Mean-field solution of the neural dynamics in a Greenberg-Hastings model with excitatory and inhibitory units.

We present a mean-field solution of the dynamics of a Greenberg-Hastings neural network with both excitatory and inhibitory units. We analyze the dynamical phase transitions that appear in the stationary state as the model parameters are varied. Analytical solutions are compared with numerical simulations of the microscopic model defined on a fully connected network. We found that the stationary state of this system exhibits a first-order dynamical phase transition (with the associated hysteresis) when the fraction of inhibitory units f is smaller than some critical value f_{t}≲1/2, even for a finite system. Moreover, any solution for f<1/2 can be mapped to a solution for purely excitatory systems (f=0). In finite systems, when the system is dominated by inhibition (f>f_{t}), the first-order transition is replaced by a pseudocritical one, namely a continuous crossover between regions of low and high activity that resembles the finite size behavior of a continuous phase transition order parameter. However, in the thermodynamic limit (i.e., infinite-system-size limit), we found that f_{t}→1/2 and the activity for the inhibition dominated case (f≥f_{t}) becomes negligible for any value of the parameters, while the first-order transition between low- and high-activity phases for f

Scale-free correlations in the dynamics of a small (N∼10000) cortical network.

The advent of novel optogenetics technology allows the recording of brain activity with a resolution never seen before. The characterization of these very large data sets offers new challenges as well as unique theory-testing opportunities. Here we discuss whether the spatial and temporal correlations of the collective activity of thousands of neurons are tangled as predicted by the theory of critical phenomena. The analysis shows that both the correlation length ξ and the correlation time τ scale as predicted as a function of the system size. With some peculiarities that we discuss, the analysis uncovers evidence consistent with the view that the large-scale brain cortical dynamics corresponds to critical phenomena.

Evaluation of a polymeric topical formulation of Endoxifen in an estrogen receptor positive breast cancer murine model.

Breast cancer (BC) has surpassed lung cancer as the most diagnosed cancer and, in terms of mortality, is the fifth leading cause with 684,996 new deaths (6.7% of all cancer-related deaths) and the highest mortality amongst all cancers (15.5%) in women. Selective estrogen-receptor modulators (SERMs) have been used for the last thirty years for estrogen receptor-positive (ER+) BC prevention and treatment. Tamoxifen (TAM), the most widely used SERM, is orally administered and its long-term oral administration has been associated to toxicity and adverse side effects. Endoxifen (EDX) is one of the known active metabolites of TAM, with an affinity to ERα 100 times higher than TAM. Furthermore, EDX has shown antiproliferative activity against the ER+ BC cell line MCF-7. Alternative administration routes that avoid the metabolic processing of TAM seem an appealing alternative to its oral administration. With this aim, we have prepared a polymeric gel-like solution of Pluronic® F127 as vehicle for topical administration of EDX. In order to shed light on the potential clinical use of this formulation, we have compared it with the standard pharmaceutical form, i.e. orally administered TAM. The biodistribution, antitumor efficacy and toxic effects of topical EDX and oral TAM were evaluated in ER+ tumor xenograft athymic nu/nu mouse models. The results showed a statistically significant antitumor effect and reduced toxicity of topical EDX as compared to oral TAM or empty F127 gel. This novel administration route of SERMs could also have a strong impact in the prevention of BC at early development stages and could help to ameliorate the mortality and morbidity related to this disease.

Self-tuned criticality: Controlling a neuron near its bifurcation point via temporal correlations.

Previous work showed that the collective activity of large neuronal networks can be tamed to remain near its critical point by a feedback control that maximizes the temporal correlations of the mean-field fluctuations. Since such correlations behave similarly near instabilities across nonlinear dynamical systems, it is expected that the principle should control also low-dimensional dynamical systems exhibiting continuous or discontinuous bifurcations from fixed points to limit cycles. Here we present numerical evidence that the dynamics of a single neuron can be controlled in the vicinity of its bifurcation point. The approach is tested in two models: a two-dimensional generic excitable map and the paradigmatic FitzHugh-Nagumo neuron model. The results show that in both cases, the system can be self-tuned to its bifurcation point by modifying the control parameter according to the first coefficient of the autocorrelation function.

Finite-size correlation behavior near a critical point: A simple metric for monitoring the state of a neural network.

