alexa Macular Pigment Quantification with Multispectral Retinal Image Analysis | Open Access Journals
ISSN: 2155-9570
Journal of Clinical & Experimental Ophthalmology
Like us on:
Make the best use of Scientific Research and information from our 700+ peer reviewed, Open Access Journals that operates with the help of 50,000+ Editorial Board Members and esteemed reviewers and 1000+ Scientific associations in Medical, Clinical, Pharmaceutical, Engineering, Technology and Management Fields.
Meet Inspiring Speakers and Experts at our 3000+ Global Conferenceseries Events with over 600+ Conferences, 1200+ Symposiums and 1200+ Workshops on
Medical, Pharma, Engineering, Science, Technology and Business

Macular Pigment Quantification with Multispectral Retinal Image Analysis

Antonio Calcagni1,2*, Hannah E Bartlett1, Ela Claridge2, Frank Eperjesi1, Jonathan M Gibson1,3, Andrew D Palmer2, Yuan Shen2 and Iain B Styles2

1Ophthalmic Research Group, School of Life and Health Sciences, Aston University, Aston Triangle, Birmingham, B4 7ET, United Kingdom

2School of Computer Science, University of Birmingham, Edgbaston, Birmingham, B15 2TT, United Kingdom

3Heart of England Foundation Trust, MIDRU, Birmingham Heartlands Hospital, Bordesley Green East, Birmingham, B9 5SS, United Kingdom

*Corresponding Author:
Antonio Calcagni
Ophthalmic Research Group, School of Life and Health Sciences
Aston University, Aston Triangle, Birmingham, B4 7ET, United Kingdom
Tel: + 44(0)1212043711
Fax: + 44(0)1213334048
E-mail: a.calcagni@aston.ac.uk

Received date: November 18, 2016; Accepted date: January 23, 2017; Published date: January 28, 2017

Citation: Calcagni A, Bartlett HA, Claridge E, Eperjesi F, Gibson JM, et al. (2017) Macular Pigment Quantification with Multispectral Retinal Image Analysis. J Clin Exp Ophthalmol 8:628. doi: 10.4172/2155-9570.1000628

Copyright: © 2017 Calcagni A, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Visit for more related articles at Journal of Clinical & Experimental Ophthalmology

Abstract

Objective: Variations in macular pigment (MP) have been linked with changes in the risk of visual loss secondary to macular pathology. MP density can be modified by diet; however there doesn’t appear to be a direct link between dietary intake of MP components and MP density in the retina and clinicians therefore need a reliable and objective method for MP measurement to establish if any intervention is yielding the required results. The objective of this study was to investigate whether multispectral retinal image analysis (MRIA), a new technique for mapping retinal pigments, is useful for measuring levels and distribution of MP to find differences between individuals with no clinical evidence of macular pathology and those diagnosed with Age-related Macular Degeneration (AMD).

Methods: The study involved 90 volunteers from three subject groups: aged under 50 without AMD, aged 50 and over without AMD and aged 50 and over with AMD. The experiments yielded 607 usable data sets that were used for analysis. Multispectral image data was acquired at six selected wavelengths using a modified fundus camera. MRIA maps of MP were computed from 3 × 3 mm regions of interest (approximately 10 degrees of visual angle) centred at the fovea. Indices characterising MP distribution were computed both for individuals and for the three subject groups. For comparison Macular Pigment Optical Density (MPOD) measurements were acquired from the Macular Pigment Screener 9000 (MPS) based on the heterochromatic flicker photometry (HFP). Correlations for MP quantities measured with the two methods were computed between MP quantity and age, MP quantity and AMD diagnosis, and between the two methods.

Results: MP maps obtained from MRIA were consistent with known histology and in agreement with expectations based on previous studies. Pooled results from the three groups suggest that the overall levels of MP across both the fovea and the parafovea are on average higher in healthy under-50 individuals than that over-50 with or without AMD. MP distribution might be more irregular in the over-50 groups than in the younger group. The correlation between age and MP levels was weak as measured individually by both techniques. The MRIA indices were not correlated with HFP-MPOD measurements for individuals, but high correlation was found between mean HFP-MPOD and mean MRIA peak value for pooled results.

