Wearable sensors have received a major recent attention owing to their considerable promise for monitoring the wearer’s health and wellness [1,2]. The medical interest for wearable systems arises from the need for monitoring patients over long periods of time. These devices have the potential to continuously collect vital health information from a person’s body and provide this information to them or their healthcare provider in a timely fashion. Such sensing platforms provide new avenues to continuously and non-invasively monitor individuals and can thus tender crucial real-time information regarding a wearer’s health. This presentation will discuss recent developments in the field of wearable electrochemical sensors integrated directly on the epidermis, under the skin, or within the mouth for various non-invasive and minimally-invasive biomedical monitoring applications [3]. Particular attention will be given to non-invasive monitoring of metabolites and electrolytes using flexible electrochemical sensors, to multiplexed microneedle sensor arrays, along with related materials, energy and integration considerations. The preparation and characterization of such wearable electrochemical sensors will be described, along with their current status and future prospects and challenges. Abstract As a surface configuration that made electrowetting practical for engineering, electrowetting-on-dielectric (EWOD) is an elegantly simple liquid handling method that has witnessed explosive advancements in research and persistent commercialization activities since around Year 2000. By allowing users to handle liquid droplets with only electric signals on a chip, EWOD gave birth to digital microfluidics, which led to many applications including the commercial products of today. Despite the success, EWOD is known to suffer from unique reliability problems originated from its reliance on the dielectric layer and hydrophobic topcoat, which narrow the path to commercial applications. Furthermore, many researchers who decide to utilize EWOD because of its apparent simplicity find themselves consumed by developing devices rather than utilizing them for their real purpose. To help addressing the problems associated with hydrophobic topcoat, even an opposite mechanism named electrodewetting has recently been developed to provide another option for realizing digital microfluidics. Based on either electrowetting or electrodewetting, the community of digital microfluidics is still rather small, especially considering its true potential. To eliminate the bottlenecks that have been preventing far more researchers from joining and staying in the field, we are cultivating a cybermanufacturing ecosystem, where a wide range of users (e.g., researchers, entrepreneurs, students, hobbyists) can focus on their own ideas and applications without worrying about the engineering and manufacturing hurdles of electrowetting and electrodewetting technology. Abstract We have spent the last decade developing methods to produce high functioning nanofibers from biological polymers. In this presentation, I will present our efforts to develop a hierarchical structured keratin-based system that has long-range molecular order and shape-memory properties in response to hydration. We report the metastable reconfiguration of the keratin secondary structure, the transition from α -helix to β -sheet, as an actuation mechanism to design a high-strength shape-memory material that is biocompatible and processable through fiber spinning and three-dimensional (3D) printing. We extracted keratin protofibrils from animal hair and subjected them to shear stress to induce their self-organization into a nematic phase, which recapitulates the native hierarchical organization of the protein. This self-assembly process can be adapted to produce materials with desired anisotropic structuring and responsiveness. Our combination of bottom-up assembly and top-down manufacturing allows for the scalable fabrication of strong and hierarchically structured shape-memory fibers and 3D-printed scaffolds with potential applications in bioengineering and smart textiles. Abstract There is an acute shortage of organs due to disease, trauma, congenital defects, and most importantly, age related maladies. While tissue engineering (and nanotechnology) has made great strides towards improving tissue growth, infection control has been largely forgotten. Critically, as a consequence, the Centers for Disease Control in the U.S. have predicted more deaths from antibiotic-resistant bacteria than all cancers combined by 2050, culminating into a prediction of 3 deaths every second. Moreover, there has been a lack of translation to real commercial products. This talk will summarize how nanotechnology with FDA approval can be used to increase tissue growth and decrease implant infection without using antibiotics. Studies will also be highlighted using nano sensors (while getting regulatory approval). We have grown nanoparticles and induced nanoscale surface features on numerous implants inserted today. We have further grown sensors off of currently implanted biomaterials. Lastly, we have fabricated a wide range of self-assembled materials using them to both increase tissue growth and reduce infection. This talk will emphasize both in vitro and in vivo studies. Our group has shown that nanofeatures, nano-modifications, nanoparticles, and