The wonderful colors of the hematoxylin-eosin stain in diagnostic surgical pathology

Digital analysis of the prostate tumor microenvironment with high-order chromogenic multiplexing

As our understanding of the tumor microenvironment grows, the pathology field is increasingly utilizing multianalyte diagnostic assays to understand important characteristics of tumor growth. In clinical settings, brightfield chromogenic assays represent the gold-standard and have developed significant trust as the first-line diagnostic method. However, conventional brightfield tests have been limited to low-order assays that are visually interrogated. We have developed a hybrid method of brightfield chromogenic multiplexing that overcomes these limitations and enables high-order multiplex assays. However, how compatible high-order brightfield multiplexed images are with advanced analytical algorithms has not been extensively evaluated. In the present study, we address this gap by developing a novel 6-marker prostate cancer assay that targets diverse aspects of the tumor microenvironment such as prostate-specific biomarkers (PSMA and p504s), immune biomarkers (CD8 and PD-L1), a prognostic biomarker (Ki-67), as well as an adjunctive diagnostic biomarker (basal cell cocktail) and apply the assay to 143 differentially graded adenocarcinoma prostate tissues. The tissues were then imaged on our spectroscopic multiplexing imaging platform and mined for proteomic and spatial features that were correlated with cancer presence and disease grade. Extracted features were used to train a UMAP model that differentiated healthy from cancerous tissue with an accuracy of 89% and identified clusters of cells based on cancer grade. For spatial analysis, cell-to-cell distances were calculated for all biomarkers and differences between healthy and adenocarcinoma tissues were studied. We report that p504s positive cells were at least 2× closer to cells expressing PD-L1, CD8, Ki-67, and basal cell in adenocarcinoma tissues relative to the healthy control tissues. These findings offer a powerful insight to understand the fingerprint of the prostate tumor microenvironment and indicate that high-order chromogenic multiplexing is compatible with digital analysis. Thus, the presented chromogenic multiplexing system combines the clinical applicability of brightfield assays with the emerging diagnostic power of high-order multiplexing in a digital pathology friendly format that is well-suited for translational studies to better understand mechanisms of tumor development and growth.

A Dual-Modal, Label-Free Raman Imaging Method for Rapid Virtual Staining of Large-Area Breast Cancer Tissue Sections

As one of the most common cancers, accurate, rapid, and simple histopathological diagnosis is very important for breast cancer. Raman imaging is a powerful technique for label-free analysis of tissue composition and histopathology, but it suffers from slow speed when applied to large-area tissue sections. In this study, we propose a dual-modal Raman imaging method that combines Raman mapping data with microscopy bright-field images to achieve virtual staining of breast cancer tissue sections. We validate our method on various breast tissue sections with different morphologies and biomarker expressions and compare it with the golden standard of histopathological methods. The results demonstrate that our method can effectively distinguish various types and components of tissues, and provide staining images comparable to stained tissue sections. Moreover, our method can improve imaging speed by up to 65 times compared to general spontaneous Raman imaging methods. It is simple, fast, and suitable for clinical applications.

Age-related decline in goblet cell numbers and mucin content of the human colon: Implications for lower bowel functions in the elderly

BACKGROUND & AIMS

Older people experience a greater incidence of lower bowel disorders, including constipation. Causes can include factors associated with growing older, such as use of medications or disease, but compounded by degenerative changes within the bowel wall. It has been suggested that the latter is exacerbated by loss of an effective mucosal barrier to luminal contents. In human colon, little is known about the impact of ageing on key components of this barrier, namely the goblet cells and mucin content.

METHODS

Changes in the number of goblet cells and density of mucin content were investigated in macroscopically normal human ascending (AC; n = 13) and descending (DC; n = 14) colon from elderly (≥ 67 years) and younger adults (60 years and below). Samples were serially sectioned and stained for haematoxylin and eosin to assess tissue morphology, and alcian blue periodic acid Schiff (ABPAS) and MUC-2 antibody to identify goblet cells producing mucins. New procedures in visualization and identification of goblet cells and mucin contents were employed to ensure unbiased counting and densitometric analysis.

RESULTS

Compared with the younger adults, the numbers of goblet cells per crypt were significantly lower in the elderly AC (72 ± 1.2 vs 51 ± 0.5) and DC (75 ± 2.6 vs. 54 ± 1.9), although this reduction did not reach statistical significance when assessed per mucosal area (AC: P = 0.068; DC: P = 0.096). In both regions from the elderly, numerous empty vesicles (normally containing mucins) were observed, and some areas of epithelium were devoid of goblet cells. Thus, the density of mucin content per unit mucosal area were significantly reduced with age.

CONCLUSIONS

Ageing could result in a reduced number of goblet cells and development of degenerative changes in mucin production. Together, these have implications for the mucus barrier function in the colon of elderly individuals.

ResSAT: Enhancing Spatial Transcriptomics Prediction from H&E- Stained Histology Images with Interactive Spot Transformer

Spatial transcriptomics (ST) revolutionizes RNA quantification with high spatial resolution. Hematoxylin and eosin (H&E) images, the gold standard in medical diagnosis, offer insights into tissue structure, correlating with gene expression patterns. Current methods for predicting spatial gene expression from H&E images often overlook spatial relationships. We introduce ResSAT (Residual networks - Self-Attention Transformer), a framework generating spatially resolved gene expression profiles from H&E images by capturing tissue structures and using a self-attention transformer to enhance prediction.Benchmarking on 10× Visium datasets, ResSAT significantly outperformed existing methods, promising reduced ST profiling costs and rapid acquisition of numerous profiles.

Towards an optimized model of food allergy in zebrafish

BACKGROUND

The prevalence of food allergies is on the rise, posing a significant challenge to public health. Rodents serve as the predominant animal model in food allergy research; yet, the application of rodent models proves to be a laborious and time-consuming endeavor. It is imperative to develop novel in vivo models.

METHODS

Ovalbumin (OVA) was administered as the allergen, following the recommended dosage used in other species. During the sensitization phase, a dosage of 0.25 mg per 10 tails per 1 L was administered twice daily, and during the challenge phase, the dosage was increased to 3 times the initial level. The study explored two dimensions of sensitization: the mode of exposure, which can be either continuous or intermittent, and the duration of exposure, which includes 3 days, 5 days, and 7 days. We examined midgut pathological changes, immunoglobulins contents, and mRNA expressions associated to T helper cells (Th) 2 cytokines following exposure.

RESULTS

A significant 109.3 % increase in the number of eosinophils was observed in the midgut histopathology following intermittent 5-day OVA exposure, which emerged as the most effective model. OVA exposure increased concentrations of immunoglobulin M (IgM) (105.2 %), IgZ (312.1 %), and IgD (304.3 %) in this model. The mRNA expressions of Th2-related interleukin (IL)-4 and IL-13 were also elevated by 132.8 % and 421.0 %, respectively.

CONCLUSION

The intermittent 5-day OVA exposure was suggested to be the best constructed zebrafish food allergy model, which may be a potential tool for research into food allergies.

Metallophthalocyanine as ideal antibiotics without light: Mechanisms and applications

The urgent global health problem of antimicrobial resistance (AMR) calls for the discovery of new antibiotics with innovative modes of action while considering the low toxicity to mammalian cells. This paper proposes a novel strategy for designing antibiotics with selective bacterial toxicity by exploiting the positional differences of electron transport chains (ETC) in bacterial and mammalian cells. The focus is on cytochrome c (cyt C) and its maturation system in E. coli. The catalytic oxidative activity of metallophthalocyanine (MPc), which have a distinctive M-N4 structure, is being investigated. Unlike previous applications based on light-activated reactive oxygen species (ROS) generation, this study exploits the ability of MPcs to oxidize Fe2+ to Fe3+ in cyt C and catalyze the formation of disulfide bonds between cysteine residues to interfere with cyt C maturation, disrupt the bacterial respiratory chain and selectively kills bacteria. In contrast, in mammalian cells, these MPcs are located in the lysosomes and cannot access the ETC in the mitochondria, thus achieving selective bacterial toxicity. Two MPcs that showed effective antibacterial activity in a wound infection model were identified. This study provides a valuable reference for the design of novel antibiotics based on M-N4-based metal complex molecules.

