International Journal of Biomedicine. 2018;8(4):273-279.
DOI: 10.21103/Article8(4)_RA1
Originally published December 15, 2018
The last fifty years have been the “golden era” of biomedical research and innovation. Major discoveries in genetics, genomics and various fields of “Omics”, together with the technology revolution, has created unlimited opportunities for the development, and improvements in the way the healthcare is delivered. Not a single day goes by, without an announcement of a new sensor, new app, or a new and novel technology, that can be integrated with the wealth of knowledge in biomedical research and applications. To the extent, one of the largest insurance provider, John Hancock announced, that they no longer offer policies, that do not include digital tracking. They will sell only “interactive” policies that collect health data through wearable devices, such as smart watch. The breakthroughs in biomedicine, and advances in technologies, have been miraculous. This is especially true in the USA, which is the envy of other nations, when it comes to innovations in research and technology. The fact that all of these innovations are “news makers” creates great expectations from the care receivers. Having said that, patients, clinicians, and healthcare providers feel at times a letdown, or question the slow pace of advance, escalating cost, sometimes dubious clinical values and inappropriate exploitations. Policy makers and economists are debating, about the cost-effectiveness and the return on the investment in biomedical research, as it relates to improvements in health care. Researchers worldwide are debating about the availability of “Precision Medicine” and “Personalized Medicine.” Despite the developments in biomedical research and emerging technologies, which have raised our expectations and created infinite opportunities, there seems to be some limitations in their applications. In this mini review, we will briefly discuss some of the developments in biomedical research and innovation. We will also express our views on the opportunities available and explain limitations.
- Vehom CL, Landefeld JS, Wagner DP. Measuring the contribution of biomedical research to the production of health. Res Policy. 1982;11(1):3-13.
- Bertuzzi S, Jamaleddine Z. Capturing the value of biomedical research. Sci Direct. 2016;165(1):9-12.
- Collins FS, Varmus H. A new initiative on precision medicine. N Engl J Med. 2015; 372(9):793-5. doi: 10.1056/NEJMp1500523. PubMed
- Ozaki K, Ohnishi Y, Iida A, Sekine A, Yamada R, Tsunoda T, et al. Functional SNPs in the lymphotoxin-alpha gene that are associated with susceptibility to myocardial infarction. Nat Genet. 2002;32(4):650-4. PubMed
- Manolio TA. Genomewide association studies and assessment of the risk of disease. N Engl J Med. 2010;363(2):166-76. doi: 10.1056/NEJMra0905980. PubMed
- Herzog RW, Cao O, Srivastava A. Two decades of clinical gene therapy-Success is finally mounting. Discov Med. 2010;9(45):105-11. PubMed
- Wirth T, Parker N, Yia-Herittuala S. History of gene therapy. Gene.2013;525(2):162-9. doi: 10.1016/j.gene.2013.03.137.
- Helmy KY, Patel SA, Silverio K, Pliner L, Rameshwar P. Stem cells and regenerative medicine: accomplishments to date and future promise. Ther Deliv. 2010;1(5):693-705. doi: 10.4155/tde.10.57. PubMed
- Dominici M, Le Blanc K, Mueller I, Slaper-Cortenbach I, Marini F, Krause D, et al. Minimal criteria for defining multipotent mesenchymal stromal cells. The International Society for cellular therapy position statement. Cryotherapy. 2006;8(4):315-7. PubMed
