transport patterns were similar in all the models. The establishment of low velocity, low WSS regions along the inner wall due to reasons discussed earlier led to an overall elevation of LDL flux and a reduction in oxygen flux to the inner wall. In contrast, high axial flow and high WSS along the outer wall due to axial velocity skewing caused a reduction in LDL flux and an increase in oxygen flux to the outer wall. The higher the WSS the higher the convection and the convective transport of oxygen. Hence the oxygen flux to the outer wall was more than that to the inner wall. The opposite effects were noted for LDL transport because the degree of concentration polarisation is reduced where high flow and high WSS prevail.39 As a result, the LDL accumulation was generally lower along the outer wall and higher along the inner wall. The diffusive transport of LDL and oxygen however, had minor influence on their transport. This was especially true because of their constant low diffusion coefficients. These results qualitatively agree with mass transport studies in curved arterial transport models.17,17,39
Despite the large time dependent variations in the blood flow and WSS patterns, the flow pulsation only had a minimal effect on oxygen (Fig. 7) and LDL (Fig. 9) transport to the arterial wall. Consequently, the time-averaged oxygen and LDL wall fluxes (Figs. 8 and 10) in the static pulsatile flow model were only slightly different from their instantaneous values. This less significant pulsatile effects on time-averaged mass transport patterns have also been reported by other studies.11,19,34
Schneiderman et al.34 simulated oxygen transfer in straight arterial segments and concluded that the pulsatile flow has negligible effect on oxygen transport to the wall. Ma et al.19 studied the effect of pulsatility on mass transfer in a sudden expansion region and reported that the time-averaged pulsatile mass transfer patterns were similar to the steady flow mass transfer patterns. Fatouraee et al.11 simulated LDL concentration polarisation in a straight tube model of a carotid artery under pulsatile flow conditions. The variation of LDL wall concentration in their study was less than 0.014%.
Time dependent variations in mass transport patterns were also not present in the dynamic models (Figs. 7 and 9). As a result, the magnitude of time-averaged oxygen and LDL wall fluxes were only slightly different from their corresponding time dependent flux values (Figs. 8 and 10). Although the wall motion did not significantly affect the temporal mass transfer patterns in the dynamic models, an overall change in the mass transfer patterns was evident (Figs. 7 and 9). This change was also reflected in the time-averaged mass transfer patterns in the dynamic models (Figs. 8 and 10). The percentage change in time-averaged oxygen wall flux and LDL wall flux along the inner and outer walls between the static and dynamic models under both pulsatile and steady flow conditions was in the order of 26% and 12% respectively.
The fact that the pulsatility did not alter mass transfer patterns and that there were variations in mass transport patterns in the dynamic models suggest that the wall motion may indeed be important in determining the time-averaged mass transport patterns in the dynamic models. These variations were not significant in the proximal regions of the LAD model. However, considerable changes in the mass transport patterns were present in the medial and distal regions of the LAD (Figs. 8 and 10). This regional difference occurred due to the specification of fixed inlet boundary condition. Although this is an effect of the fixed inlet boundary condition these results once again indicate that strong motion of the coronary arteries could have a significant impact on the luminal mass transport patterns.
The species transport however, does not entirely depend on the WSS distribution. Karino et al drew a scatter plot to establish a relationship between the WSS and LDL surface concentration.39 The scatter in their data suggests that the species transport is also influenced by global flow patterns, path line of fluid flow, spatial location and residence time of LDL at a site. This was also found in the present study which noticed different values of LDL and oxygen wall flux for the same WSS value along the outer and inner walls (Figs. 6, 8 and 10).
The inner walls of a curved artery where low WSS prevails is known to be a predilection site for atherosclerosis.14,21 LDL and oxygen transport studies have reported impaired oxygen flux and enhanced LDL accumulation in the low shear regions.5,17,17,39Similar to these findings the present study also predicted co-localisation of low WSS regions, high LDL flux regions and hypoxic regions along the inner wall of curvature (Figs. 6 and 10) in both the static and dynamic models. Hence our results suggest that even when the wall motion is considered the susceptibility of inner wall regions of the curved arteries to atherosclerosis is maintained.
