We use cookies to improve your experience. By continuing to browse this site, you accept our cookie policy.×
Skip main navigation
Aging Health
Bioelectronics in Medicine
Biomarkers in Medicine
Breast Cancer Management
CNS Oncology
Colorectal Cancer
Concussion
Epigenomics
Future Cardiology
Future Microbiology
Future Neurology
Future Oncology
Future Rare Diseases
Future Virology
Hepatic Oncology
HIV Therapy
Immunotherapy
International Journal of Endocrine Oncology
International Journal of Hematologic Oncology
Journal of 3D Printing in Medicine
Journal of Comparative Effectiveness Research
Lung Cancer Management
Melanoma Management
Nanomedicine
Neurodegenerative Disease Management
Pain Management
Pediatric Health
Personalized Medicine
Pharmacogenomics
Regenerative Medicine
Published Online:https://doi.org/10.2217/pgs.10.184

The discovery, biological qualification and analytical validation of genomic biomarkers requires extensive collaborations between individuals with expertise in biology, statistics, bioinformatics, chemistry, clinical medicine, regulatory science and so on. For clinical utility, blood-borne biomarkers (e.g., mRNA and miRNA) of organ damage, drug toxicity and/or response would be preferred to those that are tissue based. Currently used biomarkers such as serum creatinine (indicating renal dysfunction) denote organ damage whether caused by disease, physical injury or drugs. Therefore, it is anticipated that studies of disease will discover biomarkers that can also be used to identify drug-induced injury and vice versa. This article describes transcriptomic blood-borne biomarkers that have been reported to be connected with disease and drug toxicity. Much more qualification and validation needs to be carried out before many of these biomarkers can prove useful. Discussed here are some of the lessons learned and roadblocks to success.

Papers of special note have been highlighted as: ▪ of interest ▪▪ of considerable interest

Bibliography

  • Mendrick DL, Daniels KK: Biomarkers of drug-induced adverse events. Expert Rev. Clin. Pharmacol.1,81–91 (2008).Crossref, Medline, CASGoogle Scholar
  • Marchionni L, Wilson RF, Wolff AC et al.: Systematic review: gene expression profiling assays in early-stage breast cancer. Ann. Intern. Med.148,358–369 (2008).Crossref, MedlineGoogle Scholar
  • Liew CC, Ma J, Tang HC, Zheng R, Dempsey AA: The peripheral blood transcriptome dynamically reflects system wide biology: a potential diagnostic tool. J. Lab. Clin. Med.147,126–132 (2006).Crossref, Medline, CASGoogle Scholar
  • Powell EE, Kroon PA: Low density lipoprotein receptor and 3-hydroxy-3-methylglutaryl coenzyme A reductase gene expression in human mononuclear leukocytes is regulated coordinately and parallels gene expression in human liver. J. Clin. Invest.93,2168–2174 (1994).Crossref, Medline, CASGoogle Scholar
  • Aggarwal D, Freake HC, Soliman GA, Dutta A, Fernandez ML: Validation of using gene expression in mononuclear cells as a marker for hepatic cholesterol metabolism. Lipids Health Dis.5,22 (2006).Crossref, MedlineGoogle Scholar
  • Guan JZ, Tamasawa N, Murakami H et al.: HMG-CoA reductase inhibitor, simvastatin improves reverse cholesterol transport in Type 2 diabetic patients with hyperlipidemia. J. Atheroscler. Thromb.15,20–25 (2008).Crossref, MedlineGoogle Scholar
  • Mikael LG, Rozen R: Homocysteine modulates the effect of simvastatin on expression of ApoA-I and NF-κB/iNOS. Cardiovasc. Res.80,151–158 (2008).Crossref, Medline, CASGoogle Scholar
  • Pham MX, Teuteberg JJ, Kfoury AG et al.: Gene-expression profiling for rejection surveillance after cardiac transplantation. N. Engl. J. Med.362(20),1890–1900 (2010).Crossref, Medline, CASGoogle Scholar
