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Aït-Kaddour A, Loudiyi M, Boukria O, Safarov J, Sultanova S, Andueza D, Listrat A, Cahyana Y. Beef muscle discrimination based on two-trace two-dimensional correlation spectroscopy (2T2D COS) combined with snapshot visible-near infrared multispectral imaging. Meat Sci 2024; 214:109533. [PMID: 38735067 DOI: 10.1016/j.meatsci.2024.109533] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2023] [Revised: 04/29/2024] [Accepted: 05/05/2024] [Indexed: 05/14/2024]
Abstract
The purpose of this work was to assess the potential of 2T2D COS PLS-DA (two-trace two-dimensional correlation spectroscopy and partial least squares discriminant analysis) in conjunction with Visible Near infrared multispectral imaging (MSI) as a quick, non-destructive, and precise technique for classifying three beef muscles -Longissimus thoracis, Semimembranosus, and Biceps femoris- obtained from three breeds - the Blonde d'Aquitaine, Limousine, and Aberdeen Angus. The experiment was performed on 240 muscle samples. Before performing PLS-DA, spectra were extracted from MSI images and processed by SNV (Standard Normal Variate), MSC (Multivariate Scattering Correction) or AREA (area under curve equal 1) and converted in synchronous and asynchronous 2T2D COS maps. The results of the study highlighted that combining synchronous and asynchronous 2T2D COS maps before performing PLS-DA was the best strategy to discriminate between the three muscles (100% of classification accuracy and 0% of error).
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Zhang S, Duan X, Yan X, Yuan X, Zhang D, Liu Y, Wang Y, Shen S, Xuan S, Zhao J, Chen X, Luo S, Gu A. Multispectral detection of dietary fiber content in Chinese cabbage leaves across different growth periods. Food Chem 2024; 447:138895. [PMID: 38492298 DOI: 10.1016/j.foodchem.2024.138895] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 02/13/2024] [Accepted: 02/26/2024] [Indexed: 03/18/2024]
Abstract
Multispectral imaging, combined with stoichiometric values, was used to construct a prediction model to measure changes in dietary fiber (DF) content in Chinese cabbage leaves across different growth periods. Based on all the spectral bands (365-970 nm) and characteristic spectral bands (430, 880, 590, 490, 690 nm), eight quantitative prediction models were established using four machine learning algorithms, namely random forest (RF), backpropagation neural network, radial basis function, and multiple linear regression. Finally, a quantitative prediction model of RF learning algorithm is constructed based on all spectral bands, which has good prediction accuracy and model robustness, prediction performance with R2 of 0.9023, root mean square error (RMSE) of 2.7182 g/100 g, residual predictive deviation (RPD) of 3.1220 > 3.0. In summary, this model efficiently detects changes in DF content across different growth periods of Chinese cabbage, which offers technical support for vegetable sorting and grading in the field.
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Altun HY, Secilmis M, Yang F, Akgul Caglar T, Vatandaslar E, Toy MF, Vilain S, Mann GE, Öztürk G, Eroglu E. Visualizing hydrogen peroxide and nitric oxide dynamics in endothelial cells using multispectral imaging under controlled oxygen conditions. Free Radic Biol Med 2024; 221:89-97. [PMID: 38735541 DOI: 10.1016/j.freeradbiomed.2024.05.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Revised: 04/04/2024] [Accepted: 05/10/2024] [Indexed: 05/14/2024]
Abstract
The complex interplay between hydrogen peroxide (H2O2) and nitric oxide (NO) in endothelial cells presents challenges due to technical limitations in simultaneous measurement, hindering the elucidation of their direct relationship. Previous studies have yielded conflicting findings regarding the impact of H2O2 on NO production. To address this problem, we employed genetically encoded biosensors, HyPer7 for H2O2 and geNOps for NO, allowing simultaneous imaging in single endothelial cells. Optimization strategies were implemented to enhance biosensor performance, including camera binning, temperature regulation, and environmental adjustments to mimic physiological normoxia. Our results demonstrate that under ambient oxygen conditions, H2O2 exhibited no significant influence on NO production. Subsequent exploration under physiological normoxia (5 kPa O2) revealed distinct oxidative stress levels characterized by reduced basal HyPer7 signals, enhanced H2O2 scavenging kinetics, and altered responses to pharmacological treatment. Investigation of the relationship between H2O2 and NO under varying oxygen conditions revealed a lack of NO response to H2O2 under hyperoxia (18 kPa O2) but a modest NO response under physiological normoxia (5 kPa O2). Importantly, the NO response was attenuated by l-NAME, suggesting activation of eNOS by endogenous H2O2 generation upon auranofin treatment. Our study highlights the intricate interplay between H2O2 and NO within the endothelial EA.hy926 cell line, emphasizing the necessity for additional research within physiological contexts due to differential response observed under physiological normoxia (5 kPa O2). This further investigation is essential for a comprehensive understanding of the H2O2 and NO signaling considering the physiological effects of ambient O2 levels involved.