Conclusion: MRIA has potential to offer an objective, fast and reliable method of measuring MP throughout the posterior pole.

Keywords

Multispectral imaging; Hyperspectral imaging; Macular pigment; Retina; Lutein; Zeaxanthin; MPOD; Heterochromatic flicker photometry; Age-related macular degeneration

Introduction

The macula is a small area in the central region of the retina. It contains macular pigment (MP) which gives the macula its characteristic yellow tint due to the presence of xanthophylls [1,2]. The role of MP is not entirely known, but it is believed to offer protection against age-related macular degeneration (AMD) because of its antioxidant properties [3], and by acting as an optical filter to shorter wavelengths [1,2] which are known to be phototoxic [4]. The distribution of MP across the macula has a peak at the centre of the fovea and declines with increasing eccentricity, reaching negligible levels at 4-6 degrees of the visual angle (1-1.8 mm) [1,2]. Several factors have been suggested to influence MP density and distribution [5-9]. One of the open research questions is whether the high variance in MP density amongst individuals with no evidence of macular pathology has any significance in the development of retinal/choroidal disease, and if so, whether any intervention, dietary or non-dietary, is effective [10]. Different techniques have been adopted to measure MP in vivo [11]. All have proved effective but have limitations and it is recognised that better techniques of measurement are needed [3,11].

The most commonly used technique of measuring MP is Heterochromatic Flicker Photometry (HFP). It belongs to a class of psychophysical methods that use comparisons of the perceived brightness of short- and medium-wavelength light in the fovea and parafovea to derive macular pigment optical density (MPOD) estimates. In HFP a subject is required to judge whether two lights of different colours have equal luminance [12]. HFP-MPOD is calculated by comparing responses at the fovea, where MPOD peaks, and at a parafoveal reference point. HFP, widely considered to be the “gold standard” measurement technique for MPOD, is employed in the Macular Pigment Screener 9000 (Topcon GB Ltd), a commercially available instrument designed for clinical screening.

A number of techniques for objective measurement of MP have been developed in research laboratories, but have not yet been used routinely in a clinical environment. The most promising include fundus reflectance and autofluorescence. These techniques rely on the analysis of light returning from the fundus, but differ in the origins of the measured signal, the method of measurement, the principles of the analysis and the measure returned [13]. Fundus reflectance techniques for the measurement of MP exploit the characteristic absorption of light by MP in the blue end of the spectrum, 400-530 nm with peak at 460 nm [14]. The most common form of analysis is to compare a spectrum measured at the fovea with a parafoveal spectrum, and to deduce the MPOD from the difference between the two. The measurements can be obtained over a narrow field of view (0.7-2.0 degrees), with fundus reflectance spectrometry [15,16], or in the form of images taken at a specific range of wavelengths using a variety of imaging devices [17,18]. Measurement acquisition is usually much faster using narrow-field spectrometric devices, but requires very careful eye positioning and therefore particular consideration must be given in subjects with eye fixation problems, as occurs in advanced AMD.

Recent developments in autofluorescence imaging have enabled researchers to obtain maps of MPOD through analysis of the light emission from lipofuscin granules in the retinal pigment epithelium (RPE) [19]. Lipofuscin builds up with age due to phagocytosis of outer segment membranes of rod photoreceptors. MPOD can be deduced by comparing autofluorescence images generated by light at wavelengths where MP absorption is high with images generated at wavelengths where MP absorption is low [13,20].

The multispectral retinal image analysis (MRIA) technique used in this paper is based on fundus reflectance [21]. In common with some reflectometry approaches MRIA explicitly exploits the relationship between spectral measurements and retinal architecture [15-18]. It is, however, more complex as the reflectance model it uses represents the macula in more detail. In particular it takes into account the spatial variability across the macula of five pigments: the MP, the haemoglobins within the retinal vasculature which are not present at the fovea (foveal avascular zone), RPE melanin, the concentration of which declines with eccentricity, and choroidal haemoglobins and melanin.