Intake of Special Amino Acids Mixture Leads to Blunted Murine Colon Cancer Growth In Vitro and In Vivo

Cancer cells require substantial amounts of energy and substrates for their metabolic hyperactivity, enabling the synthesis of new cells at the expense of healthy ones. Preliminary in vitro data suggest that a mix of free essential amino acids (EAA-mix) can promote cancer cell apoptosis by enhancing autophagy. This study aimed to confirm, both in vitro and in vivo, whether EAA intake could influence the development of colon cancer in mice. We investigated changes in cancer proliferation in CT26 cells treated with EAA-mix and in mice fed with EAA-rich modified diets (EAARD) as compared to those on a standard laboratory diet (StD). CT26 cells were injected subcutaneously (s.c.) or intraperitoneally (i.p.). After 21 days, tumors were removed and measured. In vitro data corroborated that EAA-mix impairs cancer growth by inducing apoptosis. In vivo data revealed that mice on StD developed significantly larger (s.c.) and more numerous (i.p.) cancers than those on EAARD. EAA administration appears to influence cancer cell survival with notable antiproliferative properties.

Enhanced anticancer activity of silver doped zinc oxide magnetic nanocarrier loaded with sorafenib for hepatocellular carcinoma treatment

Drug delivery is the process or method of delivering a pharmacological product to have therapeutic effects on humans or animals. The use of nanoparticles to deliver medications to cells is driving the present surge in interest in improving human health. Green nanodrug delivery methods are based on chemical processes that are acceptable for the environment or that use natural biomaterials such as plant extracts and microorganisms. In this study, zinc oxide-superparamagnetic iron oxide-silver nanocomposite was synthesized via green synthesis method using Fusarium oxysporum fungi mycelia then loaded with sorafenib drug. The synthesized nanocomposites were characterized by UV-visibile spectroscopy, FTIR, TEM and SEM techniques. Sorafenib is a cancer treatment and is also known by its brand name, Nexavar. Sorafenib is the only systemic medication available in the world to treat hepatocellular carcinoma. Sorafenib, like many other chemotherapeutics, has side effects that restrict its effectiveness, including toxicity, nausea, mucositis, hypertension, alopecia, and hand-foot skin reaction. In our study, 40 male albino rats were given a single dose of diethyl nitrosamine (DEN) 60 mg/kg b.wt., followed by carbon tetrachloride 2 ml/kg b.wt. twice a week for one month. The aim of our study is using the zinc oxide-superparamagnetic iron oxide-silver nanocomposite that was synthesized by Fusarium oxysporum fungi mycelia as nanocarrier for enhancement the sorafenib anticancer effect.

A systematic comparison of deep learning methods for Gleason grading and scoring

Prostate cancer is the second most frequent cancer in men worldwide after lung cancer. Its diagnosis is based on the identification of the Gleason score that evaluates the abnormality of cells in glands through the analysis of the different Gleason patterns within tissue samples. The recent advancements in computational pathology, a domain aiming at developing algorithms to automatically analyze digitized histopathology images, lead to a large variety and availability of datasets and algorithms for Gleason grading and scoring. However, there is no clear consensus on which methods are best suited for each problem in relation to the characteristics of data and labels. This paper provides a systematic comparison on nine datasets with state-of-the-art training approaches for deep neural networks (including fully-supervised learning, weakly-supervised learning, semi-supervised learning, Additive-MIL, Attention-Based MIL, Dual-Stream MIL, TransMIL and CLAM) applied to Gleason grading and scoring tasks. The nine datasets are collected from pathology institutes and openly accessible repositories. The results show that the best methods for Gleason grading and Gleason scoring tasks are fully supervised learning and CLAM, respectively, guiding researchers to the best practice to adopt depending on the task to solve and the labels that are available.

Elucidating the hepatoprotective mechanisms of cholic acid against CCl4-Induced acute liver injury: A transcriptomic and metabolomic study

ETHNOPHARMACOLOGICAL RELEVANCE

Cholic acid (CA) is one of the main active ingredients in Calculus Bovis, a traditional Chinese medicine, which helps to regulate the heart and liver meridians, clearing the heart, opening the mouth, cooling the liver and calming the wind. However, the molecular mechanism of its liver protective effect is still unclear.

AIM OF THE STUDY

Growing attention has been directed towards traditional Chinese medicine (TCM), particularly Calculus Bovis, as a potential solution for liver protection. Despite this interest, a comprehensive understanding of its hepatoprotective mechanisms remains lacking. This research seeks to explore the potential protective properties of cholic acid (CA) against CCl4-induced acute liver injury (ALI) in mice, while also examining the mechanisms involved.

MATERIALS AND METHODS

In the experiment, a mouse model was employed to ALI using CCl4, and the potential therapeutic effects of orally administered CA at varying doses (15, 30, and 60 mg/kg) were assessed. The study employed a multi-faceted approach, integrating liver transcriptomics with serum metabolomics, and conducting thorough analyses of serum biochemical markers and liver histopathological sections.

RESULTS

Oral CA administration markedly reduced the organ indices of the liver, spleen, and thymus in comparison with the model group. It also elevated the expression of superoxide dismutase (SOD) in serum while diminishing the concentrations of ALT, AST, MDA, IL-6, and TNF-α. Moreover, CA ameliorated the pathological damage induced by CCl4. Integrated metabolomic and transcriptomic analyses indicated that the hepatoprotective action of CA on ALI is mediated through the modulation of lipid metabolic pathways-specifically, metabolisms of glycerophospholipid, arachidonic acid, as well as linoleic acid-and by altering the expression of genes such as Ptgr1, PLpp1, Tbxas1, and Cyp2c37.

CONCLUSIONS

The current investigation offers insights into the hepatoprotective mechanisms by which CA mitigates ALI caused by CCl4 exposure, thus supporting the further evaluation and development of CA-based therapeutics for ALI.

Global research trends on systemic lupus erythematosus and thyroid cancers (1964-2023): A scientometric and visualized study

Over the past 3 decades numerous studies have reported an association between systemic lupus erythematosus (SLE) and thyroid cancers. However, there has been no scientometric analysis in this area of research. To perform a comprehensive scientometric analysis of the global literature published on the association between SLE and thyroid cancers. The data on publications within thyroid cancers in SLE patients were retrieved from the Scopus database using a defined search strategy from its first publication in 1964 to 2023. To conduct a collaboration mapping analysis among keywords, authors, journals, and territories, VOSviewer was utilized. Our final research resulted in 246 scientific publications with 8072 citations, which were published in 198 journals affiliated to 48 countries. A global upward trend has been observed in the last 20 years, with the highest number of publications in the year 2022 (n = 28; 11.4%). The United States led the global productivity ranking with 74 publications (30.1%), followed by China with 25 publications (10.2%). The most popular journals in this field were "Arthritis Research and Therapy" and "Frontiers in Endocrinology," while the most co-cited journal was "Autoimmunity Reviews." The top 3 most prolific authors were Bernatsky, S., Clarke, A.E., and Ramsey-Goldman, R with 9 publications each. This first scientometric study comprehensively offered an overview of the status of thyroid cancers in SLE patients, assessing scholarly productivity in this domain over a period of 50 years.