In this article, a correlation metric κ_{c} is proposed for the inference of the dynamical state of neuronal networks. κ_{C} is computed from the scaling of the correlation length with the size of the observation region, which shows qualitatively different behavior near and away from the critical point of a continuous phase transition. The implementation is first studied on a neuronal network model, where the results of this new metric coincide with those obtained from neuronal avalanche analysis, thus well characterizing the critical state of the network. The approach is further tested with brain optogenetic recordings in behaving mice from a publicly available database. Potential applications and limitations for its use with currently available optical imaging techniques are discussed.

Tricritical behavior in a neural model with excitatory and inhibitory units.

While the support for the relevance of critical dynamics to brain function is increasing, there is much less agreement on the exact nature of the advocated critical point. Thus, a considerable number of theoretical efforts are currently concentrated on which mechanisms and what type(s) of transition can be exhibited by neuronal network models. In that direction, the present work describes the effect of incorporating a fraction of inhibitory neurons on the collective dynamics. As we show, this results in the appearance of a tricritical point for highly connected networks and a nonzero fraction of inhibitory neurons.

Box scaling as a proxy of finite size correlations.

The scaling of correlations as a function of size provides important hints to understand critical phenomena on a variety of systems. Its study in biological structures offers two challenges: usually they are not of infinite size, and, in the majority of cases, dimensions can not be varied at will. Here we discuss how finite-size scaling can be approximated in an experimental system of fixed and relatively small extent, by computing correlations inside of a reduced field of view of various widths (we will refer to this procedure as "box-scaling"). A relation among the size of the field of view, and measured correlation length, is derived at, and away from, the critical regime. Numerical simulations of a neuronal network, as well as the ferromagnetic 2D Ising model, are used to verify such approximations. Numerical results support the validity of the heuristic approach, which should be useful to characterize relevant aspects of critical phenomena in biological systems.

Similar local neuronal dynamics may lead to different collective behavior.

This report is concerned with the relevance of the microscopic rules that implement individual neuronal activation, in determining the collective dynamics, under variations of the network topology. To fix ideas we study the dynamics of two cellular automaton models, commonly used, rather in-distinctively, as the building blocks of large-scale neuronal networks. One model, due to Greenberg and Hastings (GH), can be described by evolution equations mimicking an integrate-and-fire process, while the other model, due to Kinouchi and Copelli (KC), represents an abstract branching process, where a single active neuron activates a given number of postsynaptic neurons according to a prescribed "activity" branching ratio. Despite the apparent similarity between the local neuronal dynamics of the two models, it is shown that they exhibit very different collective dynamics as a function of the network topology. The GH model shows qualitatively different dynamical regimes as the network topology is varied, including transients to a ground (inactive) state, continuous and discontinuous dynamical phase transitions. In contrast, the KC model only exhibits a continuous phase transition, independently of the network topology. These results highlight the importance of paying attention to the microscopic rules chosen to model the interneuronal interactions in large-scale numerical simulations, in particular when the network topology is far from a mean-field description. One such case is the extensive work being done in the context of the Human Connectome, where a wide variety of types of models are being used to understand the brain collective dynamics.

Normalization of endometrial histopathology and endometrial NK cells concentration predict successful pregnancy in repeated implantation failure.

OBJECTIVE: The primary objective was to establish the endometrial predictors of clinical pregnancy in a population of repeated implantation failure with oocyte donation after specific endometrial treatment. The secondary one was to evaluate reproduction outcomes in terms of Implantation rate (IR), Clinical pregnancy (CP), Live birth delivery rate (LBDR) and Prematurity, in relation to normalization or no-normalization of the predictors. METHODS: 66 patients were assigned to the study. We ran a Pipelle endometrial biopsy to investigate the endometrium lymphocyte population by Flow Cytometry and abnormal/normal patterns by histopathology in pre/post-treatment. We employed the binary logistic regression model to identify the predictors for CP. For the secondary objective, we assessed the clinical outcomes in function to the normalization or no normalization in post-treatment. RESULTS: Endometrial histopathology and endometrial NK cell counts resulted in CP predictors (Wald chi2 test (p=0.044 and 0.001)), respectively. We had a higher IR, CP and LBDR when both predictors were normalized in comparison with no normalization (p<0.001). There was a high percentage of prematurity in both normalized vs. non-normalized groups (34.4% (11/32) and 71.43% (5/7), respectively) without significant differences. CONCLUSION: Endometrial histopathology and endometrial NK cell counts showed that they are valid predictors of pregnancy outcome in repeated implantation failure after treatment. In post-treatment, the pregnancy outcomes were significantly higher in the presence of both normalized predictors. Pregnancy rates were zero in the no-normalization of both predictors. There was a high percentage of prematurity in both groups.

