Enteroblastic adenocarcinoma of the ampulla of Vater / Adenocarcinoma enteroblástico de la ampolla de Vater

Adenocarcinoma with enteroblastic differentiation is a rare histologic subtype of adenocarcinoma of the gastrointestinal tract that shows unique histologic and immunohistochemical features that resemble fetal intestinal epithelium. This histological subtype has been widely described in the stomach, where it most frequently appears, but, in other locations, it is misdiagnosed because of the poor experience in routine diagnostic setting. Here we present a case of an 87-year-old male with an adenocarcinoma of the ampulla of Vater with enteroblastic differentiation with a literature review of the cases described of this subtype in this location to date. The anatomical peculiarity of the ampulla, joined with the infrequent nature of this histological subtype, makes this case of great interest to aid to better characterize the biological behavior of these tumors. (AU)

El adenocarcinoma con diferenciación enteroblástica es un subtipo histológico poco frecuente de adenocarcinoma gastrointestinal que muestra características histológicas e inmunohistoquímicas únicas que se asemejan al epitelio intestinal fetal. Este subtipo histológico ha sido ampliamente descrito en el estómago, donde aparece con mayor frecuencia, pero en otras localizaciones es mal diagnosticado debido a la poca experiencia en el diagnóstico de rutina. Presentamos un caso de un varón de 87 años con adenocarcinoma de ampolla de Vater con diferenciación enteroblástica, junto a una revisión bibliográfica de los casos descritos de este subtipo en esta localización hasta el momento. La peculiaridad anatómica de la ampolla, sumada al carácter poco frecuente de este subtipo histológico, dotan a este caso de gran interés para ayudar a caracterizar mejor el comportamiento biológico de estos tumores. (AU)

Rethinking c-Fos for understanding drug action in the brain.

Understanding the mechanisms of drug action in the brain, from the genetic to the neural circuit level, is crucial for the development of new agents that act upon the central nervous system. Determining the brain regions and neurons affected by a drug is essential for revealing its mechanism of action in the brain. c-Fos, a marker of neuronal activation, has been widely used to detect neurons activated by stimuli with high spatial resolution. In this review, the use of c-Fos for the visualization and manipulation of activated neurons is introduced. I also explain that a higher temporal resolution can be achieved by changing the staining method for visualization of c-Fos. Moreover, a new method that allows labeling and manipulating commonly activated neurons using two different stimuli is proposed.

Walking a thin line between fixation and epitope binding - characterization of antigen retrieval methods suitable for eosinophil and HSV-2 staining in formalin-fixed female reproductive tissue.

Antibody-based fluorescence analysis of female reproductive tissues in research of sexually transmitted diseases allows for an in-depth understanding of protein localization, interactions, and pathogenesis. However, in many cases, cryosectioning is not compatible with biosafety regulations; at all times, exposure of lab personnel and the public to potentially harmful pathogens from biological infectious material must be avoided; thus, formaldehyde fixation is essential. Due to formaldehyde's cross-linking properties, protein detection with antibodies can be impeded. To allow effective epitope binding during immunofluorescence of formalin-fixed paraffin-embedded vaginal tissue, we investigated two antigen retrieval methods. We tested these methods regarding their suitability for automated image analysis, facilitating reproducible quantitative microscopic data acquisition in sexually transmitted disease research. Heat-based retrieval at 80°C in citrate buffer proved to increase antibody binding to eosinophil protein and HSV-2 visibly and tissue morphology best, and was the most efficient for sample processing and quantitative analysis.

An algorithm-based technique for counting mitochondria in cells using immunohistochemical staining of formalin-fixed and paraffin-embedded sections.