In this study MRIA was used to compute spatially resolved quantitative maps of MP. The main objective was to investigate whether MP levels and distribution, as measured by a number of indices, differ between individuals with no evidence of macular pathology and those diagnosed with AMD, and whether MP levels correlate with age. The MRIA indices were compared with MPOD measured with the Macular Pigment Screener 9000 (MPS) based on HFP.

Methodology

Construction of the model of fundus reflectance

MRIA uses a computer simulation of the passage of light through the fundus tissues in order to establish a link between tissue composition and fundus appearance at different wavelengths (multispectral images); this is referred to as a model of fundus reflectance.

This model and its formation have been extensively described elsewhere [20-24]. In summary, distribution of pigments (retinal haemoglobins, RPE melanin, choroidal haemoglobins, choroidal melanin, and MP) varies across the fundus, and to reflect this variability, computer simulations were carried out for multiple combinations of the histologically plausible concentrations, independently altering each of the pigment quantities within every layer of the tissue [21,24,25]. For each such combination a spectral reflectance curve is computed using Monte Carlo simulation [26]. A collection of the predicted spectra forms the reflectance model in which each spectrum corresponds to one, and only one, combination of concentration of the five pigments.

Computation of measures for pigment quantification

The “forward” model described above predicts fundus reflectance given the pigment concentrations. Its “inverse” can be used to compute pigment concentrations from fundus reflectance images. The inversion method used in this work is based on Gaussian Process Emulation [27,28]. One of its merits is that it can take into account general characteristics of spatial distribution of the relevant pigments. In the macular region the MP is assumed to decrease monotonically from the central peak. Retinal haemoglobins are assumed to be negligible in the foveal avascular zone that extends to approximately 2.5 degrees of eccentricity, and then are monotonically increasing. RPE melanin is assumed to have elevated levels at the macula, decreasing monotonically with eccentricity.

MP maps are computed for 151 × 151 pixel (approximately 3 × 3 mm) regions of interest (ROI) centred at the fovea and chosen manually. The values in the maps represent MP optical density in the range 0-0.6 [13]. In addition to reporting the peak density, as is common in published data, after fitting a two-dimensional Gaussian function to the shape of the computed MP map, five further numerical quantities that approximately characterise the overall quantity of MP and the shape of its distribution are calculated from the maps (Figure 1): absolute fitted peak height, background level (adjacent parafoveal region), fitted peak height in relation to the background, fitted peak full-width at half-maximum (FWHM), and fitted peak volume. These quantities are then used in statistical analyses to investigate their correlation with the three subject groups (see subjects).

clinical-experimental-ophthalmology-Numerical-indicators

Figure 1: Numerical indicators that describe MP distribution computed from parametric maps, based on parameters mathematically describing a two-dimensional Gaussian function fitted to the shape of MP distribution. X axis shows spatial coordinates (in pixels) of the region of interest containing the macula; Y axis shows the computed MP concentration. From the equation of two-dimensional Gaussian G distribution above, base=B, absolute MP peak=max (G), relative MP peak=max (G)-B, image

Experimental Work

Mria image acquisition

A high intensity white-light source (OSL1 with 150 W 3250 K halogen bulb; Thorlabs Inc, Newton, NJ, USA) is connected via a multi-core optic fibre bundle to a tunable filter (VariSpec CRI, U.S.A.) and then, via a liquid light guide, to a modified Zeiss RCM250 fundus camera fitted with a Hamamatsu ImageEM C1300-13 cooled EM-CCD. The light source provides sufficient intensity to guarantee short exposure times whilst remaining well within the accepted safety limits. Images are acquired at the selected wavelengths (507 nm, 525 nm, 552 nm, 585 nm, 596 nm, and 611 nm), chosen so as to ensure quantification of MP with the smallest possible margin of error and the minimum number of acquisitions [24].

