Directed assembly of nano and microscale particles is of great interest and has widespread applications in various fields including electronics, nanomaterials and tissue engineering. Bottom-up tissue engineering is motivated by the occurrence of repeating functional units in vivo. The bottom-up approach requires novel techniques to assemble engineered functional units as building blocks at a high speed with spatial control over three-dimensional (3D) micro-architecture. Here, we report a magnetic assembler that utilizes nanoparticles and microscale hydrogels as building blocks to create 3D complex multi-layer constructs via external magnetic fields using different concentrations of magnetic nanoparticles. This approach holds potential for 3D assembly processes that could be utilized in various tissue engineering and regenerative medicine applications.

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Non-invasive Brain-Computer Interfaces (BCI) have demonstrated great promise for neuroprosthetics and assistive devices. Here we aim to investigate methods to combine Electroencephalography (EEG) and functional Near-Infrared Spectroscopy (fNIRS) in an asynchronous Sensory Motor rhythm (SMR)-based BCI. We attempted to classify 4 different executed movements, namely, Right-Arm—Left-Arm—Right-Hand—Left-Hand tasks. Previous studies demonstrated the benefit of EEG-fNIRS combination. However, since normally fNIRS hemodynamic response shows a long delay, we investigated new features, involving slope indicators, in order to immediately detect changes in the signals. Moreover, Common Spatial Patterns (CSPs) have been applied to both EEG and fNIRS signals. 15 healthy subjects took part in the experiments and since 25 trials per class were available, CSPs have been regularized with information from the entire population of participants and optimized using genetic algorithms. The different features have been compared in terms of performance and the dynamic accuracy over trials shows that the introduced methods diminish the fNIRS delay in the detection of changes.

We demonstrate an integrated platform that merges a microfluidic chip with lensless imaging to target CD4+ T-lymphocyte counts for HIV point-of-care testing at resource-limited settings. The chips were designed and fabricated simply with a laser cutter without using expensive cleanroom equipment. To capture CD4+ T lymphocytes from blood, anti-CD4 antibody was immobilized on only one side of the microfluidic chip. These captured cells were detected through an optically clear chip using a charge coupled device (CCD) sensor by lensless shadow imaging techniques. Gray scale image of the captured cells in a 24 mm × 4 mm × 50 μm microfluidic chip was obtained by the lensless imaging platform. The automatic cell counting software enumerated the captured cells in three seconds. Captured cells were also imaged with a fluorescence microscope and manually counted to characterize functionality of the integrated platform. The integrated platform achieved 70.2 ± 6.5% capture efficiency, 88.8 ± 5.4% capture specificity for CD4+ T-lymphocytes, 96 ± 1.6% CCD efficiency, and 83.5 ± 2.4% overall platform performance (n = 9 devices) compared to the gold standard, i.e. flow cytometry count. The integrated system gives a CD4 count from blood within 10 minutes. The integrated platform points a promising direction for point-of-care testing (POCT) to rapidly capture, image and count subpopulations of cells from blood samples in an automated matter.

We demonstrate an integrated platform that merges a microfluidic chip with lensless imaging to target CD4 + T-lymphocyte counts for HIV point-of-care testing at resource-limited settings. The chips were designed and fabricated simply with a laser cutter without using expensive cleanroom equipment. To capture CD4 + T lymphocytes from blood, anti-CD4 antibody was immobilized on only one side of the microfluidic chip. These captured cells were detected through an optically clear chip using a charge coupled device (CCD) sensor by lensless shadow imaging techniques. Gray scale image of the captured cells in a 24 mm × 4 mm × 50 μm microfluidic chip was obtained by the lensless imaging platform. The automatic cell counting software enumerated the captured cells in three seconds. Captured cells were also imaged with a fluorescence microscope and manually counted to characterize functionality of the integrated platform. The integrated platform achieved 70.2 ± 6.5% capture efficiency, 88.8 ± 5.4% capture specificity for CD4 + T-lymphocytes, 96 ± 1.6% CCD efficiency, and 83.5 ± 2.4% overall platform performance (n = 9 devices) compared to the gold standard, i.e. flow cytometry count. The integrated system gives a CD4 count from blood within 10 minutes. The integrated platform points a promising direction for point-of-care testing (POCT) to rapidly capture, image and count subpopulations of cells from blood samples in an automated matter.

