High-resolution compound-specific mapping in works of art via data fusion of MA-XRPD with hyperspectral data (part 1: Method evaluation).

BACKGROUND: Hyperspectral imaging techniques have emerged as powerful tools for non-invasive investigation of artworks. This paper employs either reflectance imaging spectroscopy (RIS) or macroscopic X-ray fluorescence (MA-XRF) imaging in combination with macroscopic X-ray powder diffraction (MA-XRPD) for state-of-the-art chemical imaging of painted cultural heritage artefacts. While RIS can provide molecular information and MA-XRF can offer elemental distribution maps of paintings of high lateral resolution, the unique advantage of MA-XRPD lies in its ability to visualize the distributions of specific pigments and estimate in a quantitative manner the relative concentrations of the crystalline phases at the surface of artworks. However, MA-XRPD is more time-consuming and offers a lower lateral resolution than RIS and MA-XRF. RESULTS: This study introduces a machine learning (ML) approach to obtain the distribution of specific compounds on the surface of artworks with a resolution that is comparable to that of RIS and MA-XRF data but with the compound specificity of MA-XRPD. The general aim is to expedite non-destructive artwork imaging analysis by fusing data from different imaging modalities via machine learning models. The effect of preprocessing techniques to enhance the predictive accuracy of the models is explored. The paper demonstrates the method's efficacy on a 16th-century illuminated manuscript, showcasing the feasibility of predicting compound-specific distribution maps. Three evaluation methods-visual examination of the predicted distribution, root mean square errors (RMSE), and feature permutation importance (FPI)-are employed to assess model performance. Fusing MA-XRF with MA-XRPD led to the best RMSE scores overall. However, fusing the RIS and MA-XRPD data blocks also yield very satisfactory and easily interpretable high-resolution compound maps. SIGNIFICANCE: While MA-XRPD allows for highly specific imaging of artworks, its time-consuming nature and limited resolution presents a bottleneck during non-invasive imaging of painted works of art. By integrating data from more time-efficient hyperspectral techniques such as MA-XRF and RIS, and employing machine learning, we expedite the process without compromising accuracy. The fusion process can also denoise the distribution maps, improving their readability for heritage professionals and art historical scholars.

A Label-free Optical Biosensor-Based Point-of-Care Test for the Rapid Detection of Monkeypox Virus.

Diagnostic approaches that combine the high sensitivity and specificity of laboratory-based digital detection with the ease of use and affordability of point-of-care (POC) technologies could revolutionize disease diagnostics. This is especially true in infectious disease diagnostics, where rapid and accurate pathogen detection is critical to curbing the spread of disease. We have pioneered an innovative label-free digital detection platform that utilizes Interferometric Reflectance Imaging Sensor (IRIS) technology. IRIS leverages light interference from an optically transparent thin film, eliminating the need for complex optical resonances to enhance the signal by harnessing light interference and the power of signal averaging in shot-noise-limited operation to achieve virtually unlimited sensitivity. In our latest work, we have further improved our previous 'Single-Particle' IRIS (SP-IRIS) technology by allowing the construction of the optical signature of target nanoparticles (whole virus) from a single image. This new platform, 'Pixel-Diversity' IRIS (PD-IRIS), eliminated the need for z-scan acquisition, required in SP-IRIS, a time-consuming and expensive process, and made our technology more applicable to POC settings. Using PD-IRIS, we quantitatively detected the Monkeypox virus (MPXV), the etiological agent for Monkeypox (Mpox) infection. MPXV was captured by anti-A29 monoclonal antibody (mAb 69-126-3) on Protein G spots on the sensor chips and were detected at a limit-of-detection (LOD) - of 200 PFU/ml (~3.3 attomolar). PD-IRIS was superior to the laboratory-based ELISA (LOD - 1800 PFU/mL) used as a comparator. The specificity of PD-IRIS in MPXV detection was demonstrated using Herpes simplex virus, type 1 (HSV-1), and Cowpox virus (CPXV). This work establishes the effectiveness of PD-IRIS and opens possibilities for its advancement in clinical diagnostics of Mpox at POC. Moreover, PD-IRIS is a modular technology that can be adapted for the multiplex detection of pathogens for which high-affinity ligands are available that can bind their surface antigens to capture them on the sensor surface.

Characterization of Receptor Binding Affinity for Vascular Endothelial Growth Factor with Interferometric Imaging Sensor.

