Computational evaluation of laparoscopic sleeve gastrectomy

AbstractLSG is one of the most performed bariatric procedures worldwide. It is a safe and effective operation with a low complication rate. Unsatisfactory weight loss/regain may occur, suggesting that the operation design could be improved. A bioengineering approach might significantly help in avoiding the most common complications. Computational models of the sleeved stomach after LSG were developed according to bougie size (range 27–54 Fr). The endoluminal pressure and the basal volume were computed at different intragastric pressures. At an inner pressure of 22.5 mmHg, the basal volume of the 54 Fr configuration was approximately 6 times greater than that of the 27 Fr configuration (57.92 ml vs 9.70 ml). Moreover, the elongation distribution of the gastric wall was assessed to quantify the effect on mechanoreceptors impacting satiety by differencing regions and layers. An increasing trend in elongation strain with increasing bougie size was observed in all cases. The most stressed region and layer were the antrum (approximately 25% higher stress than that in the corpus at 37.5 mmHg) and mucosa layer (approximately 7% higher stress than that in the muscularis layer at 22.5 mmHg), respectively. In addition, the pressure–volume behaviors were reported. Computational models and bioengineering methods can help to quantitatively identify some critical aspects of the “design” of bariatric operations to plan interventions, and predict and increase the success rate. Moreover, computational tools can support the development of innovative bariatric procedures, potentially skipping invasive approaches.

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The daunting polygenicity of mental illness: making a new map

Philosophical Transactions of the Royal Society B Biological Sciences ◽

10.1098/rstb.2017.0031 ◽

2018 ◽

Vol 373 (1742) ◽

pp. 20170031 ◽

Cited By ~ 18

Author(s):

Steven E. Hyman

Keyword(s):

Mental Health ◽

Large Scale ◽

Computational Models ◽

Model Organisms ◽

Human Biology ◽

Genetic Studies ◽

Computational Tools ◽

Experimental Systems ◽

Terra Incognita ◽

Phenotypic Complexity

An epochal opportunity to elucidate the pathogenic mechanisms of psychiatric disorders has emerged from advances in genomic technology, new computational tools and the growth of international consortia committed to data sharing. The resulting large-scale, unbiased genetic studies have begun to yield new biological insights and with them the hope that a half century of stasis in psychiatric therapeutics will come to an end. Yet a sobering picture is coming into view; it reveals daunting genetic and phenotypic complexity portending enormous challenges for neurobiology. Successful exploitation of results from genetics will require eschewal of long-successful reductionist approaches to investigation of gene function, a commitment to supplanting much research now conducted in model organisms with human biology, and development of new experimental systems and computational models to analyse polygenic causal influences. In short, psychiatric neuroscience must develop a new scientific map to guide investigation through a polygenic terra incognita . This article is part of a discussion meeting issue ‘Of mice and mental health: facilitating dialogue between basic and clinical neuroscientists’.

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Individual-Based Modelling of Bacterial Ecologies and Evolution

Comparative and Functional Genomics ◽

10.1002/cfg.368 ◽

2004 ◽

Vol 5 (1) ◽

pp. 100-104 ◽

Cited By ~ 13

Author(s):

C. Vlachos ◽

R. Gregory ◽

R. C. Paton ◽

J. R. Saunders ◽

Q. H. Wu

Keyword(s):

Computational Models ◽

Classifier Systems ◽

Simulation Experiments ◽

Computational Tools ◽

Agent Based ◽

Fine Grained ◽

Learning Classifier ◽

Potential Applications ◽

The Individual ◽

Behavioural Strategies

This paper presents two approaches to the individual-based modelling of bacterial ecologies and evolution using computational tools. The first approach is a fine-grained model that is based on networks of interactivity between computational objects representing genes and proteins. The second approach is a coarser-grained, agent-based model, which is designed to explore the evolvability of adaptive behavioural strategies in artificial bacteria represented by learning classifier systems. The structure and implementation of these computational models is discussed, and some results from simulation experiments are presented. Finally, the potential applications of the proposed models to the solution of real-world computational problems, and their use in improving our understanding of the mechanisms of evolution, are briefly outlined.

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THE IMPACT OF VARIOUS MODELLING DECISIONS ON FLOW FIELD PREDICTIONS IN A CENTRIFUGAL COMPRESSOR

Journal of Turbomachinery ◽

10.1115/1.4050674 ◽

2021 ◽

pp. 1-13

Author(s):

William Gooding ◽

Matthew Meier ◽

Nicole L. Key

Keyword(s):