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Anichini G, Leiloglou M, Hu Z, O'Neill K, Daniel Elson. Hyperspectral and multispectral imaging in neurosurgery: a systematic literature review and meta-analysis. EUROPEAN JOURNAL OF SURGICAL ONCOLOGY 2024:108293. [PMID: 38658267 DOI: 10.1016/j.ejso.2024.108293] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Revised: 01/21/2024] [Accepted: 03/20/2024] [Indexed: 04/26/2024]
Abstract
INTRODUCTION The neuro-surgical community is witnessing a rising interest for surgical application of multispectral/hyperspectral imaging. Several potential technical applications of this optical imaging are reported, but the set-up is variable and so are the processing methods. We present a systematic review of the relevant literature on the topic. MATERIALS AND METHODS A literature search based on the PRISMA principles was performed on PubMed, SCOPUS, and Web of Science, using MESH terms and Boolean operators. Papers regarding intra-operative in-vivo application of multispectral and/or hyperspectral imaging in humans during neurosurgical procedures were included. Papers reporting technologies related to radiological applications were excluded. A meta-analysis on the performance metrics was also conducted. RESULTS Our search string retrieved 20 papers. The main applications of optical imaging during neurosurgery concern tumour detection and improvement of the extent of resection (15 papers) or visualization of perfusion changes during neuro-oncology or neuro-vascular surgery (5 papers). All the retrieved articles were pilot studies, proof of concepts, or case reports, with limited number of patients recruited. Sensitivity, specificity, and accuracy were promising in most of the reports, but the metanalysis showed heterogeneous approaches and results among studies. CONCLUSIONS The present review shows that several approaches are currently being tested to integrate hyperspectral imaging in neurosurgery, but most of the studies reported a limited pool of patients, with different approaches to data collection and analysis. Further studies on larger cohorts of patients are therefore desirable to fully explore the potential of this imaging technique.
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Yu S, Dwight J, Siska RC, Burkart H, Quan P, Yi F, Du S, Daoud Y, Plant K, Criscitiello A, Molnar J, Thatcher JE. Feasibility of intra-operative image guidance in burn excision surgery with multispectral imaging and deep learning. Burns 2024; 50:115-122. [PMID: 37821282 DOI: 10.1016/j.burns.2023.07.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 06/28/2023] [Accepted: 07/19/2023] [Indexed: 10/13/2023]
Abstract
BACKGROUND Exposing a healthy wound bed for skin grafting is an important step during burn surgery to ensure graft take and maintain good functional outcomes. Currently, the removal of non-viable tissue in the burn wound bed during excision is determined by expert clinician judgment. Using a porcine model of tangential burn excision, we investigated the effectiveness of an intraoperative multispectral imaging device combined with artificial intelligence to aid clinician judgment for the excision of non-viable tissue. METHODS Multispectral imaging data was obtained from serial tangential excisions of thermal burn injuries and used to train a deep learning algorithm to identify the presence and location of non-viable tissue in the wound bed. Following algorithm development, we studied the ability of two surgeons to estimate wound bed viability, both unaided and aided by the imaging device. RESULTS The deep learning algorithm was 87% accurate in identifying the viability of a burn wound bed. When paired with the surgeons, this device significantly improved their abilities to determine the viability of the wound bed by 25% (p = 0.03). Each time a surgeon changed their decision after seeing the AI model output, it was always a change from an incorrect decision to excise more tissue to a correct decision to stop excision. CONCLUSION This study provides insight into the feasibility of image-guided burn excision, its effect on surgeon decision making, and suggests further investigation of a real-time imaging system for burn surgery could reduce over-excision of burn wounds.
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Saldarriaga OA, Wanninger TG, Arroyave E, Gosnell J, Krishnan S, Oneka M, Bao D, Millian DE, Kueht ML, Moghe A, Jiao J, Sanchez JI, Spratt H, Beretta L, Rao A, Burks JK, Stevenson HL. Heterogeneity in intrahepatic macrophage populations and druggable target expression in patients with steatotic liver disease-related fibrosis. JHEP Rep 2024; 6:100958. [PMID: 38162144 PMCID: PMC10757256 DOI: 10.1016/j.jhepr.2023.100958] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 08/18/2023] [Accepted: 09/25/2023] [Indexed: 01/03/2024] Open
Abstract
Background & Aims Clinical trials for reducing fibrosis in steatotic liver disease (SLD) have targeted macrophages with variable results. We evaluated intrahepatic macrophages in patients with SLD to determine if activity scores or fibrosis stages influenced phenotypes and expression of druggable targets, such as CCR2 and galectin-3. Methods Liver biopsies from controls or patients with minimal or advanced fibrosis were subject to gene expression analysis using nCounter to determine differences in macrophage-related genes (n = 30). To investigate variability among individual patients, we compared additional biopsies by staining them with multiplex antibody panels (CD68/CD14/CD16/CD163/Mac387 or CD163/CCR2/galectin-3/Mac387) followed by spectral imaging and spatial analysis. Algorithms that utilize deep learning/artificial intelligence were applied to create cell cluster plots, phenotype profile maps, and to determine levels of protein expression (n = 34). Results Several genes known to be pro-fibrotic (e.g. CD206, TREM2, CD163, and ARG1) showed either no significant differences or significantly decreased with advanced fibrosis. Although marked variability in gene expression was observed in individual patients with cirrhosis, several druggable targets and their ligands (e.g. CCR2, CCR5, CCL2, CCL5, and LGALS3) were significantly increased when compared to patients with minimal fibrosis. Antibody panels identified populations that were significantly increased (e.g. Mac387+), decreased (e.g. CD14+), or enriched (e.g. interactions of Mac387) in patients that had progression of disease or advanced fibrosis. Despite heterogeneity in patients with SLD, several macrophage phenotypes and druggable targets showed a positive correlation with increasing NAFLD activity scores and fibrosis stages. Conclusions Patients with SLD have markedly varied macrophage- and druggable target-related gene and protein expression in their livers. Several patients had relatively high expression, while others were like controls. Overall, patients with more advanced disease had significantly higher expression of CCR2 and galectin-3 at both the gene and protein levels. Impact and implications Appreciating individual differences within the hepatic microenvironment of patients with SLD may be paramount to developing effective treatments. These results may explain why such a small percentage of patients have responded to macrophage-targeting therapies and provide additional support for precision medicine-guided treatment of chronic liver diseases.
