@article{GarciaSantiagoHammerschmidtSachsetal.2022, author = {Garcia Santiago, Xavier and Hammerschmidt, Martin and Sachs, Johannes and Burger, Sven and Kwon, Hyunah and Kn{\"o}ller, Marvin and Arens, Tilo and Fischer, Peer and Fernandez-Corbaton, Ivan and Rockstuhl, Carsten}, title = {Toward maximally electromagnetically chiral scatterers at optical frequencies}, volume = {9}, journal = {ACS Photonics}, arxiv = {http://arxiv.org/abs/2112.04422}, doi = {10.1021/acsphotonics.1c01887}, pages = {1954}, year = {2022}, language = {en} } @article{FischerHolzhausenRoeblitz2022, author = {Fischer-Holzhausen, Sophie and R{\"o}blitz, Susanna}, title = {Hormonal regulation of ovarian follicle growth in humans: Model-based exploration of cycle variability and parameter sensitivities}, journal = {Journal of Theoretical Biology}, doi = {https://doi.org/10.1016/j.jtbi.2022.111150}, year = {2022}, abstract = {We present a modelling and simulation framework for the dynamics of ovarian follicles and key hormones along the hypothalamic-pituitary-gonadal axis throughout consecutive human menstrual cycles. All simulation results (hormone concentrations and ovarian follicle sizes) are in biological units and can easily be compared to clinical data. The model takes into account variability in follicles' response to stimulating hormones, which introduces variability between cycles. The growth of ovarian follicles in waves is an emergent property in our model simulations and further supports the hypothesis that follicular waves are also present in humans. We use Approximate Bayesian Computation and cluster analysis to construct a population of virtual subjects and to study parameter distributions and sensitivities. The model can be used to compare and optimize treatment protocols for ovarian hyperstimulation, thus potentially forming the integral part of a clinical decision support system in reproductive endocrinology.}, language = {en} } @article{FischerHolzhausenRoeblitz2022, author = {Fischer-Holzhausen, Sophie and R{\"o}blitz, Susanna}, title = {Mathematical modelling of follicular growth and ovarian stimulation}, journal = {Current Opinion in Endocrine and Metabolic Research}, doi = {https://doi.org/10.1016/j.coemr.2022.100385}, year = {2022}, language = {en} }