Emergence and interpretation of oscillatory behaviour similar to brain waves and rhythms

Electroencephalography (EEG) monitors -by either intrusive or noninvasive electrodes-time and frequency variations and spectral content of voltage fluctuations or waves, known as brain rhythms, which in some way uncover activity during both rest periods and specific events in which the subject is under stimulus. This is a useful tool to explore brain behavior, as it complements imaging techniques that have a poorer temporal resolution. We here approach the understanding of EEG data from first principles by numerical simulating and studying a networked model of excitatory and inhibitory neurons which generates a variety of comparable waves. In fact, we thus numerically reproduce oscillatory behavior similar to alpha, beta, gamma and other rhythms as observed by EEG recordings, and identify the details of the respectively involved complex phenomena, including a precise relationship between an input and the collective response to it. It ensues the potentiality of our model to better understand actual brain oscillatory activity in normal and pathological situations, and we also describe kind of stochastic resonance phenomena which could be useful to locate main qualitative changes of brain activity in (e.g.) humans. (C) 2019 Elsevier B.V. All rights reserved.We acknowledge the Spanish Ministry for Science and Technology and the "Agencia Espanola de Investigacion"(AEI) for financial support under grant FIS2017-84256-P (FEDER funds)

Matrix architecture plays a pivotal role in 3D osteoblast migration: The effect of interstitial fluid flow

Osteoblast migration is a crucial process in bone regeneration, which is strongly regulated by interstitial fluid flow. However, the exact role that such flow exerts on osteoblast migration is still unclear. To deepen the understanding of this phenomenon, we cultured human osteoblasts on 3D microfluidic devices under different fluid flow regimes. Our results show that a slow fluid flow rate by itself is not able to alter the 3D migratory patterns of osteoblasts in collagen-based gels but that at higher fluid flow rates (increased flow velocity) may indirectly influence cell movement by altering the collagen microstructure. In fact, we observed that high fluid flow rates (1 µl/min) are able to alter the collagen matrix architecture and to indirectly modulate the migration pattern. However, when these collagen scaffolds were crosslinked with a chemical crosslinker, specifically, transglutaminase II, we did not find significant alterations in the scaffold architecture or in osteoblast movement. Therefore, our data suggest that high interstitial fluid flow rates can regulate osteoblast migration by means of modifying the orientation of collagen fibers. Together, these results highlight the crucial role of the matrix architecture in 3D osteoblast migration. In addition, we show that interstitial fluid flow in conjunction with the matrix architecture regulates the osteoblast morphology in 3D

3D collagen migration patterns reveal a SMAD3-dependent and TGF-β1-independent mechanism of recruitment for tumour-associated fibroblasts in lung adenocarcinoma

The TGF-β1 transcription factor SMAD3 is epigenetically repressed in tumour-associated fibroblasts (TAFs) from lung squamous cell carcinoma (SCC) but not adenocarcinoma (ADC) patients, which elicits a compensatory increase in SMAD2 that renders SCC-TAFs less fibrotic. Here we examined the effects of altered SMAD2/3 in fibroblast migration and its impact on the desmoplastic stroma formation in lung cancer.We used a microfluidic device to examine descriptors of early protrusions and subsequent migration in 3D collagen gels upon knocking down SMAD2 or SMAD3 by shRNA in control fibroblasts and TAFs.High SMAD3 conditions as in shSMAD2 fibroblasts and ADC-TAFs exhibited a migratory advantage in terms of protrusions (fewer and longer) and migration (faster and more directional) selectively without TGF-β1 along with Erk1/2 hyperactivation. This enhanced migration was abrogated by TGF-β1 as well as low glucose medium and the MEK inhibitor Trametinib. In contrast, high SMAD2 fibroblasts were poorly responsive to TGF-β1, high glucose and Trametinib, exhibiting impaired migration in all conditions.The basal migration advantage of high SMAD3 fibroblasts provides a straightforward mechanism underlying the larger accumulation of TAFs previously reported in ADC compared to SCC. Moreover, our results encourage using MEK inhibitors in ADC-TAFs but not SCC-TAFs.© 2022. The Author(s)

A multiscale orchestrated computational framework to reveal emergent phenomena in neuroblastoma

Neuroblastoma is a complex and aggressive type of cancer that affects children. Current treatments involve a combination of surgery, chemotherapy, radiotherapy, and stem cell transplantation. However, treatment outcomes vary due to the heterogeneous nature of the disease. Computational models have been used to analyse data, simulate biological processes, and predict disease progression and treatment outcomes. While continuum cancer models capture the overall behaviour of tumours, and agent-based models represent the complex behaviour of individual cells, multiscale models represent interactions at different organisational levels, providing a more comprehensive understanding of the system. In 2018, the PRIMAGE consortium was formed to build a cloud-based decision support system for neuroblastoma, including a multi-scale model for patient-specific simulations of disease progression. In this work we have developed this multi-scale model that includes data such as patient's tumour geometry, cellularity, vascularization, genetics and type of chemotherapy treatment, and integrated it into an online platform that runs the simulations on a high-performance computation cluster using Onedata and Kubernetes technologies. This infrastructure will allow clinicians to optimise treatment regimens and reduce the number of costly and time-consuming clinical trials. This manuscript outlines the challenging framework's model architecture, data workflow, hypothesis, and resources employed in its development