Thickness-Resolved Electrochemiluminescence Microscopy of Extracellular Matrix at Tumor Tissues for Rapid Cancer Diagnosis

The traditional recognition of extracellular matrix (ECM) at tissue sections relies on the time-consuming immunofluorescence that could not meet the demand of rapid diagnosis. Herein, we introduce a thickness-resolved electrochemiluminescence (ECL) microscopy to image thin-layer ECM at tissue sections for fast histopathological analysis. The unique surface-confined ECL mechanism enables to unveil the diversity and complexity of multiple tissue structures with varying thicknesses. Notably, the short lifetimes and the limited diffusion of electrogenerated coreactant radicals combined with their chemical reactivity result in a 2-fold increase in ECL intensity on ECM structures compared to the remaining tissue, enabling ECM visualization without specific labeling. The further quantitation of the ECM localization within tissue sections furnishes crucial insights into tumor progression and, more importantly, differentiates carcinoma and paracancerous tissues from patients in less than 30 min. Moreover, the reported electrochemistry-based microscopy is a dynamic approach allowing to investigate the transport, tortuosity, and trafficking properties through the tissues. This thickness-resolved recognition strategy not only opens new avenues for imaging complex samples but also holds promise for expediting tissue pathologic diagnosis, offering a more automated protocol with enhanced quantitative data compared to current intraoperative pathology methods.

Polarization enhancement mechanism from tissue staining in multispectral Mueller matrix microscopy

Mueller matrix microscopy can provide comprehensive polarization-related optical and structural information of biomedical samples label-freely. Thus, it is regarded as an emerging powerful tool for pathological diagnosis. However, the staining dyes have different optical properties and staining mechanisms, which can put influence on Mueller matrix microscopic measurement. In this Letter, we quantitatively analyze the polarization enhancement mechanism from hematoxylin and eosin (H&E) staining in multispectral Mueller matrix microscopy. We examine the influence of hematoxylin and eosin dyes on Mueller matrix-derived polarization characteristics of fibrous tissue structures. Combined with Monte Carlo simulations, we explain how the dyes enhance diattenuation and linear retardance as the illumination wavelength changed. In addition, it is demonstrated that by choosing an appropriate incident wavelength, more visual Mueller matrix polarimetric information can be observed of the H&E stained tissue sample. The findings can lay the foundation for the future Mueller matrix-assisted digital pathology.

The return of natural dyes: the case of logwood tree (Haematoxylum campechianum L.)

In recent years, a worldwide reassessment of natural dyes has occurred, driven by the health and environmental issues associated with synthetic dyes. Haematoxylum campechianum L. is a tropical tree from which wood extracts were widely used in the textile industry during the 16th century. The logwood tree extract serves as a contemporary source of hematoxylin, a key dye in the globally prevalent hematoxylin-eosin staining method, a cornerstone in histopathological procedures. This paper will initially explore the re-emergence of natural dyes. Subsequently, it will focus on the historical, conventional, and innovative applications of logwood in the fields of medicine, histopathology, and nanotechnology, along with the status and alternative uses of the hematoxylin-eosin stain. Lastly, this paper will examine the current state of conservation and utilization of Haematoxylum campechianum in Campeche, Mexico, a leading global producer of hematoxylin.

Prognostic Analysis Combining Histopathological Features and Clinical Information to Predict Colorectal Cancer Survival from Whole-Slide Images

BACKGROUND

Colorectal cancer (CRC) is a malignant tumor within the digestive tract with both a high incidence rate and mortality. Early detection and intervention could improve patient clinical outcomes and survival.

METHODS

This study computationally investigates a set of prognostic tissue and cell features from diagnostic tissue slides. With the combination of clinical prognostic variables, the pathological image features could predict the prognosis in CRC patients. Our CRC prognosis prediction pipeline sequentially consisted of three modules: (1) A MultiTissue Net to delineate outlines of different tissue types within the WSI of CRC for further ROI selection by pathologists. (2) Development of three-level quantitative image metrics related to tissue compositions, cell shape, and hidden features from a deep network. (3) Fusion of multi-level features to build a prognostic CRC model for predicting survival for CRC.

RESULTS

Experimental results suggest that each group of features has a particular relationship with the prognosis of patients in the independent test set. In the fusion features combination experiment, the accuracy rate of predicting patients' prognosis and survival status is 81.52%, and the AUC value is 0.77.

CONCLUSION

This paper constructs a model that can predict the postoperative survival of patients by using image features and clinical information. Some features were found to be associated with the prognosis and survival of patients.

Development and deployment of a histopathology-based deep learning algorithm for patient prescreening in a clinical trial

Accurate identification of genetic alterations in tumors, such as Fibroblast Growth Factor Receptor, is crucial for treating with targeted therapies; however, molecular testing can delay patient care due to the time and tissue required. Successful development, validation, and deployment of an AI-based, biomarker-detection algorithm could reduce screening cost and accelerate patient recruitment. Here, we develop a deep-learning algorithm using >3000 H&E-stained whole slide images from patients with advanced urothelial cancers, optimized for high sensitivity to avoid ruling out trial-eligible patients. The algorithm is validated on a dataset of 350 patients, achieving an area under the curve of 0.75, specificity of 31.8% at 88.7% sensitivity, and projected 28.7% reduction in molecular testing. We successfully deploy the system in a non-interventional study comprising 89 global study clinical sites and demonstrate its potential to prioritize/deprioritize molecular testing resources and provide substantial cost savings in the drug development and clinical settings.

Comparison of staining quality between rapid and routine hematoxylin and eosin staining of frozen breast tissue sections: an observational study

OBJECTIVE

To compare the staining quality between rapid hematoxylin and eosin (H&E) staining and routine H&E staining of frozen breast tissue sections.

METHODS

In this cross-sectional observational study, 120 frozen breast tissue sections were randomly assigned to rapid or routine H&E staining (n = 60 per group). Rapid H&E staining used a 7:1 mixture of modified Gill's hematoxylin and alcohol-soluble 1% eosin Y. The staining quality of each section was evaluated and scored. A score of >7 was considered excellent, a score of 6 to 7 good, and a score of ≤5 poor.

RESULTS

The staining time for rapid staining was approximately 3 minutes, whereas that of routine staining was approximately 12 minutes. There were no significant differences in the staining quality scores or proportions of sections in each grade between the two staining methods. The proportions of sections that were classified as excellent or good were 96.7% and 98.3% for rapid and routine staining, respectively.

CONCLUSIONS

In frozen breast tissue sections, rapid H&E staining may provide staining quality that is comparable to that of routine staining, while markedly reducing the staining time.

ALKBH5 modulates bone cancer pain in a rat model by suppressing NR2B expression

Currently, the clinical treatment of bone cancer pain (BCP) is mainly related to its pathogenesis. The aim of the present study was to elucidate the potential role of N6-methyladenosine (m6A) in BCP in the spinal cord dorsal root ganglia (DRG) of BCP rats and its specific regulatory mechanism in N-methyl-d-aspartate receptor subunit 2B (NR2B). A rat model of BCP was constructed by tibial injection of Walker256 cells, and ALKBH5 and NR2B expression in the spinal cord DRG was detected. ALKBH5 was silenced or overexpressed in PC12 cells to verify the regulatory effect of ALKBH5 on NR2B. The specific mechanism underlying the interaction between ALKBH5 and NR2B was investigated using methylated RNA immunoprecipitation and dual-luciferase reporter gene assays. The results showed increased expression of m6A, decreased expression of ALKBH5, and increased expression of NR2B in the DRG of the BCP rat model. Overexpression of ALKBH5 inhibited NR2B expression, whereas interference with ALKBH5 caused an increase in NR2B expression. In NR2B, interference with ALKBH5 caused an increase in m6A modification, which caused an increase in NR2B. Taken together, ALKBH5 affected the expression of NR2B by influencing the stability of the m6A modification site of central NR2B, revealing that ALKBH5 is a therapeutic target for BCP.

