Comparing Knee Kinetics and Kinematics in Healthy Individuals and Those With Knee Osteoarthritis, With and Without Flat Feet

Individuals with knee osteoarthritis (KOA) and flat feet are more likely to experience increased pain and cartilage damage. This study aimed to investigate the knee kinetics, kinematics, pain, and physical function in individuals with moderate symptomatic KOA, in comparison to asymptomatic control participants. Thirty volunteers with moderate KOA (with flat feet n = 15, with normal feet n = 15) and 30 asymptomatic people (with flat feet n = 15, with normal feet n = 15) were evaluated. The knee adduction angular impulse, knee flexion moment, knee flexion angular impulse, and knee flexion angle were measured during level walking. The pain was assessed in patients with KOA. The study found that individuals with KOA had a significant increase in the knee adduction angular impulse compared with the asymptomatic people (P < .05). The KOA with flat feet group had significantly lower knee flexion moment, knee flexion angular impulse, and knee flexion angle values than the KOA with normal feet group (P < .05). Furthermore, the KOA with flat feet group had a higher pain score than the KOA with normal feet group. Individuals with osteoarthritis and flat feet had lower knee flexion moments which may indicate reduced knee force exerted through compensatory mechanisms. Despite this reduction, they reported significantly higher levels of pain compared with those without flat feet, a finding that warrants further investigation in future studies.

Exploring the dynamic interplay between learning and working memory within various cognitive contexts

Introduction

The intertwined relationship between reinforcement learning and working memory in the brain is a complex subject, widely studied across various domains in neuroscience. Research efforts have focused on identifying the specific brain areas responsible for these functions, understanding their contributions in accomplishing the related tasks, and exploring their adaptability under conditions such as cognitive impairment or aging.

Methods

Numerous models have been introduced to formulate either these two subsystems of reinforcement learning and working memory separately or their combination and relationship in executing cognitive tasks. This study adopts the RLWM model as a computational framework to analyze the behavioral parameters of subjects with varying cognitive abilities due to age or cognitive status. A related RLWM task is employed to assess a group of subjects across different age groups and cognitive abilities, as measured by the Montreal Cognitive Assessment tool (MoCA).

Results

Analysis reveals a decline in overall performance accuracy and speed with differing age groups (young vs. middle-aged). Significant differences are observed in model parameters such as learning rate, WM decay, and decision noise. Furthermore, among the middle-aged group, distinctions emerge between subjects categorized as normal vs. MCI based on MoCA scores, notably in speed, performance accuracy, and decision noise.

Momentum-independent magnetic excitation continuum in the honeycomb iridate H3LiIr2O6

Understanding the interplay between the inherent disorder and the correlated fluctuating-spin ground state is a key element in the search for quantum spin liquids. H3LiIr2O6 is considered to be a spin liquid that is proximate to the Kitaev-limit quantum spin liquid. Its ground state shows no magnetic order or spin freezing as expected for the spin liquid state. However, hydrogen zero-point motion and stacking faults are known to be present. The resulting bond disorder has been invoked to explain the existence of unexpected low-energy spin excitations, although data interpretation remains challenging. Here, we use resonant X-ray spectroscopies to map the collective excitations in H3LiIr2O6 and characterize its magnetic state. In the low-temperature correlated state, we reveal a broad bandwidth of magnetic excitations. The central energy and the high-energy tail of the continuum are consistent with expectations for dominant ferromagnetic Kitaev interactions between dynamically fluctuating spins. Furthermore, the absence of a momentum dependence to these excitations are consistent with disorder-induced broken translational invariance. Our low-energy data and the energy and width of the crystal field excitations support an interpretation of H3LiIr2O6 as a disordered topological spin liquid in close proximity to bond-disordered versions of the Kitaev quantum spin liquid.

Dysconnection and cognition in schizophrenia: A spectral dynamic causal modeling study

Schizophrenia (SZ) is a severe mental disorder characterized by failure of functional integration (aka dysconnection) across the brain. Recent functional connectivity (FC) studies have adopted functional parcellations to define subnetworks of large-scale networks, and to characterize the (dys)connection between them, in normal and clinical populations. While FC examines statistical dependencies between observations, model-based effective connectivity (EC) can disclose the causal influences that underwrite the observed dependencies. In this study, we investigated resting state EC within seven large-scale networks, in 66 SZ and 74 healthy subjects from a public dataset. The results showed that a remarkable 33% of the effective connections (among subnetworks) of the cognitive control network had been pathologically modulated in SZ. Further dysconnection was identified within the visual, default mode and sensorimotor networks of SZ subjects, with 24%, 20%, and 11% aberrant couplings. Overall, the proportion of discriminative connections was remarkably larger in EC (24%) than FC (1%) analysis. Subsequently, to study the neural correlates of impaired cognition in SZ, we conducted a canonical correlation analysis between the EC parameters and the cognitive scores of the patients. As such, the self-inhibitions of supplementary motor area and paracentral lobule (in the sensorimotor network) and the excitatory connection from parahippocampal gyrus to inferior temporal gyrus (in the cognitive control network) were significantly correlated with the social cognition, reasoning/problem solving and working memory capabilities of the patients. Future research can investigate the potential of whole-brain EC as a biomarker for diagnosis of brain disorders and for neuroimaging-based cognitive assessment.

A biological and a mathematical model of SLE treated by mesenchymal stem cells covering all the stages of the disease

In this study, we proposed a biological model explaining the progress of autoimmune activation along different stages of systemic lupus erythematosus (SLE). For any upcoming stage of SLE, any new component is introduced, when it is added to the model. Particularly, the interaction of mesenchymal stem cells, with the components of the model, is specified in a way that both the inflammatory and anti-inflammatory functions of these cells would be covered. The biological model is then recapitulated to a model with less complexity that explains the main features of the problem. Later, a 7th-order mathematical model for SLE is proposed, based on this simplified model. Finally, the range of validity of the proposed mathematical model was assessed. For this purpose, we simulated the model and analyzed the simulation results in case of some known behaviors of the disease, such as tolerance breach, the appearance of systemic inflammation, development of clinical signs, and occurrence of flares and improvements. The model was able to reproduce these events, qualitatively.

