Glioblastoma multiforme (GBM) is the most common and malignant of all human primary brain cancers, in which drug treatment is still one of the most effective treatments. However, existing drug discovery and development methods rely on the use of conventional two-dimensional (2D) cell cultures, which have been proven to be poor representatives of native physiology. Here, we developed a novel three-dimensional (3D) brain cancer chip composed of photo-polymerizable poly(ethylene) glycol diacrylate (PEGDA) hydrogel for drug screening. This chip can be produced after a few seconds of photolithography and requires no silicon wafer, replica molding, and plasma bonding like microfluidic devices made of poly(dimethylsiloxane) (PDMS). We then cultured glioblastoma cells (U87), which formed 3D brain cancer tissues on the chip, and used the GBM chip to perform combinatorial treatment of Pitavastatin and Irinotecan. The results indicate that this chip is capable of high-throughput GBM cancer spheroids formation, multiple-simultaneous drug administration, and a massive parallel testing of drug response. Our approach is easily reproducible, and this chip has the potential to be a powerful platform in cases such as high-throughput drug screening and prolonged drug release. The chip is also commercially promising for other clinical applications, including 3D cell culture and micro-scale tissue engineering.

The author describes how, because of their compatibility with nonstationary random processes, wavelet transforms are particularly powerful when it comes to analyzing biomedical signals. In this paper, he presents a brief survey of the field which makes this fact clear. Applications discussed include detecting coronary artery disease, and heart sounds related to turbulent blood flow analysis.

We provide an open access dataset of High densitY Surface Electromyogram (HD-sEMG) Recordings (named “Hyser”), a toolbox for neural interface research, and benchmark results for pattern recognition and EMG-force applications. Data from 20 subjects were acquired twice per subject on different days following the same experimental paradigm. We acquired 256-channel HD-sEMG from forearm muscles during dexterous finger manipulations. This Hyser dataset contains five sub-datasets as: (1) pattern recognition (PR) dataset acquired during 34 commonly used hand gestures, (2) maximal voluntary muscle contraction (MVC) dataset while subjects contracted each individual finger, (3) one-degree of freedom (DoF) dataset acquired during force-varying contraction of each individual finger, (4) N-DoF dataset acquired during prescribed contractions of combinations of multiple fingers, and (5) random task dataset acquired during random contraction of combinations of fingers without any prescribed force trajectory. Dataset 1 can be used for gesture recognition studies. Datasets 2-5 also recorded individual finger forces, thus can be used for studies on proportional control of neuroprostheses. Our toolbox can be used to: (1) analyze each of the five datasets using standard benchmark methods and (2) decompose HD-sEMG signals into motor unit action potentials via independent component analysis. We expect our dataset, toolbox and benchmark analyses can provide a unique platform to promote a wide range of neural interface research and collaboration among neural rehabilitation engineers.

Goal: Systemic sclerosis (SSc) is a rare autoimmune, systemic disease with prominent fibrosis of skin and internal organs. Early diagnosis of the disease is crucial for designing effective therapy and management plans. Machine learning algorithms, especially deep learning, have been found to be greatly useful in biology, medicine, healthcare, and biomedical applications, in the areas of medical image processing and speech recognition. However, the need for a large training data set and the requirement for a graphics processing unit (GPU) have hindered the wide application of machine learning algorithms as a diagnostic tool in resource-constrained environments (e.g., clinics). Methods: In this paper, we propose a novel mobile deep learning network for the characterization of SSc skin. The proposed network architecture consists of the UNet, a dense connectivity convolutional neural network (CNN) with added classifier layers that when combined with limited training data, yields better image segmentation and more accurate classification, and a mobile training module. In addition, to improve the computational efficiency and diagnostic accuracy, the highly efficient training model called “MobileNetV2,” which is designed for mobile and embedded applications, was used to train the network. Results: The proposed network was implemented using a standard laptop (2.5 GHz Intel Core i7). After fine tuning, our results showed the proposed network reached 100% accuracy on the training image set, 96.8% accuracy on the validation image set, and 95.2% on the testing image set. The training time was less than 5 hours. We also analyzed the same normal vs SSc skin image sets using the CNN using the same laptop. The CNN reached 100% accuracy on the training image set, 87.7% accuracy on the validation image set, and 82.9% on the testing image set. Additionally, it took more than 14 hours to train the CNN architecture. We also utilized the MobileNetV2 model to analyze an additional dataset of images and classified them as normal, early (mid and moderate) SSc or late (severe) SSc skin images. The network reached 100% accuracy on the training image set, 97.2% on the validation set, and 94.8% on the testing image set. Using the same normal, early and late phase SSc skin images, the CNN reached 100% accuracy on the training image set, 87.7% accuracy on the validation image set, and 82.9% on the testing image set. These results indicated that the MobileNetV2 architecture is more accurate and efficient compared to the CNN to classify normal, early and late phase SSc skin images. Conclusions: Our preliminary study, intended to show the efficacy of the proposed network architecture, holds promise in the characterization of SSc. We believe that the proposed network architecture could easily be implemented in a clinical setting, providing a simple, inexpensive, and accurate screening tool for SSc.

Glioblastoma (GBM) is the most malignant brain tumor characterized by intrinsic or acquired resistance to chemotherapy. GBM tumors show nuclear factor-κB (NF-κB) activity that has been associated with tumor formation, growth, and increased resistance to therapy. We investigated the effect of NF-κB inhibitor BAY 11-7082 with Temozolomide (TMZ) on the signaling pathways in GBM pathogenesis. GBM cells and patient-derived GBM cells cultured in 3D microwells were co-treated with BAY 11-7082 and TMZ or BAY 11-7082 and TMZ alone, and combined experiments of cell proliferation, apoptosis, wound healing assay, as well as reverse-phase protein arrays, western blot and immunofluorescence staining were used to evaluate the effects of drugs on GBM cells. The results revealed that the co-treatment significantly altered cell proliferation by decreasing GBM viability, suppressed NF-κB pathway and enhanced apoptosis. Moreover, it was found that the co-treatment of BAY 11-7082 and TMZ significantly contributed to a decrease in the migration pattern of patient-derived GBM cells by modulating actin cytoskeleton pathway. These findings suggest that in addition to TMZ treatment, NF-κB can be used as a potential target to increase the treatment’s outcomes. The drug combination strategy, which is significantly improved by NF-κB inhibitor could be used to better understand the underlying mechanism of GBM pathways in vivo and as a potential therapeutic tool for GBM treatment.

Glioblastoma multiforme (GBM), an extremely invasive and high-grade (grade IV) glioma, is the most common and aggressive form of brain cancer. It has a poor prognosis, with a median overall survival of only 11 months in the general GBM population and 14.6 to 21 months in clinical trial participants with standard GBM therapies, including maximum safe craniotomy, adjuvant radiation, and chemotherapies. Therefore, new approaches for developing effective treatments, such as a tool for assessing tumor cell drug response before drug treatments are administered, are urgently needed to improve patient survival. To address this issue, we developed an improved brain cancer chip with a diffusion prevention mechanism that blocks drugs crossing from one channel to another. In the current study, we demonstrate that the chip has the ability to culture 3D spheroids from patient tumor specimen-derived GBM cells obtained from three GBM patients. Two clinical drugs used to treat GBM, temozolomide (TMZ) and bevacizumab (Avastin, BEV), were applied and a range of relative concentrations was generated by the microfluidic channels in the brain cancer chip. The results showed that TMZ works more effectively when used in combination with BEV compared to TMZ alone. We believe that this low-cost brain cancer chip could be further developed to generate optimal combination of chemotherapy drugs tailored to individual GBM patients.