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Stamford J, Kasznicki P, Lawson T. Spectral Reflectance Measurements. Methods Mol Biol 2024; 2790:333-353. [PMID: 38649579 DOI: 10.1007/978-1-0716-3790-6_17] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/25/2024]
Abstract
This chapter provides a methodology for evaluating plant health and leaf characteristics using spectral reflectance. It provides a step-by-step guide to using spectrometers for high-resolution point measurements of leaf spectral reflectance and multispectral imaging for capturing spatial data, emphasizing the importance of consistent measurement conditions. The chapter further explores the intricacies of multispectral imaging, including calibration, data collection, and image processing. Finally, this chapter delves into the application of various spectral indices for the quantification of key traits such as pigment content, the status of the xanthophyll cycle, water content, and how to identify spectral regions of interest for further research and development. Serving as a guide for researchers and practitioners in plant science, this chapter provides a straightforward framework for plant health assessment using spectral reflectance.
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Mróz T, Shafiee S, Crossa J, Montesinos-Lopez OA, Lillemo M. Multispectral-derived genotypic similarities from budget cameras allow grain yield prediction and genomic selection augmentation in single and multi-environment scenarios in spring wheat. MOLECULAR BREEDING : NEW STRATEGIES IN PLANT IMPROVEMENT 2024; 44:5. [PMID: 38230361 PMCID: PMC10789716 DOI: 10.1007/s11032-024-01449-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Accepted: 01/08/2024] [Indexed: 01/18/2024]
Abstract
With abundant available genomic data, genomic selection has become routine in many plant breeding programs. Multispectral data captured by UAVs showed potential for grain yield (GY) prediction in many plant species using machine learning; however, the possibilities of utilizing this data to augment genomic prediction models still need to be explored. We collected high-throughput phenotyping (HTP) multispectral data in a genotyped multi-environment large-scale field trial using two cost-effective cameras to fill this gap. We tested back to back the prediction ability of GY prediction models, including genomic (G matrix), multispectral-derived (M matrix), and environmental (E matrix) relationships using best linear unbiased predictor (BLUP) methodology in single and multi-environment scenarios. We discovered that M allows for GY prediction comparable to the G matrix and that models using both G and M matrices show superior accuracies and errors compared with G or M alone, both in single and multi-environment scenarios. We showed that the M matrix is not entirely environment-specific, and the genotypic relationships become more robust with more data capture sessions over the season. We discovered that the optimal time for data capture occurs during grain filling and that camera bands with the highest heritability are important for GY prediction using the M matrix. We showcased that GY prediction can be performed using only an RGB camera, and even a single data capture session can yield valuable data for GY prediction. This study contributes to a better understanding of multispectral data and its relationships. It provides a flexible framework for improving GS protocols without significant investments or software customization. Supplementary Information The online version contains supplementary material available at 10.1007/s11032-024-01449-w.
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Balandra A, Doll Y, Hirose S, Kajiwara T, Kashino Z, Inami M, Koshimizu S, Fukaki H, Watahiki MK. P-MIRU, a Polarized Multispectral Imaging System, Reveals Reflection Information on the Biological Surface. PLANT & CELL PHYSIOLOGY 2023; 64:1311-1322. [PMID: 37217180 DOI: 10.1093/pcp/pcad045] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 04/12/2023] [Accepted: 05/20/2023] [Indexed: 05/24/2023]
Abstract
Reflection light forms the core of our visual perception of the world. We can obtain vast information by examining reflection light from biological surfaces, including pigment composition and distribution, tissue structure and surface microstructure. However, because of the limitations in our visual system, the complete information in reflection light, which we term 'reflectome', cannot be fully exploited. For example, we may miss reflection light information outside our visible wavelengths. In addition, unlike insects, we have virtually no sensitivity to light polarization. We can detect non-chromatic information lurking in reflection light only with appropriate devices. Although previous studies have designed and developed systems for specialized uses supporting our visual systems, we still do not have a versatile, rapid, convenient and affordable system for analyzing broad aspects of reflection from biological surfaces. To overcome this situation, we developed P-MIRU, a novel multispectral and polarization imaging system for reflecting light from biological surfaces. The hardware and software of P-MIRU are open source and customizable and thus can be applied for virtually any research on biological surfaces. Furthermore, P-MIRU is a user-friendly system for biologists with no specialized programming or engineering knowledge. P-MIRU successfully visualized multispectral reflection in visible/non-visible wavelengths and simultaneously detected various surface phenotypes of spectral polarization. The P-MIRU system extends our visual ability and unveils information on biological surfaces.
