Benkő, E.; Ilič, I.G.; Kristó, K.; Regdon, G., Jr.; Csóka, I.; Pintye-Hódi, K.; Srčič, S.; Sovány, T.
Predicting Drug Release Rate of Implantable Matrices and Better Understanding of the Underlying Mechanisms through Experimental Design and Artificial Neural Network-Based Modelling. Pharmaceutics 2022, 14, 228.
https://doi.org/10.3390/pharmaceutics14020228
AMA Style
Benkő E, Ilič IG, Kristó K, Regdon G Jr., Csóka I, Pintye-Hódi K, Srčič S, Sovány T.
Predicting Drug Release Rate of Implantable Matrices and Better Understanding of the Underlying Mechanisms through Experimental Design and Artificial Neural Network-Based Modelling. Pharmaceutics. 2022; 14(2):228.
https://doi.org/10.3390/pharmaceutics14020228
Chicago/Turabian Style
Benkő, Ernő, Ilija German Ilič, Katalin Kristó, Géza Regdon, Jr., Ildikó Csóka, Klára Pintye-Hódi, Stane Srčič, and Tamás Sovány.
2022. "Predicting Drug Release Rate of Implantable Matrices and Better Understanding of the Underlying Mechanisms through Experimental Design and Artificial Neural Network-Based Modelling" Pharmaceutics 14, no. 2: 228.
https://doi.org/10.3390/pharmaceutics14020228
APA Style
Benkő, E., Ilič, I. G., Kristó, K., Regdon, G., Jr., Csóka, I., Pintye-Hódi, K., Srčič, S., & Sovány, T.
(2022). Predicting Drug Release Rate of Implantable Matrices and Better Understanding of the Underlying Mechanisms through Experimental Design and Artificial Neural Network-Based Modelling. Pharmaceutics, 14(2), 228.
https://doi.org/10.3390/pharmaceutics14020228