Digital Staining Research Articles

Transformation from hematoxylin-and-eosin staining to Ki-67 immunohistochemistry digital staining images using deep learning: experimental validation on the labeling index.

Endometrial cancer (EC) is one of the most common types of cancer affecting women. While the hematoxylin-and-eosin (H&E) staining remains the standard for histological analysis, the immunohistochemistry (IHC) method provides molecular-level visualizations. Our study proposes a digital staining method to generate the hematoxylin-3,3'-diaminobenzidine (H-DAB) IHC stain of Ki-67 for the whole slide image of the EC tumor from its H&E stain counterpart. We employed a color unmixing technique to yield stain density maps from the optical density (OD) of the stains and utilized the U-Net for end-to-end inference. The effectiveness of the proposed method was evaluated using the Pearson correlation between the digital and physical stain's labeling index (LI), a key metric indicating tumor proliferation. Two different cross-validation schemes were designed in our study: intraslide validation and cross-case validation (CCV). In the widely used intraslide scheme, the training and validation sets might include different regions from the same slide. The rigorous CCV validation scheme strictly prohibited any validation slide from contributing to training. The proposed method yielded a high-resolution digital stain with preserved histological features, indicating a reliable correlation with the physical stain in terms of the Ki-67 LI. In the intraslide scheme, using intraslide patches resulted in a biased accuracy (e.g., ) significantly higher than that of CCV. The CCV scheme retained a fair correlation (e.g., ) between the LIs calculated from the digital stain and its physical IHC counterpart. Inferring the OD of the IHC stain from that of the H&E stain enhanced the correlation metric, outperforming that of the baseline model using the RGB space. Our study revealed that molecule-level insights could be obtained from H&E images using deep learning. Furthermore, the improvement brought via OD inference indicated a possible method for creating more generalizable models for digital staining via per-stain analysis.

Automatic Annotation Diagnostic Framework for Nasopharyngeal Carcinoma via Pathology-Fidelity GAN and Prior-Driven Classification.

Non-keratinizing carcinoma is the most common subtype of nasopharyngeal carcinoma (NPC). Its poorly differentiated tumor cells and complex microenvironment present challenges to pathological diagnosis. AI-based pathological models have demonstrated potential in diagnosing NPC, but the reliance on costly manual annotation hinders development. To address the challenges, this paper proposes a deep learning-based framework for diagnosing NPC without manual annotation. The framework includes a novel unpaired generative network and a prior-driven image classification system. With pathology-fidelity constraints, the generative network achieves accurate digital staining from H&E to EBER images. The classification system leverages staining specificity and pathological prior knowledge to annotate training data automatically and to classify images for NPC diagnosis. This work used 232 cases for study. The experimental results show that the classification system reached a 99.59% accuracy in classifying EBER images, which closely matched the diagnostic results of pathologists. Utilizing PF-GAN as the backbone of the framework, the system attained a specificity of 0.8826 in generating EBER images, markedly outperforming that of other GANs (0.6137, 0.5815). Furthermore, the F1-Score of the framework for patch level diagnosis was 0.9143, exceeding those of fully supervised models (0.9103, 0.8777). To further validate its clinical efficacy, the framework was compared with experienced pathologists at the WSI level, showing comparable NPC diagnosis performance. This low-cost and precise diagnostic framework optimizes the early pathological diagnosis method for NPC and provides an innovative strategic direction for AI-based cancer diagnosis.

Digital histological staining of tissue slide images from optical coherence microscopy.

The convergence of staining-free optical imaging and digital staining technologies has become a central focus in digital pathology, presenting significant advantages in streamlining specimen preparation and expediting the rapid acquisition of histopathological information. Despite the inherent merits of optical coherence microscopy (OCM) as a staining-free technique, its widespread application in observing histopathological slides has been constrained. This study introduces a novel approach by combining wide-field OCM with digital staining technology for the imaging of histopathological slides. Through the optimization of the histology slide production process satisfying the ground growth for digital staining as well as pronounced contrast for OCM imaging, successful imaging of various mouse tissues was achieved. Comparative analyses with conventional staining-based bright field images were executed to evaluate the proposed methodology's efficacy. Moreover, the study investigates the generalization of digital staining color appearance to ensure consistent histopathology, considering tissue-specific and thickness-dependent variations.

Characterizing colon cancer stages through optical polarimetry-assisted digital staining.