The overall system is controlled by custom-written software that synchronises the filters with the camera shutter and allows the operator to tune image acquisition parameters such as exposure time and gain. Three sets of six images at the selected wavelengths (eighteen in total) are taken in quick succession, with the average total acquisition time of approximately 0.5 seconds. With the typical duration of the intersaccadic interval being 0.67 seconds, this design minimises the chances of eye movement between exposures at different wavelengths [29]. The experiments confirmed that at least one high-quality image set of six, defined as less than a 3 pixel movement between frames, could usually be guaranteed by acquiring three data sets. Pixel size is of the order of 20 μm (Figure 2 for examples of MRIA maps).

clinical-experimental-ophthalmology-Sample-MRIA

Figure 2: Sample MRIA results for representative data from the three groups. Group 1: subjects aged under 50 without AMD; group 2: subjects aged 50 and over without AMD; group 3: subjects aged 50 and over with AMD. First row: Fundus image at 552 nm; second row: MP map overlaid on fundus Image; third row: MRIA MP map; fourth row: MP distribution presented as a 3D height map.

Subjects

Images were acquired from 90 volunteers, distributed equally between three subject groups: (1) subjects aged under 50 without AMD [18 males, 12 females. Mean age: 32; range: 10-49. Ethnic background: 28 white; 1 Asian-Indian Subcontinent; 1 mixed race (Afro-Caribbean-White)]; (2) subjects aged 50 and over without AMD [13 males, 17 females. Mean age: 70; range: 50-83. Ethnic background: 30 white]; (3) subjects aged 50 and over with AMD (early or late) [30] [10 males, 20 females. Mean age: 73; range: 53-91. Ethnic background: 30 white]. Exclusion criteria for this study were: significant media opacities (defined as inability to clearly visualize the fundus on slit lamp biomicroscopy with a 90 dioptre lens); significant refractive error (spherical equivalent of more than 6 dioptres); and/or known retinal or choroidal pathology other than AMD. In all healthy participants and those with early AMD, best corrected LogMAR visual acuity was 0.0 or better.

The investigators certify that all applicable institutional and governmental regulations concerning the ethical use of human volunteers were followed during this research and the study protocol adhered to tenets of the Declaration of Helsinki. Ethical approval was obtained from the Aston University Research Ethics Committee.

Procedure

Each participant was required to attend twice and by random allocation 50% of subjects had MPOD at 0.5 degrees retinal eccentricity evaluated using HFP first, followed by MRIA measurements at the first visit, with the order reversed at the second visit; the other 50% of participants had MRIA measurements first, followed by HFP during the first visit, with the order reversed during the second visit. This ensured that fatigue levels were equivalent for the two techniques being compared. The same protocol for HFP measurement was followed as described in Howells et al. [31].

Subject assessment proceeded as follows:

The logarithm of the minimal angle of resolution (LogMAR) visual acuity was measured and slit-lamp biomicroscopy was performed.

In subjects having HFP first, following a brief explanation of the procedure, the subject was seated at the HFP equipment at a comfortable height, a test run was performed monocularly and then the test was carried out for each eye (approximately 10 minutes/eye), followed by dilating drops [Tropicamide 1% and Phenylephrine 2.5% (if not contraindicated)].

Following pupil dilatation, the subject was positioned at the MRIA fundus camera at the correct height, the macula was focused by an experienced operator (AC), and the subject was asked to maintain fixation on a red LED target for the duration of the dataset acquisition (approximately 0.5 seconds for 18 images); the whole process was repeated several times, until clear images of the macula of each eye were obtained (by adjusting exposure time, brightness and gain of the equipment and fixation target) or a 10 minute/eye limit was reached.

In subjects having MRIA first, drops were instilled immediately after slit lamp biomicroscopy, and the above described procedures were carried out, but in reverse order (i.e. MRIA first followed by HFP assessment).

Image analysis and parameter recovery

The following procedure was followed to recover the parameter values: Three image sets were acquired through the six selected filters. Any set containing images displaced by more than three pixels was discarded. The six images were corrected for exposure time, gain and offset. To compensate for uneven fundus illumination, image values in the first five images (507 nm-596 nm) were divided by the image values of the corresponding pixels at 611 nm [24]; at this wavelength the absorption by ocular pigments is negligible and image variations are predominantly due to spatial variations in illumination. This process generated five illumination-normalised images (image ratios). The concentration of MP was computed using the inversion method described above. From the generated MP map (Figure 2), quantitative indicators of MP were extracted.

