3D-printed smartphone-based device for label-free cell separation

Machine learning-enabled optimization of extrusion-based 3D printing

Machine learning (ML) and three-dimensional (3D) printing are among the fastest-growing branches of science. While ML can enable computers to independently learn from available data to make decisions with minimal human intervention, 3D printing has opened up an avenue for modern, multi-material, manufacture of complex 3D structures with a rapid turn-around ability for users with limited manufacturing experience. However, the determination of optimum printing parameters is still a challenge, increasing pre-printing process time and material wastage. Here, we present the first integration of ML and 3D printing through an easy-to-use graphical user interface (GUI) for printing parameter optimization. Unlike the widely held orthogonal design used in most of the 3D printing research, we, for the first time, used nine different computer-aided design (CAD) images and in order to enable ML algorithms to distinguish the difference between designs, we devised a self-designed method to calculate the "complexity index" of CAD designs. In addition, for the first time, the similarity of the print outcomes and CAD images are measured using four different self-designed labeling methods (both manually and automatically) to figure out the best labeling method for ML purposes. Subsequently, we trained eight ML algorithms on 224 datapoints to identify the best ML model for 3D printing applications. The "gradient boosting regression" model yields the best prediction performance with an R-2 score of 0.954. The ML-embedded GUI developed in this study enables users (either skilled or unskilled in 3D printing and/or ML) to simply upload a design (desired to print) to the GUI along with desired printing temperature and pressure to obtain the approximate similarity in the case of actual 3D printing of the uploaded design. This ultimately can prevent error-and-trial steps prior to printing which in return can speed up overall design-to-end-product time with less material waste and more cost-efficiency.

Deep Learning-Enabled Technologies for Bioimage Analysis

Deep learning (DL) is a subfield of machine learning (ML), which has recently demonstrated its potency to significantly improve the quantification and classification workflows in biomedical and clinical applications. Among the end applications profoundly benefitting from DL, cellular morphology quantification is one of the pioneers. Here, we first briefly explain fundamental concepts in DL and then we review some of the emerging DL-enabled applications in cell morphology quantification in the fields of embryology, point-of-care ovulation testing, as a predictive tool for fetal heart pregnancy, cancer diagnostics via classification of cancer histology images, autosomal polycystic kidney disease, and chronic kidney diseases.

HologLev: A Hybrid Magnetic Levitation Platform Integrated with Lensless Holographic Microscopy for Density-Based Cell Analysis

In clinical practice, a variety of diagnostic applications require the identification of target cells. Density has been used as a physical marker to distinguish cell populations since metabolic activities could alter the cell densities. Magnetic levitation offers great promise for separating cells at the single cell level within heterogeneous populations with respect to cell densities. Traditional magnetic levitation platforms need bulky and precise optical microscopes to visualize levitated cells. Moreover, the evaluation process of cell densities is cumbersome, which also requires trained personnel for operation. In this work, we introduce a device (HologLev) as a fusion of the magnetic levitation principle and lensless digital inline holographic microscopy (LDIHM). LDIHM provides ease of use by getting rid of bulky and expensive optics. By placing an imaging sensor just beneath the microcapillary channel without any lenses, recorded holograms are processed for determining cell densities through a fully automated digital image processing scheme. The device costs less than $100 and has a compact design that can fit into a pocket. We perform viability tests on the device by levitating three different cell lines (MDA-MB-231, U937, D1 ORL UVA) and comparing them against their dead correspondents. We also tested the differentiation of mouse osteoblastic (7F2) cells by monitoring characteristic variations in their density. Last, the response of MDA-MB-231 cancer cells to a chemotherapy drug was demonstrated in our platform. HologLev provides cost-effective, label-free, fully automated cell analysis in a compact design that could be highly desirable for laboratory and point-of-care testing applications.

