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Letting the genome out of the bottle.

The New England journal of medicine

Audeh MW.
PMID: 18494080
N Engl J Med. 2008 May 15;358(20):2184-5; author reply 2185.

No abstract available.

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Audeh MW. Letting the genome out of the bottle. N Engl J Med. 2008;358(20):2184-5; author reply 2185
Audeh, M. W. (2008). Letting the genome out of the bottle. The New England journal of medicine, 358(20), 2184-5; author reply 2185.
Audeh, M William. "Letting the genome out of the bottle." The New England journal of medicine vol. 358,20 (2008): 2184-5; author reply 2185.
Audeh MW. Letting the genome out of the bottle. N Engl J Med. 2008 May 15;358(20):2184-5; author reply 2185. PMID: 18494080.
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Genetic risk prediction--are we there yet?.

The New England journal of medicine

Kraft P, Hunter DJ.
PMID: 19369656
N Engl J Med. 2009 Apr 23;360(17):1701-3. doi: 10.1056/NEJMp0810107. Epub 2009 Apr 15.

No abstract available.

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Kraft P, Hunter DJ. Genetic risk prediction--are we there yet?. N Engl J Med. 2009;360(17):1701-3doi: 10.1056/NEJMp0810107.
Kraft, P., & Hunter, D. J. (2009). Genetic risk prediction--are we there yet?. The New England journal of medicine, 360(17), 1701-3. https://doi.org/10.1056/NEJMp0810107
Kraft, Peter, and Hunter, David J. "Genetic risk prediction--are we there yet?." The New England journal of medicine vol. 360,17 (2009): 1701-3. doi: https://doi.org/10.1056/NEJMp0810107
Kraft P, Hunter DJ. Genetic risk prediction--are we there yet?. N Engl J Med. 2009 Apr 23;360(17):1701-3. doi: 10.1056/NEJMp0810107. Epub 2009 Apr 15. PMID: 19369656.
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Number needed to harm should be measured for treatments.

BMJ (Clinical research ed.)

Zermansky A.
PMID: 9765180
BMJ. 1998 Oct 10;317(7164):1014. doi: 10.1136/bmj.317.7164.1014.

No abstract available.

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Zermansky A. Number needed to harm should be measured for treatments. BMJ. 1998;317(7164):1014doi: 10.1136/bmj.317.7164.1014.
Zermansky, A. (1998). Number needed to harm should be measured for treatments. BMJ (Clinical research ed.), 317(7164), 1014. https://doi.org/10.1136/bmj.317.7164.1014
Zermansky, A. "Number needed to harm should be measured for treatments." BMJ (Clinical research ed.) vol. 317,7164 (1998): 1014. doi: https://doi.org/10.1136/bmj.317.7164.1014
Zermansky A. Number needed to harm should be measured for treatments. BMJ. 1998 Oct 10;317(7164):1014. doi: 10.1136/bmj.317.7164.1014. PMID: 9765180; PMCID: PMC1114024.
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Genetic predisposition to COVID-19 may increase the risk of hypertension disorders in pregnancy: A two-sample Mendelian randomization study.

Pregnancy hypertension

Tan JS, Liu NN, Guo TT, Hu S, Hua L.
PMID: 34428710
Pregnancy Hypertens. 2021 Dec;26:17-23. doi: 10.1016/j.preghy.2021.08.112. Epub 2021 Aug 19.

AIMS: The aim of this study was to apply the Mendelian randomization (MR) design to explore the potential causal association between COVID-19 and the risk of hypertension disorders in pregnancy.METHODS: Our primary genetic instrument comprised 8 single-nucleotide polymorphisms (SNPs)...

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Tan JS, Liu NN, Guo TT, et al. Genetic predisposition to COVID-19 may increase the risk of hypertension disorders in pregnancy: A two-sample Mendelian randomization study. Pregnancy Hypertens. 2021;26:17-23doi: 10.1016/j.preghy.2021.08.112.
Tan, J. S., Liu, N. N., Guo, T. T., Hu, S., & Hua, L. (2021). Genetic predisposition to COVID-19 may increase the risk of hypertension disorders in pregnancy: A two-sample Mendelian randomization study. Pregnancy hypertension, 2617-23. https://doi.org/10.1016/j.preghy.2021.08.112
Tan, Jiang-Shan, et al. "Genetic predisposition to COVID-19 may increase the risk of hypertension disorders in pregnancy: A two-sample Mendelian randomization study." Pregnancy hypertension vol. 26 (2021): 17-23. doi: https://doi.org/10.1016/j.preghy.2021.08.112
Tan JS, Liu NN, Guo TT, Hu S, Hua L. Genetic predisposition to COVID-19 may increase the risk of hypertension disorders in pregnancy: A two-sample Mendelian randomization study. Pregnancy Hypertens. 2021 Dec;26:17-23. doi: 10.1016/j.preghy.2021.08.112. Epub 2021 Aug 19. PMID: 34428710.
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Chronic Non-Communicable Disease Risk Calculators - An Overview, Part II.

Maturitas

Juchli F, Zangger M, Schueck A, von Wolff M, Stute P.
PMID: 33308619
Maturitas. 2021 Jan;143:132-144. doi: 10.1016/j.maturitas.2020.10.003. Epub 2020 Oct 16.

The aim of this review was to identify the different risk assessment tools that stratify the individual's risk of four of the eight leading causes of death: stroke, ischaemic heart diseases, type 2 diabetes mellitus, and dementia. It follows...