Back et al.1 investigated the relationship between oxygen transport and lipoprotein accumulation in various arterial configurations. They noticed a correspondence between oxygen deprivation (hypoxia) and enhanced lipoprotein accumulation in decelerated flow regions and separation locations and concluded that the oxygen transport behaves in an opposite way to lipoprotein accumulation. This contrasting behaviour of oxygen and LDL transport was also noticed in our present study. It would be interesting to study the indirect effects of hypoxia on LDL transport through pathways involving enhancement of endothelial permeability for macromolecules.38
The present study assumed a constant endothelial permeability of oxygen and LDL which, in reality, is spatially variable and is dependent on the concentration of species at the arterial wall surface and the WSS.26,36Pulsatile variations in the WSS has been shown to alter the morphology of endothelial cells, increase macromolecular permeability to LDL, promote transendothelial diffusion and intimal entrapment of atherogenic particles and enhance oxygen wall flux.13,21Therefore, it is possible that the temporal variations in WSS caused both by the pulsatility of blood flow and wall motion may have an effect on LDL and oxygen transport if shear dependent variations in physiological parameters including the endothelial permeability are considered in mass transport studies. So far their effects on species transport have only been considered in static models30,36and it remains to be investigated how these factors would influence mass transport in moving arteries. This will be the subject of our future study.
The limitations of this study centre on the assumptions that were made in the construction of computational models which include idealized geometry, wall motion, and biophysical factors governing LDL and oxygen transport. An idealised, uniformly curved model without complex geometric features such as tortuosity, twisting and non-uniform cross sections was used. Inclusion of these geometric features would be expected to create local regions of flow acceleration, deceleration, separation, recirculation and secondary motion1,27 which may exacerbate or reduce the effects of wall motion on haemodynamics and species transport. While we accept that the neglect of complex geometry may introduce some simplifications in the results it should be recognised that the geometric simplification enabled us to isolate the effects of geometry and to concentrate on the role of wall motion on species transport.
The results predicted in this study are specific to the flow and wall motion used in our model and therefore would be expected to change if a different flow or wall motion were imposed. For instance, if the flow and wall motion were more out of phase it may be expected that this would reduce the instantaneous Dean number and therefore one might expect the impact of the dynamic wall motion to be decreased from what was predicted. Although the wall motion effects were secondary to the pulsatile flow effects this does not mean that the wall motion is unimportant. Given the considerable variability in coronary artery motion, geometry and flow patterns in vivo there may be instances where wall motion could play a more significant role and therefore its effects should be considered on a case by case basis.
Our study assumed that the non-Newtonian effects caused by the presence of red blood cells are negligible and therefore used constant diffusivities for LDL and oxygen. However, the effective diffusivity of a species changes due to the shear induced microscopic motion of red blood cells.6 In regions of high WSS, the effective diffusivities of LDL and oxygen would increase. This would in turn enhance oxygen wall flux but reduce LDL wall flux due to LDL concentration polarisation. Therefore, the magnitude of LDL and oxygen wall fluxes predicted in our study would be expected to change but the general patterns of flux will remain the same.
Conclusion
In this study, we have incorporated the effects of idealised motion into a simulation of oxygen and LDL transport in the LAD. The LDL and oxygen transport were modelled differently due to their differences in in-vivo transport mechanisms. We carried out simulations under both the steady and pulsatile flow conditions in the static and dynamic models to quantify the relative importance of pulsatility and wall motion on mass transport patterns. This study showed that the effects of wall motion on mass transport in the coronary arteries could be modelled in the commercial CFD code Fluent.
Our results predicted elevation of LDL flux, impaired oxygen flux and low wall shear stress (WSS) along the inner wall of curvature, a region known for its predilection to atherosclerosis. The temporal variations in velocity and WSS patterns in the dynamic model did not influence time dependent mass transport and were only secondary to the pulsatile flow effects. However, wall motion may influence mass transport if shear dependent variations in species permeability and other factors are considered in the calculations. Nevertheless, the wall motion did alter the time-averaged mass transfer in the medial and distal regions in the order of 26% and 12% for oxygen and LDL transport when compared to the corresponding static models. Taken together, these results suggest that wall motion may play an important role in coronary arterial transport processes. However, future studies on more physiologic models are warranted to gain a fundamental understanding of the role of wall motion on mass transport and atherosclerosis.