Computer-aided colorectal cancer diagnosis: AI-driven image segmentation and classification

Colorectal cancer is an enormous health concern since it is among the most lethal types of malignancy. The manual examination has its limitations, including subjectivity and data overload. To overcome these challenges, computer-aided diagnostic systems focusing on image segmentation and abnormality classification have been developed. This study presents a two-stage approach for the automatic detection of five types of colorectal abnormalities in addition to a control group: polyp, low-grade intraepithelial neoplasia, high-grade intraepithelial neoplasia, serrated adenoma, adenocarcinoma. In the first stage, UNet3+ was used for image segmentation to locate the anomalies, while in the second stage, the Cross-Attention Multi-Scale Vision Transformer deep learning model was used to predict the type of anomaly after highlighting the anomaly on the raw images. In anomaly segmentation, UNet3+ achieved values of 0.9872, 0.9422, 0.9832, and 0.9560 for Dice Coefficient, Jaccard Index, Sensitivity, Specificity respectively. In anomaly detection, the Cross-Attention Multi-Scale Vision Transformer model attained a classification performance of 0.9340, 0.9037, 0.9446, 0.8723, 0.9102, 0.9849 for accuracy, F1 score, precision, recall, Matthews correlation coefficient, and specificity, respectively. The proposed approach proves its capacity to alleviate the overwhelm of pathologists and enhance the accuracy of colorectal cancer diagnosis by achieving high performance in both the identification of anomalies and the segmentation of regions.

A prediction model based on digital breast pathology image information

BACKGROUND

The workload of breast cancer pathological diagnosis is very heavy. The purpose of this study is to establish a nomogram model based on pathological images to predict the benign and malignant nature of breast diseases and to validate its predictive performance.

METHODS

In retrospect, a total of 2,723 H&E-stained pathological images were collected from 1,474 patients at Qingdao Central Hospital between 2019 and 2022. The dataset consisted of 509 benign tumor images (adenosis and fibroadenoma) and 2,214 malignant tumor images (infiltrating ductal carcinoma). The images were divided into a training set (1,907) and a validation set (816). Python3.7 was used to extract the values of the R channel, G channel, B channel, and one-dimensional information entropy from all images. Multivariable logistic regression was used to select variables and establish the breast tissue pathological image prediction model.

RESULTS

The R channel value, B channel value, and one-dimensional information entropy of the images were identified as independent predictive factors for the classification of benign and malignant pathological images (P < 0.05). The area under the curve (AUC) of the nomogram model in the training set was 0.889 (95% CI: 0.869, 0.909), and the AUC in the validation set was 0.838 (95% CI: 0.7980.877). The calibration curve results showed that the calibration curve of this nomogram model was close to the ideal curve. The decision curve results indicated that the predictive model curve had a high value for auxiliary diagnosis.

CONCLUSION

The nomogram model for the prediction of benign and malignant breast diseases based on pathological images demonstrates good predictive performance. This model can assist in the diagnosis of breast tissue pathological images.

Cyto R-CNN and CytoNuke Dataset: Towards reliable whole-cell segmentation in bright-field histological images

BACKGROUND AND OBJECTIVE

Cell segmentation in bright-field histological slides is a crucial topic in medical image analysis. Having access to accurate segmentation allows researchers to examine the relationship between cellular morphology and clinical observations. Unfortunately, most segmentation methods known today are limited to nuclei and cannot segment the cytoplasm.

METHODS

We present a new network architecture Cyto R-CNN that is able to accurately segment whole cells (with both the nucleus and the cytoplasm) in bright-field images. We also present a new dataset CytoNuke, consisting of multiple thousand manual annotations of head and neck squamous cell carcinoma cells. Utilizing this dataset, we compared the performance of Cyto R-CNN to other popular cell segmentation algorithms, including QuPath's built-in algorithm, StarDist, Cellpose and a multi-scale Attention Deeplabv3+. To evaluate segmentation performance, we calculated AP50, AP75 and measured 17 morphological and staining-related features for all detected cells. We compared these measurements to the gold standard of manual segmentation using the Kolmogorov-Smirnov test.

RESULTS

Cyto R-CNN achieved an AP50 of 58.65% and an AP75 of 11.56% in whole-cell segmentation, outperforming all other methods (QuPath 19.46/0.91%; StarDist 45.33/2.32%; Cellpose 31.85/5.61%, Deeplabv3+ 3.97/1.01%). Cell features derived from Cyto R-CNN showed the best agreement to the gold standard (D¯=0.15) outperforming QuPath (D¯=0.22), StarDist (D¯=0.25), Cellpose (D¯=0.23) and Deeplabv3+ (D¯=0.33).

CONCLUSION

Our newly proposed Cyto R-CNN architecture outperforms current algorithms in whole-cell segmentation while providing more reliable cell measurements than any other model. This could improve digital pathology workflows, potentially leading to improved diagnosis. Moreover, our published dataset can be used to develop further models in the future.

Fine tuning deep learning models for breast tumor classification

This paper proposes an approach to enhance the differentiation task between benign and malignant Breast Tumors (BT) using histopathology images from the BreakHis dataset. The main stages involve preprocessing, which encompasses image resizing, data partitioning (training and testing sets), followed by data augmentation techniques. Both feature extraction and classification tasks are employed by a Custom CNN. The experimental results show that the proposed approach using the Custom CNN model exhibits better performance with an accuracy of 84% than applying the same approach using other pretrained models, including MobileNetV3, EfficientNetB0, Vgg16, and ResNet50V2, that present relatively lower accuracies, ranging from 74 to 82%; these four models are used as both feature extractors and classifiers. To increase the accuracy and other performance metrics, Grey Wolf Optimization (GWO), and Modified Gorilla Troops Optimization (MGTO) metaheuristic optimizers are applied to each model separately for hyperparameter tuning. In this case, the experimental results show that the Custom CNN model, refined with MGTO optimization, reaches an exceptional accuracy of 93.13% in just 10 iterations, outperforming the other state-of-the-art methods, and the other four used pretrained models based on the BreakHis dataset.

Prostatic morphological changes throughout life: Cytochemistry as a tool to reveal tissue aging markers

The prostate undergoes normal or pathological morphological changes throughout life. An understanding of these changes is fundamental for the comprehension of aging-related pathological processes such as benign prostatic hyperplasia (BPH) and cancer. In the present study, we show some of these morphological changes, as well as histochemical techniques like Weigert's resorcin-fuchsin method, Picrosirius Red, and Gömöri's reticulin for use as tools in the study of prostate tissue under light microscopy. For this purpose, prostates of the Mongolian gerbil (n = 9), an experimental model that develops BPH spontaneously, were analyzed at three life stages: young (1 month old), adult (3 months old), and old (15 months old). The results showed that fibrillar components such as collagen, and reticular and elastic fibers, change throughout life. In young animals, the prostate has cuboidal epithelium surrounded by thin layers of smooth muscle, continuous collagen fibers, winding reticular fibers, and sporadic elastic fibers. With adulthood, the epithelium becomes columnar, encircled by compacted muscle cells among slender collagen fibers, elongated reticular fibers, and linear elastic fibers. In aging individuals, the prostate's epithelium stratifies, surrounded by thick muscle layers among dense collagen fibers, disordered reticular fibers, and elastic fibers in different planes. We also identified a few accumulations of lipid droplets and lipofuscin granules in adult animals and high accumulation in old animals evidenced by Oil red O and Gömöri-Halmi techniques, respectively. The histochemical techniques presented here have been demonstrated to be useful and accessible tools in prostate studies. RESEARCH HIGHLIGHTS: Cytochemical techniques to study prostate morphology. The prostate changes with age.