  • Burczynski ME, Dorner AJ: Transcriptional profiling of peripheral blood cells in clinical pharmacogenomic studies. Pharmacogenomics7,187–202 (2006).▪▪ Excellent review of the earlier findings in the area of pharmacogenomics of the blood.Link, CASGoogle Scholar
  • 10  Tang Y, Lu A, Aronow BJ, Sharp FR: Blood genomic responses differ after stroke, seizures, hypoglycemia, and hypoxia: blood genomic fingerprints of disease. Ann. Neurol.50,699–707 (2001).Crossref, Medline, CASGoogle Scholar
  • 11  Tang Y, Nee AC, Lu A, Ran R, Sharp FR: Blood genomic expression profile for neuronal injury. J. Cereb. Blood Flow Metab.23,310–319 (2003).Crossref, Medline, CASGoogle Scholar
  • 12  Du X, Tang Y, Xu H et al.: Genomic profiles for human peripheral blood T cells, B cells, natural killer cells, monocytes, and polymorphonuclear cells: comparisons to ischemic stroke, migraine and Tourette syndrome. Genomics87,693–703 (2006).Crossref, Medline, CASGoogle Scholar
  • 13  Tang Y, Xu H, Du X et al.: Gene expression in blood changes rapidly in neutrophils and monocytes after ischemic stroke in humans: a microarray study. J. Cereb. Blood Flow Metab.26,1089–1102 (2006).Crossref, Medline, CASGoogle Scholar
  • 14  Xu H, Tang Y, Liu DZ et al.: Gene expression in peripheral blood differs after cardioembolic compared with large-vessel atherosclerotic stroke: biomarkers for the etiology of ischemic stroke. J. Cereb. Blood Flow Metab.28,1320–1328 (2008).Crossref, Medline, CASGoogle Scholar
  • 15  Lovrecic L, Kastrin A, Kobal J, Pirtosek Z, Krainc D, Peterlin B: Gene expression changes in blood as a putative biomarker for Huntington’s disease. Mov. Disord.24,2277–2281 (2009).Crossref, MedlineGoogle Scholar
  • 16  Wong B, Gilbert DL, Walker WL et al.: Gene expression in blood of subjects with Duchenne muscular dystrophy. Neurogenetics10,117–125 (2009).Crossref, Medline, CASGoogle Scholar
  • 17  Borovecki F, Lovrecic L, Zhou J et al.: Genome-wide expression profiling of human blood reveals biomarkers for Huntington’s disease. Proc. Natl Acad. Sci. USA102,11023–11028 (2005).Crossref, Medline, CASGoogle Scholar
  • 18  Runne H, Kuhn A, Wild EJ et al.: Analysis of potential transcriptomic biomarkers for Huntington’s disease in peripheral blood. Proc. Natl Acad. Sci. USA104,14424–14429 (2007).Crossref, Medline, CASGoogle Scholar
  • 19  Scherzer CR, Eklund AC, Morse LJ et al.: Molecular markers of early Parkinson’s disease based on gene expression in blood. Proc. Natl Acad. Sci. USA104,955–960 (2007).Crossref, Medline, CASGoogle Scholar
  • 20  Bowden NA, Weidenhofer J, Scott RJ et al.: Preliminary investigation of gene expression profiles in peripheral blood lymphocytes in schizophrenia. Schizophr. Res.82,175–183 (2006).Crossref, MedlineGoogle Scholar
  • 21  Takahashi M, Hayashi H, Watanabe Y et al.: Diagnostic classification of schizophrenia by neural network analysis of blood-based gene expression signatures. Schizophr. Res.119,210–218 (2010).Crossref, MedlineGoogle Scholar
  • 22  Tang Y, Glauser TA, Gilbert DL et al.: Valproic acid blood genomic expression patterns in children with epilepsy – a pilot study. Acta Neurol. Scand.109,159–168 (2004).Crossref, Medline, CASGoogle Scholar
  • 23  Le Niculescu H, Kurian SM, Yehyawi N et al.: Identifying blood biomarkers for mood disorders using convergent functional genomics. Mol. Psychiatry14(2),156–174 (2008).▪▪ Interesting approach to identifying translational biomarkers.Crossref, MedlineGoogle Scholar
  • 24  Ogden CA, Rich ME, Schork NJ et al.: Candidate genes, pathways and mechanisms for bipolar (manic-depressive) and related disorders: an expanded convergent functional genomics approach. Mol. Psychiatry9,1007–1029 (2004).Crossref, Medline, CASGoogle Scholar