Results

The experiments yielded 607 usable sets. Table 1 shows the mean values of all the measures averaged for all the individuals within each of the three groups together with correlation coefficients between the mean values of HFP-MPOD and the mean values of the six MRIA measures. Although these pooled results are not statistically significant (p>0.1), they show discernible trends as discussed below.

  Group 1 Group 2 Group 3 r
N 238 237 132  
MPS, mean MPOD value 0.38 ± 0.15 0.44 ± 0.16 0.53 ± 0.17  
MRIA (mean group values)        
Max 0.47 ± 0.15 0.50 ± 0.16 0.51 ± 0.14 0.95
Fitted max 0.50 ± 0.14 0.40 ± 0.14 0.41 ± 0.13 -0.76
Parafovea 0.21 ± 0.15 0.19 ± 0.13 0.18 ± 0.12 -0.97
Relative max 0.29 ± +0.15 0.21 ± 0.09 0.24 ± 0.09 -0.61
FWHM [pixels] 37.35 ± 8.19 37.66 ± 10.50 36.13 ± 10.71 -0.81
FWHM [mm] 0.75 ± 0.16 0.75 ± 0.21 0.72 ± 0.21 -0.81
Volume 324.91 ± 238 260.61 ± 237 262.51 ± 132 -0.80

Table 1: Mean and standard deviation data for the MP values measured using HFP and MRIA, by group. Group 1: subjects aged under 50 without AMD; group 2: subjects aged 50 and over without AMD; group 3: subjects aged 50 and over with AMD. Last column (r) is Pearon’s correlation coefficient between mean HFP-MPOD and six mean MRIA parameters from pooled samples.

The correlation coefficient for the individual indices (HFP-MPOD and five MRIA measures) was computed between every pair of variables (Table 2). The two key observations arising from these results are that on an individual level MP is uncorrelated with age (as measured with both HFP and MRIA), partly in agreement with published literature [6], and that HFP-MPOD and MRIA measures are uncorrelated. Weak correlations (|r|<0.5) were found between age and MP measured with both methods in group 1, and between the two methods of measurement within groups 2 and 3. No correlations with | r|>0.27 were evident between HFP-MPOD and any of the quantitative measures extracted from MRIA.

Group 1 (N=238) MPOD MRIA
Max
MRIA Fitted Max MRIA Relative Max MRIA FWHM MRIA Volume
Age -0.47 0.23 -0.28 -0.46 0.07 -0.31
MPOD - -0.05 0.27 0.20 -0.02 0.11
MRIA Max - - -0.09 -0.19 0.08 -0.08
MRIA Fitted Max - - - 0.48 0.10 0.37
MRIA Relative Max - - - - -0.10 0.66
MRIA FWHM - - - - - 0.60
MRIA Volume - - - - - -
Group 2 (N=237)            
Age -0.14 -0.05 -0.14 0.05 -0.24 -0.12
MPOD - -0.23 0.06 -0.12 0.11 -0.01
MRIA Max - - 0.06 0.06 0.02 0.08
MRIA Fitted Max   - - 0.44 0.27 0.43
MRIA Relative Max - - - - 0.14 0.68
MRIA FWHM - - - - - 0.74
MRIA Volume - - - - - -
Group 3 (N=132)            
Age -0.16 0.09 -0.08 0.13 -0.29 -0.22
MPOD - -0.24 0.04 -0.04 0.10 0.08
MRIA Max - - 0.01 -0.06 0.01 -0.05
MRIA Fitted Max - - - 0.45 0.01 0.22
MRIA Relative Max - - - - -0.04 0.47
MRIA Volume - - - - - 0.81
MRIA FWHM - - - - - -

Table 2: Correlation analysis of each pair of variables for subjects aged under 50 without AMD (group 1), 50 and over without AMD (Group 2) and 50 and over with AMD (Group 3). In the table, MPOD refers to the MPS reading.