Exploring Radiomics Features Based on H&E Images as Potential Biomarkers for Evaluating Muscle Atrophy: A Preliminary Study

Radiomics features have been widely used as novel biomarkers in the diagnosis of various diseases, but whether radiomics features derived from hematoxylin and eosin (H&E) images can evaluate muscle atrophy has not been studied. Therefore, this study aims to establish a new biomarker based on H&E images using radiomics methods to quantitatively analyze H&E images, which is crucial for improving the accuracy of muscle atrophy assessment. Firstly, a weightless muscle atrophy model was established by laying macaques in bed, and H&E images of the shank muscle fibers of the control and bed rest (BR) macaques were collected. Muscle fibers were accurately segmented by designing a semi-supervised segmentation framework based on contrastive learning. Then, 77 radiomics features were extracted from the segmented muscle fibers, and a stable subset of features was selected through the LASSO method. Finally, the correlation between radiomics features and muscle atrophy was analyzed using a support vector machine (SVM) classifier. The semi-supervised segmentation results show that the proposed method had an average Spearman's and intra-class correlation coefficient (ICC) of 88% and 86% compared to manually extracted features, respectively. Radiomics analysis showed that the AUC of the muscle atrophy evaluation model based on H&E images was 96.87%. For individual features, GLSZM_SZE outperformed other features in terms of AUC (91.5%) and ACC (84.4%). In summary, the feature extraction based on the semi-supervised segmentation method is feasible and reliable for subsequent radiomics research. Texture features have greater advantages in evaluating muscle atrophy compared to other features. This study provides important biomarkers for accurate diagnosis of muscle atrophy.

CAISHI: A benchmark histopathological H&E image dataset for cervical adenocarcinoma in situ identification, retrieval and few-shot learning evaluation

A benchmark histopathological Hematoxylin and Eosin (H&E) image dataset for Cervical Adenocarcinoma in Situ (CAISHI), containing 2240 histopathological images of Cervical Adenocarcinoma in Situ (AIS), is established to fill the current data gap, of which 1010 are images of normal cervical glands and another 1230 are images of cervical AIS. The sampling method is endoscope biopsy. Pathological sections are obtained by H&E staining from Shengjing Hospital, China Medical University. These images have a magnification of 100 and are captured by the Axio Scope. A1 microscope. The size of the image is 3840 × 2160 pixels, and the format is ".png". The collection of CAISHI is subject to an ethical review by China Medical University with approval number 2022PS841K. These images are analyzed at multiple levels, including classification tasks and image retrieval tasks. A variety of computer vision and machine learning methods are used to evaluate the performance of the data. For classification tasks, a variety of classical machine learning classifiers such as k-means, support vector machines (SVM), and random forests (RF), as well as convolutional neural network classifiers such as Residual Network 50 (ResNet50), Vision Transformer (ViT), Inception version 3 (Inception-V3), and Visual Geometry Group Network 16 (VGG-16), are used. In addition, the Siamese network is used to evaluate few-shot learning tasks. In terms of image retrieval functions, color features, texture features, and deep learning features are extracted, and their performances are tested. CAISHI can help with the early diagnosis and screening of cervical cancer. Researchers can use this dataset to develop new computer-aided diagnostic tools that could improve the accuracy and efficiency of cervical cancer screening and advance the development of automated diagnostic algorithms.

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.

Molecular analysis of the extracellular microenvironment: from form to function

The extracellular matrix (ECM) proteome represents an important component of the tissue microenvironment that controls chemical flux and induces cell signaling through encoded structure. The analysis of the ECM represents an analytical challenge through high levels of post-translational modifications, protease-resistant structures, and crosslinked, insoluble proteins. This review provides a comprehensive overview of the analytical challenges involved in addressing the complexities of spatially profiling the extracellular matrix proteome. A synopsis of the process of synthesizing the ECM structure, detailing inherent chemical complexity, is included to present the scope of the analytical challenge. Current chromatographic and spatial techniques addressing these challenges are detailed. Capabilities for multimodal multiplexing with cellular populations are discussed with a perspective on developing a holistic view of disease processes that includes both the cellular and extracellular microenvironment.

Decoding the basis of histological variation in human cancer

Molecular abnormalities that shape human neoplasms dissociate their phenotypic landscape from that of the healthy counterpart. Through the lens of a microscope, tumour pathology optically captures such aberrations projected onto a tissue slide and has categorized human epithelial neoplasms into distinct histological subtypes based on the diverse morphogenetic and molecular programmes that they manifest. Tumour histology often reflects tumour aggressiveness, patient prognosis and therapeutic vulnerability, and thus has been used as a de facto diagnostic tool and for making clinical decisions. However, it remains elusive how the diverse histological subtypes arise and translate into pleiotropic biological phenotypes. Molecular analysis of clinical tumour tissues and their culture, including patient-derived organoids, and add-back genetic reconstruction of tumorigenic pathways using gene engineering in culture models and rodents further elucidated molecular mechanisms that underlie morphological variations. Such mechanisms include genetic mutations and epigenetic alterations in cellular identity codes that erode hard-wired morphological programmes and histologically digress tumours from the native tissues. Interestingly, tumours acquire the ability to grow independently of the niche-driven stem cell ecosystem along with these morphological alterations, providing a biological rationale for histological diversification during tumorigenesis. This Review comprehensively summarizes our current understanding of such plasticity in the histological and lineage commitment fostered cooperatively by molecular alterations and the tumour environment, and describes basic and clinical implications for future cancer therapy.

Built to Last? Reproducibility and Reusability of Deep Learning Algorithms in Computational Pathology

Recent progress in computational pathology has been driven by deep learning. While code and data availability are essential to reproduce findings from preceding publications, ensuring a deep learning model's reusability is more challenging. For that, the codebase should be well-documented and easy to integrate into existing workflows and models should be robust toward noise and generalizable toward data from different sources. Strikingly, only a few computational pathology algorithms have been reused by other researchers so far, let alone employed in a clinical setting. To assess the current state of reproducibility and reusability of computational pathology algorithms, we evaluated peer-reviewed articles available in PubMed, published between January 2019 and March 2021, in 5 use cases: stain normalization; tissue type segmentation; evaluation of cell-level features; genetic alteration prediction; and inference of grading, staging, and prognostic information. We compiled criteria for data and code availability and statistical result analysis and assessed them in 160 publications. We found that only one-quarter (41 of 160 publications) made code publicly available. Among these 41 studies, three-quarters (30 of 41) analyzed their results statistically, half of them (20 of 41) released their trained model weights, and approximately a third (16 of 41) used an independent cohort for evaluation. Our review is intended for both pathologists interested in deep learning and researchers applying algorithms to computational pathology challenges. We provide a detailed overview of publications with published code in the field, list reusable data handling tools, and provide criteria for reproducibility and reusability.

Advances in cellular and tissue-based imaging techniques for sarcoid granulomas

Sarcoidosis embodies a complex inflammatory disorder spanning multiple systems, with its origin remaining elusive. It manifests as the infiltration of inflammatory cells that coalesce into distinctive noncaseous granulomas within afflicted organs. Unraveling this disease necessitates the utilization of cellular or tissue-based imaging methods to both visualize and characterize the biochemistry of these sarcoid granulomas. Although hematoxylin and eosin stain, standard in routine use alongside cytological stains have found utility in diagnosis within clinical contexts, special stains such as Masson's trichrome, reticulin, methenamine silver, and Ziehl-Neelsen provide additional varied perspectives of sarcoid granuloma imaging. Immunohistochemistry aids in pinpointing specific proteins and gene expressions further characterizing these granulomas. Finally, recent advances in spatial transcriptomics promise to divulge profound insights into their spatial orientation and three-dimensional (3-D) molecular mapping. This review focuses on a range of preexisting imaging methods employed for visualizing sarcoid granulomas at the cellular level while also exploring the potential of the latest cutting-edge approaches like spatial transcriptomics and matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI), with the overarching goal of shedding light on the trajectory of sarcoidosis research.