Mindfulness in primary care healthcare and teaching professionals and its relationship with stress at work: a multicentric cross-sectional study

Background: Work stress is a common problem among the health personnel of the Spanish National Health System. The objective of this paper is to assess the state of mindfulness among Spanish primary care providers and to evaluate its potential relationship with work stress and basic labor and sociodemographic characteristics. Methods: Cross-sectional, multi-centric study. Primary care nurses, teachers, teaching collaborators and residents assigned to six Spanish Family Medicine/Family and Community Care Departments were invited to participate (n = 475). A template was designed in Google Forms, including sociodemographic and work-related variables. The state of mindfulness was measured with the Five Facet Mindfulness Questionnaire (FFMQ), while work-related stress was measured using an ordinal scale ranging from 0 to 10 points. Descriptive and inferential statistical analyses were carried out, as well as bivariate and multivariate statistics. Results: The mean age of participants was 40, 14 ± 13.12 (range:23–65 years); 66.9% were women, 42.5% internal medicine residents, 29.3% family physicians, and 20.2% nurses. More than half (54.5%) knew about mindfulness, with 24.0% have received training on it, and 22.5% were usual practitioners. The average level of mindfulness was 127.18 ± 15.45 (range: 89–177). The average score of stress at work was 6.00 ± 2.44; 49.9% (range: 0–10). 49.9% of participants scored 7 or more on the stress at work scale. There was an inverse correlation between the levels of mindfulness (FFMQ total score) and work-related stress (Spearman’s r = - 0.155, p = 0.003). Significant relationships between the mindfulness practice and the level of mindfulness (F = 29.80, p < 0.001), as well as between the mindfulness practice and the level of work-related stress (F = 9.68, p = 0.042), were also found. Conclusions: Levels of mindfulness in primary care health providers were in line with those levels observed in other groups of health professionals. Half of all of the primary care providers suffered from a high degree of stress. Although weak, inverse relationships were observed between levels of mindfulness and stress at work, with lower values of stress at work among those who practiced mindfulness. Trial registration: NCT03629457

CANELC: constructing an e-language corpus

This paper reports on the construction of CANELC: the Cambridge and Nottingham e-language Corpus.3 CANELC is a one million word corpus of digital communication in English, taken from online discussion boards, blogs, tweets, emails and SMS messages. The paper outlines the approaches used when planning the corpus: obtaining consent; collecting the data and compiling the corpus database. This is followed by a detailed analysis of some of the patterns of language used in the corpus. The analysis includes a discussion of the key words and phrases used as well as the common themes and semantic associations connected with the data. These discussions form the basis of an investigation of how e-language operates in both similar and different ways to spoken and written records of communication (as evidenced by the BNC - British National Corpus). 3 CANELC stands for Cambridge and Nottingham e-language Corpus. This corpus has been built as part of a collaborative project between The University of Nottingham and Cambridge University Press with whom sole copyright of the annotated corpus resides. CANELC comprises one-million words of digital English taken from SMS messages, blogs, tweets, discussion board content and private/business emails. Plans to extend the corpus are under discussion. The legal dimension to corpus ‘ownership’ of some forms of unannotated data is a complex one and is under constant review. At the present time the annotated corpus is only available to authors and researchers working for CUP and is not more generally available

Dynamic Mechanisms of Cell Rigidity Sensing: Insights from a Computational Model of Actomyosin Networks

Cells modulate themselves in response to the surrounding environment like substrate elasticity, exhibiting structural reorganization driven by the contractility of cytoskeleton. The cytoskeleton is the scaffolding structure of eukaryotic cells, playing a central role in many mechanical and biological functions. It is composed of a network of actins, actin cross-linking proteins (ACPs), and molecular motors. The motors generate contractile forces by sliding couples of actin filaments in a polar fashion, and the contractile response of the cytoskeleton network is known to be modulated also by external stimuli, such as substrate stiffness. This implies an important role of actomyosin contractility in the cell mechano-sensing. However, how cells sense matrix stiffness via the contractility remains an open question. Here, we present a 3-D Brownian dynamics computational model of a cross-linked actin network including the dynamics of molecular motors and ACPs. The mechano-sensing properties of this active network are investigated by evaluating contraction and stress in response to different substrate stiffness. Results demonstrate two mechanisms that act to limit internal stress: (i) In stiff substrates, motors walk until they exert their maximum force, leading to a plateau stress that is independent of substrate stiffness, whereas (ii) in soft substrates, motors walk until they become blocked by other motors or ACPs, leading to submaximal stress levels. Therefore, this study provides new insights into the role of molecular motors in the contraction and rigidity sensing of cells