To test reliability of measurements from HFP and MRIA the coefficient of reliability was computed using multiple measurements of each subject

image

Where image is the variance of the subject means andimage is the variance of the whole dataset. A measure is said to have a high reliability if it produces similar results under consistent conditions. The values for HFP and for MRIA were all greater than 0.79, suggesting that all the measures demonstrate very good reliability. In particular HFP=0.85 and MRIA=0.85.

Discussion

This paper has presented a new method for assessing the quantity and distribution of MP from multispectral images of the ocular fundus. Parametric maps for MP consistent with known histology [1,2,32,33] have been shown in all usable datasets acquired using the MRIA technique.

Across the three subject groups the mean MRIA peak values were lowest for subjects aged ˂50 years, intermediate for subjects aged ≥ 50 years without AMD, and highest for subjects aged ≥ 50 years with AMD. Similar trends were found for mean HFP-MPOD. The analysis applied on an individual basis, however, showed poor correlation between the two methods. Although the correlation coefficients between the mean values of HFP-MPOD and the mean values of the six MRIA measures failed to reach statistically significant values, defined as p>0.1, they show discernible trends.

The group-wise results for the MRIA mean fitted max, mean relative max, mean parafoveal level and volume showed the inverse trend to those above by being higher for the group 1 subjects (˂50 years, no AMD) than for subjects from groups 2 and 3 (≥ 50, without and with AMD respectively). This is particularly evident for the MRIA fitted max and volume and might suggest that spatial distribution of MP is less uniform in groups 2 and 3 than in group 1: in a smooth symmetric distribution the maximum peak value would be close to that of the peak value of the fitted Gaussian. This could explain the opposite trends showed by the MRIA maximum peak versus the MRIA fitted peak and relative maximum. The MRIA measurements also suggest that the overall quantity of MP within the foveal region is higher and relatively more peaked for the under-50 subjects than for the over-50 groups.

In this work MPOD measured using HFP has been taken to be the current standard for measuring MP in the clinic. A protocol for obtaining reliable measurements using HFP in the form of the MPS has previously been described by two of the authors [31]. HFP is the only technique in widespread use but although it is often considered to be the “gold standard”, there is considerable debate in the literature on the matter due to the need for the subject to perform a subjective comparison. A recent development in HFP is the use of “customised” HFP (cHFP) techniques [34], in which the flicker frequency is optimized for each subject to enable them to perform the task more proficiently. This approach to measuring MPOD has been shown to correlate strongly with two-wavelength autofluorescence (2AF) measurements (Heidelberg Spectralis® HRA+OCT MultiColor) [35]. Customised HFP and 2AF may provide a better baseline comparison for MRIA.

Whereas mean group-wise results show some discernible trends, the same is not the case on an individual level where correlations between MRIA and HFP indices are weak. Further studies are necessary to establish if MRIA correlates with MPOD assessment techniques other than the MPS. One possible factor responsible for low individual correlations could be that the MRIA considers the absorption of pigments such as melanin and haemoglobins within and outside the fovea. HFP-MPOD measurements assume that the MP is the only pigment that absorbs the stimulus light. Melanin present in the RPE absorbs strongly in the same wavelength range as MP [36] for the test stimulus, and both the haemoglobins and melanin absorb in the reference stimulus wavelength range. Simplified assumptions made for HFP techniques may lead to the ambiguity of their results. In the absence of the true “ground truth”, multiple comparisons across various techniques and on well-defined subject groups could determine veracity of the results from each.

One merit of MRIA is its use of a fairly detailed model of fundus architecture. However, the model does not include the full variation of normal eyes, for example retinal thickness and optical properties of the lens for each subject. In future work attention will be given to achieving a practical compromise between the model complexity and the quantitative accuracy of its results.

In view of the continued need for pupil dilation, MRIA would currently only be suitable for screening individuals attending ophthalmology clinics, especially those with significant risk factors for AMD, to establish whether MP levels are satisfactory and whether any targeted intervention on MP levels is yielding the desired results. One important advantage of MRIA over HFP techniques is the fact that it is an objective technique. MRIA also offers a much faster acquisition of macular pigment density compared to all the subjective techniques and many of the objective ones. Both these factors are particularly important with respect to AMD patients and elderly subjects in general, who may experience visual impairment, and may also have co-morbidities and mobility issues that preclude sitting still at an instrument for extended periods.