mass transport
wall motion
wall shear stress
oxygen transport
cfd
coronary artery motion
ldl transport
coronary atherosclerosis


A database for medicinal and aromatic plants of JK (Jammu and Kashmir) in India

High throughput screening of small molecules for a given drug target is achieved using plant materials of medicinal value. Therefore, it is important to document the availability and location of such medicinal plants in the form of a database. Here, we describe a web database containing information (botanical name, common name, local name, botany, chemistry, folklore medicinal use and medicinal uses) about the medicinal and aromatic plants available in JK (Jammu and Kashmir). The database is available for free in public domain.
Background
Medicinal plants documented in the literature from thick vegetations of the world are routinely used for high throughput
screening in small molecular drug discovery. A large number of such plants are known to be recognized in a discrete manner.
Therefore, it is important to store information related to medicinal and aromatic plants of JK in a database. This will help in
the use and exploitation of the plant materials for drug discovery. Some of the plants available in this region are also known
to have aromatic value. Here, we describe the development and use of a database containing information on medicinal and aromatic
plants from JK.
Methodology
Dataset
Folklore medicinal usage and other associated data were manually collected from the inhabitants (tribal/gujjars/local
clinicians) of JK through direct person to person survey for each plant species. The current dataset contains information for
about 133 plant species.
Database Interface
The database interface is designed for searching the dataset using a PHP (a programming language that allows web developers
to create dynamic content that interacts with databases) server enabled script for keywords such as botanical name, local name,
folklore use and medicinal use. The database is accessible for free through the internet.
Database Design
The database is developed in MySQL on a Linux Platform and updated regularly.
Search result
The search output for a given query (for example, diabetes) is given in Figure 1.
Utility
The database finds utility in the selection of medicinal plant species for high throughput screening in drug discovery.

medicinal
aromatic
plants
jammu and kashmir
india
botanical name
common name
folklore


The influence of corticosteroids on the release of novel biomarkers in human endotoxemia

Objective Sepsis intervention studies need better patient stratification methods, and one way to realize this is the introduction of stable biomarkers. A set of recently developed novel biomarkers, based upon precursor-fragments of short-lived hormones, was previously shown to be increased during sepsis. However, it is not known whether these biomarkers are influenced by sepsis intervention strategies. Therefore we investigated the markers in a model of human endotoxemia intervened by increasing doses of prednisolone.
Introduction
During the past decade numerous sepsis intervention strategies have been introduced; only few of them, however, have shown beneficial effects. Major problems in sepsis intervention trials were mixed results and unclear risk stratification methods. The lack of clear stratification methods was especially disturbing, since the effectiveness of intervention in these studies correlated closely with disease severity and risk of mortality [1]. Within this context, there is a need for biomarkers to predict disease severity and mortality risk.
During sepsis a wide range of inflammatory proteins is released and in theory all could be used as a biomarker. The vast majority of these proteins is biologically active though and as a consequence rapidly cleared from the circulation. A promising new approach is the measurement of stable prohormone fragments of bioactive peptide-hormones [2–4]. In theory, these fragments often have no known biological function during sepsis and are therefore less rapidly cleared and less sensitive to small homeostatic changes [5–7].
The most prominent prohormone sepsis marker is procalcitonin (PCT), which represents the precursor hormone of calcitonin. Its diagnostic and prognostic properties were superior to C-reactive protein (CRP) in multiple studies [8]. In addition, PCT responded to sepsis intervention strategies and represented a promising marker for monitoring of treatment effect [9, 10]. In recent years, additional assays have been developed for measurement of stable prohormone fragments, such as mid-regional pro-adrenomedullin (MR-proADM) [5], derived from the precursor of adrenomedullin (ADM); mid-regional pro-atrial natriuretic peptide (MR-proANP), derived from the precursor of atrial natriuretic peptide (ANP) [6]; and C-terminal proAVP (CT-proAVP), derived from the precursor of arginine–vasopressin (AVP; also known as the antidiuretic hormone) [7]. Levels of these biomarkers were higher in samples from patients with sepsis [2–4]. Yet it is not known whether these levels are influenced by a potential therapeutic sepsis intervention. In order to investigate the response of the novel biomarkers to a sepsis intervention strategy we applied the model of human endotoxemia and pre-treated subjects by increasing doses of corticosteroids, which is one of the few relatively successful, and among the most cost-effective, septic shock intervention strategies to date [11].