Virtual Reality Exergaming Capability to Change Muscle Strategy During the Limits of Stability Test and Reduce Fear of Falling in Primary Osteoporotic Women

Objective: Muscle strength and balance impairment change the control strategy and increase the probability of falling. This study aimed to investigate the effect of 6-week strength-balance training through virtual reality exergaming (VRE) on muscle strategy during the limits of stability (LOS) test, fear of falling, and quality of life (QOL) in osteoporotic women. Materials and Methods: Twenty volunteer postmenopausal women with osteoporosis were randomly allocated to the VRE (n = 10) and traditional training (TRT as control, n = 10) groups. The VRE and TRT strength-balance training was performed for 6 weeks and three sessions per week. Before and after exercise, the muscle activity (onset time, peak root means square [PRMS]) and hip/ankle activity ratio were assessed by the wireless electromyography system. The muscle activities of the dominant leg were recorded during LOS functional test. The fall efficacy scale and QOL were assessed. Paired t-test was used to compare results within groups, and an independent t-test was used to compare the percentage changes in parameters between the two groups. Results: The VRE improved the onset time and PRMS. The VRE significantly reduced the hip/ankle activity ratio in the LOS test's forward, backward, and right directions (P < 0.05). No significant change was seen in all directions of the LOS functional test in the TRT group (P > 0.05). VRE reduced the fall efficacy scale (P = 0.042). Both VRT and TRT improved the total QOL score (P = 0.010). Conclusion: VRE was more effective in decreasing the onset time and hip/ankle ratio of muscle activation. The VRE is recommended to induce a better ability to reduce the fear of falling and control balance during functional activity in osteoporotic women. Clinical Trial Registration number: IRCT20101017004952N9.

The simultaneous changes in motor performance and EEG patterns in beta band during learning dart throwing skill in dominant and non-dominant hand

Background: Although changes in performance during the learning of various sports skills have been studied, however, how these changes at the brain level is still unknown. The aim of this study was to investigate simultaneous changes in motor performance and EEG patterns in beta band during learning dart throwing skill in dominant and non-dominant hand. Methodology: The samples consisted of 14 non-athlete students with an average age of 23 ± 2.5, which were divided into two group dominant hand (7) and non-dominant hand (7). Repeated measures ANOVA were used to measure data at the execution level and changes in EEG activity. Results: The results of this study at the performance level showed a significant reduction in the absolute error of dart throwing and at the same time at the brain level increased EEG activity in frontal and parietal-posterior regions along with decreased central area activity in acquisition and retention stages in both groups (P<.05). Also, there was a significant difference between the activity of EEG pattern in the dominant and non-dominant hand groups except for two channels AF3 and PO4 (P<.05). Conclusion: In general, the results of this study showed that along with relatively constant changes in performance during dart skill learning, relatively constant changes in EEG activity pattern occur, so that the concept of motor learning is also visible at the brain level. Also, the results of this study supported the existence of the different motor program for dominant and non-dominant hand control in the conditions of bilateral transfer control.

Human balance control in 3D running based on virtual pivot point concept

Balance control is one of the crucial challenges in bipedal locomotion. Humans need to maintain their trunk upright while the body behaves like an inverted pendulum which is inherently unstable. Instead, the virtual pivot point (VPP) concept introduced a new virtual pendulum model to the human balance control paradigm by analyzing the ground reaction forces (GRF) in the body coordinate frame. This paper presents novel VPP-based analyses of the postural stability of human running in a 3D space. We demonstrate the relation between the VPP position and the gait speed. The experimental results suggest different control strategies in frontal and sagittal planes. The ground reaction forces intersect below the center of mass in the sagittal plane and above the center of mass in the frontal plane. These VPP locations are found for the sagittal and frontal planes at all running speeds, respectively. We introduced a 3D VPP-based model which can replicate the kinematic and kinetic behavior of human running. The similarity between the experimental and simulation results indicates the ability of the VPP concept in predicting human balance control in running and can support its applicability for gait assistance.

The Critical Modulatory Role of Spiny Stellate Cells in Seizure Onset Based on Dynamic Analysis of a Neural Mass Model

Growing evidence suggests that excitatory neurons in the brain play a significant role in seizure generation. Nonetheless, spiny stellate cells are cortical excitatory non-pyramidal neurons in the brain, whose basic role in seizure occurrence is not well understood. In the present research, we study the critical role of spiny stellate cells or the excitatory interneurons (EI), for the first time, in epileptic seizure generation using an extended neural mass model inspired by a thalamocortical model originally introduced by another research group. Applying bifurcation analysis on this modified model, we investigated the rich dynamics corresponding to the epileptic seizure onset and transition between interictal and ictal states caused by EI connectivity to other cell types. Our results indicate that the transition between interictal and ictal states (preictal signal) corresponds to a supercritical Hopf bifurcation, and thus, the extended model suggests that before seizure onset, the amplitude and frequency of neural activities gradually increase. Moreover, we showed that (1) the altered function of GABAergic and glutamatergic receptors of EI can cause seizure, and (2) the pathway between the thalamic relay nucleus and EI facilitates the transition from interictal to ictal activity by decreasing the preictal period. Thereafter, we considered both sensory and cortical periodic inputs to study model responses to various harmonic stimulations. Bifurcation analysis of the model, in this case, suggests that the initial state of the model might be the main cause for the transition between interictal and ictal states as the stimulus frequency changes. The extended thalamocortical model shows also that the amplitude jump phenomenon and non-linear resonance behavior result from the preictal state of the modified model. These results can be considered as a step forward to a deeper understanding of the mechanisms underlying the transition from normal activities to epileptic activities.