PURPOSE: Visualizing mitochondria in cancer cells from human pathological specimens may improve our understanding of cancer biology. However, using immunohistochemistry to evaluate mitochondria remains difficult because almost all cells contain mitochondria and the number of mitochondria per cell may have important effects on mitochondrial function. Herein, we established an objective system (Mito-score) for evaluating mitochondria using machine-based processing of hue, saturation, and value color spaces. METHODS: The Mito-score was defined as the number of COX4 (mitochondrial inner membrane) immunohistochemistry-positive pixels divided by the number of nuclei per cell. The system was validated using four lung cancer cell lines, normal tissues, and lung cancer tissues (199 cases). RESULTS: The Mito-score correlated with MitoTracker, a fluorescent dye used to selectively label and visualize mitochondria within cells under a microscope (R2 = 0.68) and with the number of mitochondria counted using electron microscopy (R2 = 0.79). Histologically, the Mito-score of small cell carcinoma (57.25) was significantly lower than that of adenocarcinoma (147.5, p < 0.0001), squamous cell carcinoma (120.6, p = 0.0004), and large cell neuroendocrine carcinoma (111.8, p = 0.002). CONCLUSION: The Mito-score method enables the analysis of the mitochondrial status of human formalin-fixed paraffin-embedded specimens and may provide insights into the metabolic status of cancer.

Effect of Meconium Staining Amniotic Fluid on Fetal Outcome.

Meconium-stained amniotic fluid is the passage of meconium by a fetus in utero during the antenatal period or in labour. It has for long been considered to be a bad predictor of fetal distress and meconium aspiration syndrome (MAS). The objective of this study was to find out the fetal outcome of MSAF and clear amniotic fluid. This cross- sectional comparative study was carried out in Upazilla Health Complex, Palash, Narshingdi from July 2016 to June 2017. A total of 100 pregnant women among them 50 women with MSAF and 50 women with clear liquor were studied to see the record of ANC, mode of delivery and fetal outcome by APGAR score. Study showed that among MSAF group 76.0% (n=38) had irregular ANC and 24.0% (n=12) had regular ANC whereas in clear liquor 86.0% (n=43) had regular ANC 14.0% had irregular ANC. Among MSAF (50 cases) thick meconium was in 20 cases (40.0%) and thin meconium was in 30 cases (60.0%). Regarding mode of delivery 52.0% (n=26) MSAF cases had instrumental delivery and Caesarean section compared to 24.0% (n=12) in clear liquor group. Regarding thick MSAF among 40.0% (n=20), (n=14) had low APGAR score and (n=6) had normal score at one minute and (n=9) low APGAR score and (n=11) normal score at five minutes. In clear liquor, among 100.0% (n=50), 20.0% (n=10) had low APGAR score and 80.0% (n=40) had normal score at one-minute and at five minutes 8.0% (n=4) had low APGAR score and 92.0% (n=46) had normal score. Among MSAF 26.0% (n=13) were admitted to SCBU compare to 12.0% (n=6) in clear liquor group. The mean SCBU stay was 3.1 days in MSAF whereas 1.2 days in clear liquor. Among MSAF babies 4.0% (n=2) had MAS compared to no MAS in clear liquor group. Regarding Survivalist 92.0% (n=46) were alive in MSAF whereas 100.0% all (n=50) were alive in clear liquor group.

The virtual staining method by quantitative phase imaging for label free lymphocytes based on self-supervised iteration cycle-consistent adversarial networks.

Quantitative phase imaging (QPI) provides 3D structural and morphological information for label free living cells. Unfortunately, this quantitative phase information cannot meet doctors' diagnostic requirements of the clinical "gold standard," which displays stained cells' pathological states based on 2D color features. To make QPI results satisfy the clinical "gold standard," the virtual staining method by QPI for label free lymphocytes based on self-supervised iteration Cycle-Consistent Adversarial Networks (CycleGANs) is proposed herein. The 3D phase information of QPI is, therefore, trained and transferred to a kind of 2D "virtual staining" image that is well in agreement with "gold standard" results. To solve the problem that unstained QPI and stained "gold standard" results cannot be obtained for the same label free living cell, the self-supervised iteration for the CycleGAN deep learning algorithm is designed to obtain a trained stained result as the ground truth for error evaluation. The structural similarity index of our virtual staining experimental results for 8756 lymphocytes is 0.86. Lymphocytes' area errors after converting to 2D virtual stained results from 3D phase information are less than 3.59%. The mean error of the nuclear to cytoplasmic ratio is 2.69%, and the color deviation from the "gold standard" is less than 6.67%.

Multi-domain stain normalization for digital pathology: A cycle-consistent adversarial network for whole slide images.