  • 25  Kurian SM, Le-Niculescu H, Patel SD et al.: Identification of blood biomarkers for psychosis using convergent functional genomics. Mol. Psychiatry16(1),37–58 (2011).Crossref, Medline, CASGoogle Scholar
  • 26  Burczynski ME, Peterson RL, Twine NC et al.: Molecular classification of Crohn’s disease and ulcerative colitis patients using transcriptional profiles in peripheral blood mononuclear cells. J. Mol. Diagn.8,51–61 (2006).Crossref, Medline, CASGoogle Scholar
  • 27  Knowlton N, Jiang K, Frank MB et al.: The meaning of clinical remission in polyarticular juvenile idiopathic arthritis: gene expression profiling in peripheral blood mononuclear cells identifies distinct disease states. Arthritis Rheum.60,892–900 (2009).Crossref, Medline, CASGoogle Scholar
  • 28  Griffin TA, Barnes MG, Ilowite NT et al.: Gene expression signatures in polyarticular juvenile idiopathic arthritis demonstrate disease heterogeneity and offer a molecular classification of disease subsets. Arthritis Rheum.60,2113–2123 (2009).Crossref, Medline, CASGoogle Scholar
  • 29  Mesko B, Poliska S, Szegedi A et al.: Peripheral blood gene expression patterns discriminate among chronic inflammatory diseases and healthy controls and identify novel targets. BMC Med. Genomics3,15 (2010).Crossref, MedlineGoogle Scholar
  • 30  Sinnaeve PR, Donahue MP, Grass P et al.: Gene expression patterns in peripheral blood correlate with the extent of coronary artery disease. PLoS ONE4,e7037 (2009).Crossref, MedlineGoogle Scholar
  • 31  Wingrove JA, Daniels SE, Sehnert AJ et al.: Correlation of peripheral-blood gene expression with the extent of coronary artery stenosis. Circ. Cardiovasc. Genet.1,31–38 (2008).Crossref, Medline, CASGoogle Scholar
  • 32  Camargo A, Ruano J, Fernandez JM et al.: Gene expression changes in mononuclear cells in patients with metabolic syndrome after acute intake of phenol-rich virgin olive oil. BMC Genomics11,253 (2010).Crossref, MedlineGoogle Scholar
  • 33  Hermsdorff HH, Zulet MA, Puchau B, Martinez JA: Fruit and vegetable consumption and proinflammatory gene expression from peripheral blood mononuclear cells in young adults: a translational study. Nutr. Metab. (Lond.)7,42 (2010).CrossrefGoogle Scholar
  • 34  Radom-Aizik S, Zaldivar F Jr, Leu SY, Galassetti P, Cooper DM: Effects of 30 min of aerobic exercise on gene expression in human neutrophils. J. Appl. Physiol.104,236–243 (2008).Crossref, Medline, CASGoogle Scholar
  • 35  Radom-Aizik S, Zaldivar F Jr, Leu SY, Cooper DM: Brief bout of exercise alters gene expression in peripheral blood mononuclear cells of early- and late-pubertal males. Pediatr. Res.65,447–452 (2009).Crossref, Medline, CASGoogle Scholar
  • 36  Radom-Aizik S, Zaldivar F Jr, Leu SY, Cooper DM: A brief bout of exercise alters gene expression and distinct gene pathways in peripheral blood mononuclear cells of early- and late-pubertal females. J. Appl. Physiol.107,168–175 (2009).Crossref, Medline, CASGoogle Scholar
  • 37  Hayashi Y, Kajimoto K, Iida S et al.: DNA microarray analysis of whole blood cells and insulin-sensitive tissues reveals the usefulness of blood RNA profiling as a source of markers for predicting Type 2 diabetes. Biol. Pharm. Bull.33,1033–1042 (2010).Crossref, Medline, CASGoogle Scholar
  • 38  Takamura T, Honda M, Sakai Y et al.: Gene expression profiles in peripheral blood mononuclear cells reflect the pathophysiology of Type 2 diabetes. Biochem. Biophys. Res. Commun.361,379–384 (2007).Crossref, Medline, CASGoogle Scholar
  • 39  Huang H, Dong X, Kang MX et al.: Novel blood biomarkers of pancreatic cancer-associated diabetes mellitus identified by peripheral blood-based gene expression profiles. Am. J. Gastroenterol.105,1661–1669 (2010).Crossref, Medline, CASGoogle Scholar
