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Editorial.

Chimia

Ward T, Müller D.
PMID: 27363364
Chimia (Aarau). 2016;70(6):389.

No abstract available.

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Ward T, Müller D. Editorial. Chimia (Aarau). 2016;70(6):389
Ward, T., & Müller, D. (2016). Editorial. Chimia, 70(6), 389.
Ward, Thomas, and Müller, Daniel. "Editorial." Chimia vol. 70,6 (2016): 389.
Ward T, Müller D. Editorial. Chimia (Aarau). 2016;70(6):389. PMID: 27363364.
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Effect of age on pro-inflammatory miRNAs contained in mesenchymal stem cell-derived extracellular vesicles.

Scientific reports

Fafián-Labora J, Lesende-Rodriguez I, Fernández-Pernas P, Sangiao-Alvarellos S, Monserrat L, Arntz OJ, van de Loo FJ, Mateos J, Arufe MC.
PMID: 28262816
Sci Rep. 2017 Mar 06;7:43923. doi: 10.1038/srep43923.

Stem cells possess significant age-dependent differences in their immune-response profile. These differences were analysed by Next-Generation Sequencing of six age groups from bone marrow mesenchymal stem cells. A total of 9,628 genes presenting differential expression between age groups were...

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Fafián-Labora J, Lesende-Rodriguez I, Fernández-Pernas P, et al. Effect of age on pro-inflammatory miRNAs contained in mesenchymal stem cell-derived extracellular vesicles. Sci Rep. 2017;7:43923doi: 10.1038/srep43923.
Fafián-Labora, J., Lesende-Rodriguez, I., Fernández-Pernas, P., Sangiao-Alvarellos, S., Monserrat, L., Arntz, O. J., van de Loo, F. J., Mateos, J., & Arufe, M. C. (2017). Effect of age on pro-inflammatory miRNAs contained in mesenchymal stem cell-derived extracellular vesicles. Scientific reports, 743923. https://doi.org/10.1038/srep43923
Fafián-Labora, J, et al. "Effect of age on pro-inflammatory miRNAs contained in mesenchymal stem cell-derived extracellular vesicles." Scientific reports vol. 7 (2017): 43923. doi: https://doi.org/10.1038/srep43923
Fafián-Labora J, Lesende-Rodriguez I, Fernández-Pernas P, Sangiao-Alvarellos S, Monserrat L, Arntz OJ, van de Loo FJ, Mateos J, Arufe MC. Effect of age on pro-inflammatory miRNAs contained in mesenchymal stem cell-derived extracellular vesicles. Sci Rep. 2017 Mar 06;7:43923. doi: 10.1038/srep43923. PMID: 28262816; PMCID: PMC5338265.
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Nanotube-Enabled Vesicle-Vesicle Communication: A Computational Model.

The journal of physical chemistry letters

Zhang L, Wang X.
PMID: 26266730
J Phys Chem Lett. 2015 Jul 02;6(13):2530-7. doi: 10.1021/acs.jpclett.5b00755. Epub 2015 Jun 17.

Cell-to-cell communications via the tunneling nanotubes or gap junction channels are vital for the development and maintenance of multicellular organisms. Instead of these intrinsic communication pathways, how to design artificial communication channels between cells remains a challenging but interesting...

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Zhang L, Wang X. Nanotube-Enabled Vesicle-Vesicle Communication: A Computational Model. J Phys Chem Lett. 2015;6(13):2530-7doi: 10.1021/acs.jpclett.5b00755.
Zhang, L., & Wang, X. (2015). Nanotube-Enabled Vesicle-Vesicle Communication: A Computational Model. The journal of physical chemistry letters, 6(13), 2530-7. https://doi.org/10.1021/acs.jpclett.5b00755
Zhang, Liuyang, and Wang, Xianqiao. "Nanotube-Enabled Vesicle-Vesicle Communication: A Computational Model." The journal of physical chemistry letters vol. 6,13 (2015): 2530-7. doi: https://doi.org/10.1021/acs.jpclett.5b00755
Zhang L, Wang X. Nanotube-Enabled Vesicle-Vesicle Communication: A Computational Model. J Phys Chem Lett. 2015 Jul 02;6(13):2530-7. doi: 10.1021/acs.jpclett.5b00755. Epub 2015 Jun 17. PMID: 26266730.
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Engineering of inorganic nanoparticles as magnetic resonance imaging contrast agents.