The variation in histologic staining between different medical centers is one of the most profound challenges in the field of computer-aided diagnosis. The appearance disparity of pathological whole slide images causes algorithms to become less reliable, which in turn impedes the wide-spread applicability of downstream tasks like cancer diagnosis. Furthermore, different stainings lead to biases in the training which in case of domain shifts negatively affect the test performance. Therefore, in this paper we propose MultiStain-CycleGAN, a multi-domain approach to stain normalization based on CycleGAN. Our modifications to CycleGAN allow us to normalize images of different origins without retraining or using different models. We perform an extensive evaluation of our method using various metrics and compare it to commonly used methods that are multi-domain capable. First, we evaluate how well our method fools a domain classifier that tries to assign a medical center to an image. Then, we test our normalization on the tumor classification performance of a downstream classifier. Furthermore, we evaluate the image quality of the normalized images using the Structural similarity index and the ability to reduce the domain shift using the Fréchet inception distance. We show that our method proves to be multi-domain capable, provides a very high image quality among the compared methods, and can most reliably fool the domain classifier while keeping the tumor classifier performance high. By reducing the domain influence, biases in the data can be removed on the one hand and the origin of the whole slide image can be disguised on the other, thus enhancing patient data privacy.

Establishing Conditions for Blood Smear Drying and Staining on Sysmex SP-50 for Leukocyte Differential Count.

BACKGROUND: The purpose of this study was to determine the staining conditions and appropriate fan1 start time (FAN1ST) for Sysmex SP-50 to produce blood smears (BS) that reflect the true lymphocyte morphology of patient samples. METHODS: Using different start times of fan1, we obtained a set of 84 blood smear slides from 21 blood samples and measured 10,920 lymphocyte areas, which were then converted to compare lymphocyte sizes. We also performed a leukocyte differential count using Sysmex DI-60 on 202 blood smear slides prepared before and after the change in staining conditions and compared the results. RESULTS: The mean lymphocyte sizes at FAN1ST 0 second, 5 seconds, 10 seconds, and 30 seconds were 12.55 µm, 12.14 µm, 11.27 µm, and 10.50 µm, respectively. The mean differences in the preclassification of neutrophils, lymphocytes, monocytes, eosinophils, and basophils in DI-60, according to the SP-50 staining conditions, were 0.88, -1.58, -0.24, 0.37, and 0.07, respectively. CONCLUSIONS: Wright-Giemsa staining of blood smears prepared on the SP-50 showed that changing the pH of the concentrated phosphate buffer to 6.6 and adjusting the staining time did not affect the results of the leukocyte differential count. However, since fan1 was used to dry the blood smear on the SP-50 and the lymphocyte size gradually decreased as the start time was delayed, it was necessary to set a start time for fan1 that did not affect the lymphocyte size.

Detection of pit and fissure sealant microleakage using quantitative light-induced fluorescence technology: an in vitro study.

This in vitro study aimed to evaluate the feasibility of quantitative light-induced fluorescence (QLF) technology for detecting the presence and severity of microleakage of pit and fissure sealants. The areas of interest (AOIs) were 160 pits and fissures of 40 extracted permanent teeth. Fluorescent images were acquired using a QLF device, and the maximum fluorescence loss ΔFmax of each AOI was analyzed. After staining and cross-sectioning of the teeth, histological dye penetration was scored on a scale of 0 to 3. The relationship between ΔFmax and microleakage depth was analyzed, and the areas under the curve (AUCs) were calculated. The │ΔFmax│ increased as microleakage depth increased. The ΔFmax values of microleakage areas showed a strong significant correlation with the histological scores of dye penetration (r = - 0.72, P = 0.001). AUC analysis showed a high diagnostic accuracy for microleakage depth (AUC = 0.83-0.91). The highest AUC of 0.91 was found when differentiating the outer half microleakage of the sealant (histological score 0 vs. 1-3). QLF technology is effective in assessing the presence and severity of microleakage, suggesting its potential for noninvasive detection and monitoring of sealant microleakage in clinical settings.

Registered multi-device/staining histology image dataset for domain-agnostic machine learning models.