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Ma F, Yuan M, Kozak I. Multispectral imaging: Review of current applications. Surv Ophthalmol 2023; 68:889-904. [PMID: 37321478 DOI: 10.1016/j.survophthal.2023.06.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 06/06/2023] [Accepted: 06/06/2023] [Indexed: 06/17/2023]
Abstract
Multispectral imaging (MSI) is a unique layer-by-layer imaging technique that allows the visualization of a wide array of retinal and choroidal pathologies including retinovascular disorders, retinal pigment epithelial changes, and choroidal lesions. Herein, we summarize the basic imaging principles and current applications of MSI together with recent technology advances in the field. MSI detects reflectance signal from both normal chorioretinal tissue and pathological lesions. Either hyperreflectance or hyporeflectance reveals the absorption activity of pigments such as hemoglobin and melanin and the reflection from interfaces such as the posterior hyaloid. Advances in MSI technique include creation of a retinal and choroidal oxy-deoxy map that could provide a better understanding of blood oxygen saturation within lesions as well as better interpretation of reflectance phenomenon of MSI images such as the different reflectance from the Sattler and Haller layers described in this review.
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Sijilmassi O, López Alonso JM, Del Río Sevilla A, Barrio Asensio MDC. Multispectral Imaging Method for Rapid Identification and Analysis of Paraffin-Embedded Pathological Tissues. J Digit Imaging 2023; 36:1663-1674. [PMID: 37072579 PMCID: PMC10406798 DOI: 10.1007/s10278-023-00826-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 03/28/2023] [Accepted: 03/30/2023] [Indexed: 04/20/2023] Open
Abstract
The study of the interaction between light and biological tissue is of great help in the identification of diseases as well as structural alterations in tissues. In the present study, we have developed a tissue diagnostic technique by using multispectral imaging in the visible spectrum combined with principal component analysis (PCA). We used information from the propagation of light through paraffin-embedded tissues to assess differences in the eye tissues of control mouse embryos compared to mouse embryos whose mothers were deprived of folic acid (FA), a crucial vitamin necessary for the growth and development of the fetus. After acquiring the endmembers from the multispectral images, spectral unmixing was used to identify the abundances of those endmembers in each pixel. For each acquired image, the final analysis was performed by performing a pixel-by-pixel and wavelength-by-wavelength absorbance calculation. Non-negative least squares (NNLS) were used in this research. The abundance maps obtained for the first endmember revealed vascular alterations (vitreous and choroid) in the embryos with maternal FA deficiency. However, the abundance maps obtained for the third endmember showed alterations in the texture of some tissues such as the lens and retina. Results indicated that multispectral imaging applied to paraffin-embedded tissues enhanced tissue visualization. Using this method, first, it can be seen tissue damage location and then decide what kind of biological techniques to apply.
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Barulin A, Park H, Park B, Kim I. Dual-wavelength UV-visible metalens for multispectral photoacoustic microscopy: A simulation study. PHOTOACOUSTICS 2023; 32:100545. [PMID: 37645253 PMCID: PMC10461252 DOI: 10.1016/j.pacs.2023.100545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 08/01/2023] [Accepted: 08/13/2023] [Indexed: 08/31/2023]
Abstract
Photoacoustic microscopy is advancing with research on utilizing ultraviolet and visible light. Dual-wavelength approaches are sought for observing DNA/RNA- and vascular-related disorders. However, the availability of high numerical aperture lenses covering both ultraviolet and visible wavelengths is severely limited due to challenges such as chromatic aberration in the optics. Herein, we present a groundbreaking proposal as a pioneering simulation study for incorporating multilayer metalenses into ultraviolet-visible photoacoustic microscopy. The proposed metalens has a thickness of 1.4 µm and high numerical aperture of 0.8. By arranging cylindrical hafnium oxide nanopillars, we design an achromatic transmissive lens for 266 and 532 nm wavelengths. The metalens achieves a diffraction-limited focal spot, surpassing commercially available objective lenses. Through three-dimensional photoacoustic simulation, we demonstrate high-resolution imaging with superior endogenous contrast of targets with ultraviolet and visible optical absorption bands. This metalens will open new possibilities for downsized multispectral photoacoustic microscopy in clinical and preclinical applications.
