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A CFD level-set method for soft tissue growth: theory and fundamental equations.

Marcello Lappa
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Abstract

A level-set method, specifically conceived for the case of soft organic tissue growth from feeding solutions, is introduced and described in detail. The model can handle the morphological evolution of the organic specimen under the influence of external convection (fluid-dynamics of the bioreactor). The analogies and differences between this technique and a previous volume of fraction method are discussed pointing out advantages and limitations of both formulations.

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GrowthHumansKineticsModels, BiologicalTissue EngineeringJournal ArticleResearch Support, Non-U.S. Gov't

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Marcello Lappa. 2005. A CFD level-set method for soft tissue growth: theory and fundamental equations.. https://doi.org/10.1016/j.jbiomech.2004.02.037

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Worked examples of alternative methods for the synthesis of qualitative and quantitative research in systematic reviews.

BACKGROUND: The inclusion of qualitative studies in systematic reviews poses methodological challenges. This paper presents worked examples of two methods of data synthesis (textual narrative and thematic), used in relation to one review, with the aim of enabling researchers to consider the strength of different approaches. METHODS: A systematic review of lay perspectives of infant size and growth was conducted, locating 19 studies (including both qualitative and quantitative). The data extracted from these were synthesised using both a textual narrative and a thematic synthesis. RESULTS: The processes of both methods are presented, showing a stepwise progression to the final synthesis. Both methods led us to similar conclusions about lay views toward infant size and growth. Differences between methods lie in the way they dealt with study quality and heterogeneity. CONCLUSION: On the basis of the work reported here, we consider textual narrative and thematic synthesis have strengths and weaknesses in relation to different research questions. Thematic synthesis holds most potential for hypothesis generation, but may obscure heterogeneity and quality appraisal. Textual narrative synthesis is better able to describe the scope of existing research and account for the strength of evidence, but is less good at identifying commonality.

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