Variations in color and texture of histopathology images are caused by differences in staining conditions and imaging devices between hospitals. These biases decrease the robustness of machine learning models exposed to out-of-domain data. To address this issue, we introduce a comprehensive histopathology image dataset named PathoLogy Images of Scanners and Mobile phones (PLISM). The dataset consisted of 46 human tissue types stained using 13 hematoxylin and eosin conditions and captured using 13 imaging devices. Precisely aligned image patches from different domains allowed for an accurate evaluation of color and texture properties in each domain. Variation in PLISM was assessed and found to be significantly diverse across various domains, particularly between whole-slide images and smartphones. Furthermore, we assessed the improvement in domain shift using a convolutional neural network pre-trained on PLISM. PLISM is a valuable resource that facilitates the precise evaluation of domain shifts in digital pathology and makes significant contributions towards the development of robust machine learning models that can effectively address challenges of domain shift in histological image analysis.

Unsupervised domain adaptation for histopathology image segmentation with incomplete labels.

Stain variations pose a major challenge to deep learning segmentation algorithms in histopathology images. Current unsupervised domain adaptation methods show promise in improving model generalization across diverse staining appearances but demand abundant accurately labeled source domain data. This paper assumes a novel scenario, namely, unsupervised domain adaptation based segmentation task with incompletely labeled source data. This paper propose a Stain-Adaptive Segmentation Network with Incomplete Labels (SASN-IL). Specifically, the algorithm consists of two stages. The first stage is an incomplete label correction stage, involving reliable model selection and label correction to rectify false-negative regions in incomplete labels. The second stage is the unsupervised domain adaptation stage, achieving segmentation on the target domain. In this stage, we introduce an adaptive stain transformation module, which adjusts the degree of transformation based on segmentation performance. We evaluate our method on a gastric cancer dataset, demonstrating significant improvements, with a 10.01% increase in Dice coefficient compared to the baseline and competitive performance relative to existing methods.

Clearing Properties Between Coconut Oil and Xylene in Histological Tissue Processing.

Xylene is the commonest clearing agent even though it is hazardous and costly. This study evaluated the clearing properties of coconut oil as an alternative cost-effective clearing agent for histological processes. Ten (10) prostate samples fixed in formalin were taken and each one was cut into 4 before randomly separating them into four groups (A, B, C and D). Tissues were subjected to ascending grades of alcohol for dehydration. Group A was cleared in xylene and Groups B, C, and D were cleared at varying times of 1hr 30mins, 3hrs, and 4hrs in coconut oil respectively before embedding, sectioning, and staining were carried out. Gross and histological features were compared. Results indicated a significant shrinkage in coconut oil-treated specimen compared with the xylene-treated specimen and only the tissues cleared in coconut oil for 4hrs were as rigid as the tissues cleared in xylene (p > 0.05). No significant difference was found in either of the sections when checked for cellular details and staining quality (p > 0.999). Coconut oil is an efficient substitute for xylene in prostate tissues with a minimum clearing time of 4hrs, as it is environmentally friendly and less expensive, but causes significant shrinkage to prostate tissue.

Bacterial efflux pumps excrete SYTO™ dyes and lead to false-negative staining results.

Multidrug efflux pumps excrete a range of small molecules from bacterial cells. In this study, we show that bacterial efflux pumps have affinity for a range of SYTO™ dyes that are commonly used to label bacteria. Efflux pump activity will there lead to false negative results from bacterial staining and SYTO™ dyes should be used with caution on live samples.

CellViT: Vision Transformers for precise cell segmentation and classification.

Nuclei detection and segmentation in hematoxylin and eosin-stained (H&E) tissue images are important clinical tasks and crucial for a wide range of applications. However, it is a challenging task due to nuclei variances in staining and size, overlapping boundaries, and nuclei clustering. While convolutional neural networks have been extensively used for this task, we explore the potential of Transformer-based networks in combination with large scale pre-training in this domain. Therefore, we introduce a new method for automated instance segmentation of cell nuclei in digitized tissue samples using a deep learning architecture based on Vision Transformer called CellViT. CellViT is trained and evaluated on the PanNuke dataset, which is one of the most challenging nuclei instance segmentation datasets, consisting of nearly 200,000 annotated nuclei into 5 clinically important classes in 19 tissue types. We demonstrate the superiority of large-scale in-domain and out-of-domain pre-trained Vision Transformers by leveraging the recently published Segment Anything Model and a ViT-encoder pre-trained on 104 million histological image patches - achieving state-of-the-art nuclei detection and instance segmentation performance on the PanNuke dataset with a mean panoptic quality of 0.50 and an F1-detection score of 0.83. The code is publicly available at https://github.com/TIO-IKIM/CellViT.