  • 40  Bushel PR, Heinloth AN, Li J et al.: Blood gene expression signatures predict exposure levels. Proc. Natl Acad. Sci. USA104,18211–18216 (2007).▪▪ Exciting work describing the ability of pharmacogenomic biomarkers to surpass classical toxicity measurements in rats and to impact the clinic.Crossref, Medline, CASGoogle Scholar
  • 41  Lobenhofer EK, Auman JT, Blackshear PE et al.: Gene expression response in target organ and whole blood varies as a function of target organ injury phenotype. Genome Biol.9,R100 (2008).Crossref, MedlineGoogle Scholar
  • 42  Huang J, Shi W, Zhang J et al.: Genomic indicators in the blood predict drug-induced liver injury. Pharmacogenomics J.10,267–277 (2010).Crossref, Medline, CASGoogle Scholar
  • 43  Wetmore BA, Brees DJ, Singh R et al.: Quantitative analyses and transcriptomic profiling of circulating messenger RNAs as biomarkers of rat liver injury. Hepatology51,2127–2139 (2010).Crossref, Medline, CASGoogle Scholar
  • 44  O’Toole M, Janszen DB, Slonim DK et al.: Risk factors associated with β-amyloid(1–42) immunotherapy in preimmunization gene expression patterns of blood cells. Arch. Neurol.62,1531–1536 (2005).▪ Interesting paper describing biomarkers that can identify pre-exposure individuals who will develop adverse events.Crossref, MedlineGoogle Scholar
  • 45  Yun JW, Lee TR, Kim CW et al.: Predose blood gene expression profiles might identify the individuals susceptible to carbon tetrachloride-induced hepatotoxicity. Toxicol. Sci.115,12–21 (2010).Crossref, Medline, CASGoogle Scholar
  • 46  Yun JW, Kim CW, Bae IH et al.: Determination of the key innate genes related to individual variation in carbon tetrachloride-induced hepatotoxicity using a pre-biopsy procedure. Toxicol. Appl. Pharmacol.239,55–63 (2009).Crossref, Medline, CASGoogle Scholar
  • 47  Thum T, Catalucci D, Bauersachs J: MicroRNAs: novel regulators in cardiac development and disease. Cardiovasc. Res.79,562–570 (2008).Crossref, Medline, CASGoogle Scholar
  • 48  Cortez MA, Calin GA: MicroRNA identification in plasma and serum: a new tool to diagnose and monitor diseases. Expert Opin. Biol. Ther.9,703–711 (2009).Crossref, Medline, CASGoogle Scholar
  • 49  van Rooij E, Quiat D, Johnson BA et al.: A family of microRNAs encoded by myosin genes governs myosin expression and muscle performance. Dev. Cell17,662–673 (2009).Crossref, Medline, CASGoogle Scholar
  • 50  Fichtlscherer S, De RS, Fox H et al.: Circulating microRNAs in patients with coronary artery disease. Circ. Res.107,677–684 (2010).Crossref, Medline, CASGoogle Scholar
  • 51  Liang M, Liu Y, Mladinov D et al.: MicroRNA: a new frontier in kidney and blood pressure research. Am. J. Physiol. Renal Physiol.297,F553–F558 (2009).Crossref, Medline, CASGoogle Scholar
  • 52  Ryan BM, Robles AI, Harris CC: Genetic variation in microRNA networks: the implications for cancer research. Nat. Rev. Cancer10,389–402 (2010).Crossref, Medline, CASGoogle Scholar
  • 53  Linsen SE, de Wit E, de Bruijn E, Cuppen E: Small RNA expression and strain specificity in the rat. BMC Genomics11,249 (2010).Crossref, MedlineGoogle Scholar
  • 54  Olena AF, Patton JG: Genomic organization of microRNAs. J. Cell. Physiol.222,540–545 (2010).Medline, CASGoogle Scholar
  • 55  Wittmann J , Jack HM: Serum microRNAs as powerful cancer biomarkers. Biochim. Biophys. Acta1806(2),200–207 (2010).▪▪ Good review of miRNAs as biomarkers of cancer.Medline, CASGoogle Scholar
  • 56  Liu DZ, Tian Y, Ander BP et al.: Brain and blood microRNA expression profiling of ischemic stroke, intracerebral hemorrhage, and kainate seizures. J. Cereb. Blood Flow Metab.30,92–101 (2010).Crossref, MedlineGoogle Scholar