Tissue polarimetry has been gaining importance in extracting useful diagnostic information from the structural attributes of tissues, which vary in response to the tissue health status and hence find great potential in cancer diagnosis. However, the complexities associated with cancer make it challenging to isolate the characteristic changes as the tumor progresses using polarimetry. This study attempts to experimentally characterize the polarimetric behavior in colon cancer associated with various stages of development. Bulk and unstained sections of normal and tumor colon tissue were imaged in the reflection and transmission polarimetry configurations at low and high imaging resolutions using an in-house developed Mueller polarimeter. Through this study, we observed that the information about the major contributors of scattering in colon tissue, manifesting in depolarization and retardance, can be obtained from the bulk tissue and unstained sections. These parameters aid in characterizing the polarimetric changes as the colon tumor progresses. While the unstained colon section best indicated the depolarization contrast between normal and tumor, the contrast through the retardance parameter was more pronounced in the bulk colon tissue. The results suggest that the polarimetric "digitally stained" images obtained by Mueller polarimetry are comparable with the bulk tissue counterparts, making it useful for characterizing colon cancer tissues across different stages of development.

Decoding the universal human chromatin landscape through teratoma-based profiling.

Teratoma formation is key for evaluating differentiation of human pluripotent stem cells into embryonic germ layers and serves as a model for understanding stem cell differentiation and developmental processes. Its potential for insights into epigenome and transcriptome profiling is significant. This study integrates the analysis of the epigenome and transcriptome of hESC-generated teratomas, comparing transcriptomes between hESCs and teratomas. It employs cell type-specific expression patterns from single-cell data to deconvolve RNA-Seq data and identify cell types within teratomas. Our results provide a catalog of activating and repressive histone modifications, while also elucidating distinctive features of chromatin states. Construction of an epigenetic signature matrix enabled the quantification of diverse cell populations in teratomas and enhanced the ability to unravel the epigenetic landscape in heterogeneous tissue contexts. This study also includes a single cell multiome atlas of expression (scRNA-Seq) and chromatin accessibility (scATAC-Seq) of human teratomas, further revealing the complexity of these tissues. A histology-based digital staining tool further complemented the annotation of cell types in teratomas, enhancing our understanding of their cellular composition. This research is a valuable resource for examining teratoma epigenomic and transcriptomic landscapes and serves as a model for epigenetic data comparison.

Digital Staining of White Blood Cells With Confidence Estimation.

Chemical staining of the blood smears is one of the crucial components of blood analysis. It is an expensive, lengthy and sensitive process, often prone to produce slight variations in colour and seen structures due to a lack of unified protocols across laboratories. Even though the current developments in deep generative modeling offer an opportunity to replace the chemical process with a digital one, there are specific safety-ensuring requirements due to the severe consequences of mistakes in a medical setting. Therefore digital staining system would profit from an additional confidence estimation quantifying the quality of the digitally stained white blood cell. To this aim, during the staining generation, we disentangle the latent space of the Generative Adversarial Network, obtaining separate representation s of the white blood cell and the staining. We estimate the generated image's confidence of white blood cell structure and staining quality by corrupting these representations with noise and quantifying the information retained between multiple outputs. We show that confidence estimated in this way correlates with image quality measured in terms of LPIPS values calculated for the generated and ground truth stained images. We validate our method by performing digital staining of images captured with a Differential Inference Contrast microscope on a dataset composed of white blood cells of 24 patients. The high absolute value of the correlation between our confidence score and LPIPS demonstrates the effectiveness of our method, opening the possibility of predicting the quality of generated output and ensuring trustworthiness in medical safety-critical setup.

Digital staining facilitates biomedical microscopy.

Traditional staining of biological specimens for microscopic imaging entails time-consuming, laborious, and costly procedures, in addition to producing inconsistent labeling and causing irreversible sample damage. In recent years, computational "virtual" staining using deep learning techniques has evolved into a robust and comprehensive application for streamlining the staining process without typical histochemical staining-related drawbacks. Such virtual staining techniques can also be combined with neural networks designed to correct various microscopy aberrations, such as out-of-focus or motion blur artifacts, and improve upon diffracted-limited resolution. Here, we highlight how such methods lead to a host of new opportunities that can significantly improve both sample preparation and imaging in biomedical microscopy.