Biodegradable photoresponsive nanoparticles for chemo-, photothermal- and photodynamic therapy of ovarian cancer.

Ovarian cancer (OC) is the deadliest gynecological cancer. Standard treatment of OC is based on cytoreductive surgery followed by chemotherapy with platinum drugs and taxanes; however, innate and acquired drug-resistance is frequently observed followed by a relapse after treatment, thus, more efficient therapeutic approaches are required. Combination therapies involving phototherapies and chemotherapy (the so-called chemophototherapy) may have enhanced efficacy against cancer, by attacking cancer cells through different mechanisms, including DNA-damage and thermally driven cell membrane and cytoskeleton damage. We have designed and synthesized poly(lactic-co-glycolic) nanoparticles (PLGA NPs) containing the chemo-drug carboplatin (CP), and the near infrared (NIR) photosensitizer indocyanine green (ICG). We have evaluated the drug release profile, the photodynamic ROS generation and photothermal capacities of the NPs. Also, the antitumoral efficiency of the NPs was evaluated using the SKOV-3 cell line as an in vitro OC model, observing an enhanced cytotoxic effect when irradiating cells with an 800 nm laser. Evidence here shown supports the potential application of the biodegradable photoresponsive NPs in the clinical stage due to the biocompatibility of the materials used, the spatiotemporal control of the therapy and, also, the less likely development of resistance against the combinatorial therapy.

Controlling a complex system near its critical point via temporal correlations.

Many complex systems exhibit large fluctuations both across space and over time. These fluctuations have often been linked to the presence of some kind of critical phenomena, where it is well known that the emerging correlation functions in space and time are closely related to each other. Here we test whether the time correlation properties allow systems exhibiting a phase transition to self-tune to their critical point. We describe results in three models: the 2D Ising ferromagnetic model, the 3D Vicsek flocking model and a small-world neuronal network model. We demonstrate that feedback from the autocorrelation function of the order parameter fluctuations shifts the system towards its critical point. Our results rely on universal properties of critical systems and are expected to be relevant to a variety of other settings.

Regenerative therapy by endometrial mesenchymal stem cells in thin endometrium with repeated implantation failure. A novel strategy.

OBJECTIVE: Our primary objective was to evaluate the endometrial changes before and after the transfer of endometrial mesenchymal stem cells (enMSCs) in a population of thinned endometrium women, with absence or hypo-responsiveness to estrogen and repeated implantation failure (RIF). The secondary objective was to evaluate the clinical outcomes of the intervention in terms of clinical pregnancy (CP), early abortions, ongoing pregnancy and live birth delivery rate (LBDR) per in vitro fertilization (IVF) cycle. METHODS: A longitudinal and experimental study. The intervention was defined as "subendometrial inoculation of enMSCs," and the post-intervention changes were evaluated by the following variables: endometrial thickness (Eth), endometrial flow cytometry (enFC), endometrial histopathology (enHP) and endometrial immunohistochemistry (enIHQ). The variables were analyzed after the intervention (Post-treatment) regarding previous values (Pretreatment). RESULTS: Eth values before and after treatment with enMSCs were 5.24±1.24 mm vs. 9.93±0.77 (p=0.000), respectively. Endometrial Flow Cytometry showed significant differences in favor of Normalized variables in the post-treatment assessment, associated with the pretreatment, LT/Li, LB/Li, NK/Li, CD8/CD3+ and CD4/CD8 (p≤0.015), respectively. Only two variables Li/PC and CD4/CD3 had NS (p=0.167 and 0.118). A similar analysis was performed on enHP with an HP increase post-treatment (p=0.007). The CP rate was 79.31% (23/29), a live birth delivery rate per embryo transfer was 45.45% (10/22) and ongoing pregnancy 7/29 (24.14%). CONCLUSION: Subendometrial enMSCs inoculation produces a significant increase in endometrial thickness; normalize the enHP, enIHQ and enFC. As a result, IVF after treatment with enMSCs yields a higher rate of CP and LBDR.

Evaluating the reliability of neurocognitive biomarkers of neurodegenerative diseases across countries: A machine learning approach.