Classification of Multiple H&E Images via an Ensemble Computational Scheme

In this work, a computational scheme is proposed to identify the main combinations of handcrafted descriptors and deep-learned features capable of classifying histological images stained with hematoxylin and eosin. The handcrafted descriptors were those representatives of multiscale and multidimensional fractal techniques (fractal dimension, lacunarity and percolation) applied to quantify the histological images with the corresponding representations via explainable artificial intelligence (xAI) approaches. The deep-learned features were obtained from different convolutional neural networks (DenseNet-121, EfficientNet-b2, Inception-V3, ResNet-50 and VGG-19). The descriptors were investigated through different associations. The most relevant combinations, defined through a ranking algorithm, were analyzed via a heterogeneous ensemble of classifiers with the support vector machine, naive Bayes, random forest and K-nearest neighbors algorithms. The proposed scheme was applied to histological samples representative of breast cancer, colorectal cancer, oral dysplasia and liver tissue. The best results were accuracy rates of 94.83% to 100%, with the identification of pattern ensembles for classifying multiple histological images. The computational scheme indicated solutions exploring a reduced number of features (a maximum of 25 descriptors) and with better performance values than those observed in the literature. The presented information in this study is useful to complement and improve the development of computer-aided diagnosis focused on histological images.

Routine Workflow of Spatial Proteomics on Micro-formalin-Fixed Paraffin-Embedded Tissues

In the era of single-cell biology, spatial proteomics has emerged as an important frontier. However, it still faces several challenges in technology. Formalin-fixed paraffin-embedded (FFPE) tissues are an important material in spatial proteomics, in which fixed tissues are excised using laser capture microdissection (LCM), followed by protein identification with mass spectrometry. For a satisfied spatial proteomics upon FFPE tissues, the excision area is expected to be as small as possible, and the identified proteins are countered upon as much as possible. For a general laboratory for spatial proteomics, a routine workflow is required, not relying on any special device, and is easily operating. In view of these challenges in technology, we initiated a technology evaluation throughout the entire procedure of proteomic analysis with micro-FFPE tissues. In contrast to the protocols reported previously, several innovations in technology were proposed and conducted, such as removal of destaining, decross-linking with "hang-down", solution simplification for peptide generation and balancing to excision area, and capture rate of micro-FFPE tissues. After optimization of all the necessary steps, a routine workflow was established, in which the minimized area for protein identification was 0.002 mm2, while the excision area for a consistent proteomic analysis was 0.05 mm2. Using the developed workflow and collecting the micro-FFPE tissues continuously, for the first time, a spatial proteomic atlas of mouse brain was preliminarily constructed, which exhibited the typical characteristics of spatial-dependent protein abundance and functional enrichment.

Development and validation of a multivariable model for prediction of malignant transformation and recurrence of oral epithelial dysplasia

BACKGROUND

Oral epithelial dysplasia (OED) is the precursor to oral squamous cell carcinoma which is amongst the top ten cancers worldwide. Prognostic significance of conventional histological features in OED is not well established. Many additional histological abnormalities are seen in OED, but are insufficiently investigated, and have not been correlated to clinical outcomes.

METHODS

A digital quantitative analysis of epithelial cellularity, nuclear geometry, cytoplasm staining intensity and epithelial architecture/thickness is conducted on 75 OED whole-slide images (252 regions of interest) with feature-specific comparisons between grades and against non-dysplastic/control cases. Multivariable models were developed to evaluate prediction of OED recurrence and malignant transformation. The best performing models were externally validated on unseen cases pooled from four different centres (n = 121), of which 32% progressed to cancer, with an average transformation time of 45 months.

RESULTS

Grade-based differences were seen for cytoplasmic eosin, nuclear eccentricity, and circularity in basal epithelial cells of OED (p < 0.05). Nucleus circularity was associated with OED recurrence (p = 0.018) and epithelial perimeter associated with malignant transformation (p = 0.03). The developed model demonstrated superior predictive potential for malignant transformation (AUROC 0.77) and OED recurrence (AUROC 0.74) as compared with conventional WHO grading (AUROC 0.68 and 0.71, respectively). External validation supported the prognostic strength of this model.

CONCLUSIONS

This study supports a novel prognostic model which outperforms existing grading systems. Further studies are warranted to evaluate its significance for OED prognostication.

[Gout (ICD 10: M10): a retrospective analysis of the data from the histopathological arthritis register of the DGORh]

BACKGROUND

Gout is considered to be the most common inflammatory arthritis worldwide. The histopathological arthritis register of the German Society for Orthopedic Rheumatology (DGORh) was founded in 2018. The aim of this register is a systematic collection of histopathological data on joint pathologies. As part of a master's thesis in medicine at the Sigmund Freud Private University (Vienna, Austria) the data on gout cases were retrospectively analyzed.

OBJECTIVE

The objective of this analysis was to determine the prevalence of gout, the localizations of gout in the musculoskeletal system and the sensitivity of clinical gout diagnostics.

MATERIAL AND METHODS

In cooperation with the Medical Treatment Center for Histology, Cytology and Molecular Diagnostics in Trier, Germany (MVZ-HZMD-Trier GmbH; Germany), tissue samples from 190 different orthopedic clinics and practices were analyzed and 7595 datasets were collected and stored in an arthritis register created by the DGORh. All gout cases stored between 1 January 2018 and 20 January 2020 were eligible for retrospective analysis (N = 102).

RESULTS

The prevalence of gout was calculated at 1.34%. Of 108 histopathologically confirmed urate crystal depositions and gout granulomas, 76 were found in the lower extremities (70.37%), 30 in the upper extremities (27.78%) and 2 in the spinal joints (1.85%). The sensitivity of clinical gout diagnostics could be determined at 73.53%.

CONCLUSION

Gout affects different anatomical regions with the first metatarsophalangeal joint being the main localization site. The sensitivity of clinical gout diagnostics was determined at 73.53%. These results emphasize the importance of histopathology in gout diagnostics.

MRI Directed Magnevist Effective to Study Toxicity of Gd-Doped Mesoporous Carbon Nanoparticles in Mice Model

Purpose

Magnetic resonance imaging (MRI) has been a valuable and widely used examination technique in clinical diagnosis and prognostic efficacy evaluation. The introduction of MRI contrast agent (CA) improves its sensitivity obviously, particularly with the development of nano-CA, which presents higher contrast enhancement ability. However, systematical evaluation of their toxicity is still limited, hampering their further translation in clinics.

Methods

In this paper, to systematically evaluate the toxicity of nano-CA, Gd-doped mesoporous carbon nanoparticles (Gd-MCNs) prepared by a one-step hard template method were introduced as a model and clinically used MRI CA, Magnevist (Gd-DTPA) as control. Their in vitro blood compatibility, cellular toxicity, DNA damage, oxidative stress, inflammation response as well as in vivo toxicity and MR imaging behaviors were studied and compared.

Results

The experimental results showed that compared with Gd-DTPA, Gd-MCNs displayed negligible influence on the red blood cell shape, aggregation, BSA structure, macrophage morphology and mitochondrial function. Meanwhile, limited ROS and inflammatory cytokine production also illustrated the cellular compatibility of Gd-MCNs. For in vivo toxicity evaluation, Gd-MCNs presented acceptable in vivo biosafety even under 12 times injection for 12 weeks. More importantly, at the same concentration of Gd, Gd-MCNs displayed better contrast enhancement of tumor than Gd-DTPA, mainly coming from its high MRI relaxation rate which is nearly 9 times that of Gd-DTPA.

Conclusion

In this paper, we focus on the toxicity evaluation of MRI nano-CA, Gd-MCNs from different angles. With Gd-DTPA as control, Gd-MCNs appeared to be highly biocompatible and safe nanoparticles that possessed promising potentials for the use of MRI nano-CA. In the future, more research on the long-term genotoxicity and the fate of nanoparticles after being swallowed should be performed.