Plasma lipidome and risk of atrial fibrillation: results from the PREDIMED trial

The potential role of the lipidome in atrial fibrillation (AF) development is still widely unknown. We aimed to assess the association between lipidome profiles of the Prevenci\uf3n con Dieta Mediterr\ue1nea (PREDIMED) trial participants and incidence of AF. We conducted a nested case–control study (512 incident centrally adjudicated AF cases and 735 controls matched by age, sex, and center). Baseline plasma lipids were profiled using a Nexera X2 U-HPLC system coupled to an Exactive Plus orbitrap mass spectrometer. We estimated the association between 216 individual lipids and AF using multivariable conditional logistic regression and adjusted the p values for multiple testing. We also examined the joint association of lipid clusters with AF incidence. Hitherto, we estimated the lipidomics network, used machine learning to select important network-clusters and AF-predictive lipid patterns, and summarized the joint association of these lipid patterns weighted scores. Finally, we addressed the possible interaction by the randomized dietary intervention. Forty-one individual lipids were associated with AF at the nominal level (p &lt; 0.05), but no longer after adjustment for multiple-testing. However, the network-based score identified with a robust data-driven lipid network showed a multivariable-adjusted ORper+1SD of 1.32 (95% confidence interval: 1.16–1.51; p &lt; 0.001). The score included PC plasmalogens and PE plasmalogens, palmitoyl-EA, cholesterol, CE 16:0, PC 36:4;O, and TG 53:3. No interaction with the dietary intervention was found. A multilipid score, primarily made up of plasmalogens, was associated with an increased risk of AF. Future studies are needed to get further insights into the lipidome role on AF. Current Controlled Trials number, ISRCTN35739639

A multiscale orchestrated computational framework to reveal emergent phenomena in neuroblastoma

Neuroblastoma is a complex and aggressive type of cancer that affects children. Current treatments involve a combination of surgery, chemotherapy, radiotherapy, and stem cell transplantation. However, treatment outcomes vary due to the heterogeneous nature of the disease. Computational models have been used to analyse data, simulate biological processes, and predict disease progression and treatment outcomes. While continuum cancer models capture the overall behaviour of tumours, and agent-based models represent the complex behaviour of individual cells, multiscale models represent interactions at different organisational levels, providing a more comprehensive understanding of the system. In 2018, the PRIMAGE consortium was formed to build a cloud-based decision support system for neuroblastoma, including a multi-scale model for patient-specific simulations of disease progression. In this work we have developed this multi-scale model that includes data such as patient's tumour geometry, cellularity, vascularization, genetics and type of chemotherapy treatment, and integrated it into an online platform that runs the simulations on a high-performance computation cluster using Onedata and Kubernetes technologies. This infrastructure will allow clinicians to optimise treatment regimens and reduce the number of costly and time-consuming clinical trials. This manuscript outlines the challenging framework's model architecture, data workflow, hypothesis, and resources employed in its development

Dynamic Modeling of Cell Migration and Spreading Behaviors on Fibronectin Coated Planar Substrates and Micropatterned Geometries

An integrative cell migration model incorporating focal adhesion (FA) dynamics, cytoskeleton and nucleus remodeling, actin motor activity, and lamellipodia protrusion is developed for predicting cell spreading and migration behaviors. This work is motivated by two experimental works: (1) cell migration on 2-D substrates under various fibronectin concentrations and (2) cell spreading on 2-D micropatterned geometries. These works suggest (1) cell migration speed takes a maximum at a particular ligand density (~1140 molecules/µm2) and (2) that strong traction forces at the corners of the patterns may exist due to combined effects exerted by actin stress fibers (SFs). The integrative model of this paper successfully reproduced these experimental results and indicates the mechanism of cell migration and spreading. In this paper, the mechanical structure of the cell is modeled as having two elastic membranes: an outer cell membrane and an inner nuclear membrane. The two elastic membranes are connected by SFs, which are extended from focal adhesions on the cortical surface to the nuclear membrane. In addition, the model also includes ventral SFs bridging two focal adhesions on the cell surface. The cell deforms and gains traction as transmembrane integrins distributed over the outer cell membrane bond to ligands on the ECM surface, activate SFs, and form focal adhesions. The relationship between the cell migration speed and fibronectin concentration agrees with existing experimental data for Chinese hamster ovary (CHO) cell migrations on fibronectin coated surfaces. In addition, the integrated model is validated by showing persistent high stress concentrations at sharp geometrically patterned edges. This model will be used as a predictive model to assist in design and data processing of upcoming microfluidic cell migration assays