Microfluidics for microalgal biotechnology

Microalgae have expanded their roles as renewable and sustainable feedstocks for biofuel, smart nutrition, biopharmaceutical, cosmeceutical, biosensing, and space technologies. They accumulate valuable biochemical compounds from protein, carbohydrate, and lipid groups, including pigments and carotenoids. Microalgal biomass, which can be adopted for multivalorization under biorefinery settings, allows not only the production of various biofuels but also other value-added biotechnological products. However, state-of-the-art technologies are required to optimize yield, quality, and the economical aspects of both upstream and downstream processes. As such, the need to use microfluidic-based devices for both fundamental research and industrial applications of microalgae, arises due to their microscale sizes and dilute cultures. Microfluidics-based devices are superior to their competitors through their ability to perform multiple functions such as sorting and analyzing small amounts of samples (nanoliter to picoliter) with higher sensitivities. Here, we review emerging applications of microfluidic technologies on microalgal processes in cell sorting, cultivation, harvesting, and applications in biofuels, biosensing, drug delivery, and nutrition.

Increasing the packing density of assays in paper-based microfluidic devices

Paper-based devices have a wide range of applications in point-of-care diagnostics, environmental analysis, and food monitoring. Paper-based devices can be deployed to resource-limited countries and remote settings in developed countries. Paper-based point-of-care devices can provide access to diagnostic assays without significant user training to perform the tests accurately and timely. The market penetration of paper-based assays requires decreased device fabrication costs, including larger packing density of assays (i.e., closely packed features) and minimization of assay reagents. In this review, we discuss fabrication methods that allow for increasing packing density and generating closely packed features in paper-based devices. To ensure that the paper-based device is low-cost, advanced fabrication methods have been developed for the mass production of closely packed assays. These emerging methods will enable minimizing the volume of required samples (e.g., liquid biopsies) and reagents in paper-based microfluidic devices.

Machine learning-enabled multiplexed microfluidic sensors

High-throughput, cost-effective, and portable devices can enhance the performance of point-of-care tests. Such devices are able to acquire images from samples at a high rate in combination with microfluidic chips in point-of-care applications. However, interpreting and analyzing the large amount of acquired data is not only a labor-intensive and time-consuming process, but also prone to the bias of the user and low accuracy. Integrating machine learning (ML) with the image acquisition capability of smartphones as well as increasing computing power could address the need for high-throughput, accurate, and automatized detection, data processing, and quantification of results. Here, ML-supported diagnostic technologies are presented. These technologies include quantification of colorimetric tests, classification of biological samples (cells and sperms), soft sensors, assay type detection, and recognition of the fluid properties. Challenges regarding the implementation of ML methods, including the required number of data points, image acquisition prerequisites, and execution of data-limited experiments are also discussed.

Sensing of electrolytes in urine using a miniaturized paper-based device

Analyzing electrolytes in urine, such as sodium, potassium, calcium, chloride, and nitrite, has significant diagnostic value in detecting various conditions, such as kidney disorder, urinary stone disease, urinary tract infection, and cystic fibrosis. Ideally, by regularly monitoring these ions with the convenience of dipsticks and portable tools, such as cellphones, informed decision making is possible to control the consumption of these ions. Here, we report a paper-based sensor for measuring the concentration of sodium, potassium, calcium, chloride, and nitrite in urine, accurately quantified using a smartphone-enabled platform. By testing the device with both Tris buffer and artificial urine containing a wide range of electrolyte concentrations, we demonstrate that the proposed device can be used for detecting potassium, calcium, chloride, and nitrite within the whole physiological range of concentrations, and for binary quantification of sodium concentration.

Magnetic Force-Based Microfluidic Techniques for Cellular and Tissue Bioengineering

Live cell manipulation is an important biotechnological tool for cellular and tissue level bioengineering applications due to its capacity for guiding cells for separation, isolation, concentration, and patterning. Magnetic force-based cell manipulation methods offer several advantages, such as low adverse effects on cell viability and low interference with the cellular environment. Furthermore, magnetic-based operations can be readily combined with microfluidic principles by precisely allowing control over the spatiotemporal distribution of physical and chemical factors for cell manipulation. In this review, we present recent applications of magnetic force-based cell manipulation in cellular and tissue bioengineering with an emphasis on applications with microfluidic components. Following an introduction of the theoretical background of magnetic manipulation, components of magnetic force-based cell manipulation systems are described. Thereafter, different applications, including separation of certain cell fractions, enrichment of rare cells, and guidance of cells into specific macro- or micro-arrangements to mimic natural cell organization and function, are explained. Finally, we discuss the current challenges and limitations of magnetic cell manipulation technologies in microfluidic devices with an outlook on future developments in the field.