Skin substitutes based on gellan gum with mechanical and penetration compatibility to native human skin

The study reports on a simple system to fabricate skin substitutes consisting of a naturally occurring bacterial polysaccharide gellan gum. Gelation was driven by the addition of a culture medium whose cations induced gellan gum crosslinking at physiological temperature, resulting in hydrogels. Human dermal fibroblasts were incorporated in these hydrogels and their mechanical, morphological, and penetration characteristics were studied. The mechanical properties were determined by means of oscillatory shear rheology, and a short linear viscoelastic regime was noted up to less than 1% of strain amplitude. The storage modulus increased with an increasing polymer concentration. The moduli were in the range noted for native human skin. After 2 weeks of fibroblast cultivation, the storage moduli showed signs of deterioration, so that a culture time of 2 weeks was proposed for further studies. Microscopic and fluorescent staining observations were documented. These depicted a crosslinked network structure in the hydrogels with a homogeneous distribution of cells and an assured cell viability of 2 weeks. H&E staining was also performed, which showed some traces of ECM formation in a few sections. Finally, caffeine penetration experiments were carried out with Franz diffusion cells. The hydrogels with a higher concentration of polymer containing cells showed an improved barrier function against caffeine compared to previously studied multicomponent hydrogels as well as commercially available 3D skin models. Therefore, these hydrogels displayed both mechanical and penetration compatibility with the ex vivo native human skin.

Nuclei segmentation with point annotations from pathology images via self-supervised learning and co-training

Nuclei segmentation is a crucial task for whole slide image analysis in digital pathology. Generally, the segmentation performance of fully-supervised learning heavily depends on the amount and quality of the annotated data. However, it is time-consuming and expensive for professional pathologists to provide accurate pixel-level ground truth, while it is much easier to get coarse labels such as point annotations. In this paper, we propose a weakly-supervised learning method for nuclei segmentation that only requires point annotations for training. First, coarse pixel-level labels are derived from the point annotations based on the Voronoi diagram and the k-means clustering method to avoid overfitting. Second, a co-training strategy with an exponential moving average method is designed to refine the incomplete supervision of the coarse labels. Third, a self-supervised visual representation learning method is tailored for nuclei segmentation of pathology images that transforms the hematoxylin component images into the H&E stained images to gain better understanding of the relationship between the nuclei and cytoplasm. We comprehensively evaluate the proposed method using two public datasets. Both visual and quantitative results demonstrate the superiority of our method to the state-of-the-art methods, and its competitive performance compared to the fully-supervised methods. Codes are available at https://github.com/hust-linyi/SC-Net.

Demystifying Breast Disease Markers

Breast imaging radiologists regularly perform image-guided biopsies of suspicious breast lesions based on features that are associated with a likelihood of malignancy ranging from 2% to greater than 95% (Breast Imaging Reporting and Data System categories 4 and 5). As diagnostic partners, pathologists perform histopathologic assessment of these tissue samples to confirm a diagnosis. Correlating the imaging findings with the histopathologic results is an integral aspect of multidisciplinary breast care. Assessment of radiologic-pathologic concordance is vital in guiding appropriate management, as it enables identification of discordant results, minimizing the chance of misdiagnosis. Undersampling can lead to false-negative results, with the frequencies of false-negative diagnoses varying on the basis of multiple factors, including biopsy type (eg, core needle, vacuum-assisted needle), needle gauge, and type of lesion sampled at biopsy (ie, mass, calcifications, asymmetry, architectural distortion). Improving a radiologist's knowledge of macroscopic and microscopic breast anatomy and more common breast diseases and their expected imaging findings ensures more accurate radiologic-pathologic correlation and management recommendations. The histopathologic and molecular characteristics of biopsy-sampled breast lesions aid in making an accurate diagnosis. Hematoxylin-eosin staining provides critical morphologic details, whereas immunohistochemical staining enables molecular characterization of many benign and malignant lesions, which is critical for tailored treatment. The authors review commonly encountered benign and malignant breast diseases, their corresponding histopathologic phenotypes, and the histopathologic markers that are essential to clinching the diagnosis of these entities. As part of a multidisciplinary team that provides optimal patient care, radiologists should be knowledgeable of the foundations of histopathologic diagnosis and the implications for patient management to ensure appropriate radiologic-pathologic concordance. ©RSNA, 2023 Quiz questions for this article are available in the supplemental material.

Preparation and evaluation of sustained release pirfenidone-loaded microsphere dry powder inhalation for treatment of idiopathic pulmonary fibrosis

Pirfenidone (PFND) is a recommended oral drug used to treat idiopathic pulmonary fibrosis, but have low bioavailability and high hepatotoxicity. The study, therefore, seeks to improve the therapeutic activities of the drug via increased bioavailability and reduced associated side effects by developing a novel drug delivery system. The electrostatic spray technology was used to prepare a sustained release pirfenidone-loaded microsphere dry powder inhalation with PEG-modified chitosan (PFND-mPEG-CS-MS). The entrapment efficiency, drug loading, and in vitro cumulative drug release rate (at 24 h and with a sustained release effect) of PFND-mPEG-CS-MS were 77.35±3.01%, 11.45±0.64%, and 90.4%, respectively. The Carr's index of PFND-mPEG-CS-MS powder was 17.074±2.163% with a theoretical mass median aerodynamic diameter (MMADt) of 0.99±0.07 μm, and a moisture absorption weight gain rate (Rw) of 4.61±0.72%. The emptying rate, pulmonary deposition rate (fine particle fraction) and actual mass median aerodynamic diameter (MMADa) were 90%∼95%, 48.72±7.04% and 3.10±0.16 μm, respectively. MTT bioassay showed that mPEG-CS-MS (200 μg/mL) had good biocompatibility (RGR = 90.25%) and PFND-mPEG-CS-MS (200 μg/mL) had significant inhibitory activity (RGR = 49.82%) on fibroblast growth. The pharmacokinetic data revealed that the t1/2 (5.02 h) and MRT (10.66 h) of PFND-mPEG-CS-MS were prolonged compared with the free PFND (t1/2, 1.67 h; MRT, 2.71 h). The pharmacodynamic results also showed that the formulated-drug group had slight pathological changes, lower lung hydroxyproline content, and reduced hepatotoxicity compared with the free-drug group. The PFND-mPEG-CS-MS further significantly down-regulated TGF-β cytokines, Collagen I, and α-SMA protein expression levels compared with the free drug. The findings indicated that the PFND-mPEG-CS-MS had a good sustained release effect, enhanced bioavailability, decreased toxicity, and increased anti-fibrotic activities.

Bioactive Indole Alkaloid from Aspergillus amoenus TJ507 That Ameliorates Hepatic Ischemia/Reperfusion Injury

Hepatic ischemia/reperfusion injury (IRI) is a major factor contributing to the failure of hepatic resection and liver transplantation. As part of our ongoing investigation into bioactive compounds derived from fungi, we isolated eight indole alkaloids (1-8) from the endophytic fungus Aspergillus amoenus TJ507. Among these alkaloids, one previously undescribed compound, amoenamide D (1), was identified. The planar structure of 1 was elucidated by extensive spectroscopic analysis, including HRESIMS and NMR spectra. The absolute configuration of 1 was elucidated by using electronic circular dichroism calculations. Notably, in the CoCl2-induced hepatocyte damage model, notoamide Q (3) exhibited significant anti-hypoxia injury activity. Furthermore, in a murine hepatic ischemia/reperfusion injury model, treatment with 3 prevents IRI-induced liver damage and hepatocellular apoptosis. Consequently, 3 might serve as a potential lead compound to prevent hepatic ischemia/reperfusion injury.