Accurate early diagnosis of neurodegenerative diseases represents a growing challenge for current clinical practice. Promisingly, current tools can be complemented by computational decision-support methods to objectively analyze multidimensional measures and increase diagnostic confidence. Yet, widespread application of these tools cannot be recommended unless they are proven to perform consistently and reproducibly across samples from different countries. We implemented machine-learning algorithms to evaluate the prediction power of neurocognitive biomarkers (behavioral and imaging measures) for classifying two neurodegenerative conditions -Alzheimer Disease (AD) and behavioral variant frontotemporal dementia (bvFTD)- across three different countries (>200 participants). We use machine-learning tools integrating multimodal measures such as cognitive scores (executive functions and cognitive screening) and brain atrophy volume (voxel based morphometry from fronto-temporo-insular regions in bvFTD, and temporo-parietal regions in AD) to identify the most relevant features in predicting the incidence of the diseases. In the Country-1 cohort, predictions of AD and bvFTD became maximally improved upon inclusion of cognitive screenings outcomes combined with atrophy levels. Multimodal training data from this cohort allowed predicting both AD and bvFTD in the other two novel datasets from other countries with high accuracy (>90%), demonstrating the robustness of the approach as well as the differential specificity and reliability of behavioral and neural markers for each condition. In sum, this is the first study, across centers and countries, to validate the predictive power of cognitive signatures combined with atrophy levels for contrastive neurodegenerative conditions, validating a benchmark for future assessments of reliability and reproducibility.

Universal and nonuniversal neural dynamics on small world connectomes: A finite-size scaling analysis.

Evidence of critical dynamics has been found recently in both experiments and models of large-scale brain dynamics. The understanding of the nature and features of such a critical regime is hampered by the relatively small size of the available connectome, which prevents, among other things, the determination of its associated universality class. To circumvent that, here we study a neural model defined on a class of small-world networks that share some topological features with the human connectome. We find that varying the topological parameters can give rise to a scale-invariant behavior either belonging to the mean-field percolation universality class or having nonuniversal critical exponents. In addition, we find certain regions of the topological parameter space where the system presents a discontinuous, i.e., noncritical, dynamical phase transition into a percolated state. Overall, these results shed light on the interplay of dynamical and topological roots of the complex brain dynamics.

First report of the metacestode Caulobothrium sp. in the Peruvian scallop Argopecten purpuratus from Sechura Bay, Piura, Peru.

In recent parasitological surveys performed on the Peruvian scallop, Argopecten purpuratus, from bottom cultures of Sechura Bay, Piura, Peru, free and encysted metacestodes were frequently found in their gonads. The objective of this study was to identify this metacestode, determine their prevalence and intensity and briefly assess the histopathological impact in the affected tissues. A parasitological study of 890 scallops over a 3-year period was performed in order to determine the parasite prevalence and intensity. Microscopical observation of details of the scolex and histopathological study of the affected host tissues were performed as well as molecular characterization of the parasite based on 18S and 28S rDNA sequences. The prevalence of the metacestode was 82.2% in August of 2013, 90.4% in November of 2014, and 83.1% and 85.6% in April and September of 2015, respectively. The highest average intensity (218.4) was found in spring of 2014. The histopathological study showed that plerocercoids reduced the gonadal space where the ovules develop. The molecular characterization and phylogenetic analysis revealed that the metacestodes belong to the genus Caulobothrium having high sequence similarity to Caulobothrium opisthorchis. This study constitutes the first report of Caulobothrium metacestodes in the scallop A. purpuratus.

Cisplatin-loaded PLGA nanoparticles for HER2 targeted ovarian cancer therapy.

The conventional treatment (cytoreduction combined with cisplatin/carboplatin and taxane drugs) of ovarian cancer has a high rate of failure and recurrence despite a favorable initial response. This lack of success is usually attributed to the development of multidrug resistance mechanisms by cancer cells and avoidance of the anti-growth effects of monoclonal targeted therapeutic antibodies. The disease, like other cancers, is characterized by the overexpression of molecular markers, including HER2 receptors. Preclinical and clinical studies with trastuzumab, a HER2-targeted therapeutic antibody, reveal a low improvement of the outcomes of HER2 positive ovarian cancer patients. Therefore, here, we propose a cisplatin-loaded, HER2 targeted poly(lactic-co-glycolic) nanoplatform, a system capable to escape the drug-efflux effect and to take advantage of the overexpressed HER2 receptors, using them as docks for targeted chemotherapy. The NP/trastuzumab ratio was determined after fluorescein labeling of antibodies and quantification of fluorescence in NPs. The system was also characterized in terms of size, zeta potential, drug release kinetics, cytotoxicity and cellular internalization in the epithelial ovarian cancer cell line SKOV-3, and compared with the HER2 negative breast cancer cell line HCC70. Our results show an increased cytotoxicity of NPs as compared to free cisplatin, and moreover, an enhanced internalization and cytotoxicity due to the bionfunctionalization of NPs with the monoclonal antibody.

