A computational simulation of long-term synaptic potentiation inducing protocol processes with model of CA3 hippocampal microcircuit.

An experimental study of computational model of the CA3 region presents cog-nitive and behavioural functions the hippocampus. The main property of the CA3 region is plastic recurrent connectivity, where the connections allow it to behave as an auto-associative memory. The computer simulations showed that CA3 model performs efficient long-term synaptic potentiation (LTP) induction and high rate of sub-millisecond coincidence detection. Average frequency of the CA3 pyramidal cells model was substantially higher in simulations with LTP induction protocol than without the LTP. The entropy of pyramidal cells with LTP seemed to be significantly higher than without LTP induction protocol (p = 0.0001). There was depression of entropy, which was caused by an increase of forgetting coefficient in pyramidal cells simulations without LTP (R = -0.88, p = 0.0008), whereas such correlation did not appear in LTP simulation (p = 0.4458). Our model of CA3 hippocampal formation microcircuit biologically inspired lets you understand neurophysiologic data. (Folia Morphol 2018; 77, 2: 210-220).

Memory and forgetting processes with the firing neuron model.

The aim of this paper is to present a novel algorithm for learning and forgetting within a very simplified, biologically derived model of the neuron, called firing cell (FC). FC includes the properties: (a) delay and decay of postsynaptic potentials, (b) modification of internal weights due to propagation of postsynaptic potentials through the dendrite, (c) modification of properties of the analog weight memory for each input due to a pattern of long-term synaptic potentiation. The FC model could be used in one of the three forms: excitatory, inhibitory, or receptory (gan-glion cell). The computer simulations showed that FC precisely performs the time integration and coincidence detection for incoming spike trains on all inputs. Any modification of the initial values (internal parameters) or inputs patterns caused the following changes of the interspike intervals time series on the output, even for the 10 s or 20 s real time course simulations. It is the basic evidence that the FC model has chaotic dynamical properties. The second goal is the presentation of various nonlinear methods for analysis of a biological time series. (Folia Morphol 2018; 77, 2: 221-233).

The predictive role of interim PET after the first chemotherapy cycle and sequential evaluation of response to ABVD in Hodgkin's lymphoma patients-the Polish Lymphoma Research Group (PLRG) Observational Study.

BACKGROUND: Interim PET after two ABVD cycles (iPET2) predicts treatment outcome in classical Hodgkin's lymphoma. To test whether an earlier assessment of chemosensitivity would improve the prediction accuracy, we launched a prospective, multicenter observational study aimed at assessing the predictive value of iPET after one ABVD (iPET1) and the kinetics of response assessed by sequential PET scanning. PATIENTS AND METHODS: Consecutive patients with newly diagnosed classical Hodgkin's lymphoma underwent interim PET scan after one ABVD course (iPET1). PETs were interpreted according to the Deauville score (DS) as negative (-) (DS 1-3) and positive (+) (DS 4, 5). Patients with iPET1 DS 3-5 underwent iPET2. RESULTS: About 106 early (I-IIA) and 204 advanced (IIB-IV) patients were enrolled between January 2008 and October 2014. iPET1 was (-) in 87/106 (82%) or (+) in 19/106 (18%) of early, and (-) in 133/204 (65%) or (+) in 71/204 (35%) of advanced stage patients, respectively. Twenty-four patients were excluded from response analysis due to treatment escalation. After a median follow-up of 38.2 (3.2-90.2) months, 9/102 (9%) early and 43/184 (23%) advanced patients experienced a progression-free survival event. At 36 months, negative and positive predictive value for iPET1 were 94% and 41% (early) and 84% and 43% (advanced), respectively. The kinetics of PET response was assessed in 198 patients with both iPETs. All 116 patients with iPET1(-) remained iPET2(-) (fast responders), 41/82 with IPET1(+) became iPET2(-) (slow responders), and the remaining 41 stayed iPET2(+) (non-responders); progression-free survival at 36 months for fast, slow and non-responders was 0.88, 0.79 and 0.34, respectively. CONCLUSION: The optimal tool to predict ABVD outcome in HL remains iPET2 because it distinguishes responders, whatever their time to response, from non-responders. However, iPET1 identified fast responders with the best outcome and might guide early treatment de-escalation in both early and advanced-stage HL.