From a biological template model to gait assistance with an exosuit

The invention of soft wearable assistive devices, known as exosuits, introduced a new aspect in assisting unimpaired subjects. In this study, we designed and developed an exosuit with compliant biarticular thigh actuators called BATEX. Unlike the conventional method of using rigid actuators in exosuits, the BATEX is made of serial elastic actuators (SEA) resembling artificial muscles. This bioinspired design is complemented by the novel control concept of using the ground reaction force to adjust the artificial muscles' stiffness in the stance phase. By locking the motors in the swing phase, the SEAs will be simplified to passive biarticular springs, which is sufficient for leg swinging. The key concept in our design and control approach is to synthesize human locomotion to develop an assistive device instead of copying human motor control outputs. Analyzing human walking assistance using experiment-based OpenSim simulations demonstrates the advantages of the proposed design and control of BATEX, such as 9.4% reduction in metabolic cost during normal walking condition. This metabolic reduction increases to 10.4% when the subjects carry a 38 kg load. The adaptability of our proposed model-based control to such an unknown condition outperforms the assistance level of the model-free optimal controller. Moreover, increasing the assistive system's efficiency by adjusting the actuator compliance with the force feedback supports our previous findings on the LOPES II exoskeleton.

Quantitative analysis of taste disorder in COVID-19 patients, the hypersensitivity to salty quality

Recently, many of the studies have illustrated that the new pandemic SARS-CoV-2 can affect Central Nervous System through the olfactory bulb. In addition to investigating anosmia or hyposmia induced by this virus, a quantitative analysis was needed to clarify the taste and smell disorder of the new coronavirus. The four basic taste quality with five concentrations for sweet, sour, bitter, and salty were administered to 75 subjects divided into three groups: COVID-19 patients with taste disorder, COVID-19 patients without taste disorder, and control group.

The results indicated the increment of sweet (2.68 ± 0.14), sour (3.34 ± 0.12) and bitter (3.39 ± 0.2) thresholds in COVID-19 patients with taste disorder in comparison with patients without taste disorder that the threshold were: 2 ± 0.16, 2.11 ± 0.2 and 2.55 ± 0.5 for sweet, sour, and bitter respectively. On the other hand, the patients inversely showed a significant decrease in the salty taste threshold (0.51 ± 0.03) compared to COVID-19 positive control groups (1.11 ± 0.11).

Additionally, despite taste disorder in almost all of the patients with smell deficiency, only 30% of cases with taste disorder reported smell deficiency. It may be concluded that some of the taste disorders in patients with COVID-19 disorder could be associated with taste receptors dysfunction or the spread of infection to the cranial nerves responsible for the conduction of tastes sensation.

Deep Temporal Organization of fMRI Phase Synchrony Modes Promotes Large-Scale Disconnection in Schizophrenia

Itinerant dynamics of the brain generates transient and recurrent spatiotemporal patterns in neuroimaging data. Characterizing metastable functional connectivity (FC) - particularly at rest and using functional magnetic resonance imaging (fMRI) - has shaped the field of dynamic functional connectivity (DFC). Mainstream DFC research relies on (sliding window) correlations to identify recurrent FC patterns. Recently, functional relevance of the instantaneous phase synchrony (IPS) of fMRI signals has been revealed using imaging studies and computational models. In the present paper, we identify the repertoire of whole-brain inter-network IPS states at rest. Moreover, we uncover a hierarchy in the temporal organization of IPS modes. We hypothesize that connectivity disorder in schizophrenia (SZ) is related to the (deep) temporal arrangement of large-scale IPS modes. Hence, we analyze resting-state fMRI data from 68 healthy controls (HC) and 51 SZ patients. Seven resting-state networks (and their sub-components) are identified using spatial independent component analysis. IPS is computed between subject-specific network time courses, using analytic signals. The resultant phase coupling patterns, across time and subjects, are clustered into eight IPS states. Statistical tests show that the relative expression and mean lifetime of certain IPS states have been altered in SZ. Namely, patients spend (45%) less time in a globally coherent state and a subcortical-centered state, and (40%) more time in states reflecting anticoupling within the cognitive control network, compared to the HC. Moreover, the transition profile (between states) reveals a deep temporal structure, shaping two metastates with distinct phase synchrony profiles. A metastate is a collection of states such that within-metastate transitions are more probable than across. Remarkably, metastate occupation balance is altered in SZ, in favor of the less synchronous metastate that promotes disconnection across networks. Furthermore, the trajectory of IPS patterns is less efficient, less smooth, and more restricted in SZ subjects, compared to the HC. Finally, a regression analysis confirms the diagnostic value of the defined IPS measures for SZ identification, highlighting the distinctive role of metastate proportion. Our results suggest that the proposed IPS features may be used for classification studies and for characterizing phase synchrony modes in other (clinical) populations.

How does the CNS control arm reaching movements? Introducing a hierarchical nonlinear predictive control organization based on the idea of muscle synergies

In this study, we introduce a hierarchical and modular computational model to explain how the CNS (Central Nervous System) controls arm reaching movement (ARM) in the frontal plane and under different conditions. The proposed hierarchical organization was established at three levels: 1) motor planning, 2) command production, and 3) motor execution. Since in this work we are not discussing motion learning, no learning procedure was considered in the model. Previous models mainly assume that the motor planning level produces the desired trajectories of the joints and feeds it to the next level to be tracked. In the proposed model, the motion control is described based on a regulatory control policy, that is, the output of the motor planning level is a step function defining the initial and final desired position of the hand. For the command production level, a nonlinear predictive model was developed to explain how the time-invariant muscle synergies (MSs) are recruited. We used the same computational model to explain the arm reaching motion for a combined ARM task. The combined ARM is defined as two successive ARM such that it starts from point A and reaches to point C via point B. To develop the model, kinematic and kinetic data from six subjects were recorded and analyzed during ARM task performance. The subjects used a robotic manipulator while moving their hand in the frontal plane. The EMG data of 15 muscles were also recorded. The MSs used in the model were extracted from the recorded EMG data. The proposed model explains two aspects of the motor control system by a novel computational approach: 1) the CNS reduces the dimension of the control space using the notion of MSs and thereby, avoids immense computational loads; 2) at the level of motor planning, the CNS generates the desired position of the hand at the starting, via and the final points, and this amounts to a regulatory and non-tracking structure.