- Ott HC, Matthiesen TS, Goh SK, Black LD, Kren SM, Netoff TI, Taylor DA. Perfusion-decellularized matrix: using nature’s platform to engineer a bioartificial heart. Nat Med. 2008;14(2):213-21. doi: 10.1038/nm1684. PubMed
- Robinson KA, Li J, Mathison M, Redkar A, Cui J, Chronos NA, et al. Extracellular matrix scaffold for cardiac repair. Circulation. 2005;112(9 Suppl):I135-43. PubMed
- Park SH, Su R, Jeong J, Guo SZ, Qiu K, Joung D, et al. 3D Printed Polymer Photodetectors. Adv Mater. 2018 Aug 28:e1803980. doi:10.1002/adma.201803980. PubMed
- Cohrs NH, Petrou A, Loepfe M, Yliruka M, Schumacher CM, Kohll AX, et al. A Soft Total Artificial Heart-First Concept Evaluation on a Hybrid Mock Circulation. Artif Organs. 2017;41(10):948-958. doi: 10.1111/aor.12956. PubMed
- White JR. A brief history of the development of diabetes medications. Diabetes Spectr. 2014;27(2):82-6. doi: 10.2337/diaspect.27.2.82. PubMed
- Keating C. The genesis of the Global Burden of Disease study. Lancet. 2018 Jun 9;391(10137):2316-2317. doi: 10.1016/S0140-6736(18)31261-3. PubMed
- Schwartz SS, Epstein S, Corkey BE, Grant SFA, Gavin Iii JR, Aguilar RB, Herman ME. A Unified Pathophysiological Construct of Diabetes and its Complications. Trends Endocrinol Metab. 2017;28(9):645-655. doi: 10.1016/j.tem.2017.05.005. PubMed
- Danaei G, Finucane MM, Lin JK, Singh GM, Paciorek CJ, Cowan MJ, et al.; Global Burden of Metabolic Risk Factors of Chronic Diseases Collaborating Group (Blood Pressure). National, regional, and global trends in systolic blood pressure since 1980: systematic analysis of health examination surveys and epidemiological studies with 786 country-years and 5.4 million participants. Lancet. 2011;377(9765):568-77. doi: 10.1016/S0140-6736(10)62036-3. PubMed
- Finucane MM, Stevens GA, Cowan MJ, Danaei G, Lin JK, Paciorek CJ, et al.; Global Burden of Metabolic Risk Factors of Chronic Diseases Collaborating Group (Body Mass Index). National, regional, and global trends in body-mass index since 1980: systematic analysis of health examination surveys and epidemiological studies with 960 country-years and 9.1 million participants. Lancet. 2011;377(9765):557-67. doi: 10.1016/S0140-6736(10)62037-5. PubMed
- NCD Risk Factor Collaboration (NCD-RisC).Worldwide trends in diabetes since 1980: a pooled analysis of 751 population-based studies with 4.4 million participants. Lancet. 2016;387(10027):1513-30. doi: 10.1016/S0140-6736(16)00618-8. PubMed
- Guariguata L, Whiting DR, Hambleton I, Beagley J, Linnenkamp U, Shaw JE. Global estimates of diabetes prevalence for 2013 and projections for 2035. Diabetes Res Clin Pract. 2014 Feb;103(2):137-49. doi: 10.1016/j.diabres.2013.11.002. PubMed
- Brown MS, Goldstein JL Heart attacks: gone with the century? Science. 1996;272(5262):629. PubMed
- Guttamacher AE, Collins FS. Genomic medicine--a primer. N Engl J Med. 2002;347(19):1512-20. PubMed
- Lander ES, Linton LM, Birren B, Nusbaum C, Zody MC, Baldwin J, et al.; International Human Genome Sequencing Consortium. Initial sequencing and analysis of the human genome. Nature. 2001;409(6822):860-921. PubMed
- Venter JC, Adams MD, Myers EW, Li PW, Mural RJ, Sutton GG, et al. The sequence of the human genome. Science. 2001;291(5507):1304-51. PubMed
- Todd JA. From genome to aetiology in a multifactorial disease, type 1 diabetes. Bioessays. 1999;21(2):164-174. PubMed
- Wang TJ, Larson MG, Vasan RS, Cheng S, Rhee EP, McCabe E, et al. Metabolite profiles and the risk of developing diabetes. Nat Med. 2011;17(4):448-53. doi: 10.1038/nm.2307. PubMed
- Yamaguchi N, Mahbub MH, Takahashi H, Hase R, Ishimaru Y, Sunagawa H, et al. Plasma free amino acid profiles evaluate risk of metabolic syndrome, diabetes, dyslipidemia, and hypertension in a large Asian population. Environ Health Prev Med. 2017;22(1):35. doi: 10.1186/s12199-017-0642-7. PubMed
