All Outputs (6)

Microparticles Decorated with Cell‐Instructive Surface Chemistries Actively Promote Wound Healing (2022)
Journal Article
Latif, A., Fisher, L. E., Dundas, A. A., Crucitti, V. C., Imir, Z., Lawler, K., …Ghaemmaghami, A. M. (2022). Microparticles Decorated with Cell‐Instructive Surface Chemistries Actively Promote Wound Healing. Advanced Materials, Article 2208364. https://doi.org/10.1002/adma.202208364

Wound healing is a complex biological process involving close crosstalk between various cell types. Dysregulation in any of these processes, such as in diabetic wounds, results in chronic nonhealing wounds. Fibroblasts are a critical cell type involv... Read More about Microparticles Decorated with Cell‐Instructive Surface Chemistries Actively Promote Wound Healing.

Feature importance in machine learning models: A fuzzy information fusion approach (2022)
Journal Article
Rengasamy, D., Mase, J. M., Kumar, A., Rothwell, B., Torres, M. T., Alexander, M. R., …Figueredo, G. P. (2022). Feature importance in machine learning models: A fuzzy information fusion approach. Neurocomputing, 511, 163-174. https://doi.org/10.1016/j.neucom.2022.09.053

With the widespread use of machine learning to support decision-making, it is increasingly important to verify and understand the reasons why a particular output is produced. Although post-training feature importance approaches assist this interpreta... Read More about Feature importance in machine learning models: A fuzzy information fusion approach.

Exploiting Generative Design for Multi-Material Inkjet 3D Printed Cell Instructive, Bacterial Biofilm Resistant Composites (2022)
Working Paper
he, Y., Begines, B., Trindade, G., Abdi, M., dubern, J., Prina, E., …Wildman, R. Exploiting Generative Design for Multi-Material Inkjet 3D Printed Cell Instructive, Bacterial Biofilm Resistant Composites

As our understanding of disease grows, it is becoming established that treatment needs to be personalized and targeted to the needs of the individual. In this paper we show that multi-material inkjet-based 3D printing, when backed with generative des... Read More about Exploiting Generative Design for Multi-Material Inkjet 3D Printed Cell Instructive, Bacterial Biofilm Resistant Composites.

Utilising micron scale 3D printed morphologies for particle adhesion reduction (2022)
Journal Article
Marsh, G. E., Bunker, M. J., Alexander, M. R., Wildman, R. D., Nicholas, M., & Roberts, C. J. (2022). Utilising micron scale 3D printed morphologies for particle adhesion reduction. Powder Technology, 404, Article 117418. https://doi.org/10.1016/j.powtec.2022.117418

In the pharmaceutical industry, the ability to improve the understanding of the effect of surface roughness on interparticulate interactions is critical. Dry powder inhalers often possess poor efficiency, as the powder formulations are inherently adh... Read More about Utilising micron scale 3D printed morphologies for particle adhesion reduction.

Molecular Formula Prediction for Chemical Filtering of 3D OrbiSIMS Datasets (2022)
Journal Article
Edney, M. K., Kotowska, A. M., Spanu, M., Trindade, G. F., Wilmot, E., Reid, J., …Scurr, D. J. (2022). Molecular Formula Prediction for Chemical Filtering of 3D OrbiSIMS Datasets. Analytical Chemistry, 94(11), 4703–4711. https://doi.org/10.1021/acs.analchem.1c04898

Modern mass spectrometry techniques produce a wealth of spectral data, and although this is an advantage in terms of the richness of the information available, the volume and complexity of data can prevent a thorough interpretation to reach useful co... Read More about Molecular Formula Prediction for Chemical Filtering of 3D OrbiSIMS Datasets.

Correction to “Bespoke 3D-Printed Polydrug Implants Created via Microstructural Control of Oligomers” (2022)
Journal Article
Ruiz-Cantu, L., Trindade, G. F., Taresco, V., Zhou, Z., He, Y., Burroughs, L., …Wildman, R. D. (2022). Correction to “Bespoke 3D-Printed Polydrug Implants Created via Microstructural Control of Oligomers”. ACS Applied Materials and Interfaces, 14(6), 8654. https://doi.org/10.1021/acsami.2c00035

The chemical structure of the drug trandolapril has been corrected in Figure 4c. The conclusions of the work have not been affected by this correction. (Figure present).