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.

A Protocol for the Automated Assessment of Cutaneous Pathology in a Mouse Model of Hemichannel Dysfunction.

In this chapter, we provide detailed instructions to perform quantitative reflectance imaging in a mouse model of a rare epidermal disorder caused by hyperactive connexin 26 hemichannels. Reflectance imaging is a versatile and powerful tool in dermatology, offering noninvasive, high-resolution insights into skin pathology, which is essential for both clinical practice and research. This approach offers several advantages and applications. Unlike traditional biopsy, reflectance imaging is noninvasive, allowing for real-time, in vivo examination of the skin. This is particularly valuable for monitoring chronic conditions or assessing the efficacy of treatments over time, enabling the detailed examination of skin morphology. This is crucial for identifying features of skin diseases such as cancers, inflammatory conditions, and infections. In therapeutic applications, reflectance imaging can be used to monitor the response of skin lesions to treatments. It can help in identifying the most representative area of a lesion for biopsy, thereby increasing the diagnostic accuracy. Reflectance imaging can also be used to diagnose and monitor inflammatory skin diseases, like psoriasis and eczema, by visualizing changes in skin structure and cellular infiltration. As the technology becomes more accessible, it has potential in telemedicine, allowing for remote diagnosis and monitoring of skin conditions. In academic settings, reflectance imaging can be a powerful research tool, enabling the study of skin pathology and the effects of novel treatments, including the development of monoclonal antibodies for therapeutic applications.

Diverse projected climate change scenarios affect the physiology of broccoli plants to different extents.

Climate change caused by global warming involves crucial plant growth factors such as atmospheric CO2 concentration, ambient temperature or water availability. These stressors usually co-occur, causing intricate alterations in plant physiology and development. This work focuses on how elevated atmospheric CO2 levels, together with the concomitant high temperature, would affect the physiology of a relevant crop, such as broccoli. Particular attention has been paid to those defence mechanisms that contribute to plant fitness under abiotic stress. Results show that both photosynthesis and leaf transpiration were reduced in plants grown under climate change environments compared to those grown under current climate conditions. Furthermore, an induction of carbohydrate catabolism pointed to a redistribution from primary to secondary metabolism. This result could be related to a reinforcement of cell walls, as well as to an increase in the pool of antioxidants in the leaves. Broccoli plants, a C3 crop, grown under an intermediate condition showed activation of those adaptive mechanisms, which would contribute to coping with abiotic stress, as confirmed by reduced levels of lipid peroxidation relative to current climate conditions. On the contrary, the most severe climate change scenario exceeded the adaptive capacity of broccoli plants, as shown by the inhibition of growth and reduced vigour of plants. In conclusion, only a moderate increase in atmospheric CO2 concentration and temperature would not have a negative impact on broccoli crop yields.

Receptor activator of nuclear factor-kappa B is enriched in CD9-positive extracellular vesicles released by osteoclasts.

Aim: Receptor activator of nuclear factor-kappa B (RANK)-containing extracellular vesicles (EVs) bind RANK-Ligand (RANKL) on osteoblasts, and thereby simultaneously inhibit bone resorption and promote bone formation. Because of this, they are attractive candidates for therapeutic bone anabolic agents. Previously, RANK was detected in 1 in every 36 EVs from osteoclasts by immunogold electron microscopy. Here, we have sought to characterize the subpopulation of EVs from osteoclasts that contains RANK in more detail. Methods: The tetraspanins CD9 and CD81 were localized in osteoclasts by immunofluorescence. EVs were visualized by transmission electron microscopy. A Single Particle Interferometric Reflectance Imaging Sensor (SP-IRIS) and immunoaffinity isolations examined whether RANK is enriched in specific types of EVs. Results: Immunofluorescence showed CD9 was mostly on or near the plasma membrane of osteoclasts. In contrast, CD81 was localized deeper in the osteoclast's cytosolic vesicular network. By interferometry, both CD9 and CD81 positive EVs from osteoclasts were small (56-83 nm in diameter), consistent with electron microscopy. The CD9 and CD81 EV populations were mostly distinct, and only 22% of the EVs contained both markers. RANK was detected by SP-IRIS in 2%-4% of the CD9-containing EVs, but not in CD81-positive EVs, from mature osteoclasts. Immunomagnetic isolation of CD9-containing EVs from conditioned media of osteoclasts removed most of the RANK. A trace amount of RANK was isolated with CD81. Conclusion: RANK was enriched in a subset of the CD9-positive EVs. The current study provides the first report of selective localization of RANK in subsets of EVs.

