Myxofibrosarcoma of the thyroid gland

AbstractIntroductionMyxofibrosarcoma of the thyroid is exceptional: a Medline search found a single case report. We report a new case which raised diagnostic and therapeutic problems.ObservationWe report the case of a 74-year-old woman who presented with swelling of the left thyroid lobe and ipsilateral cervical lymphadenopathy. Total thyroidectomy with cervical lymph-node dissection was performed. Histological analysis diagnosed myxofibrosarcoma. Evolution was marked by rapid local recurrence, and chemotherapy based on doxorubicin and ifosfamide was introduced.Discussion/conclusionHead and neck myxofibrosarcoma is rare. MRI is essential and should always precede treatment. Diagnosis is histological. There is elevated risk of local recurrence after resection, accompanied by worsening tumor grade, whence the need for accurate diagnosis, appropriate treatment and regular MRI follow-up

Nullclines in the Oregonator model.

<p>We display the nullclines (logarithmic scale) of the activator species (blue curve) and the inhibitor (brown curve) for the Oregonator model in the deterministic limit for the parameter set given in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0042508#pone-0042508-t006" target="_blank">Table 6</a> and . We assume that the intermediary species is in a steady-state equilibrium with and and ignore diffusion. The blue (brown) arrow illustrates the gradient in phase space of the activator (inhibitor) on either side of the nullcline and the unstable fix point is marked with . The system is in the unstable (oscillatory) regime. We plot an example trajectory (dashed curve) of a larger perturbation from the (linearly stable) trivial homogeneous state. Starting at point , the system enters a limit cycle in phase space.</p

Simulation parameters for the cell migration model.

<p>Simulation parameters for the cell migration model Eqs. (47) and (49)–(52) .</p

RMSE for the Ornstein-Uhlenbeck test problem.

<p>The root mean-square error of the simulation output is calculated relative to the analytic solution. Initially molecules are distributed at . Simulations are run with the FPT implementation (solid curve) and the FPE implementation (dashed curve). (left) Shown are results for (blue squares), (green circles) and (red triangles) subvolumes. We vary the drift field parameter over . (right) We display the RMSE for (blue squares), (green circles), and (red triangles), where is varied over .</p

Identification of symbolic species in the Oregonator model.

<p>Identification of the species in the FKN representation (after <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0042508#pone.0042508-Scott1" target="_blank">[6]</a>).</p

Simulation runtimes for the Oregonator model.

<p>Simulation runtimes for the Oregonator model of the BZ reaction Eqs. (7)–(14).</p

Formation of a spike spiral wave in the model.

<p>Shown are snap-shots of a spiral wave in the model Eqs. (18)–(26), initialized as shown in the top left panel, at in the deterministic simulation (bottom left) and in stochastic simulations for different scale factors (rightmost columns).</p

Transition probabilities on a cell-centered grid.

<p>The particle jumps to the neighboring grid cells with probabilities , , and . The probability to stay put is given by .</p

RMSE for the biased diffusion test problem.

<p>The root mean-square error of the simulation output is calculated relative to the analytic solution, with and at . The solid curves indicate results from simulations which were done with transition probabilities computed from the FPT. The dashed curves, in contrast, displays the RMSE from simulations based on a discretization of the FPE. In both cases, molecules are located in the center subvolume initially. (left) We display the RMSE as a function of for (blue squares), (green circles), and (red triangles) subvolumes. (right) Shown is the RMSE as a function of subvolume side length for (blue squares), (green circles), and (red triangles).</p

Simulation parameters for the model.

<p>Simulation parameters for the model Eqs. (18)–(26). With the length and number of grid cells given, we find for the subvolume size and can therefore convert the concentration base into the number of particles per subvolume .</p