The Concept of Transmission Coefficient Among Different Cerebellar Layers: A Computational Tool for Analyzing Motor Learning

High-fidelity regulation of information transmission among cerebellar layers is mainly provided by synaptic plasticity. Therefore, determining the regulatory foundations of synaptic plasticity in the cerebellum and translating them to behavioral output are of great importance. To date, many experimental studies have been carried out in order to clarify the effect of synaptic defects, while targeting a specific signaling pathway in the cerebellar function. However, the contradictory results of these studies at the behavioral level further add to the ambiguity of the problem. Information transmission through firing rate changes in populations of interconnected neurons is one of the most widely accepted principles of neural coding. In this study, while considering the efficacy of synaptic interactions among the cerebellar layers, we propose a firing rate model to realize the concept of transmission coefficient. Thereafter, using a computational approach, we test the effect of different values of transmission coefficient on the gain adaptation of a cerebellar-dependent motor learning task. In conformity with the behavioral data, the proposed model can accurately predict that disruption in different forms of synaptic plasticity does not have the same effect on motor learning. Specifically, impairment in training mechanisms, like in the train-induced LTD in parallel fiber-Purkinje cell synapses, has a significant negative impact on all aspects of learning, including memory formation, transfer, and consolidation, although it does not disrupt basic motor performance. In this regard, the overinduction of parallel fiber-molecular layer interneuron LTP could not prevent motor learning impairment, despite its vital role in preserving the robustness of basic motor performance. In contrast, impairment in plasticity induced by interneurons and background activity of climbing fibers is partly compensable through overinduction of train-induced parallel fiber-Purkinje cell LTD. Additionally, blockade of climbing fiber signaling to the cerebellar cortex, referred to as olivary system lesion, shows the most destructive effect on both motor learning and basic motor performance. Overall, the obtained results from the proposed computational framework are used to provide a map from procedural motor memory formation in the cerebellum. Certainly, the generalization of this concept to other multi-layered networks of the brain requires more physiological and computational researches.

Postural instability and position of the center of pressure into the base of support in postmenopausal osteoporotic and nonosteoporotic women with and without hyperkyphosis

In postmenopausal women, thoracic hyperkyphosis affects postural instability in the sagittal plane, whereas osteoporosis affects it in the frontal plane. Decrease of hip muscle strength can be changed the center of pressure distance to the center of base of support. These results may be important to design the therapeutic exercise for decreasing the postural instability.

PURPOSE

In this study, we investigated the effect of bone mineral density (BMD) and thoracic kyphosis on the center of pressure (CoP) sway and its location related to the base of support (BoS).

METHODS

Ten young and 39 postmenopausal women voluntarily participated in this study. Postmenopausal women were divided into four groups according to the thoracic kyphosis angle (normal kyphotic < 50° ≤ hyperkyphotic) and T-score values. The isometric strength of the trunk and lower limb muscles were measured. The CoP postural sway was measured in a comfortable double stance position, and the location of the CoP was then determined related to the BoS.

RESULTS

In both hyperkyphotic groups (osteoporotic and normal BMD), the strength of back extension and hip adduction showed a significant decrease compared to the normal kyphotic groups. In the osteoporotic groups (hyper- and normal kyphotic), hip abduction and ankle plantar flexion were significantly weaker than those in the nonosteoporotic groups. In both hyperkyphotic groups, velocity of the CoP displacement in the anterior-posterior (AP) direction was significantly higher than that in the young group, while, in both of the osteoporotic groups, velocity of the CoP displacement in the medio-lateral (ML) direction was significantly higher than that in the young group. In postmenopausal women, hip extensor strength negatively and significantly correlated with the CoP distance to the center of the BoS.

CONCLUSION

It appears that thoracic hyperkyphosis affects postural instability in the AP direction and that a decrease of BMD affects postural instability in the ML direction.

A modified particle swarm optimization algorithm for parameter estimation of a biological system

Background

Mathematical modeling has achieved a broad interest in the field of biology. These models represent the associations among the metabolism of the biological phenomenon with some mathematical equations such that the observed time course profile of the biological data fits the model. However, the estimation of the unknown parameters of the model is a challenging task. Many algorithms have been developed for parameter estimation, but none of them is entirely capable of finding the best solution. The purpose of this paper is to develop a method for precise estimation of parameters of a biological model.

Methods

In this paper, a novel particle swarm optimization algorithm based on a decomposition technique is developed. Then, its root mean square error is compared with simple particle swarm optimization, Iterative Unscented Kalman Filter and Simulated Annealing algorithms for two different simulation scenarios and a real data set related to the metabolism of CAD system.

Results

Our proposed algorithm results in 54.39% and 26.72% average reduction in root mean square error when applied to the simulation and experimental data, respectively.

Conclusion

The results show that the metaheuristic approaches such as the proposed method are very wise choices for finding the solution of nonlinear problems with many unknown parameters.