Timing of ocular examination and retinal imaging for the diagnosis of type 2 macular telangiectasia (MacTel).

PURPOSE: To report a case demonstrating the relevance of ocular examination and retinal imaging timing in type 2 macular telangiectasia (MacTel). CASE DESCRIPTION: A 55-year-old-female complained of blurring of vision in the left eye for the last 6 months. Corrected visual acuity was 20/20, N6 and 20/30, N8 in the right and left eye respectively. Anterior segment examination was normal in both eyes. Her both eyes' fundus was seen initially by the general ophthalmologist and was subsequently referred to the retinal specialist. RESULTS: Dilated fundus examination of the right eye appeared normal while the left eye showed no specific retinal pathology except for a dull foveal reflex. Retinal imaging with optical coherence tomography and confocal blue reflectance (CBR) imaging were not conclusive of any specific clinical diagnosis. A fluorescein angiography and repeat CBR imaging was done on the following day which now clearly showed the features of type 2 MacTel. CONCLUSION: Specific clinical and imaging features in type 2 MacTel are observed better in the absence of light exposure. Retinal imaging with CBR should not be performed immediately after dilated retinal examination in a suspected case of type 2 MacTel.

Bull's Eye Maculopathy in Near-Infrared Reflectance as An Early Sign of Hydroxychloroquine Toxicity.

Hydroxychloroquine (HCQ) ocular toxicity is rare but severe, and progression can occur even after termination of therapy. Case reports have suggested that a bull's eye maculopathy detected by near-infrared reflectance (NIR) may indicate early HCQ toxicity. This retrospective cross-sectional study evaluated patients treated with HCQ who underwent routine screening with optical coherence tomography (OCT), fundus autofluorescence (FAF) and 10-2 perimetry. NIR images captured alongside OCT were subsequently graded independently by 2 masked graders for the presence of bull's eye maculopathy, and the result was compared to the outcome of the screening. A total of 123 participants (246 eyes) were included, and 101 (90%) were female. The patients' mean age was 55.2 ± 13.8 years. The mean time of HCQ usage was 84.0 ± 72.3 months, and the mean weekly dose was 2327 ± 650 mg. Two eyes showed toxicity in all 3 routine screening exams, with one patient suspending HCQ. The prevalence of bull´s eye lesions in NIR was 13% (33 eyes) with substantial intergrader agreement, a 71.3% specificity and 88.0% negative predictive value for HCQ toxicity. We suggest that NIR changes may be a sign of early HCQ toxicity. The detection of NIR bull´s eye lesions may warrant an increased screening frequency.

Highly-Sensitive, Label-Free Detection of Microorganisms and Viruses via Interferometric Reflectance Imaging Sensor.

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.

The role of near-infrared reflectance imaging in retinal disease: A systematic review.

Near-infrared reflectance (NIR) retinal imaging aids in a better visualization of structures at the level of outer retina, retinal pigment epithelium, and choroid. It has multiple advantages, including easy acquisition in association with structural spectral domain optical coherence tomography, more comfort for patients, and enhanced contrast and spatial resolution. It helps in the diagnosis of chorioretinal diseases that present with minimal funduscopic findings and can be used to follow up many chorioretinal conditions. We describe the chorioretinal NIR imaging appearance and the clinical role of NIR imaging in ocular inflammatory disease, vascular and acquired disease, degenerative disease, tumors, associated systemic condition, toxic and traumatic disease, optic nerve head conditions, and physiological findings.

Novel Vegetation Indices to Identify Broccoli Plants Infected With Xanthomonas campestris pv. campestris.

