Untethered micro-robotic coding of three-dimensional material composition

Complex functional materials with three-dimensional micro- or nano-scale dynamic compositional features are prevalent in nature. However, the generation of three-dimensional functional materials composed of both soft and rigid microstructures, each programmed by shape and composition, is still an unsolved challenge. Herein, we describe a method to code complex materials in three-dimensions with tunable structural, morphological, and chemical features using an untethered magnetic micro-robot remotely controlled by magnetic fields. This strategy allows the micro-robot to be introduced to arbitrary microfluidic environments for remote two- and three-dimensional manipulation. We demonstrate the coding of soft hydrogels, rigid copper bars, polystyrene beads, and silicon chiplets into three-dimensional heterogeneous structures. We also use coded microstructures for bottom-up tissue engineering by generating cell-encapsulating constructs

Guided and magnetic self-assembly of tunable magnetoceptive gels

Self-assembly of components into complex functional patterns at microscale is common in nature, and used increasingly in numerous disciplines such as optoelectronics, microfabrication, sensors, tissue engineering and computation. Here, we describe the use of stable radicals to guide the self-assembly of magnetically tunable gels, which we call ‘magnetoceptive’ materials at the scale of hundreds of microns to a millimeter, each can be programmed by shape and composition, into heterogeneous complex structures. Using paramagnetism of free radicals as a driving mechanism, complex heterogeneous structures are built in the magnetic field generated by permanent magnets. The overall magnetic signature of final structure is erased via an antioxidant vitamin E, subsequent to guided self-assembly. We demonstrate unique capabilities of radicals and antioxidants in fabrication of soft systems with heterogeneity in material properties, such as porosity, elastic modulus and mass density; then in bottom-up tissue engineering and finally, levitational and selective assembly of microcomponents

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

Recent technological developments in the diagnosis and treatment of cerebral edema

Latest technological advancements in neurocritical care have translated to improved clinical outcomes and have paved the way for the effective diagnosis and treatment of cerebral edema. Effective management of cerebral edema has the potential to provide a personalized treatment by obtaining the complete pathophysiological information of the patient. The aims of this review are to inform the reader about the research and development in this field in the past decade as well as the materialization of scientific literature through patents. There is a growing interest in multimodal monitoring of the diseased brain as it provides a necessary means to implement effective intervention strategies. Although there is a gradual shift toward the adoption of noninvasive devices for research purposes, their clinical applications are hindered by their inaccuracies. However, the inherent risk of complication and high costs of implementation challenge the status quo. The role of neuroprotectants is explored and the combination of neurodiagnostic and neuroprotective approaches is proposed. Finally, the impacts of the current state of global affairs are discussed and it is predicted that the rising number of traumatic brain injury patents will inevitably translate to improvements in technologies to effectively address cerebral edema

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

Micro-a-fluidics ELISA for rapid CD4 cell count at the point-of-care

HIV has become one of the most devastating pathogens in human history. Despite fast progress in HIV-related basic research, antiretroviral therapy (ART) remains the most effective method to save AIDS patients' lives. Unfortunately, ART cannot be universally accessed, especially in developing countries, due to the lack of effective treatment monitoring diagnostics. Here, we present an inexpensive, rapid and portable micro-a-fluidic platform, which can streamline the process of an enzyme-linked immunosorbent assay (ELISA) in a fully automated manner for CD4 cell count. The micro-a-fluidic CD4 cell count is achieved by eliminating operational fluid flow via “moving the substrate”, as opposed to “flowing liquid” in traditional ELISA or microfluidic methods. This is the first demonstration of capturing and detecting cells from unprocessed whole blood using the enzyme-linked immunosorbent assay (ELISA) in a microfluidic channel. Combined with cell phone imaging, the presented micro-a-fluidic ELISA platform holds great promise for offering rapid CD4 cell count to scale up much needed ART in resource-constrained settings. The developed system can be extended to multiple areas for ELISA-related assays.the Center for Integration of Medicine and Innovative Technology ; the U.S. Army Medical Research & Materiel Command (USAMRMC) ; the Telemedicine & Advanced Technology Research Center (TATRC).publisher versio

Analysis of vaginal microbicide film hydration kinetics by quantitative imaging refractometry

We have developed a quantitative imaging refractometry technique, based on holographic phase microscopy, as a tool for investigating microscopic structural changes in water-soluble polymeric materials. Here we apply the approach to analyze the structural degradation of vaginal topical microbicide films due to water uptake. We implemented transmission imaging of 1-mm diameter film samples loaded into a flow chamber with a 1.5×2 mm field of view. After water was flooded into the chamber, interference images were captured and analyzed to obtain high resolution maps of the local refractive index and subsequently the volume fraction and mass density of film material at each spatial location. Here, we compare the hydration dynamics of a panel of films with varying thicknesses and polymer compositions, demonstrating that quantitative imaging refractometry can be an effective tool for evaluating and characterizing the performance of candidate microbicide film designs for anti-HIV drug delivery. © 2014 Rinehart et al

Dry Needling for Spine Related Disorders: a Scoping Review

Introduction/Background: The depth and breadth of research on dry needling (DN) has not been evaluated specifically for symptomatic spine related disorders (SRD) from myofascial trigger points (TrP), disc, nerve and articular structures not due to serious pathologies. Current literature appears to support DN for treatment of TrP. Goals of this review include identifying research published on DN treatment for SRD, sites of treatment and outcomes studied. Methods: A scoping review was conducted following Levac et al.’s five part methodological framework to determine the current state of the literature regarding DN for patients with SRD. Results: Initial and secondary search strategies yielded 55 studies in the cervical (C) region (71.43%) and 22 in the thoracolumbar-pelvic (TLP) region (28.57%). Most were randomized controlled trials (60% in C, 45.45% in TLP) and clinical trials (18.18% in C, 22.78% in TLP). The most commonly treated condition was TrP for both the C and TLP regions. In the C region, DN was provided to 23 different muscles, with the trapezius as treatment site in 41.88% of studies. DN was applied to 31 different structures in the TLP region. In the C region, there was one treatment session in 23 studies (41.82%) and 2–6 treatments in 25 (45.45%%). For the TLP region, one DN treatment was provided in 8 of the 22 total studies (36.36%) and 2–6 in 9 (40.9%). The majority of experimental designs had DN as the sole intervention. For both C and TLP regions, visual analogue scale, pressure pain threshold and range of motion were the most common outcomes. Conclusion: For SRD, DN was primarily applied to myofascial structures for pain or TrP diagnoses. Many outcomes were improved regardless of diagnosis or treatment parameters. Most studies applied just one treatment which may not reflect common clinical practice. Further research is warranted to determine optimal treatment duration and frequency. Most studies looked at DN as the sole intervention. It is unclear whether DN alone or in addition to other treatment procedures would provide superior outcomes. Functional outcome tools best suited to tracking the outcomes of DN for SRD should be explored.https://doi.org/10.1186/s12998-020-00310-