Fungal and Parasitic Diseases Seen in Tissue Biopsies at a Tertiary Health Center in Southwest Nigeria in a Five-Year Period

Aim: To describe the clinical and pathological features of the various fungal and parasitic diseases seen at our hospital over a five-year period.
 Study Design: This is a retrospective study of records of fungal and parasitic diseases.
 Place and Duration of Study: The study was done at the Department of Morbid Anatomy and Forensic Medicine of Obafemi Awolowo University Teaching Hospitals Complex, Ile-Ife from January 2018 to December 2022.
 Methods: The tissue sections of each case went through routine processing in the histopathology laboratory. The slides of the sections were viewed using a simple binocular microscope. The slides were scanned using APERIO CS2 digital slide scanner.
 Results: Eight cases of fungal infections and eleven cases of parasitic infections were noted during the study period. Aspergillosis accounted for five of the fungal infections, while mucormycosis accounted for two nasal infections. Schistosomiasis accounted for six cases of parasitic infections.
 Conclusion: Aspergillosis was the most diagnosed fungal infection while Schistosomiasis was the commonest parasitic infection. Digital stain separation could be an added tool in identification of fungal and parasitic stages in histological tissue sections.

Generative adversarial network based digital stain conversion for generating RGB EVG stained image from hyperspectral H&E stained image.

Quantification of elastic fiber in the tissue specimen is an important aspect of diagnosing different diseases. Though hematoxylin and eosin (H&E) staining is a routinely used and less expensive tissue staining technique, elastic and collagen fibers cannot be differentiated using it. So, in conventional pathology, special staining technique, such as Verhoeff's van Gieson (EVG), is applied physically for this purpose. However, the procedure of EVG staining is very expensive and time-consuming. The goal of our study is to propose a deep-learning-based computerized method for the generation of RGB EVG stained tissue from hyperspectral H&E stained one to save the time and cost of conventional EVG staining procedure. H&E stained hyperspectral image and EVG stained RGB whole slide image of human pancreatic tissue have been leveraged for this experiment. CycleGAN-based deep learning model has been proposed for digital stain conversion while images from source and target domains are of different modalities (hyperspectral and RGB) with different channel dimensions. A set of three basis functions have been introduced for calculating one of the losses of the proposed method, which retains the relevant features of EVG stained image within the reduced channel dimension of the H&E stained one. The experimental results showed that a set of three basis functions including linear discriminant function and transmittance spectrum of eosin and hematoxylin better retained the essential properties of the elastic fiber to be discriminated from collagen fiber within the reduced dimension of the hyperspectral H&E stained image. Also, only a smaller number of paired training data with our proposed training method contributed significantly to the generation of more realistic EVG stained image with more precise identification of elastic fiber. RGB EVG stained image is generated from hyperspectral H&E stained image for which our model has performed two types of image conversion simultaneously: hyperspectral to RGB and H&E to EVG. The experimental results show that the intentionally designed set of three basis functions contains more relevant information and prove the effectiveness of our proposed method in generating realistic RGB EVG stained image from hyperspectral H&E stained one.

Automatic normalized digital color staining in the recognition of abnormal blood cells using generative adversarial networks

Background and ObjectivesCombining knowledge of clinical pathologists and deep learning models is a growing trend in morphological analysis of cells circulating in blood to add objectivity, accuracy, and speed in diagnosing hematological and non-hematological diseases. However, the variability in staining protocols across different laboratories can affect the color of images and performance of automatic recognition models. The objective of this work is to develop, train and evaluate a new system for the normalization of color staining of peripheral blood cell images, so that it transforms images from different centers to map the color staining of a reference center (RC) while preserving the structural morphological features. MethodsThe system has two modules, GAN1 and GAN2. GAN1 uses the PIX2PIX technique to fade original color images to an adaptive gray, while GAN2 transforms them into RGB normalized images. Both GANs have a similar structure, where the generator is a U-NET convolutional neural network with ResNet and the discriminator is a classifier with ResNet34 structure. Digitally stained images were evaluated using GAN metrics and histograms to assess the ability to modify color without altering cell morphology. The system was also evaluated as a pre-processing tool before cells undergo a classification process. For this purpose, a CNN classifier was designed for three classes: abnormal lymphocytes, blasts and reactive lymphocytes. ResultsTraining of all GANs and the classifier was performed using RC images, while evaluations were conducted using images from four other centers. Classification tests were performed before and after applying the stain normalization system. The overall accuracy reached a similar value around 96% in both cases for the RC images, indicating the neutrality of the normalization model for the reference images. On the contrary, it was a significant improvement in the classification performance when applying the stain normalization to the other centers. Reactive lymphocytes were the most sensitive to stain normalization, with true positive rates (TPR) increasing from 46.3% - 66% for the original images to 81.2% - 97.2% after digital staining. Abnormal lymphocytes TPR ranged from 31.9% - 95.7% with original images to 83% - 100% with digitally stained images. Blast class showed TPR ranges of 90.3% - 94.4% and 94.4% - 100%, for original and stained images, respectively. ConclusionsThe proposed GAN-based normalization staining approach improves the performance of classifiers with multicenter data sets by generating digitally stained images with a quality similar to the original images and adaptability to a reference staining standard. The system requires low computation cost and can help improve the performance of automatic recognition models in clinical settings.