Formation of Opioid-Induced Memory and Its Prevention: A Computational Study

There are several experimental studies which suggest opioids consumption forms pathological memories in different brain regions. For example it has been empirically demonstrated that the theta rhythm which appears during chronic opioid consumption is correlated with the addiction memory formation. In this paper, we present a minimal computational model that shows how opioids can change firing patterns of the neurons during acute and chronic opioid consumption and also during withdrawal periods. The model consists of a pre- and post-synaptic neuronal circuits and the astrocyte that monitors the synapses. The output circuitry consists of inhibitory interneurons and excitatory pyramidal neurons. Our simulation results demonstrate that acute opioid consumption induces synchronous patterns in the beta frequency range, while, chronic opioid consumption provokes theta frequency oscillations. This allows us to infer that the theta rhythm appeared during chronic treatment can be an indication of brain engagement in opioid-induced memory formation. Our results also suggest that changing the inputs of the interneurons and the inhibitory neuronal network is not an appropriate method for preventing the formation of pathological memory. However, the same results suggest that prevention of pathological memory formation is possible by manipulating the input of the stimulatory network and the excitatory connections in the neuronal network. They also show that during withdrawal periods, firing rate is reduced and random fluctuations are generated in the modeled neural network. The random fluctuations disappear and synchronized patterns emerge when the activities of the astrocytic transporters are decreased. These results suggest that formation of the synchronized activities can be correlated with the relapse. Our model also predicts that reduction in gliotransmitter release can eliminate the synchrony and thereby it can reduce the likelihood of the relapse occurrence.

Assessing the Effects of Opioids on Pathological Memory by a Computational Model

Introduction:

Opioids hijack learning and memory formation mechanisms of brain and induce a pathological memory in the hippocampus. This effect is mainly mediated by modifications in glutamatergic system. Speaking more precisely, Opioids presence in a synapse inhibits blockage of N-Methyl-D-Aspartate Receptor (NMDAR) by Mg²⁺, enhances conductance of NMDAR and thus, induces false Long-Term Potentiation (LTP).

Methods:

Based on experimental observations of different researchers, we developed a mathematical model for a pyramidal neuron of the hippocampus to study this false LTP. The model contains a spine of the pyramidal neuron with NMDAR, α-Amino-3-hydroxy-5-Methyl-4-isoxazole Propionic Acid Receptors (AMPARs), and Voltage-Gated Calcium Channels (VGCCs). The model also describes Calmodulin-dependent protein Kinase II (CaMKII) and AMPAR phosphorylation processes which are assumed to be the indicators of LTP induction in the synapse.

Results:

Simulation results indicate that the effect of inhibition of blockage of NMDARs by Mg²⁺ on the false LTP is not as crucial as the effect of NMDAR’s conductance modification by opioids. We also observed that activation of VGCCs has a dominant role in inducing pathological LTP.

Conclusion:

Our results confirm that preventing this pathological LTP is possible by three different mechanisms: 1. By decreasing NMDAR’s conductance; and 2. By attenuating VGCC’s mediated current; and 3. By enhancing glutamate clearance rate from the synapse.

Attractor controllability of Boolean networks by flipping a subset of their nodes

The controllability analysis of Boolean networks (BNs), as models of biomolecular regulatory networks, has drawn the attention of researchers in recent years. In this paper, we aim at governing the steady-state behavior of BNs using an intervention method which can easily be applied to most real system, which can be modeled as BNs, particularly to biomolecular regulatory networks. To this end, we introduce the concept of attractor controllability of a BN by flipping a subset of its nodes, as the possibility of making a BN converge from any of its attractors to any other one, by one-time flipping members of a subset of BN nodes. Our approach is based on the algebraic state-space representation of BNs using semi-tensor product of matrices. After introducing some new matrix tools, we use them to derive necessary and sufficient conditions for the attractor controllability of BNs. A forward search algorithm is then suggested to identify the minimal perturbation set for attractor controllability of a BN. Next, a lower bound is derived for the cardinality of this set. Two new indices are also proposed for quantifying the attractor controllability of a BN and the influence of each network variable on the attractor controllability of the network and the relationship between them is revealed. Finally, we confirm the efficiency of the proposed approach by applying it to the BN models of some real biomolecular networks.

Computational modeling of opioid-induced synaptic plasticity in hippocampus

According to a broad range of research, opioids consumption can lead to pathological memory formation. Experimental observations suggested that hippocampal glutamatergic synapses play an indispensable role in forming such a pathological memory. It has been suggested that memory formation at the synaptic level is developed through LTP induction. Here, we attempt to computationally indicate how morphine induces pathological LTP at hippocampal CA3-CA1 synapses. Then, based on simulations, we will suggest how one can prevent this type of pathological LTP. To this purpose, a detailed computational model is presented, which consists of one pyramidal neuron and one interneuron both from CA3, one CA1 pyramidal neuron, and one astrocyte. Based on experimental findings morphine affects the hippocampal neurons in three primary ways: 1) disinhibitory mechanism of interneurons in CA3, 2) enhancement of NMDARs current by μ Opioid Receptor (μOR) activation and 3) by attenuation of astrocytic glutamate reuptake ability. By utilizing these effects, simulations were implemented. Our results indicate that morphine can induce LTP by all aforementioned possible mechanisms. Based on our simulation results, attenuation of pathologic LTP achieved mainly by stimulation of astrocytic glutamate transporters, down-regulation of the astrocytic metabotropic glutamate receptors (mGlurs) or by applying NMDAR’s antagonist. Based on our observations, we suggest that astrocyte has a dominant role in forming addiction-related memories. This finding may help researchers in exploring drug actions for preventing relapse.

An extended mathematical model of tumor growth and its interaction with the immune system, to be used for developing an optimized immunotherapy treatment protocol

BACKGROUND

Chemotherapy is usually known as the main modality for cancer treatment. Nevertheless, most of chronic cancers could not be treated with chemotherapy alone. Immunotherapy is a new modality for cancer treatment that is effective for early stages of cancer and it has fewer side effects compared to chemotherapy, specifically for those types of cancer that are resistant to it.