Methods
Subjects and study design
The study was designed according to the requirements of the Declaration of Helsinki and approved by the institutional scientific and ethics committees, and written informed consent was obtained from all subjects. The study was performed simultaneously with an investigation of the effects of prednisolone on activation of the cytokine network, coagulation and hemodynamic variables in the same cohort [12]. Thirty-two healthy male volunteers [mean (± SE) age 23.9 ± 0.7 years] were admitted to a hospital clinical research unit. Screening tests were all normal. Prednisolone (prednisolone sodium phosphate oral solution 5 mg/ml; prepared by institutional pharmacy) was administered orally at a dose of 0, 3, 10 or 30 mg (n = 8 per group) at 2 h prior to LPS injection. All participants were challenged at t = 0 h with LPS (Escherichia coli lipopolysaccharide, lot G; US Pharmacopeia, Rockville, MD) as a bolus intravenous injection at a dose of 4 ng/kg, which is a standardized dose [13].
Assays
Blood was collected at intervals from 2 h before LPS injection to 24 h thereafter. Blood was immediately centrifuged (4 °C, 10 min, 3000 rpm) and plasma was stored at –20 °C until assayed. Uptake of orally administered prednisolone was measured by HPLC-MS/MS technology (CombinatoRx, Inc., Boston, MA, USA). MR-proADM, MR-proANP, CT-proAVP and PCT were measured by chemiluminescence label-coated tube-based sandwich immunoassays (Brahms Diagnostica, Berlin, Germany) as previously described [5–7, 14].
Statistical analysis
Differences between treatment groups were analyzed using a non-parametric mixed model approach (repeated-measures ANOVA). The effects reported are treatment effects adjusted for time effects. p values < 0.05 were considered statistically significant. Values presented are given as mean ± SE.
Results
Pharmacokinetic data
As shown in Fig. 1, uptake of orally administered prednisolone was demonstrated by a dose-dependent increase in plasma levels during endotoxemia. The levels peaked from 1 h before LPS administration to 1 h after LPS administration (peak levels 302 ± 34 ng/ml, 190 ± 19 ng/ml and 71 ± 5 ng/ml in the groups receiving prednisolone 30, 10 and 3 mg respectively; t = –1 h).
Fig. 1Prednisolone. Mean (± SE) values of prednisolone concentrations after LPS administration (4 ng/kg IV, t = 0 h) to healthy male volunteers, preceded by oral administration of prednisolone 3 mg (circles), 10 mg (triangles), or 30 mg (squares) at t = –2.5 h
Novel prohormone biomarkers
MR-proADM levels increased after LPS administration from 0.31 ± 0.02 nmol/l at baseline to a peak value of 1.05 ± 0.11 nmol/l in the control group at t = 4 h (Fig. 2). The rise was inhibited after prednisolone 30 mg and 10 mg pre-treatment (levels at t = 4 h: 0.65 ± 0.06 nmol/l and 0.65 ± 0.1 nmol/l respectively; p < 0.05), but not after the low dose of prednisolone 3 mg. MR-proANP levels rose from 42.4 ± 2.2 pmol/l at baseline to 118.4 ± 17.2 pmol/l in the control group at t = 4 h. These levels were reduced after prednisolone 30 mg and 10 mg (peak levels: 91.5 ± 9.9 pmol/l and 64.8 ± 5.6 pmol/l at t = 2 h and t = 4 h, respectively; p < 0.05). CT-proAVP levels showed a modest increase from 12.5 ± 4.0 pmol/l to 18.0 ± 4.8 pmol/l in the control group at t = 4 h. The rise was inhibited after prednisolone 30 mg (peak level: 6.1 ± 1.0 pmol/l; p < 0.05) and not significantly changed by 10 mg (peak level: 10.6 ± 3.4 pmol/l; p < 0.1). In contrast, prednisolone 3 mg pre-treatment was associated with enhanced CT-proAVP release (peak level 34.7 ± 11.5 pmol/l; p < 0.05). PCT levels increased from a baseline level of 0.04 ± 0.02 ng/ml to a peak value of 16.7 ± 2.6 ng/ml in the control group. Peak PCT levels all occurred at t = 24 h and were not significantly affected by prednisolone pre-treatment (11.9 ± 3.0 ng/ml, 13.1 ± 2.5 ng/ml and 17.6 ± 3.5 ng/ml in the 30, 10 and 3 mg group, respectively).