A rapid diagnosis of black rot in brassicas, a devastating disease caused by Xanthomonas campestris pv. campestris (Xcc), would be desirable to avoid significant crop yield losses. The main aim of this work was to develop a method of detection of Xcc infection on broccoli leaves. Such method is based on the use of imaging sensors that capture information about the optical properties of leaves and provide data that can be implemented on machine learning algorithms capable of learning patterns. Based on this knowledge, the algorithms are able to classify plants into categories (healthy and infected). To ensure the robustness of the detection method upon future alterations in climate conditions, the response of broccoli plants to Xcc infection was analyzed under a range of growing environments, taking current climate conditions as reference. Two projections for years 2081-2100 were selected, according to the Assessment Report of Intergovernmental Panel on Climate Change. Thus, the response of broccoli plants to Xcc infection and climate conditions has been monitored using leaf temperature and five conventional vegetation indices (VIs) derived from hyperspectral reflectance. In addition, three novel VIs, named diseased broccoli indices (DBI1-DBI3), were defined based on the spectral reflectance signature of broccoli leaves upon Xcc infection. Finally, the nine parameters were implemented on several classifying algorithms. The detection method offering the best performance of classification was a multilayer perceptron-based artificial neural network. This model identified infected plants with accuracies of 88.1, 76.9, and 83.3%, depending on the growing conditions. In this model, the three Vis described in this work proved to be very informative parameters for the disease detection. To our best knowledge, this is the first time that future climate conditions have been taken into account to develop a robust detection model using classifying algorithms.

Finite Element Analysis and Near-Infrared Hyperspectral Reflectance Imaging for the Determination of Blueberry Bruise Grading.

Bruising of the subcutaneous tissues of blueberries is an important form of mechanical damage. Different levels of bruising have a significant effect on the post-harvest marketing of blueberries. To distinguish different grades of blueberry bruises and explore the effects of different factors, explicit dynamic simulation and near-infrared hyperspectral reflectance imaging were employed without harming the blueberries in this study. Based on the results of the compression experiment, an explicit dynamic simulation of blueberries was performed to measure the potential locations of bruises and preliminarily divide the bruise stages. A near-infrared hyperspectral reflectance imaging system was used to detect the actual blueberry bruises. According to the blueberry photos taken by the near-infrared hyperspectral reflectance imaging system, the actual bruise rates of blueberries were obtained by using the Environment for Visualizing Images software for training and classification. Bruise grades of blueberries were divided accordingly. Response surface methodology was used to determine the effects of ripeness, loading speed and loading location on the blueberry bruising rate. Under the optimized parameters, the actual damage rate of blueberries was 1.1%. The results provide an important theoretical basis for the accurate and rapid identification and classification of blueberry bruise damage.

Optical Microscopy Systems for the Detection of Unlabeled Nanoparticles.

Label-free detection of nanoparticles is essential for a thorough evaluation of their cellular effects. In particular, nanoparticles intended for medical applications must be carefully analyzed in terms of their interactions with cells, tissues, and organs. Since the labeling causes a strong change in the physicochemical properties and thus also alters the interactions of the particles with the surrounding tissue, the use of fluorescently labeled particles is inadequate to characterize the effects of unlabeled particles. Further, labeling may affect cellular uptake and biocompatibility of nanoparticles. Thus, label-free techniques have been recently developed and implemented to ensure a reliable characterization of nanoparticles. This review provides an overview of frequently used label-free visualization techniques and highlights recent studies on the development and usage of microscopy systems based on reflectance, darkfield, differential interference contrast, optical coherence, photothermal, holographic, photoacoustic, total internal reflection, surface plasmon resonance, Rayleigh light scattering, hyperspectral and reflectance structured illumination imaging. Using these imaging modalities, there is a strong enhancement in the reliability of experiments concerning cellular uptake and biocompatibility of nanoparticles, which is crucial for preclinical evaluations and future medical applications.

Multi-Class CNN for Classification of Multispectral and Autofluorescence Skin Lesion Clinical Images.

In this work, we propose to use an artificial neural network to classify limited data of clinical multispectral and autofluorescence images of skin lesions. Although the amount of data is limited, the deep convolutional neural network classification of skin lesions using a multi-modal image set is studied and proposed for the first time. The unique dataset consists of spectral reflectance images acquired under 526 nm, 663 nm, 964 nm, and autofluorescence images under 405 nm LED excitation. The augmentation algorithm was applied for multi-modal clinical images of different skin lesion groups to expand the training datasets. It was concluded from saliency maps that the classification performed by the convolutional neural network is based on the distribution of the major skin chromophores and endogenous fluorophores. The resulting classification confusion matrices, as well as the performance of trained neural networks, have been investigated and discussed.

Topographical Relationship Between Acute Macular Neuroretinopathy and Choroidal Watershed Zone or Patchy Choroidal Filling.

