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The ability to respond to injury with tissue repair is a fundamental property of all multicellular organisms. The extracellular matrix (ECM), composed of fibrillar collagens as well as a number of other components is dis-regulated during repair in many organs. In many tissues, scaring results when the balance is lost between ECM synthesis and degradation. Investigating what disrupts this balance and what effect this can have on tissue function remains an active area of research. Recent advances in the imaging of fibrillar collagen using second harmonic generation (SHG) imaging have proven useful in enhancing our understanding of the supramolecular changes that occur during scar formation and disease progression. Here, we review the physical properties of SHG, and the current nonlinear optical microscopy imaging (NLOM) systems that are used for SHG imaging. We provide an extensive review of studies that have used SHG in skin, lung, cardiovascular, tendon and ligaments, and eye tissue to understand alterations in fibrillar collagens in scar tissue. Lastly, we review the current methods of image analysis that are used to extract important information about the role of fibrillar collagens in scar formation.

Collagen, one of the major components in the mammalian connective tissues, plays an essential role in many vital physiological processes. Many common diseases, such as fibrosis, overuse injuries, and bone fracture, are associated with collagen arrangement defects. However, the underlying mechanism of collagen arrangement defects remains elusive. In this study, we applied infrared scattering-type scanning near-field optical microscopy to study collagen fibrils’ structural properties. Experimentally, we observed two types of collagen fibrils’ arrangement with different periodic characteristics. A crystal sliding model was employed to explain this observation qualitatively. Our results suggest that the collagen dislocation propagates in collagen fibrils, which may shed light on many collagen diseases’ pathogenesis. These findings help to understand the regulation mechanism of hierarchical biological structure.

Many reports state the potential hazards of microplastics (MPs) and their implications to wildlife and human health. The presence of MP in the aquatic environment is related to several origins but particularly associated to their occurrence in wastewater effluents. The determination of MP in these complex samples is a challenge. Current analytical procedures for MP monitoring are based on separation and counting by visual observation or mediated with some type of microscopy with further identification by techniques such as Raman or Fourier-transform infrared (FTIR) spectroscopy. In this work, a simple alternative for the separation, counting and identification of MP in wastewater samples is reported. The presented sample preparation technique with further polarized light optical microscopy (PLOM) observation positively identified the vast majority of MP particles occurring in wastewater samples of Montevideo, Uruguay, in the 70–600 μm range. MPs with different shapes and chemical composition were identified by PLOM and confirmed by confocal Raman microscopy. Rapid identification of polyethylene (PE), polypropylene (PP) and polyethylene terephthalate (PET) were evidenced. A major limitation was found in the identification of MP from non-birefringent polymers such as PVC (polyvinylchloride). The proposed procedure for MP analysis in wastewater is easy to be implemented at any analytical laboratory. A pilot monitoring of Montevideo WWTP effluents was carried out over 3-month period identifying MP from different chemical identities in the range 5.3–8.2 × 10 3 MP items/m 3 .

Accurate and precise nucleic-acid quantification is crucial for clinical and diagnostic decisions, as overestimation or underestimation can lead to misguided treatment of a disease or incorrect labelling of the products. Digital PCR is one of the best tools for absolute nucleic-acid copy-number determination. However, digital PCR needs to be well characterised in terms of accuracy and sources of uncertainty. With droplet digital PCR, discrepancies between the droplet volume assigned by the manufacturer and measured by independent laboratories have already been shown in previous studies. In the present study, we report on the results of an inter-laboratory comparison of different methods for droplet volume determination that is based on optical microscopy imaging and is traceable to the International System of Units. This comparison was conducted on the same DNA material, with the examination of the influence of parameters such as droplet generators, supermixes, operators, inter-cartridge and intra-cartridge variability, and droplet measuring protocol. The mean droplet volume was measured using a QX200™ AutoDG™ Droplet Digital™ PCR system and two QX100™ Droplet Digital™ PCR systems. The data show significant volume differences between these two systems, as well as significant differences in volume when different supermixes are used. We also show that both of these droplet generator systems produce droplets with significantly lower droplet volumes (13.1%, 15.9%, respectively) than stated by the manufacturer and previously measured by other laboratories. This indicates that to ensure precise quantification, the droplet volumes should be assessed for each system.