Chemical Society reviews

Ni D, Bu W, Ehlerding EB, Cai W, Shi J.
PMID: 29071327
Chem Soc Rev. 2017 Nov 27;46(23):7438-7468. doi: 10.1039/c7cs00316a.

Magnetic resonance imaging (MRI) is a highly valuable non-invasive imaging tool owing to its exquisite soft tissue contrast, high spatial resolution, lack of ionizing radiation, and wide clinical applicability. Contrast agents (CAs) can be used to further enhance the...

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Ni D, Bu W, Ehlerding EB, et al. Engineering of inorganic nanoparticles as magnetic resonance imaging contrast agents. Chem Soc Rev. 2017;46(23):7438-7468doi: 10.1039/c7cs00316a.
Ni, D., Bu, W., Ehlerding, E. B., Cai, W., & Shi, J. (2017). Engineering of inorganic nanoparticles as magnetic resonance imaging contrast agents. Chemical Society reviews, 46(23), 7438-7468. https://doi.org/10.1039/c7cs00316a
Ni, Dalong, et al. "Engineering of inorganic nanoparticles as magnetic resonance imaging contrast agents." Chemical Society reviews vol. 46,23 (2017): 7438-7468. doi: https://doi.org/10.1039/c7cs00316a
Ni D, Bu W, Ehlerding EB, Cai W, Shi J. Engineering of inorganic nanoparticles as magnetic resonance imaging contrast agents. Chem Soc Rev. 2017 Nov 27;46(23):7438-7468. doi: 10.1039/c7cs00316a. PMID: 29071327; PMCID: PMC5705441.
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An ecological compensation standard based on emergy theory for the Xiao Honghe River Basin.

Water science and technology : a journal of the International Association on Water Pollution Research

Guan X, Chen M, Hu C.
PMID: 26442487
Water Sci Technol. 2015;71(10):1463-70. doi: 10.2166/wst.2015.121.

The calculation of an ecological compensation standard is an important, but also difficult aspect of current ecological compensation research. In this paper, the factors affecting the ecological-economic system in the Xiao Honghe River Basin, China, including the flow of...

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Guan X, Chen M, Hu C. An ecological compensation standard based on emergy theory for the Xiao Honghe River Basin. Water Sci Technol. 2015;71(10):1463-70doi: 10.2166/wst.2015.121.
Guan, X., Chen, M., & Hu, C. (2015). An ecological compensation standard based on emergy theory for the Xiao Honghe River Basin. Water science and technology : a journal of the International Association on Water Pollution Research, 71(10), 1463-70. https://doi.org/10.2166/wst.2015.121
Guan, Xinjian, et al. "An ecological compensation standard based on emergy theory for the Xiao Honghe River Basin." Water science and technology : a journal of the International Association on Water Pollution Research vol. 71,10 (2015): 1463-70. doi: https://doi.org/10.2166/wst.2015.121
Guan X, Chen M, Hu C. An ecological compensation standard based on emergy theory for the Xiao Honghe River Basin. Water Sci Technol. 2015;71(10):1463-70. doi: 10.2166/wst.2015.121. PMID: 26442487.
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A Brief Review of Computer-Assisted Approaches to Rational Design of Peptide Vaccines.

International journal of molecular sciences

Nandy A, Basak SC.
PMID: 27153063
Int J Mol Sci. 2016 May 04;17(5). doi: 10.3390/ijms17050666.

The growing incidences of new viral diseases and increasingly frequent viral epidemics have strained therapeutic and preventive measures; the high mutability of viral genes puts additional strains on developmental efforts. Given the high cost and time requirements for new...

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Nandy A, Basak SC. A Brief Review of Computer-Assisted Approaches to Rational Design of Peptide Vaccines. Int J Mol Sci. 2016;17(5)doi: 10.3390/ijms17050666.
Nandy, A., & Basak, S. C. (2016). A Brief Review of Computer-Assisted Approaches to Rational Design of Peptide Vaccines. International journal of molecular sciences, 17(5), . https://doi.org/10.3390/ijms17050666
Nandy, Ashesh, and Basak, Subhash C. "A Brief Review of Computer-Assisted Approaches to Rational Design of Peptide Vaccines." International journal of molecular sciences vol. 17,5 (2016). doi: https://doi.org/10.3390/ijms17050666
Nandy A, Basak SC. A Brief Review of Computer-Assisted Approaches to Rational Design of Peptide Vaccines. Int J Mol Sci. 2016 May 04;17(5). doi: 10.3390/ijms17050666. PMID: 27153063; PMCID: PMC4881492.
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Predicting the Reliability of Drug-target Interaction Predictions with Maximum Coverage of Target Space.

