Biology subjects

Di Ieva, A.

Publications and source records attributed to Di Ieva, A..

2 recordsLinked to original sources

bioRxiv2026

What Can We Count On? Performance of Microplate Cell Counting Assays in 2D Monolayer and 3D ECM-based In Vitro Tumour Models

Accurate cell counting is essential in tissue engineering and cancer research. The ongoing transition towards advanced 3D in vitro tumour models raises a question about the validity of the standard cell counting protocols, particularly in the systems containing extracellular matrix-based scaffolds. Here, we provide a quantitative analysis of the performance of three popular plate reader-based cell counting/viability assays, such as the Alamar Blue, MTT, CellTiter Glo 3D assays, in 2D monolayer and 3D scaffold-based cultures of U251 human glioblastoma cells, including cell-laden Matrigel plugs, and original tissue engineering constructs based on the decellularised sheep brain scaffolds. We quantitatively characterized the assays linearity, precision, biological and technical reproducibility, proportionality, and inter-assay agreement. The study revealed that assays performance is highly platform-dependent, with 2D cultures allowing significantly more precise and reliable measurements than in 3D ECM scaffold-based cultures. The numerical results provided in this study can help researchers make informed decisions when working with 3D scaffold-based in vitro tumour models and for other tissue engineering purposes where precise cell counting is essential. ToC O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=184 SRC="FIGDIR/small/720021v1_ufig1.gif" ALT="Figure 1"> View larger version (39K): org.highwire.dtl.DTLVardef@16018d9org.highwire.dtl.DTLVardef@1ff7d6dorg.highwire.dtl.DTLVardef@838021org.highwire.dtl.DTLVardef@1510d5b_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

bioRxiv2020

Advanced computational and statistical multiparametric analysis of Susceptibility-Weighted Imaging to characterize gliomas and brain metastases.

Susceptibility-weighted imaging (SWI) is a technique useful for evaluation of the internal structures of brain tumors, including microvasculature and microbleeds. Intratumoral patterns of magnetic susceptibility can be quantified by means of fractal analysis. Here, we propose a radiomics methodological pipeline to merge advanced fractal-based computational modelling with statistical analysis to objectively characterize the fingerprint of gliomas and brain metastases. Forty-seven patients with glioma (grades II-IV, according to the WHO 2016 classification system) and fourteen with brain metastases underwent 3 Tesla MRI using a SWI protocol. All images underwent computational analysis aimed to quantify three Euclidean parameters (related to tumor and SWI volume) and five fractal-based parameters (related to the pixel distribution and geometrical complexity of the SWI patterns). Principal components analysis, linear and quadratic discriminant analysis, K-nearest neighbor and support vector machine methods were used to discriminate between tumor types. The combination of parameters offered an objective evaluation of the SWI pattern in gliomas and brain metastases. The model accurately predicted 88% of glioblastoma, according to the quantification of intratumoral SWI features, failing to discriminate the other types. SWI is not normally used to classify brain tumors, however fractal-based multi-parametric computational analysis can be used to characterize intratumoral SWI patterns to objectively quantify tumors-related features. Specific parameters still have to be identified to provide completely automatic computerized differential diagnosis.

neuroscience↗