Scanning near-field optical microscopy (SNOM) is a well-known powerful optical technique for visualization of surface nanostructures and fields far beyond the diffraction limit and thus indispensable in material- and nanoscience. While the SNOM resolution is theoretically unlimited, the SNOM performance is in practice constrained by the signal-to-background ratio, simply because of light scattering scaling down as the sixth power of a nanoparticle size and useful signals rapidly drowning in the background for very small objects. In modern instruments, this problem is usually ameliorated through advanced post-processing techniques. Here, we suggest using, instead or in parallel, a ‘dark’ SNOM probe designed to suppress the background light scattering, so that the scattering occurs only when the probe is very close to a nanoscopic object. We argue and demonstrate with simulations that the dark-probe SNOM imaging is much more sensitive to the presence of tiny nanoparticles or any other nanoscale features, allowing thereby for superior resolution and sensing capabilities that are invaluable for nano-optical characterization.

In conventional confocal/multiphoton fluorescence microscopy, images are typically acquired under ideal settings and after extensive optimization of parameters for a given structure or feature, often resulting in information loss from other image attributes. To overcome the problem of selective data display, we developed a new method that extends the imaging dynamic range in optical microscopy and improves the signal-to-noise ratio. Here we demonstrate how real-time and sequential high dynamic range microscopy facilitates automated three-dimensional neural segmentation. We address reconstruction and segmentation performance on samples with different size, anatomy and complexity. Finally, in vivo real-time high dynamic range imaging is also demonstrated, making the technique particularly relevant for longitudinal imaging in the presence of physiological motion and/or for quantification of in vivo fast tracer kinetics during functional imaging. Confocal and multiphoton fluorescence microscopy often suffers from low dynamic range. Here the authors develop a high dynamic range, laser scanning fluorescence technique by simultaneously recording different light intensity ranges. The method can be adapted to commercial systems.

Prostate cancer (PC) is the most frequently diagnosed cancer in North American men. Pathologists are in critical need of accurate biomarkers to characterize PC, particularly to confirm the presence of intraductal carcinoma of the prostate (IDC-P), an aggressive histopathological variant for which therapeutic options are now available. Our aim was to identify IDC-P with Raman micro-spectroscopy (RμS) and machine learning technology following a protocol suitable for routine clinical histopathology laboratories. We used RμS to differentiate IDC-P from PC, as well as PC and IDC-P from benign tissue on formalin-fixed paraffin-embedded first-line radical prostatectomy specimens (embedded in tissue microarrays [TMAs]) from 483 patients treated in 3 Canadian institutions between 1993 and 2013. The main measures were the presence or absence of IDC-P and of PC, regardless of the clinical outcomes. The median age at radical prostatectomy was 62 years. Most of the specimens from the first cohort (Centre hospitalier de l'Université de Montréal) were of Gleason score 3 + 3 = 6 (51%) while most of the specimens from the 2 other cohorts (University Health Network and Centre hospitalier universitaire de Québec-Université Laval) were of Gleason score 3 + 4 = 7 (51% and 52%, respectively). Most of the 483 patients were pT2 stage (44%-69%), and pT3a (22%-49%) was more frequent than pT3b (9%-12%). To investigate the prostate tissue of each patient, 2 consecutive sections of each TMA block were cut. The first section was transferred onto a glass slide to perform immunohistochemistry with H&E counterstaining for cell identification. The second section was placed on an aluminum slide, dewaxed, and then used to acquire an average of 7 Raman spectra per specimen (between 4 and 24 Raman spectra, 4 acquisitions/TMA core). Raman spectra of each cell type were then analyzed to retrieve tissue-specific molecular information and to generate classification models using machine learning technology. Models were trained and cross-validated using data from 1 institution. Accuracy, sensitivity, and specificity were 87% ± 5%, 86% ± 6%, and 89% ± 8%, respectively, to differentiate PC from benign tissue, and 95% ± 2%, 96% ± 4%, and 94% ± 2%, respectively, to differentiate IDC-P from PC. The trained models were then tested on Raman spectra from 2 independent institutions, reaching accuracies, sensitivities, and specificities of 84% and 86%, 84% and 87%, and 81% and 82%, respectively, to diagnose PC, and of 85% and 91%, 85% and 88%, and 86% and 93%, respectively, for the identification of IDC-P. IDC-P could further be differentiated from high-grade prostatic intraepithelial neoplasia (HGPIN), a pre-malignant intraductal proliferation that can be mistaken as IDC-P, with accuracies, sensitivities, and specificities > 95% in both training and testing cohorts. As we used stringent criteria to diagnose IDC-P, the main limitation of our study is the exclusion of borderline, difficult-to-classify lesions from our datasets. In this study, we developed classification models for the analysis of RμS data to differentiate IDC-P, PC, and benign tissue, including HGPIN. RμS could be a next-generation histopathological technique used to reinforce the identification of high-risk PC patients and lead to more precise diagnosis of IDC-P.