PURPOSE: To study the topographical relationship between acute macular neuroretinopathy (AMN) lesions and the choroidal watershed zone (CWZ) or patchy choroidal filling (PCF) using multimodal imaging. METHODS: Lesions in patients diagnosed with AMN were clinically examined using multimodal imaging, including fundus photography, near-infrared reflectance imaging, spectral-domain optical coherence tomography (OCT), fluorescein angiography, indocyanine green angiography, OCT angiography, and microperimetry. The topographical relationship between AMN and the CWZ or PCF was evaluated. RESULTS: Seven eyes of six patients were included in the study. The mean age of the patients was 35.8 ± 11.7 years. The AMN lesions were collocated with the CWZ in five eyes and the PCF in one eye. Among these eyes, three had complete patterns, and three had partial patterns. Only one eye showed no topographical relationship between AMN and the CWZ or PCF. CONCLUSION: The colocation of AMN and CWZ/PCF suggests that the AMN lesions were within an area with a dual-watershed zone: the watershed zone between the retinal deep capillary plexus and choriocapillaris, and the choroidal watershed zone or patchy choroidal filling. This retinal area was highly vulnerable to hypoperfusion. Our results suggest a novel pathophysiological mechanism for AMN.

Reflectance Imaging Spectroscopy (RIS) for Operation Night Watch: Challenges and Achievements of Imaging Rembrandt's Masterpiece in the Glass Chamber at the Rijksmuseum.

Visible and infrared reflectance imaging spectroscopy is one of the several non-invasive techniques used during Operation Night Watch for the study of Rembrandt's iconic masterpiece The Night Watch (1642). The goals of this project include the identification and mapping of the artists' materials, providing information about the painting technique used as well as documenting the painting's current state and ultimately determining the possible conservation plan. The large size of the painting (3.78 m by 4.53 m) and the diversity of the technical investigations being performed make Operation Night Watch the largest research project ever undertaken at the Rijksmuseum. To construct a complete reflectance image cube at a high spatial resolution (168 µm2) and spectral resolution (2.54 to 6 nm), the painting was imaged with two high-sensitivity line scanning hyperspectral cameras (VNIR 400 to 1000 nm, 2.54 nm, and SWIR 900 to 2500 nm, 6 nm). Given the large size of the painting, a custom computer-controlled 3-D imaging frame was constructed to move each camera, along with lights, across the painting surface. A third axis, normal to the painting, was added along with a distance-sensing system which kept the cameras in focus during the scanning. A total of 200 hyperspectral image swaths were collected, mosaicked and registered to a high-resolution color image to sub-pixel accuracy using a novel registration algorithm. The preliminary analysis of the VNIR and SWIR reflectance images has identified many of the pigments used and their distribution across the painting. The SWIR, in particular, has provided an improved visualization of the preparatory sketches and changes in the painted composition. These data sets, when combined with the results from the other spectral imaging modalities and paint sample analyses, will provide the most complete understanding of the materials and painting techniques used by Rembrandt in The Night Watch.

Artificial Intelligence for Pigment Classification Task in the Short-Wave Infrared Range.

Hyperspectral reflectance imaging in the short-wave infrared range (SWIR, "extended NIR", ca. 1000 to 2500 nm) has proven to provide enhanced characterization of paint materials. However, the interpretation of the results remains challenging due to the intrinsic complexity of the SWIR spectra, presenting both broad and narrow absorption features with possible overlaps. To cope with the high dimensionality and spectral complexity of such datasets acquired in the SWIR domain, one data treatment approach is tested, inspired by innovative development in the cultural heritage field: the use of a pigment spectral database (extracted from model and historical samples) combined with a deep neural network (DNN). This approach allows for multi-label pigment classification within each pixel of the data cube. Conventional Spectral Angle Mapping and DNN results obtained on both pigment reference samples and a Buddhist painting (thangka) are discussed.

Bimodal multispectral imaging system with cloud-based machine learning algorithm for real-time screening and detection of oral potentially malignant lesions and biopsy guidance.