Fig. 2Novel prohormone biomarkers. Mean (± SE) values of MR-proADM, MR-proANP, CT-proAVP and PCT after LPS administration (4 ng/kg IV, t = 0 h) to healthy male volunteers, preceded by oral administration of prednisolone 3 mg (open circles), 10 mg (triangles), 30 mg (squares) or, in the control group, 0 mg (filled circles) at t = –2.5 h. * p < 0.05 vs. control group. NS, non significant
Discussion
The primary objective of this study was to determine the effect of increasing doses of prednisolone on the release of novel biomarkers during human endotoxemia. We show here that prednisolone pharmacologic concentrations peaked at the time of LPS infusion and that the release of MR-proADM, MR-proANP and CT-proAVP was subsequently inhibited in a dose-dependent way. The levels of PCT were not significantly inhibited within the study period of 24 h. However, previously inhibition of PCT was shown by anti-inflammatory agents within a period of 168 h [9]. The levels of CT-proAVP were, remarkably, increased after prednisolone 3 mg, yet the reason for this remains unclear because the pattern was unique to this biomarker.
The increased levels of MR-proANP, MR-proADM and CT-proAVP reflect activation of their mature counterparts. Difficulties in measurement of the mature hormones made it difficult to determine their roles during sepsis [5–7]. The currently investigated biomarkers are produced after proteolytic processing of the same prohormone precursors as the mature hormones; thus, they reflect production levels of mature hormones in stoichiometric concentrations [7, 15]. In theory, the biomarker assays may measure levels of both cut prohormone fragments and full-length prohormones. However, the meaning of this relationship remains unclear in plasma because no well-defined methods are currently available for measurement of full-length prohormone plasma levels.
The biomarker levels in this study were dose-dependently influenced by corticosteroid intervention, which is important for treatment effect monitoring. The development of new biomarkers is needed for improvement of sepsis stratification methods, which influence trial outcomes. Several sepsis intervention strategies have shown promise [16]; in this study corticosteroids were selected because they aim primarily at inhibition of the immune response, and for this intervention in particular, effects are dose-dependent and improvement of patient stratification methods is urgently needed [11, 17]. Prednisolone is among the most frequently applied corticosteroids [18]; the oral route is convenient and potentially safer than intravenous administration [19].
The doses of prednisolone used in our model were chosen according to common dose regimens for prednisolone [18]. These doses were relatively low (equivalent to 12, 40 and 120 mg hydrocortisone) in comparison to doses used for sepsis intervention (hydrocortisone 200–300 mg per day), yet sufficient to reduce the release of the investigated biomarkers during endotoxemia. Of note, it has been demonstrated that the effects of orally administered doses of prednisolone do not differ from those of comparable doses of intravenously administered hydrocortisone [20].
The human endotoxemia model differs from sepsis in many aspects: it uses a single-dose drug intervention and bolus intravenous challenge of LPS, it is self-limiting, and it lacks an infectious source [13]. The study subjects are healthy young people not using any co-medication. The intervention must be administered before LPS injection in order to reach clinically relevant drug concentrations during endotoxemia because LPS is extremely quickly cleared from the circulation. Due to these differences, data cannot be directly extrapolated to the sepsis field and must always be confirmed in patient studies. Nonetheless, the model is the best available in healthy humans and has proven useful to obtain a proof of principle of the actions of drugs and/or to study mechanisms that contribute to the activation of inflammatory pathways [13]. With these limitations in mind, we demonstrate here that the levels of the novel biomarkers MR-proADM, MR-proANP, CT-proAVP and PCT are all increased after LPS injection, and that the levels of MR-proADM, MR-proANP and CT-proAVP can be inhibited by prednisolone. These results add to their potential as biomarkers in sepsis.

corticosteroids
sepsis
biological markers
endotoxin