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Salido J, Vallez N, González-López L, Deniz O, Bueno G. Comparison of deep learning models for digital H&E staining from unpaired label-free multispectral microscopy images. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 235:107528. [PMID: 37040684 DOI: 10.1016/j.cmpb.2023.107528] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 03/27/2023] [Accepted: 04/03/2023] [Indexed: 05/08/2023]
Abstract
BACKGROUND AND OBJECTIVE This paper presents the quantitative comparison of three generative models of digital staining, also known as virtual staining, in H&E modality (i.e., Hematoxylin and Eosin) that are applied to 5 types of breast tissue. Moreover, a qualitative evaluation of the results achieved with the best model was carried out. This process is based on images of samples without staining captured by a multispectral microscope with previous dimensional reduction to three channels in the RGB range. METHODS The models compared are based on conditional GAN (pix2pix) which uses images aligned with/without staining, and two models that do not require image alignment, Cycle GAN (cycleGAN) and contrastive learning-based model (CUT). These models are compared based on the structural similarity and chromatic discrepancy between samples with chemical staining and their corresponding ones with digital staining. The correspondence between images is achieved after the chemical staining images are subjected to digital unstaining by means of a model obtained to guarantee the cyclic consistency of the generative models. RESULTS The comparison of the three models corroborates the visual evaluation of the results showing the superiority of cycleGAN both for its larger structural similarity with respect to chemical staining (mean value of SSIM ∼ 0.95) and lower chromatic discrepancy (10%). To this end, quantization and calculation of EMD (Earth Mover's Distance) between clusters is used. In addition, quality evaluation through subjective psychophysical tests with three experts was carried out to evaluate quality of the results with the best model (cycleGAN). CONCLUSIONS The results can be satisfactorily evaluated by metrics that use as reference image a chemically stained sample and the digital staining images of the reference sample with prior digital unstaining. These metrics demonstrate that generative staining models that guarantee cyclic consistency provide the closest results to chemical H&E staining that also is consistent with the result of qualitative evaluation by experts.
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Herath S, Weerasooriya HK, Ranasinghe DYL, Bandara WGC, Herath VR, Godaliyadda RI, Ekanayake MPB, Madhujith T. Quantitative assessment of adulteration of coconut oil using transmittance multispectral imaging. JOURNAL OF FOOD SCIENCE AND TECHNOLOGY 2023; 60:1551-1559. [PMID: 37033321 PMCID: PMC10076459 DOI: 10.1007/s13197-023-05697-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Revised: 01/18/2023] [Accepted: 02/18/2023] [Indexed: 03/03/2023]
Abstract
Economical to a fault, coconut oil is a commodity related to fraudulent activities such as oil adulteration for undue profits. Unfortunately, the conventional methods used in the detection of adulteration and toxicants are laborious, destructive, and time-consuming. Hence, it is imperative to engineer a non-destructive and rapid screening test with sufficient accuracy. To that end, the proposed work has an in-house developed imaging system hardware and a method to estimate relevant quality parameters from multispectral imagery. Multispectral images of adulterated coconut oil were analyzed through a cascade of statistical algorithms: Fisher Discriminant Analysis and Bhattacharyya distance respectively. In this work, a functional relationship was developed for the estimation of adulteration level that recorded an R2 of 0.9876 for the training samples and an MSE of 0.0029 for the testing samples. Besides, the proposed imaging system offers flexibility on post-processing of raw measurements as the algorithm is designed to operate from raw multispectral images. In addition, the developed imaging system is economical in its capacity to estimate the adulteration of coconut oil with remarkable accuracy considering the low cost of production. Moreover, the proposed work validates the use of multispectral imagery as an initial screening technique instead of expensive spectroscopy methods.
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Papachristoforou A, Prodromou M, Hadjimitsis D, Christoforou M. Detecting and distinguishing between apicultural plants using UAV multispectral imaging. PeerJ 2023; 11:e15065. [PMID: 37077312 PMCID: PMC10108856 DOI: 10.7717/peerj.15065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2022] [Accepted: 02/23/2023] [Indexed: 04/21/2023] Open
Abstract
Detecting and distinguishing apicultural plants are important elements of the evaluation and quantification of potential honey production worldwide. Today, remote sensing can provide accurate plant distribution maps using rapid and efficient techniques. In the present study, a five-band multispectral unmanned aerial vehicle (UAV) was used in an established beekeeping area on Lemnos Island, Greece, for the collection of high-resolution images from three areas where Thymus capitatus and Sarcopoterium spinosum are present. Orthophotos of UAV bands for each area were used in combination with vegetation indices in the Google Earth Engine (GEE) platform, to classify the area occupied by the two plant species. From the five classifiers (Random Forest, RF; Gradient Tree Boost, GTB; Classification and Regression Trees, CART; Mahalanobis Minimum Distance, MMD; Support Vector Machine, SVM) in GEE, the RF gave the highest overall accuracy with a Kappa coefficient reaching 93.6%, 98.3%, 94.7%, and coefficient of 0.90, 0.97, 0.92 respectively for each case study. The training method used in the present study detected and distinguish the two plants with great accuracy and results were confirmed using 70% of the total score to train the GEE and 30% to assess the method's accuracy. Based on this study, identification and mapping of Thymus capitatus areas is possible and could help in the promotion and protection of this valuable species which, on many Greek Islands, is the sole foraging plant of honeybees.