METHOD

This work presents an extended mathematical model to depict interactions between cancerous and adaptive immune system in mouse. We called the model an extended model, because we embedded all those compartments that have important roles in response to tumor in one model. The model includes tumor cells, natural killers, naïve and mature cytotoxic T cells, naïve and mature helper T cells, regulatory T cells, dendritic cells and interleukin 2 cytokine. Whole cycle of cell division program of immune cells is also considered in the model. We also optimized protocol of immunotherapy with DC vaccine based on the proposed mathematical model.

RESULT

Simulation results of the proposed model are in conformity with the experimental data recorded from mouse in immunology department of Tehran University of Medical Science as well as what has been explained in the literature. Our results explain dynamics of the immune cells from the first day of cancer growth and progression. Simulation result shows that reducing intervals between immunotherapy injections, efficacy of the treatment will be increased because CD8+ cells are boosted more rapidly. Optimized protocol for immunotherapy suggests that if the effect of DC vaccines on increasing number of anti-tumor immune cells be just before the maximum number of CD8+ cells, the effect of treatment will be maximized.

Designing a deep brain stimulator to suppress pathological neuronal synchrony

Some of neuropathologies are believed to be related to abnormal synchronization of neurons. In the line of therapy, designing effective deep brain stimulators to suppress the pathological synchrony among neuronal ensembles is a challenge of high clinical relevance. The stimulation should be able to disrupt the synchrony in the presence of latencies due to imperfect knowledge about parameters of a neuronal ensemble and stimulation impacts on the ensemble. We propose an adaptive desynchronizing deep brain stimulator capable of dealing with these uncertainties. We analyze the collective behavior of the stimulated neuronal ensemble and show that, using the designed stimulator, the resulting asynchronous state is stable. Simulation results reveal the efficiency of the proposed technique.

Method to classify elderly subjects as fallers and non-fallers based on gait energy image

Falls are one of the leading causes of injuries among the elderly. Therefore, distinguishing fallers and performing preventive actions is vitally important. A new variation of the gait energy image (GEI) called coloured gait energy image (CGEI) is proposed for classifying subjects as fallers and non-fallers and for visualising their gait patterns. Eight elderly fallers, eight elderly non-fallers and eight young subjects performed timed up and go (TUG) test, which is one of the well-known clinical tools for fall risk assessment and contains two gait sequences. Subjects were also asked to perform two other variations of the TUG test, namely TUG with manual load and TUG with cognitive load. Gait sequences were extracted from the TUG test based on the opinion of three human observers. Then the gait cycles were automatically extracted from the walking sequence and divided into three phases, corresponding to double support and first and second half of single support. Next, the GEI of each phase was generated and formed one of the colour components of CGEI. Histogram-based features obtained from CGEI were then used to classify the video collected from walking sequences of elderly fallers and non-fallers. Correct classification rate was improved by approximately 27% compared with the standard TUG test.

The study of the variability of anticipatory postural adjustments in patients with recurrent non-specific low back pain

BACKGROUND AND OBJECTIVES

Intrinsic variability is present in all actions, including repetitive tasks. The aim of this study was to evaluate the variability of anticipatory postural adjustments (APAs) of trunk muscles in participants with low back pain (LBP).

MATERIAL AND METHOD

The study included 21 participants with recurrent non-specific LBP (15 men, 6 women) and 21 healthy volunteers. Standard deviation of electromyographic activity of the external oblique (EO), transverse abdominis/internal oblique (TrA/IO), and erector spinae (ES) muscles onset relative to deltoid muscle onset was recorded in 75 rapid arm flexions, and the correlation with the participants' avoidance belief (the FABQ score) and disability (the Roland-Morris Questionnaire score) was statistically analyzed.

RESULTS

participants with LBP exhibited less variability in timing of APAs of the TrA/IO muscle compared with the control group (P=0.047). The timing of APAs of the TrA/IO muscle was significantly correlated with the FABQ score (P=0.006). There was no significant correlation between this variable and disability (P=0.09). Decrease in variability of the timing of APA of the EO (P=0.45) and ES (P=0.6) muscles was not significant.

CONCLUSION

The variability of the postural responses of participants with LBP decreased. Restoring variability in postural control responses might be a goal in rehabilitating these patients.

A mathematical model of immune activation with a unified self-nonself concept

The adaptive immune system reacts against pathogenic nonself, whereas it normally remains tolerant to self. The initiation of an immune response requires a critical antigen(Ag)-stimulation and a critical number of Ag-specific T cells. Autoreactive T cells are not completely deleted by thymic selection and partially present in the periphery of healthy individuals that respond in certain physiological conditions. A number of experimental and theoretical models are based on the concept that structural differences discriminate self from nonself. In this article, we establish a mathematical model for immune activation in which self and nonself are not distinguished. The model considers the dynamic interplay of conventional T cells, regulatory T cells (Tregs), and IL-2 molecules and shows that the renewal rate ratio of resting Tregs to naïve T cells as well as the proliferation rate of activated T cells determine the probability of immune stimulation. The actual initiation of an immune response, however, relies on the absolute renewal rate of naïve T cells. This result suggests that thymic selection reduces the probability of autoimmunity by increasing the Ag-stimulation threshold of self reaction which is established by selection of a low number of low-avidity autoreactive T cells balanced with a proper number of Tregs. The stability analysis of the ordinary differential equation model reveals three different possible immune reactions depending on critical levels of Ag-stimulation: a subcritical stimulation, a threshold stimulation inducing a proper immune response, and an overcritical stimulation leading to chronic co-existence of Ag and immune activity. The model exhibits oscillatory solutions in the case of persistent but moderate Ag-stimulation, while the system returns to the homeostatic state upon Ag clearance. In this unifying concept, self and nonself appear as a result of shifted Ag-stimulation thresholds which delineate these three regimes of immune activation.