Comparison of deep learning models for digital H&E staining from unpaired label-free multispectral microscopy images

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.

Multimodal study of CHI3L1 inhibition and its effect on angiogenesis, migration, immune response and refractive index of cellular structures in glioblastoma

Glioblastoma is one of the most aggressive tumours with a poor response to treatment and a poor prognosis for patients. One of the proteins expressed in glioblastoma tissue is CHI3L1 (YKL-40), which is upregulated and known for its angiogenesis-supporting and pro-tumour immunomodulatory effects in a variety of cancers. In this paper we present the anti-angiogenic, anti-migratory and immunomodulatory effects of the compound G721–0282, an inhibitor of CHI3L1. The inhibitor-induced changes were investigated using conventional techniques as well as the novel label-free digital holographic tomography (DHT), a quantitative phase imaging technique that allows the reconstruction of the refractive index (RI), which is used as an image contrast for 3D visualisation of living cells. DHT allowed digital staining of individual cells and intercellular structures based only on their specific RI. Quantitative spatially resolved analysis of the RI data shows that the concentration of G721–0282 leads to significant changes in the density of cells and their intracellular structures (in particular the cytoplasm and nucleus), in the volume of lipid droplets and in protein concentrations. Studies in the U-87 MG glioblastoma cell line, THP-1 monocytes differentiated into macrophages, human microvascular endothelial cells (HMEC-1) and in the spheroid model of glioblastoma composed of U-87 MG, HMEC-1 and macrophages suggest that inhibition of CHI3L1 may have potential in the antitumour treatment of glioblastoma. In this paper, we also propose a spheroid model for in vitro studies that mimics this type of tumour.

Ex vivo confocal microscopy detects basic patterns of acute and chronic lesions using fresh kidney samples.

Ex vivo confocal microscopy is a real-time technique that provides high-resolution images of fresh, non-fixed tissues, with an optical resolution comparable to conventional pathology. The objective of this study was to investigate the feasibility of using ex vivo confocal microscopy in fusion mode (FuCM) and the haematoxylin and eosin (H&E)-like digital staining that results for the analysis of basic patterns of lesion in nephropathology. Forty-eight renal samples were scanned in a fourth-generation ex vivo confocal microscopy device. Samples were subjected to confocal microscopy imaging and were then processed using conventional pathology techniques. Concordance between the techniques was evaluated by means of the percentage of agreement and the κ index. Agreement between conventional microscopy and H&E-like digital staining was strong (κ=0.88) in the evaluation of acute tubular damage and was substantial (κ=0.79) in the evaluation of interstitial fibrosis, interstitial inflammation, arterial and arteriolar lesions. H&E-like digital staining also allows rapid identification of extracapillary proliferation (κ=0.88), necrosis and segmental sclerosis (κ=.88) in the glomerular compartment, but the results reported here are limited because of the small number of cases with these glomerular findings. FuCM proved to be as effective as conventional techniques in evaluating the presence of acute tubular necrosis and interstitial fibrosis changes, but in fresh tissue. The ease of acquisition of ex vivo confocal microscopy images suggests that FuCM may be useful for rapid evaluation of kidney biopsies and to restructure the clinical workflow in renal histopathology.

Multi-scale visualization of incipient CaCO3 scaling on the polyamide layer of reverse osmosis membranes

Although robust models on water transport across reverse osmosis (RO) membranes exist, detailed mechanistic insights of mineral scaling in the active polyamide (PA) layer, especially through visual investigations, are still lacking. Herein, a holistic approach of imaging the onset of mineral scales in the PA-layer is detailed at different length scales. BW30 RO membranes were scaled by running CaCO3-rich feedwater in a narrow, spacer-filled crossflow channel for 120 min. Then, microscopy and spectroscopy-based characterization tools were applied to investigate the CaCO3 scaling in the PA-layer. The vaterite phase of CaCO3 dominated at the incipient stage and was amplified at higher flux. TEM imaging, followed by digital staining with unsupervised K-means clustering, demonstrated incipient scaling primarily on top of the intricate folds of the PA-layer. Scales with a characteristic size between 1 and 6 nm were also identified deep inside the randomly distributed microvoids formed by those folds, hinting at heterogeneous crystallization of CaCO3. Thus, membranes designed to limit the complex folding of the PA-layer could show more scale resistance. The improved visualization of PA-layer via TEM–K-means and resulting insights can be broadly used in designing novel scale-resistant membranes and evaluating the efficacy of membrane cleaning operations.