most importantly, nanosensors can reduce bacterial growth without using antibiotics. This talk will summarize techniques and efforts to create nanosensors for a wide range of medical and tissue engineering applications, particularly those that have received FDA approval and are currently being implanted in humans. Moreover, our nanosensors can communicate to hand held devices cellular events at the surface of the implant and, in turn, such sensors can communicate back to release molecules that reduce infection, inhibit inflammation, and/or increase tissue growth. Nanotechnology has proven to be a technology that can be approved by the FDA to improve tissue growth, limit infection, and inhibit inflammation without the use of drugs. Further nanosensors can be implanted with biomaterials to determine their fate and even control cellular events to promote success. In this manner, nanotechnology is revolutionizing healthcare. such and and con-densed with a strong focus on innovative experiments. The experimental probes used for exploring physics at nanoscale are Raman spectroscopy, Ultrafast time resolved spectroscopies including terahertz spectroscopy, transport measurements and x-ray diffractions He has published more than 420 papers international journals and holds a few national and International patents. His work has been recognized by way of many .These include Prize, Birla Award, TWAS Prize in Physics, FICCI Prize, Goyal Prize, M.N. Saha Award and Millennium Gold Medal of Indian Science Congress, Sir C.V. Raman Award of UGC, Bhabha Medal of Science Academy, Nanotechnology Nano Abstract In recent years, ultrafast time-resolved Raman spectroscopies have proved to be excellent probes to understand photo physics of quantum materials- be it in bulk or in nano-dimensions. Ultrafast lasers offer unique possibilities to control and probe transient processes in nano materials. Following photoexcitation by a femtosecond laser pulse, the carrier dynamics includes many important processes like thermaliza-tion, energy relaxation, exciton formation and spin dynamics which are impacted by dimensionality. Their understanding is crucial not only for many optoelectronic applications, but also to gain a deeper understanding of physical processes in nano systems. My talk will discuss our work on photoconductivity of graphene and carbon nanotubes using optical pump-terahertz probe spectroscopy. A quantitative understanding of dynamic conductivity based on generalized Boltzmann transport model gives us insights into various relaxation mechanisms [1-3]. I will also discuss our recent work on Dirac surface Plasmons in nanowires of topological insulators [4]. as well as the Soft His current research program explores novel strategies for creating nanomaterial with targeted architectures and functions. He his in physics from Bar-Ilan University, he then went onto become a postdoctoral Rothschild Fellow at Harvard University and a Distinguished Goldhaber Fellow at Brookhaven National Laboratory. Gang joined the Columbia faculty in 2016, where his group develops new strategies to create materials by design using nano-assembly approaches, and to explore their properties for photonics, sensing, catalysis and biomedical applications. Gang is a Fellow of the American Physical Society and has received numerous accolades for his work, including the Gordon Battelle Prize for Scientific Discovery and 2016 Inventor of the Year Award. Abstract The ability to organize nano-components into the desired architectures with targeted properties can enable a broad range of nanotechnological applications, from designed biomaterial to optical systems and information processing. However, we are currently lacking an adaptable and broadly applicable methodology for the bottom-up 3D nanofabrication of the prescribed nanoscale structures. I will discuss our efforts in establishing a versatile platform for the formation of targeted 3D architectures from inorganic and biomolecular nano-components based on the molecularly programmable assembly. The recent advances on building periodic and hierarchical organizations from inorganic nanoparticles, proteins and enzymes using DNA-based methods will be presented. I will demonstrate how these assembly approaches can be used for a fabrication of nanomaterials with novel nano-optical, drug delivery and biocatalytic functions. Abstract Layered two-dimensional (2D) materials interact primarily via van der Waals bonding, which has created new opportunities for heterostructures that are not
Cobalt, a transition metal, is indispensable for mankind in multiple areas. It not only plays a vital role in body functions but also forms compounds of prime importance for usage in lithium-ion batteries and paint industry. Excess cobalt can have detrimental effects on the heart, thyroid gland, and lower pulmonary functions and can also cause hypertension. Its presence in improperly treated industry waste also causes heavy damage to soil and water bodies. Thus, effective detection and quantification of cobalt is a pressing need. This study presents the electrochemical detection of cobalt (II) ions with a gold-treated carbon cloth coated with multiwall carbon nanotubes (MWCNT). A linear increase in current was observed in the ranges 10 µM – 170 µM with R2 value of 0.99. The limit of detection (LOD) was determined to be 0.67 µM.
