Search Results (15)

Wet Age-related macular degeneration (AMD) is the leading cause of vision loss in industrialized nations, often resulting in blindness. Biologics, therapeutic agents derived from biological sources, have been effective in AMD, albeit at a high cost. Due to the high cost of AMD treatment, it is critical to determine the binding affinity of biologics to ensure their efficacy and make quantitative comparisons between different drugs. This study evaluates the in vitro VEGF binding affinity of two drugs used for treating wet AMD, monoclonal antibody-based bevacizumab and fusion protein-based aflibercept, performing quantitative binding measurements on an Interferometric Reflectance Imaging Sensor (IRIS) system. Both biologics can inhibit Vascular Endothelial Growth Factor (VEGF). For comparison, the therapeutic molecules were immobilized on to the same support in a microarray format, and their real-time binding interactions with recombinant human VEGF (rhVEGF) were measured using an IRIS. The results indicated that aflibercept exhibited a higher binding affinity to VEGF than bevacizumab, consistent with previous studies using ELISA and SPR. The IRIS system’s innovative and cost-effective features, such as silicon-based semiconductor chips for enhanced signal detection and multiplexed analysis capability, offer new prospects in sensor technologies. These attributes make IRISs a promising tool for future applications in the development of therapeutic agents, specifically biologics. Full article

This paper presents a study on applying satellite Interferometric Synthetic Aperture Radar (InSAR) technology for the remote monitoring of road bridges and interpreting the results from a structural standpoint. The motivation behind this study arises from the widespread deterioration observed in many road bridges worldwide, leading to the need for large-scale, economic, and effective structural health monitoring (SHM) techniques. While traditional contact-type sensors have cost sustainability limitations, remote sensing techniques, including satellite-based InSAR, offer interesting alternative solutions. The objective of this study is three-fold: (i) to process InSAR data specifically for road bridges in operational conditions through the Multi-Temporal InSAR technique and extract displacement time series of reflective targets on their decks; (ii) to interpret the observed millimetric bridge displacements to verify the consistency with expected response to environmental loads and the possibility to detect unexpected behaviours; and (iii) to investigate the correlation between bridge displacements and environmental loads as temperature and river water flow variations. The study focuses on the multi-span prestressed concrete A22 Po River Bridge in Italy, utilising a dataset of X-Band HIMAGE mode Stripmap images acquired over eight years by the satellite constellation COSMO-SkyMed. The study demonstrates the effectiveness of InSAR-based SHM in detecting temperature-induced displacements and identifying different bridge spans simply by studying the sign of the correlation between displacements and temperature variation. It also reveals an unexpected behaviour in a portion of the bridge retrofitted to prevent scour issues a few years before the dataset start date. Furthermore, the correlation between pier displacements and river level variations underscores the importance of considering environmental factors and the geotechnical characteristics of the foundation soils in bridge monitoring. The results obtained from this study are significant with a view to using this satellite InSAR-based monitoring for early detection of anomalous bridge behaviour on a large scale. Full article

Viral infections can pose a major threat to public health by causing serious illness, leading to pandemics, and burdening healthcare systems. The global spread of such infections causes disruptions to every aspect of life including business, education, and social life. Fast and accurate diagnosis of viral infections has significant implications for saving lives, preventing the spread of the diseases, and minimizing social and economic damages. Polymerase chain reaction (PCR)-based techniques are commonly used to detect viruses in the clinic. However, PCR has several drawbacks, as highlighted during the recent COVID-19 pandemic, such as long processing times and the requirement for sophisticated laboratory instruments. Therefore, there is an urgent need for fast and accurate techniques for virus detection. For this purpose, a variety of biosensor systems are being developed to provide rapid, sensitive, and high-throughput viral diagnostic platforms, enabling quick diagnosis and efficient control of the virus’s spread. Optical devices, in particular, are of great interest due to their advantages such as high sensitivity and direct readout. The current review discusses solid-phase optical sensing techniques for virus detection, including fluorescence-based sensors, surface plasmon resonance (SPR), surface-enhanced Raman scattering (SERS), optical resonators, and interferometry-based platforms. Then, we focus on an interferometric biosensor developed by our group, the single-particle interferometric reflectance imaging sensor (SP-IRIS), which has the capability to visualize single nanoparticles, to demonstrate its application for digital virus detection. Full article