Trajectory of human movement during sit to stand: a new modeling approach based on movement decomposition and multi-phase cost function

The purpose of this work is to develop a computational model to describe the task of sit to stand (STS). STS is an important movement skill which is frequently performed in human daily activities, but has rarely been studied from the perspective of optimization principles. In this study, we compared the recorded trajectories of STS with the trajectories generated by several conventional optimization-based models (i.e., minimum torque, minimum torque change and kinetic energy cost models) and also with the trajectories produced by a novel multi-phase cost model (MPCM). In the MPCM, we suggested that any complex task, such as STS, is decomposable into successive motion phases, so that each phase requires a distinct strategy to be performed. In this way, we proposed a multi-phase cost function to describe the STS task. The results revealed that the conventional optimization-based models failed to correctly predict the invariable features of STS, such as hip flexion and ankle dorsiflexion movements. However, the MPCM not only predicted the general features of STS with a sufficient accuracy, but also showed a potential flexibility to distinguish between the movement strategies from one subject to the other. According to the results, it seems plausible to hypothesize that the central nervous system might apply different strategies when planning different phases of a complex task. The application areas of the proposed model could be generating optimized trajectories of STS for clinical applications (such as functional electrical stimulation) or providing clinical and engineering insights to develop more efficient rehabilitation devices and protocols.

A phase plane analysis of neuron-astrocyte interactions

Intensive experimental studies have shown that astrocytes are active partners in modulation of synaptic transmission. In the present research, we study neuron-astrocyte signaling using a biologically inspired model of one neuron synapsing one astrocyte. In this model, the firing dynamics of the neuron is described by the Morris-Lecar model and the Ca(2+) dynamics of a single astrocyte explained by a functional model introduced by Postnov and colleagues. Using the coupled neuron-astrocyte model and based on the results of the phase plane analyses, it is demonstrated that the astrocyte is able to activate the silent neuron or change the neuron spiking frequency through bidirectional communication. This suggests that astrocyte feedback signaling is capable of modulating spike transmission frequency by changing neuron spiking frequency. This effect is described by a saddle-node on invariant circle bifurcation in the coupled neuron-astrocyte model. In this way, our results suggest that the neuron-astrocyte crosstalk has a fundamental role in producing diverse neuronal activities and therefore enhances the information processing capabilities of the brain.

Abstract P2-12-10: Psycho-spiritual therapy for improving the quality of life and spiritual well-being of women with breast cancer

Objective: Psychological distress and morbidity are common consequences of diagnosis and treatment of breast cancer and associated with poor quality of life (QOL). Among several approaches, spirituality has been shown to be significantly associated with improving the quality of life of these patients. The aim of this study was to assess the role of psycho-spiritual therapy intervention in improving the quality of life and spiritual well-being of patients with breast cancer.

Methods: This was a randomized controlled trial study which was conducted in the Breast Cancer Research Center, St. S. Al-Shohada hospital, Isfahan, Iran. Sixty-eight patients with breast cancer were randomized to either psycho-spiritual therapy intervention group or control group who received routine management and educational programs. Before and after 6 weeks of psycho-spiritual therapy sessions, the quality of life was evaluated using Cancer quality-of-life questionnaire (QLQ-C30) and its supplementary breast cancer questionnaire (QLQ-BR23) and Spiritual well-being was measured using the Functional Assessment of Chronic Illness Therapy Spiritual Well-being scale (FACIT-Sp12). Multivariate, repeated-measures ANOVA, T-test and Paired T-test were used for analysis using Predictive Analytic Soft Ware (PASW, version 18) for windows.

Results: In all sixty five patients actually completed the six-week intervention and were evaluated for the outcomes. The mean Global health status score/QOL reached from 44.37 (SD: 13.03) to 68.63 (SD: 10.86), (p &lt; 0.001), (Cohen's d effect size: 2.16, 95% CI: 1.56–2.78). There was a statistically significant difference in all functional scales of QLQ-C30 (p &lt; 0.05) as well as spiritual well-being after intervention. (p = 0.02)

Conclusions: The results of this trial showed that the psycho-spiritual therapy program can improve the spiritual well-being and QOL of women with breast cancer. This program, with its ease of implementation and home practice, could be adopted in comprehensive care programs for women with breast cancer.

Citation Information: Cancer Res 2012;72(24 Suppl):Abstract nr P2-12-10.

Tetracyclines: drugs with huge therapeutic potential

Tetracyclines are an amazing class of chemical agents with multiple therapeutic potential. Structural modification of the original natural tetracyclines led to the synthesis and development of doxycycline and minocycline, compounds with higher lipophilicity, better oral pharmacokinetics and higher potency. Due to diverse pharmacological properties, these drugs are now under extensive investigation for use in the treatment of various disparate diseases. In recent years, several studies have conclusively reported anti-inflammatory, immune-modulating and neuroprotective effects of these compounds. There are currently over 200 ongoing clinical trials on tetracyclines. These studies extend over a wide range of diseases including dermatological diseases, behavior and mental disorders, immune system disorders, cardiovascular diseases, and cancer. In this review we will discuss the chemistry and pharmacology of these agents, and describe how their inhibitory effect on matrix metalloproteinase and on pro-inflammatory cytokines has kindled renewed interest in them. Based on the reports from pre-clinical and clinical trials, the therapeutic potential and application of tetracyclines may well be redefined and extensively extended.