Cardiac tissue exhibits very low regenerative capacity. Polymeric tissue scaffolds designed for repair of damaged cardiac tissue need to have the right balance of mechanical and biological characteristics that are necessary to withstand inherent stresses generated by cyclic contraction of cardiomyocytes and to promote cell attachment and growth. Polycaprolactone (PCL), a biocompatible polymer with low biodegradability, is a promising material to develop scaffolds for tissue engineering (TE). However, its high stiffness limits its use as a matrix for cardiovascular TE. In this work, we evaluated the physical, mechanical and surface properties of PCL and cellulose nanocrystal (CNC) composite membranes, imprinted with a three-dimensional groove-ridge pattern, and their potential for cardiomyocyte adhesion and growth. This work demonstrates the impact of (a) incorporating CNCs within the PCL matrix and (b) patterning the membrane, on the surface roughness, wettability, and mechanical properties of composites, with an aim to alleviate the limitations of pure PCL scaffolds for cardiac TE applications.
The current impact of COVID-19 on global health and the economy is enormous. Considering pandemic severity, there is an urgent need to develop a smart biosensor that can provide early detection of SARS-CoV-2 viruses with robust and reliable results. In this work, we have systematically developed a plasmonic-based biosensor chip for the early detection of the COVID-19 virus by providing fast and reliable results. The label-free plasmonic sensor utilizes light and detects the resonance oscillation of surface-bound free conduction electrons in the presence of the target analyte biomarker (virus), resulting in binding and affinity incidents at the surface of plasmonic gold (Au) material, causing a shift in the resonance wavelength. The results show the ability of biosensor to exhibit an increased shift in the resonance wavelength upon binding of the COVID-19 virus because of the change in the optical property, i.e., the refractive index of the medium in the vicinity of the Au film. This study further demonstrated the fabrication and performance optimization of the plasmonic biosensor for the potential point-of-care testing device.
Osteoporosis is a common bone and metabolic disease that is characterized by bone density loss and microstructural degeneration. Human bone marrow-derived mesenchymal stem cells have great potential for bone tissue engineering and cell-based therapy due to their excellent multipotency, especially osteogenic differentiation. Although low fluid shear force plays an important role in bone osteogenic differentiation, the cellular and molecular mechanisms underlying this effect remain poorly understood due to a lack of effective tools to detect gene expression at the single-cell level. Here, we presented a double-stranded nucleic acid biosensor to examine the regulatory role of Notch signaling during osteogenic differentiation. The effects of orbital shear stress on hMSC proliferation, morphology change, osteogenic differentiation and Notch1-Dll4 signaling were examined. Osteogenic differentiation was studied by characterizing alkaline phosphatase (ALP) activity. We further investigated how orbital shear modulates Notch1-Dll4 signaling during osteogenic differentiation. Our results showed Notch1-Dll4 signaling is involved in orbital shearregulated osteogenic differentiation. Inhibition of Notch signaling will mediate the effects of shear stress on human osteogenic differentiation.
In vivo computation transforms smart tumor targeting using swarms of externally manipulable nanorobots into an optimization problem with tumor being the optimal solution to be found, the high-risk tissue as the search space, and nanorobots the computational agents. It poses a unique challenge for search space analysis (SSA) due to physical constraints of the biological phenomenon. In this paper, we propose three new SSA measures and validate them with numerical simulations on two different tumor vascular networks. The less vascularized network was found to result in a more discrete search space, less likelihood of countercurrent desirable search direction, and more significant heterogeneity in various search directions. Finally, numerical results are presented to demonstrate the principles of the proposed strategy.