Segmentation and Recognition of the Pathological Features of Squamous Cell Carcinoma of the Skin Based on Multispectral Imaging.

Cutaneous squamous cell carcinoma (cSCC) is one of the most common skin cancers, a definitive diagnosis of cSCC is crucial to prevent patients from missing out on treatment. The gold standard for the diagnosis of cSCC is still pathological biopsy. Currently, its diagnostic efficiency and accuracy largely depend on the experience of pathologists. Here, we present a simple, fast, and robust technique, a microscopic multispectral imaging system based on LED illumination, to diagnose cSCC qualitatively and quantitatively. The adaptive threshold segmentation method was used to segment the multispectral images into characteristic structures. There was a statistically significant difference between the average nucleocytoplasmic ratio of normal skin (4.239%) and cSCC tissues (15.607%) (p < 0.01), and the keratin pearls cSCC have well-defined qualitative features. These results show that the qualitative and quantitative features obtained from multispectral imaging can be used to comprehensively determine whether or not the tissue is cancerous. This work has significant implications for the development of a low-cost and easy-to-use device, which can not only reduce the complexity of pathological diagnosis but can also achieve the goal of convenient digital staining and access to critical histological information.

Diagnostic Accuracy of Digital Staining ex vivo Confocal Microscopy for Diagnosing and Subtyping Basal Cell Carcinoma in Fresh Pretherapeutic Punch Biopsies: A Monocentric Prospective Study

Background: Ex vivo confocal microscopy using fusion mode and digital staining (EVCM) scans unfixed fresh tissue and produces rapidly digitally stained images of very similar quality to classical pathology. We investigated whether EVCM could represent an alternative to the standard histological examination of the pretherapeutic basal cell carcinoma (BCC) punch biopsies. Objectives: The objective of the study was to assess diagnostic accuracy of EVCM versus traditional histopathological examination for diagnosing and subtyping clinically suspicious lesions of BCC in 3-mm fresh and nonfixed punch biopsies. Methods: In this prospective monocentric observational study, patients with clinically suspected BCC were consecutively enrolled. Punch biopsies were imaged using EVCM and subsequently processed for standard histologic examination (gold standard). EVCM images were examined by a dermatopathologist blinded to clinical aspect of the lesion and histopathological results. Concordance between the EVCM and histology analysis was calculated with Cohen’s kappa (κ) statistic. Results: Sixty-six patients were recruited, and 106 biopsies were analyzed. EVCM correctly diagnosed 70/73 BCCs and 31/33 non-BCC lesions, corresponding to a sensitivity of 96% and a specificity of 94% (positive predictive value = 97%, negative predictive value = 91%). The EVCM assessment led to over-staging and under-staging of BCC subtypes in 5% and 11% of cases, respectively. It led to over-staging and under-staging of BCC depths in 5% and 15%, respectively. The kappa coefficient for concordance was 0.78 (95% confidence interval [CI]: 0.69–0.88) when considering BCC subtypes and 0.81 (95% CI: 0.72–0.90) when considering BCC depths. Conclusions: These results render EVCM as a promising option for “real-time” pretreatment evaluation of clinically suspected BCC lesions. Further larger randomized studies are needed to assess the efficiency of EVCM versus standard care in patients with clinically suspected BCC.

Visualization of energy-based device-induced thermal tissue alterations using bimodal ex-vivo confocal microscopy with digital staining. A proof-of-concept study.