Flow Field ◽

Centrifugal Compressor ◽

Computational Models ◽

Turbulence Models ◽

Numerical Results ◽

Computational Tools ◽

Performance Trends ◽

Sst Turbulence Model ◽

Overall Performance ◽

The Impact

Abstract Computational tools have become increasingly important in design and research applications in recent years due to increasing computational resources. In most cases, model geometry and flow-physics are simplified to reduce the complexity of the computational model. While this was necessary historically, modern computational tools are capable of including realistic features such as fillets, surface roughness, and heat transfer. This work presents extensive and systematic numerical results from a simulation of a centrifugal compressor stage for an aero-engine application. Numerical results are compared to detailed experimental data to investigate the effect of various modelling decisions, including turbulence models, on the predicted aerodynamics developing through the diffuser passage. Roughness and the inclusion of fillets significantly impact the flow development, especially with the SST turbulence model. This approach leads to the conclusion that the BSL-EARSM model is best able to predict the experimentally determined diffuser flow profiles and overall performance trends with the inclusion of the previously mentioned model features. Additionally, the misleading conclusions can be reached if modelling decisions are based on merely matching overall performance values. Finally, frozen rotor simulations are used to roughly approximate the impact of unsteadiness on the flow field. The results show a significant impact and also that the inclusion of approximate unsteady effects tends to further improve the predictive capability of the computational models that were considered.

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Recent advances in the analysis of nanotube-reinforced polymeric biomaterials

Journal of the Mechanical Behavior of Materials ◽

10.1515/jmbm-2013-0021 ◽

2013 ◽

Vol 22 (5-6) ◽

pp. 137-148 ◽

Cited By ~ 1

Author(s):

J.N. Reddy ◽

Vinu U. Unnikrishnan ◽

Ginu U. Unnikrishnan

Keyword(s):

Atomistic Simulation ◽

Membrane Bioreactor ◽

Computational Models ◽

Hollow Fiber Membrane ◽

Element Model ◽

Computational Techniques ◽

Computational Tools ◽

Polymeric Biomaterials ◽

Hollow Fiber Membrane Bioreactor ◽

Made In

AbstractConventional experimental or computational techniques are often inadequate for the analysis and development of nanocomposite-based materials as they are tedious (e.g., experimental methods) or are unsuitable to capture the properties of these novel materials (e.g., conventional computational techniques), thereby requiring multiscale computational strategies. During the last 5 years, major developments were made by the authors on the formulation and implementation of multiscale computational models, using atomistic simulation and micro-mechanics-based techniques, to study the mechanical and thermal behavior of nanocomposite-based materials. In this article, the advances made in the computational analysis of nanocomposites for tissue engineering applications (e.g., scaffolds and bioreactors) would be discussed. The material properties of the nanocomposites in the lower scales were determined using molecular dynamics, and were then transferred to the macroscale using various homogenization techniques. Also in this article, the authors discuss the development of a theory of mixture-based finite element model for nutrient flow in a hollow fiber membrane bioreactor and the use of computational tools to improve the efficiency of the bioreactor.

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A Full Computational Evaluation of Two Novel Chalcone Derivatives as Inhibitors for Colon Cancer Related Proteins.

10.20944/preprints202103.0062.v1 ◽

2021 ◽

Author(s):

Manos C. Vlasiou ◽

Christos C. Petrou ◽

Yiannis Sarigiannis ◽

Kyriaki S. Pafiti

Keyword(s):

Colon Cancer ◽

Stage Iv ◽

Binding Affinities ◽

Computational Tools ◽

Prenylated Flavonoids ◽

Anticancer Properties ◽

Computational Evaluation ◽

Related Proteins ◽

Derivatives Of ◽

Molecular Docking And Dynamics

Colorectal cancer is a major threat to the society causing the death through metastasis to several patients with stage IV. Computational tools provide a relatively quick procedure in order to evaluate several molecules for their drug activity. Prenylated flavonoids are well known for their anticancer properties even in colon cancer. Here, we provided altered structures of chalcones, based on theoretical studies that are showing better binding affinities to several colon cancer related proteins. Using molecular docking and dynamics, alongside with density function theory and ADMET studies we are representing two new derivatives of Xanthohumol prenylated flavonoids having promising results against this disease.

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Modeling misretrieval and feature substitution in agreement attraction: A computational evaluation

10.31234/osf.io/957e3 ◽

2020 ◽

Author(s):

Dario Paape ◽

Serine Avetisyan ◽

Sol Lago ◽

Shravan Vasishth

Keyword(s):

Computational Models ◽

Word Reading ◽

Specific Pattern ◽

Number Agreement ◽

Computational Evaluation ◽

Trial Basis ◽

Predictive Fit ◽

The Subject ◽

Attraction Effects ◽

Reading Study

We present a self-paced reading study investigating attraction effects on number agreement in Eastern Armenian. Both word-by-word reading times and open-ended responses to sentence-final comprehension questions were collected, allowing us to relate reading times and sentence interpretations on a trial-by-trial basis. Results indicate that readers sometimes misinterpret the number feature of the subject in agreement attraction configurations, which is in line with agreement attraction being due to memory encoding errors. Our data also show that readers sometimes misassign the thematic roles of the critical verb. While such a tendency is principally in line with agreement attraction being due to incorrect memory retrievals, the specific pattern observed in our data is not predicted by existing models. We implement four computational models of agreement attraction in a Bayesian framework, finding that our data are better accounted for by an encoding-based model of agreement attraction, rather than a retrieval-based model. A novel contribution of our computational modeling is the finding that the best predictive fit to our data comes from a model that allows number features from the verb to overwrite number features on noun phrases during encoding.

