Highly Effective and Efficient Self-Assembled Multilayer-Based Electrode Passivation for Operationally Stable and Reproducible Electrolyte-Gated Transistor Biosensors

To ensure the operational stability of transistor-based biosensors in aqueous electrolytes during multiple measurements, effective electrode passivation is crucially important for reliable and reproducible device performances. This paper presents a highly effective and efficient electrode passivation method using a facile solution-processed self-assembled multilayer (SAML) with excellent insulation property to achieve operational stability and reproducibility of electrolyte-gated transistor (EGT) biosensors. The SAML is created by the consecutive self-assembly of three different molecular layers of 1,10-decanedithiol, vinyl-polyhedral oligomeric silsesquioxane, and 1-octadecanethiol. This passivation enables EGT to operate stably in phosphate-buffered saline (PBS) during repeated measurements over multiple cycles without short-circuiting. The SAML-passivated EGT biosensor is fabricated with a solution-processed In2O3 thin film as an amorphous oxide semiconductor working both as a semiconducting channel in the transistor and as a functionalizable biological interface for a bioreceptor. The SAML-passivated EGT including In2O3 thin film is demonstrated for the detection of Tau protein as a biomarker of Alzheimer’s disease while employing a Tau-specific DNA aptamer as a bioreceptor and a PBS solution with a low ionic strength to diminish the charge-screening (Debye length) effect. The SAML-passivated EGT biosensor functionalized with the Tau-specific DNA aptamer exhibits ultrasensitive, quantitative, and reliable detection of Tau protein from 1 × 10–15 to 1 × 10–10 M, covering a much larger range than clinical needs, via changes in different transistor parameters. Therefore, the SAML-based passivation method can be effectively and efficiently utilized for operationally stable and reproducible transistor-based biosensors. Furthermore, this presented strategy can be extensively adapted for advanced biomedical devices and bioelectronics in aqueous or physiological environments

Radiomics-based neural network predicts recurrence patterns in glioblastoma using dynamic susceptibility contrast-enhanced MRI

© 2021, The Author(s).Glioblastoma remains the most devastating brain tumor despite optimal treatment, because of the high rate of recurrence. Distant recurrence has distinct genomic alterations compared to local recurrence, which requires different treatment planning both in clinical practice and trials. To date, perfusion-weighted MRI has revealed that perfusional characteristics of tumor are associated with prognosis. However, not much research has focused on recurrence patterns in glioblastoma: namely, local and distant recurrence. Here, we propose two different neural network models to predict the recurrence patterns in glioblastoma that utilizes high-dimensional radiomic profiles based on perfusion MRI: area under the curve (AUC) (95% confidence interval), 0.969 (0.903–1.000) for local recurrence; 0.864 (0.726–0.976) for distant recurrence for each patient in the validation set. This creates an opportunity to provide personalized medicine in contrast to studies investigating only group differences. Moreover, interpretable deep learning identified that salient radiomic features for each recurrence pattern are related to perfusional intratumoral heterogeneity. We also demonstrated that the combined salient radiomic features, or “radiomic risk score”, increased risk of recurrence/progression (hazard ratio, 1.61; p = 0.03) in multivariate Cox regression on progression-free survival.11Nsciescopu