Delivering light to the nanoscale using a flexible and easily integrated fiber platform holds potential in various fields of quantum science and bioscience. However, rigorous optical alignment, sophisticated fabrication process, and low spatial resolution of the fiber-based nanoconcentrators limit the practical applications. Here, a broadband azimuthal plasmon interference nanofocusing technique on a fiber-coupled spiral tip is demonstrated for fiber-based near-field optical nanoimaging. The spiral plasmonic fiber tip fabricated through a robust and reproducible process can reverse the polarization and modulate the mode field of the surface plasmon polaritons in three-dimensionally azimuthal direction, resulting in polarization-insensitive, broad-bandwidth, and azimuthal interference nanofocusing. By integrating this with a basic scanning near-field optical microscopy, a high optical resolution of 31 nm and beyond is realized. The high performance and the easy incorporation with various existing measurement platforms offered by this fiber-based nanofocusing technique have great potential in near-field optics, tip-enhanced Raman spectroscopy, nonlinear spectroscopy, and quantum sensing.

Hardwood species have flourished in the recent evolutionary history of angiosperms and exhibit pronounced diversity in cell anatomy, such as cell arrangement, cell type, ultrastructure, and cellulose microfibril angle. Optimization of the mass transportation and load-bearing functions of wood can be realized by complex combinations of these structural features. To decipher species-by-species structural optimization strategies, multiscale wood cell structures should ideally be measured simultaneously. However, methodological restrictions have hampered progress in this respect. To address this problem, the present study aimed to measure and analyze the cell-by-cell cellulose microfibril angle and anatomy of Japanese hardwood species using polarized optical microscopy-based retardation imaging and deep learning-based semantic segmentation to achieve cell-by-cell microfibril angle imaging in a wide field of view. Microfibril angle imaging revealed that characteristic ultrastructure and microfibril angle distributions were distinguishable depending on the wood species and cell type, such as fibers, axial parenchyma, and vessel elements. The results implied that cell wall thickness and microfibril angle are correlated in fibers or tracheids of certain vesselless and ring-porous species. The combination of microfibril angle imaging and semantic segmentation presents the opportunity to gain insights into optimization strategies in the hierarchical structure of diverse wood species.

A visible and sensitive assay for the quantitative detection of β-glucosidase (β-glu) activity based on Au@CeO 2 core-shell nanoparticles (Au@CeO 2 NPs) is described. As a hydrolytic enzyme, β-glu can promote the hydrolysis of β-arbutin to hydroquinone (HQ), which can trigger the decomposition of the CeO 2 shell. With the single-particle enumeration (SPE) strategy coupled with dark field optical microscopy (DFM), an obvious color alteration of single Au@CeO 2 NPs during the etching process can be observed in real-time. By statistically calculating the number of the etched nanoparticles, the β-glu activity level can be quantified accurately. This assay displays a broad linear range from 0.5 to 50 mU⋅mL −1 and low detection limit of 0.12 mU⋅mL −1 . In addition, this method was successfully used to determine β-glu in real samples and acquires satisfactory recoveries in the range of 97.1-102.0%. This study provides a visualization analysis method for β-glu, which may be helpful for monitoring other targets in the future. Graphical Abstract