With a mortality rate over 580,000 per year, cancer is still one of the leading causes of death worldwide. However, the emerging field of microfluidics can potentially shed light on this puzzling disease. Unique characteristics of microfluidic chips (also known as micro-total analysis system) make them excellent candidates for biological applications. The ex vivo approach of tumor-on-a-chip is becoming an indispensable part of personalized medicine and can replace in vivo animal testing as well as conventional in vitro methods. In tumor-on-a-chip, the complex three-dimensional (3D) nature of malignant tumor is co-cultured on a microfluidic chip and high throughput screening tools to evaluate the efficacy of anticancer drugs are integrated on the same chip. In this article, we critically review the cutting edge advances in this field and mainly categorize each tumor-on-a-chip work based on its primary organ. Specifically, design, fabrication and characterization of tumor microenvironment; cell culture technique; transferring mechanism of cultured cells into the microchip; concentration gradient generators for drug delivery; in vitro screening assays of drug efficacy; and pros and cons of each microfluidic platform used in the recent literature will be discussed separately for the tumor of following organs: (1) Lung; (2) Bone marrow; (3) Brain; (4) Breast; (5) Urinary system (kidney, bladder and prostate); (6) Intestine; and (7) Liver. By comparing these microchips, we intend to demonstrate the unique design considerations of each tumor-on-a-chip based on primary organ, e.g., how microfluidic platform of lung-tumor-on-a-chip may differ from liver-tumor-on-a-chip. In addition, the importance of heart–liver–intestine co-culture with microvasculature in tumor-on-a-chip devices for in vitro chemosensitivity assay will be discussed. Such system would be able to completely evaluate the absorption, distribution, metabolism, excretion and toxicity (ADMET) of anticancer drugs and more realistically recapitulate tumor in vivo-like microenvironment.

With a growing geriatric population estimated to triple by 2050, minimally invasive procedures that are image-guided are becoming both more popular and necessary for treating a variety of diseases. To lower the learning curve for new procedures, it is necessary to develop better guidance systems and methods to analyze procedure performance. Since fluoroscopy remains the primary mode of visualizations, the ability to perform catheter tracking from fluoroscopic images is an important part of this endeavor. This paper explores the use of deep learning to perform the landmark detection of a catheter from fluoroscopic images in 3D-printed heart models. We show that a two-stage deep-convolutional-neural-network-based model architecture can provide improved performance by initially locating a region of interest before determining the coordinates of the catheter tip within the image. This model has an average error of less than 2% of the image resolution and can be performed within 4 milliseconds, allowing for its potential use for real-time intraprocedural tracking. Coordinate regression models have the advantage of directly outputting values that can be used for quantitative tracking in future applications and are easier to create ground truth values (~50× faster), as compared to semantic segmentation models that require entire masks to be made. Therefore, we believe this work has better long-term potential to be used for a broader class of cardiac devices, catheters, and guidewires.

This paper introduces a low-cost, rapid, and simple method for fabricating 3D-printed microfluidic chips, inspired by LEGO® bricks, to accommodate modular microfluidic platforms. A fused filament fabrication (FFF) 3D printer was operated and modified to print a single 400 μm layer of thermoplastic material onto a polymethyl methacrylate substrate, resulting in a very smooth and transparent microchannel by minimizing the limitations of this additive manufacturing. Specific male/female chip-to-world and chip-to-chip connectors have been developed to implement the device’s modularity. In addition, various post-processing procedures were considered because of the inherent surface roughness of the FFF method. Several microfluidic components were designed and fabricated, including T-shaped and cross-shaped microchannels, zig-zag pattern micromixers, and four oval-shaped parallel microchannels. The chips’ validity was examined by injecting food dye into a group of modules to observe the chips’ leakage and fluid circulation behavior. At last, a fluorescent test was implemented to observe the mixing efficiency of the micromixer chip. The proposed fabrication method, from materials’ cost, accessibility, and commercialized perspectives, offers a high throughput process. In other words, it could be fabricated and implemented in most lab environments with limited facilities and budget without expensive equipment. All microfluidic chips in this work have been designed using a modular concept. This relatively new approach allows users to reconfigure connections and microfluidic components to obtain the desired system.

The increasing rate of minimally invasive procedures and the growing prevalence of cardiovascular disease have led to a demand for higher-quality guidance systems for catheter tracking. Traditional methods for catheter tracking, such as detection based on single points and applying masking techniques, have been limited in their ability to provide accurate pose information. In this paper, we propose a novel deep learning-based method for catheter tracking and pose detection. Our method uses a Yolov5 bounding box neural network with postprocessing to perform landmark detection in four regions of the catheter: the tip, radio-opaque marker, bend, and entry point. This allows us to track the catheter’s position and orientation in real time, without the need for additional masking or segmentation techniques. We evaluated our method on a dataset of fluoroscopic images from two distinct datasets and achieved state-of-the-art results in terms of accuracy and robustness. Our model was able to detect all four landmark features (tip, marker, bend, and entry) used to generate a pose for a catheter with 0.285 ± 0.143 mm, 0.261 ± 0.138 mm, 0.424 ± 0.361 mm, and 0.235 ± 0.085 mm accuracy. We believe that our method has the potential to significantly improve the accuracy and efficiency of catheter tracking in medical procedures that utilize bi-plane fluoroscopy guidance.

Hydrostatic pressure is one of the most fundamental and common mechanical stimuli in the body, playing a critical role in the homeostasis of all organ systems. Kidney function is affected by high blood pressure, namely hypertension, by the increased pressure acting on the glomerular capillary walls. This general effect of hypertension is diagnosed as a chronic disease, but underlying mechanistic causes are still not well understood. This paper reports a portable and adaptive device for studying the effects of hydrostatic pressure on a monolayer of cells. The fabricated device fits within a conventional incubation system and microscope. The effects of various pressures and durations were evaluated on a confluent layer of human endothelial cells. We found that a fluid pressure (i.e. hydrostatic pressure) can alter the morphology of the cells and that returning to an ambient pressure can reverse the changes in morphology. Thus, this study provides a proof-of-principle demonstration that this tool can be utilized for exploring the effects of hydrostatic pressure on mammalian cells.