- Bi X, Tey SL, Loo YT, Henry CJ. Central adiposity-induced plasma-free amino acid alterations are associated with increased insulin resistance in healthy Singaporean adults. Eur J Clin Nutr. 2017;71(9):1080-1087. doi: 10.1038/ejcn.2017.34. PubMed
- Yamakado M, Nagao K, Imaizumi A, Tani M, Toda A, Tanaka T, et al. Plasma Free Amino Acid Profiles Predict Four-Year Risk of Developing Diabetes, Metabolic Syndrome, Dyslipidemia, and Hypertension in Japanese Population. Sci Rep. 2015;5:11918. doi: 10.1038/srep11918. PubMed
- Magnusson M, Lewis GD, Ericson U, Orho-Melander M, Hedblad B, Engström G, et al. A diabetes-predictive amino acid score and future cardiovascular disease. Eur Heart J. 2013;34(26):1982-9. doi: 10.1093/eurheartj/ehs424. PubMed
- Fujisaka S, Avila-Pacheco J, Soto M, Kostic A, Dreyfuss JM, Pan H, et al. Diet, Genetics and the gut microbiome drive dynamic changes in plasma metabolites. Cell Rep. 2018; 22(11):3072-3086. doi: 10.1016/j.celrep.2018.02.060. PubMed
- Bouter KE, van Raalte DH, Groen AK, Nieuwdorp M. Role of the Gut Microbiome in the Pathogenesis of Obesity and Obesity-Related Metabolic Dysfunction. Gastroenterology. 2017;152(7):1671-1678. doi: 10.1053/j.gastro.2016.12.048. PubMed
- Billings LK, Florez JC. The genetics of type-2 diabetes: what have we learned from GWAS? Ann NY Acad Sci. 2010;1212:59-77. doi: 10.1111/j.1749-6632.2010.05838.x. PubMed
- Gerrard JM Stuart MJ, Rao GH, Steffes MW, Mauer SM, Brown DM, White JG. Alteration in the balance of prostaglandin and thromboxane synthesis in diabetic rats. J Lab Clin Med. 1980;95(6):950-8. PubMed
- Lundberg MS, Baldwin JT, Buxton DB. Building a bioartificial heart: Obstacles and opportunities. J Thorac Cardiovasc Surg. 2017;153(4):748-750. doi: 10.1016/j.jtcvs.2016.10.103. PubMed
- Miller DG. Bioengineering a successful bioartificial liver system: Requirements for a comprehensive solution. In: Rao GHR & Reddy M, editors. Handbook of Biotechnology, Bioengineering and Biomedical Applications. National Design Research Foundation (NDRF), Institutions of Engineers, Bengaluru, India; 2016.
- O’Donnell VB, Murphy RC, Watson SP. Platelet lipidomics: modern day perspective on lipid discovery and characterization in platelets. Circ Res. 2014;114(7):1185-203. doi: 10.1161/CIRCRESAHA.114.301597. PubMed
- Slatter DA, Aldrovandi M, O'Connor A, Allen SM, Brasher CJ, Murphy RC, et al. Mapping the Human Platelet Lipidome Reveals Cytosolic Phospholipase A2 as a Regulator of Mitochondrial Bioenergetics during Activation. Cell Metab. 2016;23(5):930-44. doi: 10.1016/j.cmet.2016.04.001. PubMed
- Murphy DP, Bai O, Gorgey AS, Fox J, Lovegreen WT, Burkhardt BW, et al. Electroencephalogram-Based Brain-Computer Interface and Lower-Limb Prosthesis Control: A Case Study. Front Neurol. 2017 Dec 15;8:696. doi: 10.3389/fneur.2017.00696. eCollection 2017. PubMed
- Wolpaw JR. Brain-computer interfaces. In Michael PB, David CG, editors. Handbook of Clinical Neurology. Elsevier: Amsterdam, The Netherlands, 2013;110:67–74.
- Handford ML, Srinivasan M. Robotic lower limb prosthesis design through simultaneous computer optimization of human and prosthesis costs. Sci Rep. 2016 Feb 9;6:19983. doi: 10.1038/srep19983. PubMed
- Anderson FC, Pandy MG. Dynamic optimization of human walking. J Biomech Eng. 2001;123(5):381-90. PubMed
- Moses H 3rd, Martin JB. Biomedical research and health advances. N Engl J Med. 2011;364(6):567-71. doi: 10.1056/NEJMsb1007634. PubMed
Download Article
Received September 26, 2018.
Accepted October 14, 2018.
©2018 International Medical Research and Development Corporation.