Functional contributions of astrocytes in synchronization of a neuronal network model

In the present study, a biologically plausible neuronal population model is developed, which considers functional outcome of neuron-astrocyte interactions. Based on established neurophysiologic findings, astrocytes dynamically regulate the synaptic transmission of neuronal networks. The employed structure is based on the main physiological and anatomical features of the CA1 subfield of the hippocampus. Utilizing our model, we demonstrate that healthy astrocytes provide appropriate feedback control in regulating neural activity. In this way, the astrocytes compensate the increase of excitation coupling strength among neurons and stabilize the normal level of synchronized behavior. Next, malfunction of astrocytes in the regulatory feedback loop is investigated. In this way, pathologic astrocytes are no longer able to regulate and/or compensate the excessive increase of the excitation level. Consequently, disruption of astrocyte signaling initiates hypersynchronous firing of neurons. Our results suggest that diminishing of neuron-astrocyte cross-talk leads to an abnormal synchronized neuronal firing, which suggests that astrocytes could be a proximal target for the treatment of related disorders including epilepsy.

On the role of astrocytes in synchronization of two coupled neurons: a mathematical perspective

Based on recent findings, astrocytes, a subtype of glial cells, dynamically regulate the synaptic transmission of neuronal networks. In this research, a biologically inspired neuronal network model is constructed by connecting two Morris-Lecar neuron models. In this minimal network model, neuron-astrocyte interactions are considered in a functional-based procedure. Utilizing the developed model and according to the theoretical analysis carried out in the article, it is confirmed that, the astrocyte increases the threshold value of synchronization and provides appropriate feedback control in regulating the neural activities. Therefore, the healthy astrocyte has the potential to desynchronize the synchrony between two coupled neurons. Next, we investigate malfunction of the astrocyte in the regulatory feedback loop. Mathematically, we verify that pathologic astrocyte is no longer able to increase the synchronization threshold and therefore, it cannot compensate excessive increase in the excitation level. The main reason behind this is the fact that healthy astrocyte can optimally increase the input current of the individual neurons, while the so-called pathological astrocyte is unable to modify correctly the amount of this current. Consequently, disruptions of the signaling function of astrocyte initiate the hypersynchronous firing of neurons. In other words, reduction in neuron-astrocyte cross-talk will lead to synchronized firing of neurons. Therefore, our results propose that the astrocyte could have a key role in stabilizing neural activity.

MODEM: a multi-agent hierarchical structure to model the human motor control system

In this study, based on behavioral and neurophysiological facts, a new hierarchical multi-agent architecture is proposed to model the human motor control system. Performance of the proposed structure is investigated by simulating the control of sit to stand movement. To develop the model, concepts of mixture of experts, modular structure, and some aspects of equilibrium point hypothesis were brought together. We have called this architecture MODularized Experts Model (MODEM). Human motor system is modeled at the joint torque level and the role of the muscles has been embedded in the function of the joint compliance characteristics. The input to the motor system, i.e., the central command, is the reciprocal command. At the lower level, there are several experts to generate the central command to control the task according to the details of the movement. The number of experts depends on the task to be performed. At the higher level, a "gate selector" block selects the suitable subordinate expert considering the context of the task. Each expert consists of a main controller and a predictor as well as several auxiliary modules. The main controller of an expert learns to control the performance of a given task by generating appropriate central commands under given conditions and/or constraints. The auxiliary modules of this expert learn to scrutinize the generated central command by the main controller. Auxiliary modules increase their intervention to correct the central command if the movement error is increased due to an external disturbance. Each auxiliary module acts autonomously and can be interpreted as an agent. Each agent is responsible for one joint and, therefore, the number of the agents of each expert is equal to the number of joints. Our results indicate that this architecture is robust against external disturbances, signal-dependent noise in sensory information, and changes in the environment. We also discuss the neurophysiological and behavioral basis of the proposed model (MODEM).

Arsenic rich iron plaque on macrophyte roots--an ecotoxicological risk?

Arsenic is known to accumulate with iron plaque on macrophyte roots. Three to four years after the Aznalcóllar mine spill (Spain), residual arsenic contamination left in seasonal wetland habitats has been identified in this form by scanning electron microscopy. Total digestion has determined arsenic concentrations in thoroughly washed 'root+plaque' material in excess of 1000 mg kg(-1), and further analysis using X-ray absorption spectroscopy suggests arsenic exists as both arsenate and arsenite. Certain herbivorous species feed on rhizomes and bulbs of macrophytes in a wide range of global environments, and the ecotoxicological impact of consuming arsenic rich iron plaque associated with such food items remains to be quantified. Here, greylag geese which feed on Scirpus maritimus rhizome and bulb material in areas affected by the Aznalcóllar spill are shown to have elevated levels of arsenic in their feces, which may originate from arsenic rich iron plaque.

Type of cell death and the role of acetylcholinesterase activity in neurotoxicity induced by paraoxon in cultured rat hippocampal neurons

Organophosphate (Ops) neurotoxicity is attributed both to its well-known cholinergic and non-cholinergic effects. In the present study we compared enzymatic and morphologic changes in neurons exposed to paraoxon during one day and one week. The effect of exposure time is important in neurotoxicity of Ops. The longer the exposure time is the more damage is observed in neurons, although there are few investigations about the effect in the post-exposure period. Hippocampal cells were obtained from rat neonates and cultured in Neurobasal/B27. Paraoxon at 50 and 100 microM were added. Inverted microscope and electron microscope were used to study cell morphology and Neutral Red staining was used to measure viability. We also assayed caspase-3 and (acetylcholinesterase) AChE activity. Hoechst staining was utilized to determine the type of cell death. Culture medium was replaced after 24 h in one-day group, however, tests were all carried out at the end of the first week in both group. The results indicate that paraoxon reduced the viability in a dose-dependent manner. Our results do not confirm apoptosis in either group; it seems that the cell death in one-day exposure group was not AChE dependent. In conclusion, present data imply that the toxicity of paraoxon is both dose and duration dependent, which may even remain after the cessation of exposure.