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Juchli F, Zangger M, Schueck A, et al. Chronic Non-Communicable Disease Risk Calculators - An Overview, Part II. Maturitas. 2020;143:132-144doi: 10.1016/j.maturitas.2020.10.003.
Juchli, F., Zangger, M., Schueck, A., von Wolff, M., & Stute, P. (2021). Chronic Non-Communicable Disease Risk Calculators - An Overview, Part II. Maturitas, 143132-144. https://doi.org/10.1016/j.maturitas.2020.10.003
Juchli, Fabienne, et al. "Chronic Non-Communicable Disease Risk Calculators - An Overview, Part II." Maturitas vol. 143 (2021): 132-144. doi: https://doi.org/10.1016/j.maturitas.2020.10.003
Juchli F, Zangger M, Schueck A, von Wolff M, Stute P. Chronic Non-Communicable Disease Risk Calculators - An Overview, Part II. Maturitas. 2021 Jan;143:132-144. doi: 10.1016/j.maturitas.2020.10.003. Epub 2020 Oct 16. PMID: 33308619.
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What drives public acceptance of nanotechnology?.

Nature nanotechnology

Currall SC, King EB, Lane N, Madera J, Turner S.
PMID: 18654170
Nat Nanotechnol. 2006 Dec;1(3):153-5. doi: 10.1038/nnano.2006.155.

How do the risks and benefits of nanotechnology, as viewed by the public, compare with those associated with other technologies such as genetically modified organisms, stem cells, biotechnology and nuclear power? And when deciding to use a specific nanotechnology...

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Currall SC, King EB, Lane N, et al. What drives public acceptance of nanotechnology?. Nat Nanotechnol. 2006;1(3):153-5doi: 10.1038/nnano.2006.155.
Currall, S. C., King, E. B., Lane, N., Madera, J., & Turner, S. (2006). What drives public acceptance of nanotechnology?. Nature nanotechnology, 1(3), 153-5. https://doi.org/10.1038/nnano.2006.155
Currall, Steven C, et al. "What drives public acceptance of nanotechnology?." Nature nanotechnology vol. 1,3 (2006): 153-5. doi: https://doi.org/10.1038/nnano.2006.155
Currall SC, King EB, Lane N, Madera J, Turner S. What drives public acceptance of nanotechnology?. Nat Nanotechnol. 2006 Dec;1(3):153-5. doi: 10.1038/nnano.2006.155. PMID: 18654170.
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Nanoecotoxicology: Environmental risks of nanomaterials.

Nature nanotechnology

Scheringer M.
PMID: 18654538
Nat Nanotechnol. 2008 Jun;3(6):322-3. doi: 10.1038/nnano.2008.145.

No abstract available.

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Scheringer M. Nanoecotoxicology: Environmental risks of nanomaterials. Nat Nanotechnol. 2008;3(6):322-3doi: 10.1038/nnano.2008.145.
Scheringer, M. (2008). Nanoecotoxicology: Environmental risks of nanomaterials. Nature nanotechnology, 3(6), 322-3. https://doi.org/10.1038/nnano.2008.145
Scheringer, Martin. "Nanoecotoxicology: Environmental risks of nanomaterials." Nature nanotechnology vol. 3,6 (2008): 322-3. doi: https://doi.org/10.1038/nnano.2008.145
Scheringer M. Nanoecotoxicology: Environmental risks of nanomaterials. Nat Nanotechnol. 2008 Jun;3(6):322-3. doi: 10.1038/nnano.2008.145. PMID: 18654538.
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Artificial Intelligence, Machine Learning, and Surgical Science: Reality Versus Hype.

The Journal of surgical research

El Hechi M, Ward TM, An GC, Maurer LR, El Moheb M, Tsoulfas G, Kaafarani HM.
PMID: 33743995
J Surg Res. 2021 Aug;264:A1-A9. doi: 10.1016/j.jss.2021.01.046. Epub 2021 Mar 18.

Artificial intelligence (AI) has made increasing inroads in clinical medicine. In surgery, machine learning-based algorithms are being studied for use as decision aids in risk prediction and even for intraoperative applications, including image recognition and video analysis. While AI...

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El Hechi M, Ward TM, An GC, et al. Artificial Intelligence, Machine Learning, and Surgical Science: Reality Versus Hype. J Surg Res. 2021;264:A1-A9doi: 10.1016/j.jss.2021.01.046.
El Hechi, M., Ward, T. M., An, G. C., Maurer, L. R., El Moheb, M., Tsoulfas, G., & Kaafarani, H. M. (2021). Artificial Intelligence, Machine Learning, and Surgical Science: Reality Versus Hype. The Journal of surgical research, 264A1-A9. https://doi.org/10.1016/j.jss.2021.01.046
El Hechi, Majed, et al. "Artificial Intelligence, Machine Learning, and Surgical Science: Reality Versus Hype." The Journal of surgical research vol. 264 (2021): A1-A9. doi: https://doi.org/10.1016/j.jss.2021.01.046
El Hechi M, Ward TM, An GC, Maurer LR, El Moheb M, Tsoulfas G, Kaafarani HM. Artificial Intelligence, Machine Learning, and Surgical Science: Reality Versus Hype. J Surg Res. 2021 Aug;264:A1-A9. doi: 10.1016/j.jss.2021.01.046. Epub 2021 Mar 18. PMID: 33743995.
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Showing 217 to 224 of 224 entries