SIGNIFICANCE: Screening and early detection of oral potentially malignant lesions (OPMLs) are of great significance in reducing the mortality rates associated with head and neck malignancies. Intra-oral multispectral optical imaging of tissues in conjunction with cloud-based machine learning (CBML) can be used to detect oral precancers at the point-of-care (POC) and guide the clinician to the most malignant site for biopsy. AIM: Develop a bimodal multispectral imaging system (BMIS) combining tissue autofluorescence and diffuse reflectance (DR) for mapping changes in oxygenated hemoglobin (HbO2) absorption in the oral mucosa, quantifying tissue abnormalities, and guiding biopsies. APPROACH: The hand-held widefield BMIS consisting of LEDs emitting at 405, 545, 575, and 610 nm, 5MPx monochrome camera, and proprietary Windows-based software was developed for image capture, processing, and analytics. The DR image ratio (R610/R545) was compared with pathologic classification to develop a CBML algorithm for real-time assessment of tissue status at the POC. RESULTS: Sensitivity of 97.5% and specificity of 92.5% were achieved for discrimination of OPML from patient normal in 40 sites, whereas 82% sensitivity and 96.6% specificity were obtained for discrimination of abnormal (OPML + SCC) in 89 sites. Site-specific algorithms derived for buccal mucosa (27 sites) showed improved sensitivity and specificity of 96.3% for discrimination of OPML from normal. CONCLUSIONS: Assessment of oral cancer risk is possible by mapping of HbO2 absorption in tissues, and the BMIS system developed appears to be suitable for biopsy guidance and early detection of oral cancers.

Quantitative Multispectral Imaging Differentiates Melanoma from Seborrheic Keratosis.

Melanoma is a melanocytic tumor that is responsible for the most skin cancer-related deaths. By contrast, seborrheic keratosis (SK) is a very common benign lesion with a clinical picture that may resemble melanoma. We used a multispectral imaging device to distinguish these two entities, with the use of autofluorescence imaging with 405 nm and diffuse reflectance imaging with 525 and 660 narrow-band LED illumination. We analyzed intensity descriptors of the acquired images. These included ratios of intensity values of different channels, standard deviation and minimum/maximum values of intensity of the lesions. The pattern of the lesions was also assessed with the use of particle analysis. We found significantly higher intensity values in SKs compared with melanoma, especially with the use of the autofluorescence channel. Moreover, we found a significantly higher number of particles with high fluorescence in SKs. We created a parameter, the SK index, using these values to differentiate melanoma from SK with a sensitivity of 91.9% and specificity of 57.0%. In conclusion, this imaging technique is potentially applicable to distinguish melanoma from SK based on the analysis of various quantitative parameters. For this application, multispectral imaging could be used as a screening tool by general physicians and non-experts in the everyday practice.

Characterization of extracellular vesicles and synthetic nanoparticles with four orthogonal single-particle analysis platforms.

We compared four orthogonal technologies for sizing, counting, and phenotyping of extracellular vesicles (EVs) and synthetic particles. The platforms were: single-particle interferometric reflectance imaging sensing (SP-IRIS) with fluorescence, nanoparticle tracking analysis (NTA) with fluorescence, microfluidic resistive pulse sensing (MRPS), and nanoflow cytometry measurement (NFCM). EVs from the human T lymphocyte line H9 (high CD81, low CD63) and the promonocytic line U937 (low CD81, high CD63) were separated from culture conditioned medium (CCM) by differential ultracentrifugation (dUC) or a combination of ultrafiltration (UF) and size exclusion chromatography (SEC) and characterized by transmission electron microscopy (TEM) and Western blot (WB). Mixtures of synthetic particles (silica and polystyrene spheres) with known sizes and/or concentrations were also tested. MRPS and NFCM returned similar particle counts, while NTA detected counts approximately one order of magnitude lower for EVs, but not for synthetic particles. SP-IRIS events could not be used to estimate particle concentrations. For sizing, SP-IRIS, MRPS, and NFCM returned similar size profiles, with smaller sizes predominating (per power law distribution), but with sensitivity typically dropping off below diameters of 60 nm. NTA detected a population of particles with a mode diameter greater than 100 nm. Additionally, SP-IRIS, MRPS, and NFCM were able to identify at least three of four distinct size populations in a mixture of silica or polystyrene nanoparticles. Finally, for tetraspanin phenotyping, the SP-IRIS platform in fluorescence mode was able to detect at least two markers on the same particle, while NFCM detected either CD81 or CD63. Based on the results of this study, we can draw conclusions about existing single-particle analysis capabilities that may be useful for EV biomarker development and mechanistic studies.