In this study, we characterize biosamples using a microstrip patch based Radio Frequency (RF) sensor by integrating both resonant and non-resonant parameters. In particular, the resonance frequency, the resonance amplitude, −10 dB bandwidth, range of operation, and return loss at center frequency were employed to perform the analysis. The idea was to utilize multiple parameters to build a comprehensive understanding of different bio-samples. In order to demonstrate the working of the methodology, two sets of analytes were studied-(i) aqueous solutions of biomolecules and (ii) body fluids. For both sets of analytes, the method demonstrated significant efficiency in performing qualitative characterization.
This paper proposes a novel fuzzy-inspired biosensing strategy for contrast-enhanced tumor classification by using multiple features of the disease. Specifically, the proposed strategy considers two features of breast cancers, i.e., tissue malignancy and distance between cancerous cells, to determine the cancer status through the fuzzy relation transfer analysis. The analysis enables an intuitive yet systematic way to characterize the variation of classification fuzziness occurred in the biosensing process when nanoscale materials are utilized as contrast agents. Subsequently, root-mean-square-error of the membership function is introduced to evaluate the sensing integrity. Finally, numerical results are presented to demonstrate the principles of the proposed strategy.
Viral diagnostic is essential to the fields of medicine and bio-nanotechnology, but such analyses can present some complex analytical challenges. While molecular methods that are mostly used in clinical laboratories, for instance, reverse transcription-polymerase chain reaction (RT-PCR) and antigens tests require long acquisition times, and often provides unreliable results for COVID-19 virus detection, the piezo-based sensors coupled with MEMS have demonstrated a significant role in robust viral detection. In this work, we have designed and simulated a piezoelectric MEMS-based biosensor integrated into a wearable face mask for early detection of the SARS-CoV-2 virus droplets. We systematically investigated the influence of virus droplets in changing the applied stress on the cantilever receptor pit with change in mass when viruses (pathogens) from airborne coughing droplets-nuclei binds with coated antibodies on the sensor's cantilever layer with receptor pit thereby generating electric potential. Additionally, Bio-MEMS sensor results have manifested that it has the ability to detect a single size particle of 1 virion with a diameter ≥100 nm and mass of 1fg in a single cough containing droplet nuclei of radius 0.05µm in a less amount of time. Additionally, we empirically set electrical potential as thresholds parameter for our wearable biosensor embedded in the face mask for public monitoring to detect contagious virus particle droplets. Furthermore, this study presented the prospective use of MEMS-based sensing method to identify and detect other biological (bacteria and toxins) analytes.
The field of small-scale microdevices is growing rapidly towards the development of tethered/untethered microsensors and therapeutic agents that can move through narrow vascular networks in living organisms to diagnose and treat for example vascular diseases. However, imaging and localization of such microdevices are challenging under scattering tissue due to the lack of high spatial and temporal resolution that compromises the penetration depth and therefore the application scenario. Here, we report the tracking of magnetically-driven rolling micromotors (20 µm in diameter) through scattering tissue (1.2 mm thickness) using reflective infrared (IR) imaging. This technique provides high spatiotemporal resolution for the tracking of micro-agents in a narrow vascular network. As a potential application, we suggest the use of such micro-agents as micro-viscosimeters, in which by analyzing their dynamics, one can extract some basic information on the surrounding fluid properties which might be related to certain health status.
Plasmonic gold nanoparticles (AuNPs) exhibit a phenomenon called localized surface plasmon resonance (LSPR), making them suitable for several applications in nanotheranostics, bio-imaging and optoelectronic sensing. However, this property needs further tuning and enhancement to improve their sensitivity. Here, we developed hybrid nanocomposites by synthesizing AuNPs on the surface of cellulose nanocrystals (CNCs), with the hypothesis that confinement of collective oscillations of electrons at the interface of CNCs and AuNPs would lead to LSPR enhancement. This study uses a seed-mediated approach for growth of AuNPs on CNC surface and the ratio of gold salt to reducing agent was optimized to tune the growth. Ultraviolet/visible/near-infrared Spectroscopy, Dynamic Light Scattering, Atomic Force Microscopy and X-Ray Diffraction were used to validate hybrid formation and associated enhancement in LSPR.