In this study, a laser interferometric sensing measurement (ISM) system based on a 4R manipulator system is developed to achieve detection of mechanical targets, which aims to realize the real-time, online detection of workpieces with high precision during processing. The 4R mobile manipulator (MM) system is flexible and can move in the workshop, aiming to preliminarily track the position of the workpiece to be measured and locate it at millimeter level. The reference plane of the ISM system is driven by piezoelectric ceramics with the spatial carrier frequency realized and the interferogram obtained by a charge coupled device (CCD) image sensor. The subsequent processing of the interferogram includes fast Fourier transform (FFT), spectrum filtering, phase demodulation, tilt elimination for wave-surface, etc., so as to further restore the surface shape of the measured surface and obtain the surface quality indexes. A novel cosine banded cylindrical (CBC) filter is used to improve the FFT processing accuracy, and a bidirectional extrapolation and interpolation (BEI) technique is proposed for the preprocessing operation of real-time interferograms before FFT processing. Compared with the results from a ZYGO interferometer, the real-time online detection results show the reliability and practicability of this design. The relative error of peak–valley value reflecting the processing accuracy can reach about 0.63%, with the root-mean-square value reaching about 1.36%. Some possible applications of this work include the surface of mechanical parts in the process of online machining, the end face of shaft-like structures, annular surfaces, etc. Full article

Pathogenic microorganisms and viruses can easily transfer from one host to another and cause disease in humans. The determination of these pathogens in a time- and cost-effective way is an extreme challenge for researchers. Rapid and label-free detection of pathogenic microorganisms and viruses is critical in ensuring rapid and appropriate treatment. Sensor technologies have shown considerable advancements in viral diagnostics, demonstrating their great potential for being fast and sensitive detection platforms. In this review, we present a summary of the use of an interferometric reflectance imaging sensor (IRIS) for the detection of microorganisms. We highlight low magnification modality of IRIS as an ensemble biomolecular mass measurement technique and high magnification modality for the digital detection of individual nanoparticles and viruses. We discuss the two different modalities of IRIS and their applications in the sensitive detection of microorganisms and viruses. Full article

Small extracellular vesicles (sEV) hold enormous potential as biomarkers, drug carriers, and therapeutic agents. However, due to previous limitations in the phenotypic characterization of sEV at the single vesicle level, knowledge of cell type-specific sEV signatures remains sparse. With the introduction of next-generation sEV analysis devices, such as the single-particle interferometric reflectance imaging sensor (SP-IRIS)-based ExoView R100 platform, single sEV analyses are now possible. While the tetraspanins CD9, CD63, and CD81 were generally considered pan-sEV markers, it became clear that sEV of different cell types contain several combinations and amounts of these proteins on their surfaces. To gain better insight into the complexity and heterogeneity of sEV, we used the ExoView R100 platform to analyze the CD9/CD63/CD81 phenotype of sEV released by different cell types at a single sEV level. We demonstrated that these surface markers are sufficient to distinguish cell-type-specific sEV phenotypes. Furthermore, we recognized that tetraspanin composition in some sEV populations does not follow a random pattern. Notably, the tetraspanin distribution of sEV derived from mesenchymal stem cells (MSCs) alters depending on cell culture conditions. Overall, our data provide an overview of the cell-specific characteristics of sEV populations, which will increase the understanding of sEV physiology and improve the development of new sEV-based therapeutic approaches. Full article

Burned area (BA) mapping of a forest after a fire is required for its management and the determination of the impacts on ecosystems. Different remote sensing sensors and their combinations have been used due to their individual limitations for accurate BA mapping. This study analyzes the contribution of different features derived from optical, thermal, and Synthetic Aperture Radar (SAR) images to extract BA information from the Turkish red pine (Pinus brutia Ten.) forest in a Mediterranean ecosystem. In addition to reflectance values of the optical images, Normalized Burn Ratio (NBR) and Land Surface Temperature (LST) data are produced from both Sentinel-2 and Landsat-8 data. The backscatter of C-band Sentinel-1 and L-band ALOS-2 SAR images and the coherence feature derived from the Interferometric SAR technique were also used. The pixel-based random forest image classification method is applied to classify the BA detection in 24 scenarios created using these features. The results show that the L-band data provided a better contribution than C-band data and the combination of features created from Landsat LST, NBR, and coherence of L-band ALOS-2 achieved the highest accuracy, with an overall accuracy of 96% and a Kappa coefficient of 92.62%. Full article

Anthrax lethal factor (LF) is one of the enzymatic components of the anthrax toxin responsible for the pathogenic responses of the anthrax disease. The ability to screen multiplexed ligands against LF and subsequently estimate the effective kinetic rates ($k_{on}$ and $k_{off}$) and complementary binding behavior provides critical information useful in diagnostic and therapeutic development for anthrax. Tools such as biolayer interferometry (BLI) and surface plasmon resonance imaging (SPRi) have been developed for this purpose; however, these tools suffer from limitations such as signal jumps when the solution in the chamber is switched or low sensitivity. Here, we present multiplexed antibody affinity measurements obtained by the interferometric reflectance imaging sensor (IRIS), a highly sensitive, label-free optical biosensor, whose stability, simplicity, and imaging modality overcomes many of the limitations of other multiplexed methods. We compare the multiplexed binding results obtained with the IRIS system using two ligands targeting the anthrax lethal factor (LF) against previously published results obtained with more traditional surface plasmon resonance (SPR), which showed consistent results, as well as kinetic information previously unattainable with SPR. Additional exemplary data demonstrating multiplexed binding and the corresponding complementary binding to sequentially injected ligands provides an additional layer of information immediately useful to the researcher. Full article