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Galactosidase-catalyzed fluorescence amplification method (GAFAM): sensitive fluorescent immunohistochemistry using novel fluorogenic β-galactosidase substrates and its application in multiplex immunostaining. Histochem Cell Biol 2023; 159:233-246. [PMID: 36374321 DOI: 10.1007/s00418-022-02162-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/16/2022] [Indexed: 11/16/2022]
Abstract
Multiplex immunohistochemistry/multiplex immunofluorescence (mIHC/mIF) enables the simultaneous detection of multiple markers in a single tissue section by visualizing the markers in different colors. Currently, tyramide signal amplification (TSA) is the most commonly used method because it is heat resistant to multiplexing. SPiDER-βGal (6'-(diethylamino)-4'-(fluoromethyl)spiro[isobenzofuran-1(3H),9'-[9H]xanthen]-3'-yl β-D-galactopyranoside), a novel fluorogenic substrate of β-galactosidase (β-gal) was reported recently. Its properties are favorable for application in sensitive mIF based on quinone methide chemistry. Combining SPiDER-βGal with its related substrates, a novel, sensitive fluorescent IHC method for formalin-fixed paraffin-embedded (FFPE) sections was developed, named the galactosidase-catalyzed fluorescence amplification method (GAFAM). Evaluation of GAFAM indicated the following characteristics: (1) the entire GAFAM procedure was complete within a few hours; (2) the optimal working concentration of the substrates was 20 μM; (3) the fluorescent product was heat resistant; (4) the GAFAM exhibited sensitivity comparable with that of TSA, which was higher than that of conventional IF; and (5) the GAFAM was applicable to mIF and multispectral imaging. GAFAM is expected to be applicable to IF (or mIF in combination with TSA), and is a promising tool for facilitating morphological research in various fields of life science.
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van den Brand FF, Masrati H, Jordanova ES, Bloemena E, Lissenberg-Witte BI, de Boer YS, Bontkes HJ, Mebius R, Bouma G. MAdCAM-1 does not play a central role in the early pathophysiology of autoimmune hepatitis. Clin Res Hepatol Gastroenterol 2023; 47:102099. [PMID: 36841352 DOI: 10.1016/j.clinre.2023.102099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Revised: 02/13/2023] [Accepted: 02/21/2023] [Indexed: 02/27/2023]
Abstract
INTRODUCTION CD4+ T cells are thought to have a central role in the pathogenesis of autoimmune hepatitis (AIH). Mucosal addressin cell adhesion molecule-1 (MAdCAM-1) directs homing of CD4+ T cells in the alimentary tract and is a therapeutic target in inflammatory bowel diseases. Here we assessed MAdCAM-1 expression in AIH and viral hepatitis and related its expression with immune infiltrate analysis and histopathological key features. METHODS Hepatic portal areas of pretreatment biopsies (n=10) and follow-up biopsies (n=9) of patients with a confirmed diagnosis of AIH were assessed for MAdCAM-1 expression and infiltrate composition using immunohistochemistry and multispectral imaging (Vectra® Polaris™). Controls consisted of biopsies of patients with untreated chronic viral hepatitis B or C (n=22). RESULTS MAdCAM-1 expression on endothelium was sparsely present in portal fields of two treatment-naïve AIH patients. Three patients showed MAdCAM-1 expression within lymphoid aggregates. No expression of significance (including single-cell expression) was observed in the remaining 6 patients. In contrast, viral hepatitis C biopsies showed endothelial MAdCAM-1 expression in 8 of 13 untreated patients. Densities of both B-cells (CD20+) and CD4+ T-cells (CD3+ CD8-) were increased in AIH and viral hepatitis patients with MAdCAM-1 expression. CONCLUSION MAdCAM-1 was detected in liver biopsies in a minority of patients with AIH at the time of diagnosis suggesting no central role in its pathophysiology. Lymphoid or reticular MAdCAM-1 pattern expression was associated with more dense infiltrates of both B-cells and CD4+ T-cells, and may be related to the formation of secondary lymphoid follicles.
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Marafioti T, Lozano MD, de Andrea CE. Characterization of the immune infiltrate in mouse tissue by multiplex immunofluorescence. Methods Cell Biol 2023; 174:43-53. [PMID: 36710050 DOI: 10.1016/bs.mcb.2022.07.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Multiplexed immunofluorescence imaging of formalin-fixed, paraffin-embedded (FFPE) specimens mounted on glass slides allow the identification of multiple cell phenotypes while retaining spatial and morphological context. Multiplex immunofluorescence protocols have already been developed and validated for mouse tissues. Immunophenotyping analysis reliably depicts the immune landscape of cancer tissues that has been demonstrated to influence cancer development and progression as well as to have an impact on therapy responsiveness and resistance. Here, we describe a method for multiplexed fluorescence image analysis, enabling analysis of mouse cancer morphology and cell phenotypes in FFPE sections.
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Sadashivaiah V, Tippani M, Page SC, Kwon SH, Bach SV, Bharadwaj RA, Hyde TM, Kleinman JE, Jaffe AE, Maynard KR. SUFI: an automated approach to spectral unmixing of fluorescent multiplex images captured in mouse and post-mortem human brain tissues. BMC Neurosci 2023; 24:6. [PMID: 36698068 PMCID: PMC9878864 DOI: 10.1186/s12868-022-00765-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Accepted: 12/06/2022] [Indexed: 01/26/2023] Open
Abstract
BACKGROUND Multispectral fluorescence imaging coupled with linear unmixing is a form of image data collection and analysis that allows for measuring multiple molecular signals in a single biological sample. Multiple fluorescent dyes, each measuring a unique molecule, are simultaneously measured and subsequently "unmixed" to provide a read-out for each molecular signal. This strategy allows for measuring highly multiplexed signals in a single data capture session, such as multiple proteins or RNAs in tissue slices or cultured cells, but can often result in mixed signals and bleed-through problems across dyes. Existing spectral unmixing algorithms are not optimized for challenging biological specimens such as post-mortem human brain tissue, and often require manual intervention to extract spectral signatures. We therefore developed an intuitive, automated, and flexible package called SUFI: spectral unmixing of fluorescent images. RESULTS This package unmixes multispectral fluorescence images by automating the extraction of spectral signatures using vertex component analysis, and then performs one of three unmixing algorithms derived from remote sensing. We evaluate these remote sensing algorithms' performances on four unique biological datasets and compare the results to unmixing results obtained using ZEN Black software (Zeiss). We lastly integrate our unmixing pipeline into the computational tool dotdotdot, which is used to quantify individual RNA transcripts at single cell resolution in intact tissues and perform differential expression analysis, and thereby provide an end-to-end solution for multispectral fluorescence image analysis and quantification. CONCLUSIONS In summary, we provide a robust, automated pipeline to assist biologists with improved spectral unmixing of multispectral fluorescence images.