Scientific reports

Peón A, Naulaerts S, Ballester PJ.
PMID: 28630414
Sci Rep. 2017 Jun 19;7(1):3820. doi: 10.1038/s41598-017-04264-w.

Many computational methods to predict the macromolecular targets of small organic molecules have been presented to date. Despite progress, target prediction methods still have important limitations. For example, the most accurate methods implicitly restrict their predictions to a relatively...

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Peón A, Naulaerts S, Ballester PJ. Predicting the Reliability of Drug-target Interaction Predictions with Maximum Coverage of Target Space. Sci Rep. 2017;7(1):3820doi: 10.1038/s41598-017-04264-w.
Peón, A., Naulaerts, S., & Ballester, P. J. (2017). Predicting the Reliability of Drug-target Interaction Predictions with Maximum Coverage of Target Space. Scientific reports, 7(1), 3820. https://doi.org/10.1038/s41598-017-04264-w
Peón, Antonio, et al. "Predicting the Reliability of Drug-target Interaction Predictions with Maximum Coverage of Target Space." Scientific reports vol. 7,1 (2017): 3820. doi: https://doi.org/10.1038/s41598-017-04264-w
Peón A, Naulaerts S, Ballester PJ. Predicting the Reliability of Drug-target Interaction Predictions with Maximum Coverage of Target Space. Sci Rep. 2017 Jun 19;7(1):3820. doi: 10.1038/s41598-017-04264-w. PMID: 28630414; PMCID: PMC5476590.
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Optimal Spatial Prediction Using Ensemble Machine Learning.

The international journal of biostatistics

Davies MM, van der Laan MJ.
PMID: 27130244
Int J Biostat. 2016 May 01;12(1):179-201. doi: 10.1515/ijb-2014-0060.

Spatial prediction is an important problem in many scientific disciplines. Super Learner is an ensemble prediction approach related to stacked generalization that uses cross-validation to search for the optimal predictor amongst all convex combinations of a heterogeneous candidate set....

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Davies MM, van der Laan MJ. Optimal Spatial Prediction Using Ensemble Machine Learning. Int J Biostat. 2016;12(1):179-201doi: 10.1515/ijb-2014-0060.
Davies, M. M., & van der Laan, M. J. (2016). Optimal Spatial Prediction Using Ensemble Machine Learning. The international journal of biostatistics, 12(1), 179-201. https://doi.org/10.1515/ijb-2014-0060
Davies, Molly Margaret, and van der Laan, Mark J. "Optimal Spatial Prediction Using Ensemble Machine Learning." The international journal of biostatistics vol. 12,1 (2016): 179-201. doi: https://doi.org/10.1515/ijb-2014-0060
Davies MM, van der Laan MJ. Optimal Spatial Prediction Using Ensemble Machine Learning. Int J Biostat. 2016 May 01;12(1):179-201. doi: 10.1515/ijb-2014-0060. PMID: 27130244.
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MEK Binding to KSR Promotes Allosteric Activation of BRAF.

Cancer discovery

[No authors listed]
PMID: 29475885
Cancer Discov. 2018 Apr;8(4):385. doi: 10.1158/2159-8290.CD-RW2018-033. Epub 2018 Feb 23.

KSR-MEK complexes allosterically activate BRAF, which then phosphorylates a second MEK molecule.

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MEK Binding to KSR Promotes Allosteric Activation of BRAF. Cancer Discov. 2018;8(4):385doi: 10.1158/2159-8290.CD-RW2018-033.
(2018). MEK Binding to KSR Promotes Allosteric Activation of BRAF. Cancer discovery, 8(4), 385. https://doi.org/10.1158/2159-8290.CD-RW2018-033
"MEK Binding to KSR Promotes Allosteric Activation of BRAF." Cancer discovery vol. 8,4 (2018): 385. doi: https://doi.org/10.1158/2159-8290.CD-RW2018-033
MEK Binding to KSR Promotes Allosteric Activation of BRAF. Cancer Discov. 2018 Apr;8(4):385. doi: 10.1158/2159-8290.CD-RW2018-033. Epub 2018 Feb 23. PMID: 29475885.
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Predictive modeling studies for the ecotoxicity of ionic liquids towards the green algae Scenedesmus vacuolatus.

Chemosphere

Das RN, Roy K.
PMID: 24296027
Chemosphere. 2014 Jun;104:170-6. doi: 10.1016/j.chemosphere.2013.11.002. Epub 2013 Dec 02.

