The surface topography of biomaterials can have an important impact on cellular adhesion, growth and proliferation. Apart from the overall roughness, the detailed morphological features, at all length scales, significantly affect the cell-biomaterial interactions in a plethora of applications including structural implants, tissue engineering scaffolds and biosensors. In this study, we present a simple, one-step direct laser patterning technique to fabricate nanoripples and dual-rough hierarchical micro/nano structures to control SW10 cell attachment and migration. It is shown that, depending on the laser processing conditions, distinct cell-philic or cell-repellant patterned areas can be attained with a desired motif. We envisage that our technique could enable spatial patterning of cells in a controllable manner, giving rise to advanced capabilities in cell biology research.

Systematic reviews with meta‐analyses are powerful tools that can answer research questions based on data from published studies. Ideally, all relevant data is directly available in the text or tables, but often it is only presented in graphs. In those cases, the data can be extracted from graphs, but this potentially introduces errors. Here, we investigate to what extent the extracted outcome and error values differ from the original data and if these differences could affect the results of a meta‐analysis. Six extractors extracted 36 outcome values and corresponding errors from 22 articles. Differences between extractors were compared using overall concordance correlation coefficients (OCCC), differences between the original and extracted data were compared using concordance correlation coefficients (CCC). To test the possible influence on meta‐analyses, random‐effects meta‐analyses on mean difference comparing original and extracted data were performed. The OCCCs and CCCs were high for both outcome values and errors, CCCs were >0.99 for the outcome and >0.92 for errors. The meta‐analyses showed that the overall effect on outcome was very small (median: 0.025, interquartile range: 0.016–0.046). Therefore, data extraction from graphs is a good method to harvest data if it is not provided in the text or tables, and the original authors cannot provide the data.

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