Conclusion

MRIA has potential to offer an objective, fast and reliable method of measuring MP throughout the posterior pole. Pooled results from the three subject groups suggest that the overall levels of MP across both the fovea and the parafovea are on average higher in healthy under-50 individuals than either for healthy over-50 or individuals diagnosed with AMD. Results also suggest indirectly that MP distribution might be more irregular in the over-50 groups than in the younger group. The MRIA indices were not correlated with MPOD measured with HFP for individual measurements, but high correlation was found between mean HFP-MPOD and mean MRIA absolute peak for pooled results. MRIA can therefore be recommended for cohort-level studies such as the effects of dietary interventions for prevention of AMD. Further studies are warranted to refine the MRIA imaging method and to devise more discriminative quantitative indicators for MP so they can identify individuals at risk of AMD before symptoms occur and vision can still be salvaged.

Acknowledgements

The authors gratefully acknowledge financial support from the Dunhill Medical Trust under grant number R116/0509.

Financial Disclosure

Project funded by Dunhill Medical Trust, grant number R116/0509

References

Select your language of interest to view the total content in your interested language
Post your comment

Share This Article

Recommended Conferences

Article Usage

  • Total views: 227
  • [From(publication date):
    February-2017 - Apr 29, 2017]
  • Breakdown by view type
  • HTML page views : 199
  • PDF downloads :28
 

Post your comment

captcha   Reload  Can't read the image? click here to refresh

Peer Reviewed Journals
 
Make the best use of Scientific Research and information from our 700 + peer reviewed, Open Access Journals
International Conferences 2017-18
 
Meet Inspiring Speakers and Experts at our 3000+ Global Annual Meetings

Contact Us

Agri, Food, Aqua and Veterinary Science Journals

Dr. Krish

agrifoodaquavet@omicsonline.com

1-702-714-7001 Extn: 9040

Clinical and Biochemistry Journals

Datta A

clinical_biochem@omicsonline.com

1-702-714-7001Extn: 9037

Business & Management Journals

Ronald

business@omicsonline.com

1-702-714-7001Extn: 9042

Chemical Engineering and Chemistry Journals

Gabriel Shaw

chemicaleng_chemistry@omicsonline.com

1-702-714-7001 Extn: 9040

Earth & Environmental Sciences

Katie Wilson

environmentalsci@omicsonline.com

1-702-714-7001Extn: 9042

Engineering Journals

James Franklin

engineering@omicsonline.com

1-702-714-7001Extn: 9042

General Science and Health care Journals

Andrea Jason

generalsci_healthcare@omicsonline.com

1-702-714-7001Extn: 9043

Genetics and Molecular Biology Journals

Anna Melissa

genetics_molbio@omicsonline.com

1-702-714-7001 Extn: 9006

Immunology & Microbiology Journals

David Gorantl

immuno_microbio@omicsonline.com

1-702-714-7001Extn: 9014

Informatics Journals

Stephanie Skinner

omics@omicsonline.com

1-702-714-7001Extn: 9039

Material Sciences Journals

Rachle Green

materialsci@omicsonline.com

1-702-714-7001Extn: 9039

Mathematics and Physics Journals

Jim Willison

mathematics_physics@omicsonline.com

1-702-714-7001 Extn: 9042

Medical Journals

Nimmi Anna

medical@omicsonline.com

1-702-714-7001 Extn: 9038

Neuroscience & Psychology Journals

Nathan T

neuro_psychology@omicsonline.com

1-702-714-7001Extn: 9041

Pharmaceutical Sciences Journals

John Behannon

pharma@omicsonline.com

1-702-714-7001Extn: 9007

Social & Political Science Journals

Steve Harry

social_politicalsci@omicsonline.com

1-702-714-7001 Extn: 9042

 
© 2008-2017 OMICS International - Open Access Publisher. Best viewed in Mozilla Firefox | Google Chrome | Above IE 7.0 version