Optimizing Ideal Thickness of Sections for Epidermis in Intra-Operative Frozen Sections.

AIM AND OBJECTIVES: To estimate and evaluate the optimal thickness of tissue section for skin and dermal appendages intra-operatively and to compare the morphology and architecture of the epidermis and its appendages at 7 and 10 microns. METHODOLOGY: After obtaining clearance from the institutional human ethical committee, 101 skin margins were prospectively analyzed using a two-step embedding technique. After multiple trials and errors, 7- and 10-micron thicknesses were selected for the present study. Artefacts, staining characteristics, cellular morphology, cellular outline, and nuclear outline were assessed and scored as unacceptable or acceptable. The data were entered in a Microsoft Excel spreadsheet and analyzed using SPSS software. RESULTS: There was a statistically significant difference between the sections obtained at 7 microns and 10 microns (p-value: <0.001), the latter were better in all the parameters analyzed. However, no difference was noted in the characteristics of the dermal appendages (p-value: >0.05). CONCLUSION: While mucosal margins can be obtained at the usual 5-7 microns, the same thickness is not optimal for skin margins intra-operatively. Frozen sections for the skin margins may be set at 10 microns, to save time, minimize artefacts, and for better readability.

Using Low-cost Dyes to Visualize Glycogen Accumulation and Gut Integrity in Caenorhabditis elegans.

Caenorhabditis elegans (C. elegans) is a transparent, non-parasitic nematode with a simple biology, which makes it a great tool for biological sciences teaching through the staining of the cells or their molecular content. Lugol dye (iodine-potassium iodide solution) has been widely used in biochemistry to stain glycogen stores. In this context, it is possible to observe differences between fed and starved animals, besides the effects of different conditions, such as different diets and oxygen levels. Erioglaucine is a blue dye that indicates the loss of the intestinal barrier. When the intestinal barrier is intact, the blue dye stains inside the lumen; however, when this integrity is disrupted, the dye leaks into the body cavity. Using a stereomicroscope or a microscope, teachers can demonstrate physiological and biochemical alterations, or they can instigate students to ask a scientific question and hypothesize and test their hypothesis using these assays. The present protocol describes two staining techniques in C. elegans that can be easily carried out by students.

NuInsSeg: A fully annotated dataset for nuclei instance segmentation in H&E-stained histological images.

In computational pathology, automatic nuclei instance segmentation plays an essential role in whole slide image analysis. While many computerized approaches have been proposed for this task, supervised deep learning (DL) methods have shown superior segmentation performances compared to classical machine learning and image processing techniques. However, these models need fully annotated datasets for training which is challenging to acquire, especially in the medical domain. In this work, we release one of the biggest fully manually annotated datasets of nuclei in Hematoxylin and Eosin (H&E)-stained histological images, called NuInsSeg. This dataset contains 665 image patches with more than 30,000 manually segmented nuclei from 31 human and mouse organs. Moreover, for the first time, we provide additional ambiguous area masks for the entire dataset. These vague areas represent the parts of the images where precise and deterministic manual annotations are impossible, even for human experts. The dataset and detailed step-by-step instructions to generate related segmentation masks are publicly available on the respective repositories.

Plastic vs. glass: the effect of the synthetic material in contact with DMEK tissue during staining on endothelial cell loss.

PURPOSE: Endothelial cell loss (ECL) during Descemet membrane endothelial keratoplasty (DMEK) graft preparation has been shown to affect graft survival and the need for re-grafting. The purpose of this study was to quantitatively assess the impact of the plastic and glass mediums in contact with DMEK donor tissue during intra-operative graft staining on ECL. METHODS: Retrospective study that included patients who underwent DMEK surgery between January 2019 and June 2021 at Hôpital Maisonneuve-Rosemont and the Jewish General Hospital in Montreal, Canada. DMEK grafts were stained with 0.06% Trypan blue ophthalmic solution (VisionBlue®, Dutch Ophthalmic, USA, Exeter, NH) for 120 s in either a plastic or glass medium prior to delivery into the recipient's eye. The ECL was compared between the two groups 12-30 months post-operatively. RESULTS: ECL at 12-30 months was significantly less in the eyes that had received grafts stained in a plastic medium compared to those stained in a glass medium. Graft survival and re-bubbling was higher in the glass group however this difference was not statistically significant. CONCLUSION: Staining of the DMEK graft in a plastic medium caused less ECL compared to the glass medium.