  • 57  Cox MB, Cairns MJ, Gandhi KS et al.: MicroRNAs miR-17 and miR-20a inhibit T cell activation genes and are under-expressed in MS whole blood. PLoS ONE5,e12132 (2010).Crossref, MedlineGoogle Scholar
  • 58  Wang K, Zhang S, Marzolf B et al.: Circulating microRNAs, potential biomarkers for drug-induced liver injury. Proc. Natl Acad. Sci. USA106,4402–4407 (2009).Crossref, Medline, CASGoogle Scholar
  • 59  Ji X, Takahashi R, Hiura Y, Hirokawa G, Fukushima Y, Iwai N: Plasma miR-208 as a biomarker of myocardial injury. Clin. Chem.55,1944–1949 (2009).Crossref, Medline, CASGoogle Scholar
  • 60  van Rooij E, Sutherland LB, Qi X, Richardson JA, Hill J, Olson EN: Control of stress-dependent cardiac growth and gene expression by a microRNA. Science316,575–579 (2007).Crossref, Medline, CASGoogle Scholar
  • 61  Callis TE, Pandya K, Seok HY et al.: MicroRNA-208a is a regulator of cardiac hypertrophy and conduction in mice. J. Clin. Invest.119,2772–2786 (2009).Crossref, Medline, CASGoogle Scholar
  • 62  Wang GK, Zhu JQ, Zhang JT et al.: Circulating microRNA: a novel potential biomarker for early diagnosis of acute myocardial infarction in humans. Eur. Heart J.31,659–666 (2010).Crossref, MedlineGoogle Scholar
  • 63  Satoh M, Minami Y, Takahashi Y, Tabuchi T, Nakamura M: Expression of microRNA-208 is associated with adverse clinical outcomes in human dilated cardiomyopathy. J. Card. Fail.16,404–410 (2010).Crossref, Medline, CASGoogle Scholar
  • 64  Radom-Aizik S, Zaldivar F Jr, Oliver S, Galassetti P, Cooper DM: Evidence for microRNA involvement in exercise-associated neutrophil gene expression changes. J. Appl. Physiol.109,252–261 (2010).Crossref, Medline, CASGoogle Scholar
  • 65  Michiels S, Koscielny S, Hill C: Prediction of cancer outcome with microarrays: a multiple random validation strategy. Lancet365,488–492 (2005).Crossref, Medline, CASGoogle Scholar
  • 66  Ioannidis JP: Microarrays and molecular research: noise discovery? Lancet365,454–455 (2005).Crossref, MedlineGoogle Scholar
  • 67  Shi L, Campbell G, Jones WD et al.: The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models. Nat. Biotechnol.28,827–838 (2010).▪▪ Excellent evaluation by the microarray quality control consortia of the current issues regarding identifying and statistically validating pharmacogenomic biomarkers.Crossref, Medline, CASGoogle Scholar
  • 68  Shi W, Bessarabova M, Dosymbekov D et al.: Functional analysis of multiple genomic signatures demonstrates that classification algorithms choose phenotype-related genes. Pharmacogenomics J.10,310–323 (2010).Crossref, Medline, CASGoogle Scholar
  • 69  Parry RM, Jones W, Stokes TH et al.: k-nearest neighbor models for microarray gene expression analysis and clinical outcome prediction. Pharmacogenomics J.10,292–309 (2010).Crossref, Medline, CASGoogle Scholar
  • 70  Luo J, Schumacher M, Scherer A et al.: A comparison of batch effect removal methods for enhancement of prediction performance using MAQC-II microarray gene expression data. Pharmacogenomics J.10,278–291 (2010).Crossref, Medline, CASGoogle Scholar
  • 71  Oberthuer A, Juraeva D, Li L et al.: Comparison of performance of one-color and two-color gene-expression analyses in predicting clinical endpoints of neuroblastoma patients. Pharmacogenomics J.10,258–266 (2010).Crossref, Medline, CASGoogle Scholar
  • 72  Fan X, Lobenhofer EK, Chen M et al.: Consistency of predictive signature genes and classifiers generated using different microarray platforms. Pharmacogenomics J.10,247–257 (2010).Crossref, Medline, CASGoogle Scholar