Seepage of geological methane through sediments of Arctic lakes might contribute conceivably to the atmospheric methane budget. However, the abundance and precise locations of such seeps are poorly quantified. For Lake Neyto, one of the largest lakes on the Yamal Peninsula in Northwestern Siberia, temporally expanding regions of anomalously low backscatter in C-band SAR imagery acquired in late winter and spring have been suggested to be related to seepage of methane from hydrocarbon reservoirs. However, this hypothesis has not been verified using in-situ observations so far. Similar anomalies have also been identified for other lakes on Yamal, but it is still uncertain whether or how many of them are related to methane seepage. This study aimed to document similar lake ice backscatter anomalies on a regional scale over four study regions (the Yamal Peninsula and Tazovskiy Peninsulas; the Lena Delta in Russia; the National Petroleum Reserve Alaska) during different years using a time series based approach on Google Earth Engine (GEE) that quantifies changes of ${\sigma }^0$ from the Sentinel-1 C-band SAR sensor over time. An algorithm for assessing the coverage that takes the number of acquisitions and maximum time between acquisitions into account is presented, and differences between the main operating modes of Sentinel-1 are evaluated. Results show that better coverage can be achieved in extra wide swath (EW) mode, but interferometric wide swath (IW) mode data could be useful for smaller study areas and to substantiate EW results. A classification of anomalies on Lake Neyto from EW $\mathsf{\Delta }{\sigma }^0$ images derived from GEE showed good agreement with the classification presented in a previous study. Automatic threshold-based per-lake counting of years where anomalies occurred was tested, but a number of issues related to this approach were identified. For example, effects of late grounding of the ice and anomalies potentially related to methane emissions could not be separated efficiently. Visualizations of $\mathsf{\Delta }{\sigma }^0$ images likely reflect the temporal expansions of anomalies and are expected to be particularly useful for identifying target areas for future field-based research. Characteristic anomalies that clearly resemble the ones observed for Lake Neyto could be identified solely visually in the Yamal and Tazovskiy study regions. All data and algorithms produced in the framework of this study are openly provided to the scientific community for future studies and might potentially aid our understanding of geological lake seepage upon the progression of related field-based studies and corresponding evaluations of formation hypotheses. Full article

Extracellular vesicles (EVs) have attracted significant attention as impactful diagnostic biomarkers, since their properties are closely related to specific clinical conditions. However, designing experiments that involve EVs phenotyping is usually highly challenging and time-consuming, due to laborious optimization steps that require very long or even overnight incubation durations. In this work, we demonstrate label-free, real-time detection, and phenotyping of extracellular vesicles binding to a multiplexed surface. With the ability for label-free kinetic binding measurements using the Interferometric Reflectance Imaging Sensor (IRIS) in a microfluidic chamber, we successfully optimize the capture reaction by tuning various assay conditions (incubation time, flow conditions, surface probe density, and specificity). A single (less than 1 h) experiment allows for characterization of binding affinities of the EVs to multiplexed probes. We demonstrate kinetic characterization of 18 different probe conditions, namely three different antibodies, each spotted at six different concentrations, simultaneously. The affinity characterization is then analyzed through a model that considers the complexity of multivalent binding of large structures to a carpet of probes and therefore introduces a combination of fast and slow association and dissociation parameters. Additionally, our results confirm higher affinity of EVs to aCD81 with respect to aCD9 and aCD63. Single-vesicle imaging measurements corroborate our findings, as well as confirming the EVs nature of the captured particles through fluorescence staining of the EVs membrane and cargo. Full article

The manufacture of a very high-quality microarray support is essential for the adoption of this assay format in clinical routine. In fact, poorly surface-bound probes can affect the diagnostic sensitivity or, in worst cases, lead to false negative results. Here we report on a reliable and easy quality control method for the evaluation of spotted probe properties in a microarray test, based on the Interferometric Reflectance Imaging Sensor (IRIS) system, a high-resolution label free technique able to evaluate the variation of the mass bound to a surface. In particular, we demonstrated that the IRIS analysis of microarray chips immediately after probe immobilization can detect the absence of probes, which recognizably causes a lack of signal when performing a test, with clinical relevance, using fluorescence detection. Moreover, the use of the IRIS technique allowed also to determine the optimal concentration of the probe, that has to be immobilized on the surface, to maximize the target recognition, thus the signal, but to avoid crowding effects. Finally, through this preliminary quality inspection it is possible to highlight differences in the immobilization chemistries. In particular, we have compared NHS ester versus click chemistry reactions using two different surface coatings, demonstrating that, in the diagnostic case used as an example (colorectal cancer) a higher probe density does not reflect a higher binding signal, probably because of a crowding effect. Full article