In this study, we employed a low-cost and facile soaking method to prepare terbium-doped mesoporous silica nanoparticles (SiO 2 - Tb NPs) suitable for bioimaging purposes. Morphological analysis showed that prepared silica NPs possess spherical morphology with a mean diameter of ~ 82–1 07 nm. Photoluminescence analysis of SiO 2 - Tb NPs yielded several luminescent peaks associated with electronic 5 D 4 → 7Fj (where j = 3, 4, 5, 6) transitions within terbium ions. The successful labeling of L-929 cells with SiO 2 - Tb NPs was tested using a fluorescence microscope. Obtained results suggested that SiO 2 - Tb NPs can be utilized as promising optically-stable nanoprobes for cells/tissues labeling and imaging.
By drawing stimulating parallels between the optimization and nanobiosensing processes, a novel framework of computational nanobiosensing (CONA) employs nanorobots as computing agents to perform “smart” searching of tumours, greatly improving targeting performance. Optimal routing in the vascular network and learning the biological gradient field (BGF), a peritumoral field triggered by the tumour, are two fundamental problems in CONA. In this paper, we present a novel reinforcement learning (RL)-based CONA strategy for tumour targeting, which can address these two problems together. The overall reward of the Q-Learning mission includes the Markov reward and BGF reward. The Markov reward is employed to train nanorobots to avoid colliding vessel obstacles during the searching process, resulting in learn the structure of the vascular network. While the BGF reward, learning prior information of BGF, benefit in faster convergence of searching process. In other words, this hybrid strategy, taking advantage of feasible path planning of the vessels and the information of BGF together, accelerates finding the optimal path planning for the tumour in a complicated vessel network. By introducing different weights between the Markov and BGF rewards, it is found that the best weight is roughly the same for different scenarios.
Liposomes are used and heavily researched in drug delivery. These nanocarriers encapsulate therapeutic agents efficiently and deliver their contents to tumors with minimal undesired side effects. In this study, we investigated the preparation of Estrone liposomes in a commercially available microfluidic chip (a Herringbone micromixer). Using this chip, we synthesized Estrone liposomes with high uniformity and reproducibility, contrary to conventional methods (e.g., the thin film hydration), which are cumbersome and non-reliable. In addition, we studied the effect of the lipids concentration and the flow rate ratio between the lipids stream and the aqueous stream on the size and polydispersity of the resulting liposomes. The smallest liposomes obtained were 91.8 ± 4.8nm in radius (mean ± SD) with a polydispersity of 17.2%.
Identifying nonhormonal contraceptives will have profound impacts on avoiding side effects of hormonal birth control methods, minimizing pregnancy complications and infant mortality rates, and promoting family planning. However, phenotypic screening of contraceptives is challenging due to the diverse procedures associated with oocyte culture, biochemical assays, and molecular imaging. This study reports a multifunctional microfluidic platform comprising reconfigurable building blocks and interfaces to implement various cell-based drug screening protocols. This versatile platform has three major layers. The top layer consists of interchangeable 3D microfluidic building blocks (e.g., branching microchannels, chemical gradient generators, pumpless flow controllers, and emulsion generators) or an open interface. The middle layer incorporates a multiwell array with embedded membrane filters for live cell culture, medium exchange, enzymatic cumulus cell removal, washing, and fluorescence staining. The bottom layer is also reconfigurable for waste collection, oocyte culture, plate reader measurement, and high-resolution microscopy. We demonstrate an 8 by 16 (128 wells) system for performing the cumulus-oocyte complex (COC) expansion and oocyte maturation assays for screening nonhormonal contraceptives. The microfluidic building block platform is scalable and can be reconfigured for a variety of drug screening applications in the future.
The motivation of this study was to develop a Geant4-based novel three dimensional (3D) digital mouse model derived from in vivo cone-beam computed tomography (CBCT) images of a mouse injected with gold nanoparticles (GNPs) for x-ray tomography simulation studies. Pixel-by-pixel CT numbers from axial CBCT images of a mouse, obtained before and after injecting GNPs, were converted into material densities and then transformed into identical 3D voxels using a custom Geant4 application. The consecutive materialized slices were then stacked along the longitudinal axis to build the whole-body mouse models. The first mouse model showed no GNPs inside any organs of interest or the skeleton, whereas the second model showed the actual GNP biodistributions inside the kidneys along with the anatomical features. The applicability of the mouse models for x-ray imaging was investigated by a whole-body CBCT and x-ray fluorescence computed tomography (XFCT) Monte Carlo (MC) simulations. The current investigation showed the feasibility of developing digital mouse models using CBCT images of a mouse injected with GNPs and their application to simulation studies of preclinical CBCT or XFCT or multimodal CBCT+XFCT imaging.