  • 73  Miclaus K, Chierici M, Lambert C et al.: Variability in GWAS analysis: the impact of genotype calling algorithm inconsistencies. Pharmacogenomics J.10,324–335 (2010).Crossref, Medline, CASGoogle Scholar
  • 74  Miclaus K, Wolfinger R, Vega S et al.: Batch effects in the BRLMM genotype calling algorithm influence GWAS results for the Affymetrix 500K array. Pharmacogenomics J.10,336–346 (2010).Crossref, Medline, CASGoogle Scholar
  • 75  Zhang L, Yin S, Miclaus K et al.: Assessment of variability in GWAS with CRLMM genotyping algorithm on WTCCC coronary artery disease. Pharmacogenomics J.10,347–354 (2010).Crossref, Medline, CASGoogle Scholar
  • 76  Hong H, Shi L, Su Z et al.: Assessing sources of inconsistencies in genotypes and their effects on genome-wide association studies with HapMap samples. Pharmacogenomics J.10,364–374 (2010).Crossref, Medline, CASGoogle Scholar
  • 77  Chierici M, Miclaus K, Vega S, Furlanello C: An interactive effect of batch size and composition contributes to discordant results in GWAS with the CHIAMO genotyping algorithm. Pharmacogenomics J.10,355–363 (2010).Crossref, Medline, CASGoogle Scholar
  • 78  Fannin RD, Russo M, O’Connell TM et al.: Acetaminophen dosing of humans results in blood transcriptome and metabolome changes consistent with impaired oxidative phosphorylation. Hepatology51,227–236 (2010).Crossref, Medline, CASGoogle Scholar
  • 79  Stamova BS, Apperson M, Walker WL et al.: Identification and validation of suitable endogenous reference genes for gene expression studies in human peripheral blood. BMC Med. Genomics2,49 (2009).Crossref, MedlineGoogle Scholar
  • 80  Walker WL, Liao IH, Gilbert DL et al.: Empirical Bayes accomodation of batch-effects in microarray data using identical replicate reference samples: application to RNA expression profiling of blood from Duchenne muscular dystrophy patients. BMC Genomics9,494 (2008).Crossref, MedlineGoogle Scholar
  • 81  Tillinghast GW: Microarrays in the clinic. Nat. Biotechnol.28,810–812 (2010).Crossref, Medline, CASGoogle Scholar
  • 82  Baggerly KA, Coombes KR: Deriving chemosensitivity from cell lines: forensic bioinformatics and reproducible research in high-throughput biology. Ann. Appl. Stat.3,1309–1334 (2009).▪▪ Important work that noted the problems with data that was being used to determine clinical treatments options.CrossrefGoogle Scholar
  • 83  Goodsaid F, Papaluca M: Evolution of biomarker qualification at the health authorities. Nat. Biotechnol.28,441–443 (2010).Crossref, Medline, CASGoogle Scholar
  • 84  Goodsaid FM, Mendrick DL: Translational medicine and the value of biomarker qualification. Sci. Transl. Med.2(47),ps44 (2010).CrossrefGoogle Scholar
  • 85  Dieterle F, Sistare F, Goodsaid F et al.: Renal biomarker qualification submission: a dialog between the FDA–EMEA and Predictive Safety Testing Consortium. Nat. Biotechnol.28,455–462 (2010).Crossref, Medline, CASGoogle Scholar
  • 86  Mendrick DL: Genomic and genetic biomarkers of toxicity. Toxicology245,175–181 (2008).Crossref, Medline, CASGoogle Scholar
  • 87  Mattes WB: Public consortium efforts in toxicogenomics. In: Essential Concepts in Toxicogenomics. Mendrick DL, Mattes WB (Eds.). Humana, NJ, USA 221–238 (2008).Google Scholar
  • 101  US FDA: MicroArray Quality Control www.fda.gov/ScienceResearch/BioinformaticsTools/MicroarrayQualityControlProject/default.htmGoogle Scholar
  • 102  FDA: Guidance for Industry: Qualification Process for Drug Development Tools. Draft Guidance (2010) www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/UCM230597.pdfGoogle Scholar
  • 103  Center for Disease Control and Prevention Public Health Image Library http://phil.cdc.gov/phil/home.aspGoogle Scholar
  • 104  Professional Royalty-Free Stock Photos www.photos.comGoogle Scholar