Investigating the binding kinetics of small molecule analytes to larger ligands, such as proteins and antibodies, is a compelling task for the field of drug and biomarker development, as well as the food industry and agro-biotechnology. Here, we improve the limit of detection of the Interferometric Reflectance Imaging Sensor (IRIS), a label-free, highly multiplexed biosensor, to perform real-time affinity measurement of small molecules binding to immobilized antibodies in a microarray format. As the analytes bind to the surface probes, the biomass accumulation on the surface is quantified by measuring the optical reflectance from the layered Si/SiO2 chip through the solution, in a common-path interferometer configuration. As a proof of concept, label-free detection of biotin molecules binding to immobilized streptavidin probes is performed, achieving 1 pg/mm2 sensitivity through signal averaging in a shot noise limited operation. Furthermore, we apply the optimized sensor to the screening of a 20-multiplexed antibody chip (MW~150 kDa ligands) against Fumonisin B1 (MW = 721.8 Da), one of the most prevalent mycotoxins found in many cereal grains such as corn and wheat. The simultaneously recorded binding curves of the toxin to the multiplexed sensor yield a signal-to-noise ratio of ≈8 when noise reduction methods of spatial and temporal averaging are utilized. Full article

Protein microarrays have gained popularity as an attractive tool for various fields, including drug and biomarker development, and diagnostics. Thus, multiplexed binding affinity measurements in microarray format has become crucial. The preparation of microarray-based protein assays relies on precise dispensing of probe solutions to achieve efficient immobilization onto an active surface. The prohibitively high cost of equipment and the need for trained personnel to operate high complexity robotic spotters for microarray fabrication are significant detriments for researchers, especially for small laboratories with limited resources. Here, we present a low-cost, instrument-free dispensing technique by which users who are familiar with micropipetting can manually create multiplexed protein assays that show improved capture efficiency and noise level in comparison to that of the robotically spotted assays. In this study, we compare the efficiency of manually and robotically dispensed α-lactalbumin probe spots by analyzing the binding kinetics obtained from the interaction with anti-α-lactalbumin antibodies, using the interferometric reflectance imaging sensor platform. We show that the protein arrays prepared by micropipette manual spotting meet and exceed the performance of those prepared by state-of-the-art robotic spotters. These instrument-free protein assays have a higher binding signal (~4-fold improvement) and a ~3-fold better signal-to-noise ratio (SNR) in binding curves, when compared to the data acquired by averaging 75 robotic spots corresponding to the same effective sensor surface area. We demonstrate the potential of determining antigen-antibody binding coefficients in a 24-multiplexed chip format with less than 5% measurement error. Full article

An integrated sensor system comprised of a terrestrial laser scanner (TLS), corner reflectors (CRs), and high precision linear rail is utilized to validate ground-based synthetic aperture radar (GB-SAR) interferometric micro-displacement measurements. A rail with positioning accuracy of 0.1 mm is deployed to ensure accurate and controllable deformation. The rail is equipped with a CR on a sliding platform for mobility. Three smaller CRs are installed nearby, each with a reflective sticker attached to the CR’s vertex; the CRs present as high-amplitude points both in the GB-SAR images and the TLS point cloud to allow for accurate data matching. We analyze the GB-SAR zero-baseline repeated rail differential interferometry signal model to obtain 2D interferograms of the test site in time series, and then use TLS to obtain a 3D surface model. The model is matched with interferograms to produce more intuitive 3D products. The CR displacements can also be extracted via surface reconstruction algorithm. Finally, we compared the rail sensor measurement and TLS results to optimize coherent scatterer selection and filter the data. The proposed method yields accurate target displacement results via quantitative analysis of GB-SAR interferometry. Full article

Over the last decade, the growing need in disease diagnostics has stimulated rapid development of new technologies with unprecedented capabilities. Recent emerging infectious diseases and epidemics have revealed the shortcomings of existing diagnostics tools, and the necessity for further improvements. Optical biosensors can lay the foundations for future generation diagnostics by providing means to detect biomarkers in a highly sensitive, specific, quantitative and multiplexed fashion. Here, we review an optical sensing technology, Interferometric Reflectance Imaging Sensor (IRIS), and the relevant features of this multifunctional platform for quantitative, label-free and dynamic detection. We discuss two distinct modalities for IRIS: (i) low-magnification (ensemble biomolecular mass measurements) and (ii) high-magnification (digital detection of individual nanoparticles) along with their applications, including label-free detection of multiplexed protein chips, measurement of single nucleotide polymorphism, quantification of transcription factor DNA binding, and high sensitivity digital sensing and characterization of nanoparticles and viruses. Full article