Herein, a cost-efficient, easy realizable and customized laser-induced flexible graphene (LIFG) have been explored as Enzymatic Biofuel Cell (EBFC) bioelectrodes. These LIFG bioelectrodes were created on a polyamide substrate directly by irradiation with a CO2 laser at optimized laser properties (speed and power). The bioelectrodes were rigorously characterized using Raman spectroscopic technique. Further, the surface morphological study of polyamide film, LIFG, and LIFG with the relevant enzymes modified bioelectrodes has been accomplished using Scanning Electron Microscope (SEM) and Energy Dispersive Spectroscopy (EDS). Subsequently, the voltammetric electrochemical analysis of modified bioelectrodes has been carried out using Linear Sweep Voltammetry (LSV), Cyclic Voltammetry (CV) and Open Circuit Potential (OCP). Such electrochemical characterizations have shown excellent performance and further motivate us towards future studies at the microfluidics level.
Aiming at the irregular distribution of kidney stones inside the body, traditional clinical surgery can not achieve complete resection of the lesion, the targeted drug damage to the affected body is too large, can not effectively identify the target information of the lesion, and the probability of recurrence is high, In this paper, an enhanced energizing module microsystem with lesion target recognition and cutting is developed. The system mainly includes: a neural network signal extraction module for target recognition, a logic control target chip for lesion resection, and a targeted drug with jet ability. The overall structure of the micro-system is micro/mesoscopic size, and the sensitive structure is an execution probe of “small grain size”. After the enhanced energizing module enters the patient from the vein, it begins to extract the cell growth and reproduction information of the surrounding environment and the echo information of the stone lesions. enhanced energizing module set treatment mode selection, each of the enhanced energizing module has two or more targeted drug heads, which can be set according to the characteristics of the lesions. The targeted drug head with jet function bombards the surface of the affected area, which can produce a local high temperature of 1500 °C, which can reliably destroy the lesions of the affected area.
Automating human preimplantation embryo grading offers the potential for higher success rates with in vitro fertilization (IVF) by providing new quantitative and objective measures of embryo quality. Current IVF procedures typically use only qualitative manual grading, which is limited in the identification of genetically abnormal embryos. The automatic quantitative assessment of blastocyst expansion can potentially improve sustained pregnancy rates and reduce health risks from abnormal pregnancies through a more accurate identification of genetic abnormality. The expansion rate of a blastocyst is an important morphological feature to determine the quality of a developing embryo. In this work, a deep learning based human blastocyst image segmentation method is presented, with the goal of facilitating the challenging task of segmenting irregularly shaped blastocysts. The type of blastocysts evaluated here has undergone laser ablation of the zona pellucida, which is required prior to trophectoderm biopsy. This complicates the manual measurements of the expanded blastocyst's size, which shows a correlation with genetic abnormalities. The experimental results on the test set demonstrate segmentation greatly improves the accuracy of expansion measurements, resulting in up to 99.4% accuracy, 98.1% precision, 98.8% recall, a 98.4% Dice Coefficient, and a 96.9% Jaccard Index.
We propose a new framework of tracking nanoswimmers for cancer detection and targeted drug delivery in microscale scenarios. The framework includes a novel multimodal complex vascular topological model to emulate the real vasculature inside the human body, and an efficient system model that is able to track nanoswimmers in an in vivo environment. The multimodal vascular model consists of three consecutive subnetworks to represent normal arteries, normal subcutaneous capillaries and tumor vasculature, aiming for setting up the trajectories towards tumors. The proposed tracking system model, based on Kalman filters, very significantly decreases the noise induced by a variety of sources by approximately 51.3%-75% to provide accurate position information of the nanoswimmers.