Hazardous potential of ionic liquids is becoming an issue of high concern with increasing application of these compounds in various industrial processes. Predictive toxicological modeling on ionic liquids provides a rational assessment strategy and aids in developing suitable guidance...

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Das RN, Roy K. Predictive modeling studies for the ecotoxicity of ionic liquids towards the green algae Scenedesmus vacuolatus. Chemosphere. 2013;104:170-6doi: 10.1016/j.chemosphere.2013.11.002.
Das, R. N., & Roy, K. (2014). Predictive modeling studies for the ecotoxicity of ionic liquids towards the green algae Scenedesmus vacuolatus. Chemosphere, 104170-6. https://doi.org/10.1016/j.chemosphere.2013.11.002
Das, Rudra Narayan, and Roy, Kunal. "Predictive modeling studies for the ecotoxicity of ionic liquids towards the green algae Scenedesmus vacuolatus." Chemosphere vol. 104 (2014): 170-6. doi: https://doi.org/10.1016/j.chemosphere.2013.11.002
Das RN, Roy K. Predictive modeling studies for the ecotoxicity of ionic liquids towards the green algae Scenedesmus vacuolatus. Chemosphere. 2014 Jun;104:170-6. doi: 10.1016/j.chemosphere.2013.11.002. Epub 2013 Dec 02. PMID: 24296027.
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[Study on quantitative methods of cleistocalycis operculati cortex].

Zhongguo Zhong yao za zhi = Zhongguo zhongyao zazhi = China journal of Chinese materia medica

Chen LS, Ou JJ, Li SY, Lu SG.
PMID: 25509300
Zhongguo Zhong Yao Za Zhi. 2014 Aug;39(16):3128-30.

Cleistocalycis Operculati Cortex is the dry bark of Cleistocalyx operculatus. It is the raw material of Compound Hibiscuse which is external sterilization antipruritic drugs. The quality standard of Cleistocalycis Operculati Cortex in Guangdong Province "standard for the traditional Chinese...

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Chen LS, Ou JJ, Li SY, et al. [Study on quantitative methods of cleistocalycis operculati cortex]. Zhongguo Zhong Yao Za Zhi. 2014;39(16):3128-30
Chen, L. S., Ou, J. J., Li, S. Y., & Lu, S. G. (2014). [Study on quantitative methods of cleistocalycis operculati cortex]. Zhongguo Zhong yao za zhi = Zhongguo zhongyao zazhi = China journal of Chinese materia medica, 39(16), 3128-30.
Chen, Li-Si, et al. "[Study on quantitative methods of cleistocalycis operculati cortex]." Zhongguo Zhong yao za zhi = Zhongguo zhongyao zazhi = China journal of Chinese materia medica vol. 39,16 (2014): 3128-30.
Chen LS, Ou JJ, Li SY, Lu SG. [Study on quantitative methods of cleistocalycis operculati cortex]. Zhongguo Zhong Yao Za Zhi. 2014 Aug;39(16):3128-30. Chinese. PMID: 25509300.
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Editorial: Advances in cheminformatics: drug discovery, computational toxicology and nanomaterials (part I).

Combinatorial chemistry & high throughput screening

Melagraki G, Afantitis A.
PMID: 25929702
Comb Chem High Throughput Screen. 2015;18(3):236-7. doi: 10.2174/138620731803150423131038.

No abstract available.

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Melagraki G, Afantitis A. Editorial: Advances in cheminformatics: drug discovery, computational toxicology and nanomaterials (part I). Comb Chem High Throughput Screen. 2015;18(3):236-7doi: 10.2174/138620731803150423131038.
Melagraki, G., & Afantitis, A. (2015). Editorial: Advances in cheminformatics: drug discovery, computational toxicology and nanomaterials (part I). Combinatorial chemistry & high throughput screening, 18(3), 236-7. https://doi.org/10.2174/138620731803150423131038
Melagraki, Georgia, and Afantitis, Antreas. "Editorial: Advances in cheminformatics: drug discovery, computational toxicology and nanomaterials (part I)." Combinatorial chemistry & high throughput screening vol. 18,3 (2015): 236-7. doi: https://doi.org/10.2174/138620731803150423131038
Melagraki G, Afantitis A. Editorial: Advances in cheminformatics: drug discovery, computational toxicology and nanomaterials (part I). Comb Chem High Throughput Screen. 2015;18(3):236-7. doi: 10.2174/138620731803150423131038. PMID: 25929702.
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Showing 13 to 24 of 1929 entries