Enhanced γ-Glutamyltranspeptidase Imaging That Unravels the Glioma Recurrence in Post-radio/Chemotherapy Mixtures for Precise Pathology via Enzyme-Triggered Fluorescent Probe

Accurate pathological diagnosis of gliomas recurrence is crucial for the optimal management and prognosis prediction. The study here unravels that our newly developed γ-glutamyltranspeptidase (GGT) fluorescence probe (Figure 1A) imaging in twenty recurrent glioma tissues selectively recognizes the most malignant portion from treatment responsive tissues induced by radio/chemo-therapy (Figure 1B). The overexpression of GGT in recurrent gliomas and low level in radiation necrosis were validated by western blot analysis and immunohistochemistry. Furthermore, the ki-67 index evaluation demonstrated the significant increase of malignancy, aided by the GGT-responsive fluorescent probe to screen out the right specimen through fast enhanced imaging of enzyme activity. Importantly, our GGT-targeting probe can be used for accurate determination of pathologic evaluation of tumor malignancy, and eventually for guiding the following management in patients with recurrent gliomas

Machine Learning Models for Multiparametric Glioma Grading With Quantitative Result Interpretations

Gliomas are the most common primary malignant brain tumors in adults. Accurate grading is crucial as therapeutic strategies are often disparate for different grades and may influence patient prognosis. This study aims to provide an automated glioma grading platform on the basis of machine learning models. In this paper, we investigate contributions of multi-parameters from multimodal data including imaging parameters or features from the Whole Slide images (WSI) and the proliferation marker Ki-67 for automated brain tumor grading. For each WSI, we extract both visual parameters such as morphology parameters and sub-visual parameters including first-order and second-order features. On the basis of machine learning models, our platform classifies gliomas into grades II, III, and IV. Furthermore, we quantitatively interpret and reveal the important parameters contributing to grading with the Local Interpretable Model-Agnostic Explanations (LIME) algorithm. The quantitative analysis and explanation may assist clinicians to better understand the disease and accordingly to choose optimal treatments for improving clinical outcomes. The performance of our grading model was evaluated with cross-validation, which randomly divided the patients into non-overlapping training and testing sets and repeatedly validated the model on the different testing sets. The primary results indicated that this modular platform approach achieved the highest grading accuracy of 0.90 ± 0.04 with support vector machine (SVM) algorithm, with grading accuracies of 0.91 ± 0.08, 0.90 ± 0.08, and 0.90 ± 0.07 for grade II, III, and IV gliomas, respectively

Numerical Simulations of Manipulation of Microparticles by Droplets in Digital Microfluidics

Manipulation of microparticles by droplets is a very useful and important technique for many microfluidics applications. Due to the large specific surface necessary for chemical binding and easy recovery from a dispersion, utilization of nanospheres or microspheres has become more and more popular for different medical, biological, and optical applications. The goal of this research is to understand the mechanism for the manipulation of microparticles by droplets. Dissipative particle dynamics (DPD), which is extensively used to model mesoscale flow phenomena, is applied as the numerical tool for this study. A model for solid microparticles is designed to study the interactions among microparticles, liquid droplets, and solid substrates. A spherical shell is used to represent the microparticle, and the shell surface is packed by dense enough beads to avoid undesired penetration of liquid beads into solid microparticles, conserving the momentum automatically. After that, the interaction between a rigid microparticle and a solid substrate is modeled based on contact mechanics, including adhesion forces, normal forces, and friction forces. After the model for microparticles is built, a baseline case simulating the pickup and transport of a hydrophobic microparticle by a droplet is demonstrated and compared with experimental observations. Then, the flow structures within a droplet containing a hydrophobic microparticle are revealed.With this developed numerical tool, parametric studies are conducted to investigate the effect on the manipulation processes (including pickup, transport, and drop off) of a microparticle by droplet sizes, wetting properties of microparticles, and particle-substrate friction coefficients. The increase of droplet size can speed up the transport of microparticles. However, the increase of particle-substrate friction coefficients can lead to drop-off of a hydrophobic microparticle. The mechanism for the drop-off, or delivery, is analyzed by checking the development of the friction force and driving force on the microparticle during the transport process. The critical velocity, defined as the instantaneous velocity of the microparticle right before the occurrence of delivery, is measured, and it is found that the critical velocity is about same for different sizes of droplets. Based on the numerical results, two different designs, namely passive delivery and active delivery, have been demonstrated to be capable of controlling the location for the delivery of single hydrophobic microparticle without any trap design or external field forces. These numerical results provide a fundamental understanding of interactions among the microparticle, the droplet and the substrate to facilitate the optimal experimental design of digital microfluidic system utilizing microparticles

Numerical Simulations of the Digital Microfluidic Manipulation of Single Microparticles

Single-cell analysis techniques have been developed as a valuable bioanalytical tool for elucidating cellular heterogeneity at genomic, proteomic, and cellular levels. Cell manipulation is an indispensable process for single-cell analysis. Digital microfluidics (DMF) is an important platform for conducting cell manipulation and single-cell analysis in a high-throughput fashion. However, the manipulation of single cells in DMF has not been quantitatively studied so far. In this article, we investigate the interaction of a single microparticle with a liquid droplet on a flat substrate using numerical simulations. The droplet is driven by capillary force generated from the wettability gradient of the substrate. Considering the Brownian motion of microparticles, we utilize many-body dissipative particle dynamics (MDPD), an off-lattice mesoscopic simulation technique, in this numerical study. The manipulation processes (including pickup, transport, and drop-off) of a single microparticle with a liquid droplet are simulated. Parametric studies are conducted to investigate the effects on the manipulation processes from the droplet size, wettability gradient, wetting properties of the microparticle, and particle–substrate friction coefficients. The numerical results show that the pickup, transport, and drop-off processes can be precisely controlled by these parameters. On the basis of the numerical results, a trap-free delivery of a hydrophobic microparticle to a destination on the substrate is demonstrated in the numerical simulations. The numerical results not only provide a fundamental understanding of interactions among the microparticle, the droplet, and the substrate but also demonstrate a new technique for the trap-free immobilization of single hydrophobic microparticles in the DMF design. Finally, our numerical method also provides a powerful design and optimization tool for the manipulation of microparticles in DMF systems