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MHC Class II Binding Prediction—A Little Help from a Friend

Journal of Biomedicine and Biotechnology ◽

10.1155/2010/705821 ◽

2010 ◽

Vol 2010 ◽

pp. 1-8 ◽

Cited By ~ 14

Author(s):

Ivan Dimitrov ◽

Panayot Garnev ◽

Darren R. Flower ◽

Irini Doytchinova

Keyword(s):

Mhc Class Ii ◽

Computational Models ◽

Class Ii ◽

T Cell Epitopes ◽

Binding Prediction ◽

Reliable Prediction ◽

Computational Tools ◽

Robust Identification ◽

Successful Design ◽

Single Instrument

Vaccines are the greatest single instrument of prophylaxis against infectious diseases, with immeasurable benefits to human wellbeing. The accurate and reliable prediction of peptide-MHC binding is fundamental to the robust identification of T-cell epitopes and thus the successful design of peptide- and protein-based vaccines. The prediction of MHC class II peptide binding has hitherto proved recalcitrant and refractory. Here we illustrate the utility of existing computational tools for in silico prediction of peptides binding to class II MHCs. Most of the methods, tested in the present study, detect more than the half of the true binders in the top 5% of all possible nonamers generated from one protein. This number increases in the top 10% and 15% and then does not change significantly. For the top 15% the identified binders approach 86%. In terms of lab work this means 85% less expenditure on materials, labour and time. We show that while existing caveats are well founded, nonetheless use of computational models of class II binding can still offer viable help to the work of the immunologist and vaccinologist.

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Experimental and Computational Evaluation of Performance and Water Management Characteristics of a Bio-Inspired Proton Exchange Membrane Fuel Cell

Journal of Fuel Cell Science and Technology ◽

10.1115/1.4032041 ◽

2015 ◽

Vol 12 (6) ◽

Cited By ~ 8

Author(s):

Bhaskar P. Saripella ◽

Umit O. Koylu ◽

Ming C. Leu

Keyword(s):

Water Management ◽

Fuel Cell ◽

Proton Exchange Membrane ◽

Computational Models ◽

Proton Exchange ◽

Computational Evaluation ◽

Evaluation Of Performance ◽

And Performance ◽

Exchange Membrane ◽

Power Curves

A bio-inspired proton exchange membrane (PEM) fuel cell with a flow field that mimics a leaf pattern is experimentally and computationally evaluated. Experiments are conducted using a transparent assembly for direct visualization of liquid water within the microchannels. Polarization and power curves are also obtained while advanced simulations are performed to predict distributions of pressure, velocity, and concentrations. The same measurements and computations are also performed for a single serpentine fuel cell. The results establish the superior water management and performance characteristics of the bio-inspired fuel cell in comparison to a conventional one. They also help elucidate the underlying transport mechanisms, validate the computational models, and guide the optimization of bio-inspired fuel cells.

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Quality assessment tools used in systematic reviews of in vitro studies: A systematic review

BMC Medical Research Methodology ◽

10.1186/s12874-021-01295-w ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Linh Tran ◽

Dao Ngoc Hien Tam ◽

Abdelrahman Elshafay ◽

Thao Dang ◽

Kenji Hirayama ◽

...

Keyword(s):

Quality Assessment ◽

Systematic Reviews ◽

In Vitro Studies ◽

Assessment Tools ◽

Increasing Trend ◽

Meta Analyses ◽

Critical Aspects

Abstract Background Systematic reviews (SRs) and meta-analyses (MAs) are commonly conducted to evaluate and summarize medical literature. This is especially useful in assessing in vitro studies for consistency. Our study aims to systematically review all available quality assessment (QA) tools employed on in vitro SRs/MAs. Method A search on four databases, including PubMed, Scopus, Virtual Health Library and Web of Science, was conducted from 2006 to 2020. The available SRs/MAs of in vitro studies were evaluated. DARE tool was applied to assess the risk of bias of included articles. Our protocol was developed and uploaded to ResearchGate in June 2016. Results Our findings reported an increasing trend in publication of in vitro SRs/MAs from 2007 to 2020. Among the 244 included SRs/MAs, 126 articles (51.6%) had conducted the QA procedure. Overall, 51 QA tools were identified; 26 of them (51%) were developed by the authors specifically, whereas 25 (49%) were pre-constructed tools. SRs/MAs in dentistry frequently had their own QA tool developed by the authors, while SRs/MAs in other topics applied various QA tools. Many pre-structured tools in these in vitro SRs/MAs were modified from QA tools of in vivo or clinical trials, therefore, they had various criteria. Conclusion Many different QA tools currently exist in the literature; however, none cover all critical aspects of in vitro SRs/MAs. There is a need for a comprehensive guideline to ensure the quality of SR/MA due to their precise nature.

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