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Slomka B, Duan S, Knapp TG, Lima N, Sontz R, Merchant JL, Sawyer TW. Design, fabrication, and preclinical testing of a miniaturized, multispectral, chip-on-tip, imaging probe for intraluminal fluorescence imaging of the gastrointestinal tract. FRONTIERS IN PHOTONICS 2023; 3:1067651. [PMID: 37691859 PMCID: PMC10488317 DOI: 10.3389/fphot.2022.1067651] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
Gastrointestinal cancers continue to account for a disproportionately large percentage of annual cancer deaths in the US. Advancements in miniature imaging technology combined with a need for precise and thorough tumor detection in gastrointestinal cancer screenings fuel the demand for new, small-scale, and low-cost methods of localization and margin identification with improved accuracy. Here, we report the development of a miniaturized, chip-on-tip, multispectral, fluorescence imaging probe designed to port through a gastroscope working channel with the aim of detecting cancerous lesions in point-of-care endoscopy of the gastrointestinal lumen. Preclinical testing has confirmed fluorescence sensitivity and supports that this miniature probe can locate structures of interest via detection of fluorescence emission from exogenous contrast agents. This work demonstrates the design and preliminary performance evaluation of a miniaturized, single-use, chip-on-tip fluorescence imaging system, capable of detecting multiple fluorochromes, and devised for deployment via the accessory channel of a standard gastroscope.
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McGue JJ, Edwards JJ, Griffith BD, Frankel TL. Multiplex Fluorescent Immunohistochemistry for Preservation of Tumor Microenvironment Architecture and Spatial Relationship of Cells in Tumor Tissues. Methods Mol Biol 2023; 2660:235-246. [PMID: 37191801 DOI: 10.1007/978-1-0716-3163-8_16] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/17/2023]
Abstract
The tumor microenvironment (TME), composed of immune cells, antigens, and local soluble factors, is integral to cancer development and progression. Traditional techniques such as immunohistochemistry, immunofluorescence, or flow cytometry limit the analysis of spatial data and cellular interactions within the TME, as they are restricted to colocalization of a small number of antigens or the loss of tissue architecture. Multiplex fluorescent immunohistochemistry (mfIHC) allows for detection of multiple antigens within a single tissue sample, providing a more comprehensive description of tissue composition and spatial interactions within the TME. This technique utilizes antigen retrieval, application of primary and secondary antibodies, followed by a tyramide-based chemical reaction to covalently bind a fluorophore to an epitope of interest and, eventually, stripping of the antibodies. This allows for multiple rounds of antibody application without concern for species cross-reactivity, as well as signal amplification which abrogates the autofluorescence that frequently plagues analysis of fixed tissues. As such, mfIHC can be used to quantify multiple cellular populations and their interactions, in situ, unlocking key biologic data that was previously unavailable. This chapter provides an overview of the experimental design, staining, and imaging strategies using a manual technique in formalin-fixed paraffin-embedded tissue sections.
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Arjoune Y, Sugunaraj N, Peri S, Nair SV, Skurdal A, Ranganathan P, Johnson B. Soybean cyst nematode detection and management: a review. PLANT METHODS 2022; 18:110. [PMID: 36071455 PMCID: PMC9450454 DOI: 10.1186/s13007-022-00933-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 08/04/2022] [Indexed: 06/15/2023]
Abstract
Soybeans play a key role in global food security. U.S. soybean yields, which comprise [Formula: see text] of the total soybeans planted in the world, continue to experience unprecedented grain loss due to the soybean cyst nematode (SCN) plant pathogen. SCN remains one of the primary disruptive pests despite the existence of advanced management techniques such as crop rotation and SCN-resistant varieties. SCN detection is a key step in managing this disease; however, early detection is challenging because soybeans do not show any above ground symptoms unless they are significantly damaged. Direct soil sampling remains the most common method for SCN detection, however, this method has several problems. For example, the threshold damage methods-adopted by most of the laboratories to make recommendations-is not reliable as it does not consider soil pH, N, P, and K values and relies solely on the egg count instead of assessment of the root infection. To overcome the challenges of manual soil sampling methods, deep learning and hyperspectral imaging are important current topics in precision agriculture for plant disease detection and have been proposed as cost-effective and efficient detection methods that can work at scale. We have reviewed more than 150 research papers focusing on soybean cyst nematodes with an emphasis on deep learning techniques for detection and management. First: we describe soybean vegetation and reproduction stages, SCN life cycles, and factors influencing this disease. Second: we highlight the impact of SCN on soybean yield loss and the challenges associated with its detection. Third: we describe direct sampling methods in which the soil samples are procured and analyzed to evaluate SCN egg counts. Fourth: we highlight the advantages and limitations of these direct methods, then review computer vision- and remote sensing-based detection methods: data collection using ground, aerial, and satellite approaches followed by a review of machine learning methods for image analysis-based soybean cyst nematode detection. We highlight the evaluation approaches and the advantages of overall detection workflow in high-performance and big data environments. Lastly, we discuss various management approaches, such as crop rotation, fertilization, SCN resistant varieties such as PI 88788, and SCN's increasing resistance to these strategies. We review machine learning approaches for soybean crop yield forecasting as well as the influence of pesticides, herbicides, and fertilizers on SCN infestation reduction. We provide recommendations for soybean research using deep learning and hyperspectral imaging to accommodate the lack of the ground truth data and training and testing methodologies, such as data augmentation and transfer learning, to achieve a high level of detection accuracy while keeping costs as low as possible.