5.3 million American couples of reproductive age (9%) are affected by infertility, among which male factors account for up to 50% of cases, which necessitates the identification of parameters defining sperm quality, including sperm count and motility. In vitro fertilization (IVF) with or without intra cytoplasmic sperm injection (ICSI) has become the most widely used assisted reproductive technology (ART) in modern clinical practice to overcome male infertility challenges. One of the obstacles of IVF and ICSI lies in identifying and isolating the most motile and presumably healthiest sperm from semen samples that have low sperm counts (oligozoospermia) and/or low sperm motility (oligospermaesthenia). Microfluidic systems have shown potential to sort sperm with flow systems. However, the small field of view (FOV) of conventional microscopes commonly used to image sperm motion presents challenges in tracking a large number of sperm cells simultaneously. To address this challenge, we have integrated a lensless charge-coupled device (CCD) with a microfluidic chip to enable wide FOV and automatic recording as the sperm move inside a microfluidic channel. The integrated system enables the sorting and tracking of a population of sperm that have been placed in a microfluidic channel. This channel can be monitored in both horizontal and vertical configuration similar to a swim-up column method used clinically. Sperm motilities can be quantified by tracing the shadow paths for individual sperm. Moreover, as the sperm are sorted by swimming from the inlet towards the outlet of a microfluidic channel, motile sperm that reach the outlet can be extracted from the channel at the end of the process. This technology can lead to methods to evaluate each sperm individually in terms of motility response in a wide field of view, which could prove especially useful, when working with oligozoospermic or oligospermaesthenic samples, in which the most motile sperm need to be isolated from a pool of small number of sperm.

Selective capture of cells from bodily fluids in microchannels has broadly transformed medicine enabling circulating tumor cell isolation, rapid CD4+ cell counting for HIV monitoring, and diagnosis of infectious diseases. Although cell capture methods have been demonstrated in microfluidic systems, the release of captured cells remains a significant challenge. Viable retrieval of captured label-free cells in microchannels will enable a new era in biological sciences by allowing cultivation and post-processing. The significant challenge in release comes from the fact that the cells adhere strongly to the microchannel surface, especially when immuno-based immobilization methods are used. Even though fluid shear and enzymes have been used to detach captured cells in microchannels, these methods are known to harm cells and affect cellular characteristics. This paper describes a new technology to release the selectively captured label-free cells in microchannels without the use of fluid shear or enzymes. We have successfully released the captured CD4+ cells (3.6% of the mononuclear blood cells) from blood in microfluidic channels with high specificity (89% ± 8%), viability (94% ± 4%), and release efficiency (59% ± 4%). We have further validated our system by specifically capturing and controllably releasing the CD34+ stem cells from whole blood, which were quantified to be 19 cells per million blood cells in the blood samples used in this study. Our results also indicated that both CD4+ and CD34+ cells released from the microchannels were healthy and amenable for in vitro culture. Manual flow based microfluidic method utilizes inexpensive, easy to fabricate microchannels allowing selective label-free cell capture and release in less than 10 minutes, which can also be used at the point-of-care. The presented technology can be used to isolate and purify a broad spectrum of cells from mixed populations offering widespread applications in applied biological sciences, such as tissue engineering, regenerative medicine, rare cell and stem cell isolation, proteomic/genomic research, and clonal/population analyses.

Lab-chip device analysis often requires high throughput quantification of fluorescent cell images, obtained under different conditions of fluorescent intensity, illumination, focal depth, and optical magnification. Many laboratories still use manual counting - a tedious, expensive process prone to inter-observer variability. The manual counting process can be automated for fast and precise data gathering and reduced manual bias. We present a method to segment and count cells in microfluidic chips that are labeled with a single stain, or multiple stains, using image analysis techniques in Matlab and discuss its advantages over manual counting. Microfluidic based cell capturing devices for HIV monitoring were used to validate our method. Captured CD4+ CD3+ T lymphocytes were stained with DAPI, AF488-anti CD4, and AF647-anti CD3 for cell identification. Altogether 4788 (76 × 3 × 21) gray color images were obtained from devices using discarded 10 HIV infected patient whole blood samples (21 devices). We observed that the automatic method performs similarly to manual counting for a small number of cells. However, automated counting is more accurate and more than 100 times faster than manual counting for multiple-color stained cells, especially when large numbers of cells need to be quantified (>500 cells). The algorithm is fully automatic for subsequent microscope images that cover the full device area. It accounts for problems that generally occur in fluorescent lab-chip cell images such as: uneven background, overlapping cell images and cell detection with multiple stains. This method can be used in laboratories to save time and effort, and to increase cell counting accuracy of lab-chip devices for various applications, such as circulating tumor cell detection, cell detection in biosensors, and HIV monitoring devices, i.e. CD4 counts.