Sustainable and effective reconditioning of SPR gold chips functionalized with molecularly imprinted polynorepinephrine.

Surface Plasmon Resonance (SPR) technology has revolutionized the study of affinity-based biomolecular interactions, offering label-free and real-time analysis capabilities. However, traditional SPR gold chips have been viewed as disposable due to challenges in post-use reconditioning, leading to significant resource wastage and increased costs. To address this issue, we propose a novel approach utilizing polynorepinephrine-based (PNE) Molecularly Imprinted Bio-Polymers (MIBPs) as alternative receptors to conventional antibodies. Self-adhesive MIBPs do not require covalent immobilization. This enables efficient and rapid chip functionalization and post-use removal, facilitating multiple reuses of the plasmon source without compromising analytical performance. We conducted a thorough characterization and data analysis, confirming the robustness and repeatability of a single MIBP-functionalized chip for human IgG detection. 10 cycles of reconditioning and reuse, assayed by 60 kinetic calibrations, were performed. Our findings demonstrate the potential indefinite reuse of SPR chips facilitated by PNE MIBPs, with implications for streamlining test development and routine implementation in SPR biosensing applications. Therefore, they represent a sustainable solution to the longstanding challenge of disposable SPR gold chips also by reducing the reliance on animal-derived Abs for bioanalytic testing. Being also extremely low-cost and green, PNE-based MIBPs minimize the ecological footprint associated with traditional SPR assays. Overall, our work represents a significant advancement towards the development of reusable SPR biosensors. It promises a more sustainable and cost-effective future for biomedical research and diagnostic applications, with application on other transducers and bioanalytical platforms.

Test-retest repeatability of myocardial radiomic features from quantitative cardiac magnetic resonance T1 and T2 mapping.

Radiomics of cardiac magnetic resonance (MR) imaging has proved to be potentially useful in the study of various myocardial diseases. Therefore, assessing the repeatability degree in radiomic features measurement is of fundamental importance. The aim of this study was to assess test-retest repeatability of myocardial radiomic features extracted from quantitative T1 and T2 maps. A representative group of 24 subjects (mean age 54 ± 18 years) referred for clinical cardiac MR imaging were enrolled in the study. For each subject, T1 and T2 mapping through MOLLI and T2-prepared TrueFISP acquisition sequences, respectively, were performed at 1.5 T. Then, 98 radiomic features of different classes (shape, first-order, second-order) were extracted from a region of interest encompassing the whole left ventricle myocardium in a short axis slice. The repeatability was assessed performing different and complementary analyses: intraclass correlation coefficient (ICC) and limits of agreement (LOA) (i.e., the interval within which 95% of the percentage differences between two repeated measures are expected to lie). Radiomic features were characterized by a relatively wide range of repeatability degree in terms of both ICC and LOA. Overall, 44.9% and 38.8% of radiomic features showed ICC values > 0.75 for T1 and T2 maps, respectively, while 25.5% and 23.4% of radiomic features showed LOA between ±10%. A subset of radiomic features for T1 (Mean, Median, 10Percentile, 90Percentile, RootMeanSquared, Imc2, RunLengthNonUniformityNormalized, RunPercentage and ShortRunEmphasis) and T2 (MaximumDiameter, RunLengthNonUniformityNormalized, RunPercentage, ShortRunEmphasis) maps presented both ICC > 0.75 and LOA between ±5%. Overall, radiomic features extracted from T1 maps showed better repeatability performance than those extracted from T2 maps, with shape features characterized by better repeatability than first-order and textural features. Moreover, only a limited subset of 9 and 4 radiomic features for T1 and T2 maps, respectively, showed high repeatability degree in terms of both ICC and LOA. These results confirm the importance of assessing test-retest repeatability degree in radiomic feature estimation and might be useful for a more effective/reliable use of myocardial T1 and T2 mapping radiomics in clinical or research studies.

Efficacy of MRI data harmonization in the age of machine learning: a multicenter study across 36 datasets.

Pooling publicly-available MRI data from multiple sites allows to assemble extensive groups of subjects, increase statistical power, and promote data reuse with machine learning techniques. The harmonization of multicenter data is necessary to reduce the confounding effect associated with non-biological sources of variability in the data. However, when applied to the entire dataset before machine learning, the harmonization leads to data leakage, because information outside the training set may affect model building, and potentially falsely overestimate performance. We propose a 1) measurement of the efficacy of data harmonization; 2) harmonizer transformer, i.e., an implementation of the ComBat harmonization allowing its encapsulation among the preprocessing steps of a machine learning pipeline, avoiding data leakage by design. We tested these tools using brain T1-weighted MRI data from 1740 healthy subjects acquired at 36 sites. After harmonization, the site effect was removed or reduced, and we showed the data leakage effect in predicting individual age from MRI data, highlighting that introducing the harmonizer transformer into a machine learning pipeline allows for avoiding data leakage by design.