Isoegomaketone alleviates inflammatory response and oxidative stress in sepsis lung injury

Background: Sepsis is a life-threatening condition characterized by acute organ dysfunction, which frequently leads to acute lung injury (ALI) in approximately 40% of cases. Isoegomaketone (IK) is a constituent of essential oil found in P. frutescens, known for its diverse biological properties, including anti-inflammatory and antitumor effects. However, the regulatory impact of IK on ALI in the context of sepsis remains poorly understood. Methods: Pathological alterations in lung tissues were assessed using hematoxylin and eosin staining. Enumeration of total leukocytes and neutrophils in bronchoalveolar lavage fluid (BALF) was performed using a hematocytometer, while the levels of interleukin (IL)-6, IL-1β, IL-10, and IL-17 in BALF were quantified using enzyme-linked immunosorbent serological assay. In addition, the levels of malondialdehyde (MDA), myeloperoxidase (MPO), superoxide dismutase (SOD), and glutathione (GSH) in lung tissues were assessed using respective commercial kits; cell apoptosis was evaluated using the terminal deoxynucleotide transferase--mediated dUTP nick end-labeling assay, and protein expressions were determined through Western blot analysis. Results: Our findings revealed that cecal ligation and puncture (CLP) treatment in mice induced severe lung injury, characterized by increased lung injury scores, significant bleeding, neutrophil infiltration, and alveolar edema. However, treatment with IK at a dose of 10 mg/kg ameliorated CLP-induced lung injury, while IK dose of 5 mg/kg showed no significant effect. Additionally, IK treatment at 10 mg/kg reduced CLP-induced inflammation by decreasing levels of IL-6, IL-1β, IL-10, and IL-17. Furthermore, IK at 10 mg/kg attenuated CLP-induced oxidative stress by modulating levels of MDA, MPO, SOD, and GSH... (AU)

Structurally constrained and pathology-aware convolutional transformer generative adversarial network for virtual histology staining of human coronary optical coherence tomography images.

Significance: There is a significant need for the generation of virtual histological information from coronary optical coherence tomography (OCT) images to better guide the treatment of coronary artery disease (CAD). However, existing methods either require a large pixel-wise paired training dataset or have limited capability to map pathological regions. Aim: The aim of this work is to generate virtual histological information from coronary OCT images, without a pixel-wise paired training dataset while capable of providing pathological patterns. Approach: We design a structurally constrained, pathology-aware, transformer generative adversarial network, namely structurally constrained pathology-aware convolutional transformer generative adversarial network (SCPAT-GAN), to generate virtual stained H&E histology from OCT images. We quantitatively evaluate the quality of virtual stained histology images by measuring the Fréchet inception distance (FID) and perceptual hash value (PHV). Moreover, we invite experienced pathologists to evaluate the virtual stained images. Furthermore, we visually inspect the virtual stained image generated by SCPAT-GAN. Also, we perform an ablation study to validate the design of the proposed SCPAT-GAN. Finally, we demonstrate 3D virtual stained histology images. Results: Compared to previous research, the proposed SCPAT-GAN achieves better FID and PHV scores. The visual inspection suggests that the virtual histology images generated by SCPAT-GAN resemble both normal and pathological features without artifacts. As confirmed by the pathologists, the virtual stained images have good quality compared to real histology images. The ablation study confirms the effectiveness of the combination of proposed pathological awareness and structural constraining modules. Conclusions: The proposed SCPAT-GAN is the first to demonstrate the feasibility of generating both normal and pathological patterns without pixel-wisely supervised training. We expect the SCPAT-GAN to assist in the clinical evaluation of treating the CAD by providing 2D and 3D histopathological visualizations.