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Mihailova A, Liebisch B, Islam MD, Carstensen JM, Cannavan A, Kelly SD. The use of multispectral imaging for the discrimination of Arabica and Robusta coffee beans. Food Chem X 2022; 14:100325. [PMID: 35586030 PMCID: PMC9108882 DOI: 10.1016/j.fochx.2022.100325] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Revised: 04/19/2022] [Accepted: 05/03/2022] [Indexed: 11/16/2022] Open
Abstract
Arabica coffee beans are sold at twice the price, or more, compared to Robusta beans and consequently are susceptible to economically motivated adulteration by substitution. There is a need for rapid, non-destructive, and efficient analytical techniques for monitoring the authenticity of Arabica coffee beans in the supply chain. In this study, multispectral imaging (MSI) was applied to discriminate roasted Arabica and Robusta coffee beans and perform quantitative prediction of Arabica coffee bean adulteration with Robusta. The Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) model, built using selected spectral and morphological features from individual coffee beans, achieved 100% correct classification of the two coffee species in the test dataset. The OPLS regression model was able to successfully predict the level of adulteration of Arabica with Robusta. MSI analysis has potential as a rapid screening tool for the detection of fraud issues related to the authenticity of Arabica coffee beans.
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Schellenberg M, Dreher KK, Holzwarth N, Isensee F, Reinke A, Schreck N, Seitel A, Tizabi MD, Maier-Hein L, Gröhl J. Semantic segmentation of multispectral photoacoustic images using deep learning. PHOTOACOUSTICS 2022; 26:100341. [PMID: 35371919 PMCID: PMC8968659 DOI: 10.1016/j.pacs.2022.100341] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 02/15/2022] [Accepted: 02/20/2022] [Indexed: 05/08/2023]
Abstract
Photoacoustic (PA) imaging has the potential to revolutionize functional medical imaging in healthcare due to the valuable information on tissue physiology contained in multispectral photoacoustic measurements. Clinical translation of the technology requires conversion of the high-dimensional acquired data into clinically relevant and interpretable information. In this work, we present a deep learning-based approach to semantic segmentation of multispectral photoacoustic images to facilitate image interpretability. Manually annotated photoacoustic and ultrasound imaging data are used as reference and enable the training of a deep learning-based segmentation algorithm in a supervised manner. Based on a validation study with experimentally acquired data from 16 healthy human volunteers, we show that automatic tissue segmentation can be used to create powerful analyses and visualizations of multispectral photoacoustic images. Due to the intuitive representation of high-dimensional information, such a preprocessing algorithm could be a valuable means to facilitate the clinical translation of photoacoustic imaging.
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Hirasawa T, Tachi K, Miyashita M, Okawa S, Kushibiki T, Ishihara M. Spectroscopic photoacoustic microscopic imaging during single spatial scan using broadband excitation light pulses with wavelength-dependent time delay. PHOTOACOUSTICS 2022; 26:100364. [PMID: 35574189 PMCID: PMC9096666 DOI: 10.1016/j.pacs.2022.100364] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 04/15/2022] [Accepted: 04/29/2022] [Indexed: 06/15/2023]
Abstract
In most multispectral optical-resolution photoacoustic microscopy (OR-PAM), spatial scanning is repeated for each excitation wavelength, which decreases throughput and causes motion artifacts during spectral processing. This study proposes a new spectroscopic OR-PAM technique to acquire information on the photoacoustic signal intensity and excitation wavelength from single spatial scans. The technique involves irradiating an imaging target with two broadband optical pulses with and without wavelength-dependent time delays. The excitation wavelength of the sample is then calculated by measuring the time delay between the photoacoustic signals generated by the two optical pulses. This technique is validated by measuring the excitation wavelengths of dyes in tubes. Furthermore, we demonstrate the three-dimensional spectroscopic OR-PAM of cells stained with suitable dyes. Although the tradeoff between excitation efficiency and excitation bandwidth must be adjusted based on the application, combining the proposed technique with fast spatial scanning methods can significantly contribute to recent OR-PAM applications, such as monitoring quick biological events and microscale tracking of moving materials.
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