Ex-vivo confocal microscopy (EVCM) enables examination of tissue alterations immediately after treatment with energy-based devices (EBDs). This proof-of-concept study aimed to describe EBD-induced tissue effects in ex-vivo porcine skin after treatment with microneedle radiofrequency (MNRF) and ablative fractional CO2 -laser (AFL) using EVCM. Ex-vivo porcine skin was treated with MNRF and AFL. Three cryosections from each intervention were stained with acridine orange (AO) and scanned with EVCM. Reflectance confocal microscopy (RCM, 638nm) and fluorescence confocal microscopy (FCM, 488nm) images were captured and evaluated individually, after image fusion, and after digital hematoxylin and eosin (H&E) staining. Bimodal EVCM was able to visualize EBD-induced thermal alterations in porcine skin. In RCM mode, the full width and depth of the vertically aligned microscopic treatment zones (MTZs) were displayed with clear demarcation to surrounding intact skin. In FCM mode, the ablation of the epidermis after AFL was prominent in contrast with the almost intact epidermis observed in MNRF treated skin. In fusion mode, fluorescence signal from AO marked the surrounding coagulation zone (CZ) from both interventions, with enhanced discrimination between ablation and coagulation. Digitally H&E-stained images closely resembled conventional histopathology but proved superior in terms of visualization of the CZ. Bimodal EVCM with digital H&E-staining facilitates the identification and qualitative evaluation of thermal alterations induced by treatment with EBD. By providing high-resolution images comparable to standard histology, EVCM is a useful tool in the research and development of EBD to visualize and evaluate device-tissue interactions.

Ex vivo confocal laser scanning microscopy: A diagnostic technique for easy real-time evaluation of benign and malignant skin tumours.

Ex vivo confocal laser scanning microscopy (ex vivo CLSM) is a novel diagnostic tool for a quick bedside evaluation of freshly excised tissue, comparable to histology. We aimed to assess the sensitivity and specificity of ex vivo CLSM in detecting malignant features, to validate its reliability in identifying various skin tumours based on a combination of confocal features and to evaluate the digital staining mode (DS). One-hundred twenty freshly excised skin samples from 91 patients were evaluated. Each lesion was screened for the presence of 23 predefined confocal criteria with ex vivo CLSM, followed by a histopathological examination. The diagnostic agreement between ex vivo CLSM and histology was 89.2%. The diagnostic accuracy of ex vivo CLSM in detecting malignancy reached a sensitivity of 98% and a specificity of 76%. Ex vivo CLSM enabled a rapid identification of the most common skin tumours, the tumour dignity and cytological features. The DS demonstrated a close resemblance to conventional histopathology.

Calcium-sensing receptor is not expressed in the absorptive enterocytes of weaned piglets.

The calcium-sensing receptor (CaSR) is a kokumi receptor that plays an essential role in nutrient sensing and animal physiology, growth, and development. Pig CaSR (pCaSR) was identified and characterized in the intestine. However, further research is still needed to confirm the expression of CaSR in the epithelial cells isolated from weaned piglets. In this study, primary enterocytes were isolated and characterized from the ileum of weaned piglets by the Weiser distended intestinal sac technique and fluorescence-activated cell sorting (FACS) based on sucrase-isomaltase (SI) as an enterocyte-specific marker. The expression of CaSR was investigated in both primary enterocytes and the intestinal porcine enterocyte cell line-j2 (IPEC-J2) by droplet digital polymerase chain reaction (ddPCR), immunofluorescence staining, and Western blotting. Results demonstrated that porcine enterocytes could be obtained using FACS with the SI as the enterocyte-specific marker and that pCaSR is not expressed in both porcine ileal enterocytes and IPEC-J2 cells, which specifically identified the expression of pCaSR in ileal enterocytes with sensitive and specific approaches.

Single-shot hyperspectral imaging based on dual attention neural network with multi-modal learning.

Hyperspectral imaging is being extensively investigated owing to its promising future in critical applications such as medical diagnostics, sensing, and surveillance. However, current techniques are complex with multiple alignment-sensitive components and spatiospectral parameters predetermined by manufacturers. In this paper, we demonstrate an end-to-end snapshot hyperspectral imaging technique and build a physics-informed dual attention neural network with multimodal learning. By modeling the 3D spectral cube reconstruction procedure and solving that compressive-imaging inverse problem, the hyperspectral volume can be directly recovered from only one scene RGB image. Spectra features and camera spectral sensitivity are jointly leveraged to retrieve the multiplexed spatiospectral correlations and realize hyperspectral imaging. With the help of integrated attention mechanism, useful information supplied by disparate modal components is adaptively learned and aggregated to make our network flexible for variable imaging systems. Results show that the proposed method is ultra-faster than the traditional scanning method, and 3.4 times more precise than the existing hyperspectral imaging convolutional neural network. We provide theory for network design, demonstrate training process, and present experimental results with high accuracy. Without bulky benchtop setups and strict experimental limitations, this simple and effective method offers great potential for future spectral imaging applications such as pathological digital stain, computational imaging and virtual/augmented reality display, etc.