Nrf2 stabilization prevents critical oxidative damage in Down syndrome cells.

Mounting evidence implicates chronic oxidative stress as a critical driver of the aging process. Down syndrome (DS) is characterized by a complex phenotype, including early senescence. DS cells display increased levels of reactive oxygen species (ROS) and mitochondrial structural and metabolic dysfunction, which are counterbalanced by sustained Nrf2-mediated transcription of cellular antioxidant response elements (ARE). Here, we show that caspase 3/PKCδdependent activation of the Nrf2 pathway in DS and Dp16 (a mouse model of DS) cells is necessary to protect against chronic oxidative damage and to preserve cellular functionality. Mitochondria-targeted catalase (mCAT) significantly reduced oxidative stress, restored mitochondrial structure and function, normalized replicative and wound healing capacity, and rendered the Nrf2-mediated antioxidant response dispensable. These results highlight the critical role of Nrf2/ARE in the maintenance of DS cell homeostasis and validate mitochondrial-specific interventions as a key aspect of antioxidant and antiaging therapies.

Fibronectin rescues estrogen receptor α from lysosomal degradation in breast cancer cells.

Estrogen receptor α (ERα) is expressed in tissues as diverse as brains and mammary glands. In breast cancer, ERα is a key regulator of tumor progression. Therefore, understanding what activates ERα is critical for cancer treatment in particular and cell biology in general. Using biochemical approaches and superresolution microscopy, we show that estrogen drives membrane ERα into endosomes in breast cancer cells and that its fate is determined by the presence of fibronectin (FN) in the extracellular matrix; it is trafficked to lysosomes in the absence of FN and avoids the lysosomal compartment in its presence. In this context, FN prolongs ERα half-life and strengthens its transcriptional activity. We show that ERα is associated with ß1-integrin at the membrane, and this integrin follows the same endocytosis and subcellular trafficking pathway triggered by estrogen. Moreover, ERα+ vesicles are present within human breast tissues, and colocalization with ß1-integrin is detected primarily in tumors. Our work unravels a key, clinically relevant mechanism of microenvironmental regulation of ERα signaling.

Preparation of PLGA/Rose Bengal colloidal particles by double emulsion and layer-by-layer for breast cancer treatment.

The use of colloidal particles (CPs) in the transport of drugs is developing rapidly thanks to its effectiveness and biosafety, especially in the treatment of various types of cancer. In this study Rose Bengal/PLGA CPs synthesized by double emulsion (W/O/W) and by electrostatic adsorption (layer-by-layer), were characterized and evaluated as potential breast cancer treatment. CPs were evaluated in terms of size, zeta potential, drug release kinetics and cell viability inhibition efficacy with the triple negative breast cancer cell line HCC70. The results showed that both types of CPs can be an excellent alternative to conventional cancer treatment by taking advantage of the enhanced permeation and retention (EPR) effect, manifested by solid tumors; however, the double emulsion CPs showed more suitable delivery times of up to 60% within two days, while layer-by-layer showed fast release of 50% in 90 min. Both types of CPs were capable to decrease cell viability, which encourage us to further testing in in vivo models to prove their efficacy and feasible use in the treatment of triple negative breast cancer.

Mitochondrial network complexity emerges from fission/fusion dynamics.

Mitochondrial networks exhibit a variety of complex behaviors, including coordinated cell-wide oscillations of energy states as well as a phase transition (depolarization) in response to oxidative stress. Since functional and structural properties are often interwinded, here we characterized the structure of mitochondrial networks in mouse embryonic fibroblasts using network tools and percolation theory. Subsequently we perturbed the system either by promoting the fusion of mitochondrial segments or by inducing mitochondrial fission. Quantitative analysis of mitochondrial clusters revealed that structural parameters of healthy mitochondria laid in between the extremes of highly fragmented and completely fusioned networks. We confirmed our results by contrasting our empirical findings with the predictions of a recently described computational model of mitochondrial network emergence based on fission-fusion kinetics. Altogether these results offer not only an objective methodology to parametrize the complexity of this organelle but also support the idea that mitochondrial networks behave as critical systems and undergo structural phase transitions.