In Vivo Ultrasound Molecular Imaging in the Evaluation of Complex Ovarian Masses: A Practical Guide to Correlation with Ex Vivo Immunohistochemistry

Ovarian cancer is the fifth leading cause of cancer-related deaths in women and the most lethal gynecologic cancer. It is curable when discovered at an early stage, but usually remains asymptomatic until advanced stages. It is crucial to diagnose the disease before it metastasizes to distant organs for optimal patient management. Conventional transvaginal ultrasound imaging offers limited sensitivity and specificity in the ovarian cancer detection. With molecularly targeted ligands addressing targets, such as kinase insert domain receptor (KDR), attached to contrast microbubbles, ultrasound molecular imaging (USMI) can be used to detect, characterize and monitor ovarian cancer at a molecular level. In this article, the authors propose a standardized protocol is proposed for the accurate correlation between in- vivo transvaginal KDR-targeted USMI and ex vivo histology and immunohistochemistry in clinical translational studies. The detailed procedures of in vivo USMI and ex vivo immunohistochemistry are described for four molecular markers, CD31 and KDR with a focus on how to enable the accurate correlation between in vivo imaging findings and ex vivo expression of the molecular markers, even if not the entire tumor could can be imaged by USMI, which is not an uncommon scenario in clinical translational studies. This work aims to enhance the workflow and the accuracy of characterization of ovarian masses on transvaginal USMI using histology and immunohistochemistry as reference standards, which involves sonographers, radiologists, surgeons, and pathologists in a highly collaborative research effort of USMI in cancer.

Color restoration based on digital pathology image

OBJECTIVE

Protective color restoration of faded digital pathology images based on color transfer algorithm.

METHODS

Twenty fresh tissue samples of invasive breast cancer from the pathology department of Qingdao Central Hospital in 2021 were screened. After HE staining, HE stained sections were irradiated with sunlight to simulate natural fading, and every 7 days was a fading cycle, and a total of 8 cycles were experienced. At the end of each cycle, the sections were digitally scanned to retain clear images, and the color changes of the sections during the fading process were recorded. The color transfer algorithm was applied to restore the color of the faded images; Adobe Lightroom Classic software presented the histogram of the image color distribution; UNet++ cell recognition segmentation model was used to identify the color restored images; Natural Image Quality Evaluator (NIQE), Information Entropy (Entropy), and Average Gradient (AG) were applied to evaluate the quality of the restored images.

RESULTS

The restored image color met the diagnostic needs of pathologists. Compared with the faded images, the NIQE value decreased (P<0.05), Entropy value increased (P<0.01), and AG value increased (P<0.01). The cell recognition rate of the restored image was significantly improved.

CONCLUSION

The color transfer algorithm can effectively repair faded pathology images, restore the color contrast between nucleus and cytoplasm, improve the image quality, meet the diagnostic needs and improve the cell recognition rate of the deep learning model.

Cetacean Intracytoplasmic Eosinophilic Globules: A Cytomorphological, Histological, Histochemical, Immunohistochemical, and Proteomic Characterization

The nature, etiopathogenesis, and clinicopathologic relevance of the prevalent intracytoplasmic eosinophilic globules (IEGs) within hepatocytes of cetaceans are unknown. This study aims to evaluate the presence and characterize the IEGs in the hepatocytes of cetaceans using histochemical and immunohistochemical electron microscopy, Western blot, lectin histochemistry, and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry techniques. A total of 95/115 (83%) animals (16 species) exhibited histologically evident intracytoplasmic round to oval, single to multiple, hyaline eosinophilic globules within the hepatocytes. These globules were largely PAS-positive, diastase resistant, and were immunopositive for fibrinogen (FB, 97%), albumin (Alb, 85%), and α1-antitrypsine (A1AT, 53%). The IEG positivity for FB and A1AT were correlated with live-stranding, hepatic congestion and a good nutritional status. The cetaceans lacking IEGs were consistently dead stranded and had poor body conditions. The IEGs in 36 bycaught cetaceans were, all except one, FB-positive and A1AT-negative. The IEGs exhibited morphologic and compositional variations at the ultrastructural level, suggesting various stages of development and/or etiopathogenesis(es). The glycocalyx analysis suggested an FB- and A1AT-glycosylation pattern variability between cetaceans and other animals. The proteomic analyses confirmed an association between the IEGs and acute phase proteins, suggesting a relationship between acute stress (i.e., bycatch), disease, and cellular protective mechanisms, allowing pathologists to correlate this morphological change using the acute hepatocytic cell response under certain stress conditions.

Enhancement of Scaffold In Vivo Biodegradability for Bone Regeneration Using P28 Peptide Formulations

The field of bone tissue engineering has shown a great variety of bone graft substitute materials under development to date, with the aim to reconstruct new bone tissue while maintaining characteristics close to the native bone. Currently, insufficient scaffold degradation remains the critical limitation for the success of tailoring the bone formation turnover rate. This study examines novel scaffold formulations to improve the degradation rate in vivo, utilising chitosan (CS), hydroxyapatite (HAp) and fluorapatite (FAp) at different ratios. Previously, the P28 peptide was reported to present similar, if not better performance in new bone production to its native protein, bone morphogenetic protein-2 (BMP-2), in promoting osteogenesis in vivo. Therefore, various P28 concentrations were incorporated into the CS/HAp/FAp scaffolds for implantation in vivo. H&E staining shows minimal scaffold traces in most of the defects induced after eight weeks, showing the enhanced biodegradability of the scaffolds in vivo. The HE stain highlighted the thickened periosteum indicating a new bone formation in the scaffolds, where CS/HAp/FAp/P28 75 µg and CS/HAp/FAp/P28 150 µg showed the cortical and trabecular thickening. CS/HAp/FAp 1:1 P28 150 µg scaffolds showed a higher intensity of calcein green label with the absence of xylenol orange label, which indicates that mineralisation and remodelling was not ongoing four days prior to sacrifice. Conversely, double labelling was observed in the CS/HAp/FAp 1:1 P28 25 µg and CS/HAp/FAp/P28 75 µg, which indicates continued mineralisation at days ten and four prior to sacrifice. Based on the HE and fluorochrome label, CS/HAp/FAp 1:1 with P28 peptides presented a consistent positive osteoinduction following the implantation in the femoral condyle defects. These results show the ability of this tailored formulation to improve the scaffold degradation for bone regeneration and present a cost-effective alternative to BMP-2.

Integrated single-nucleus sequencing and spatial architecture analysis identified distinct injured-proximal tubular types in calculi rats

BACKGROUND

Urolithiasis with high prevalence and recurrence rate, has impacts on kidney injury in patients, becomes a socioeconomic and healthcare problem in worldwide. However, the biology of kidney with crystal formation and proximal tubular injury remains essentially unclear. The present study aims to evaluate the cell biology and immune-communications in urolithiasis mediated kidney injury, to provide new insights in the kidney stone treatment and prevention.

RESULTS

We identified 3 distinct injured-proximal tubular cell types based on the differentially expression injury markers (Havcr1 and lcn2) and functional solute carriers (slc34a3, slc22a8, slc38a3 and slc7a13), and characterized 4 main immune cell types in kidney and one undefined cell population, where F13a1^+/high/CD163^+/high monocyte & macrophage and Sirpa/Fcgr1a/Fcgr2a^+/high granulocyte were the most enriched. We performed intercellular crosstalk analysis based on the snRNA-seq data and explored the potential immunomodulation of calculi stone formation, and founded that the interaction between ligand Gas6 and its receptors (Gas6-Axl, Gas6-Mertk) was specifically observed in the injured-PT1 cells, but not injured-PT2 and -PT3 cells. The interaction of Ptn-Plxnb2 was only observed between the injured-PT3 cells and its receptor enriched cells.

CONCLUSIONS

Present study comprehensively characterized the gene expression profile in the calculi rat kidney at single nucleus level, identified novel marker genes for all cell types of rat kidney, and determined 3 distinct sub-population of injured-PT clusters, as well as intercellular communication between injured-PTs and immune cells. Our collection of data provides a reliable resource and reference for studies on renal cell biology and kidney disease.

Circ_0069094 regulates malignant phenotype and paclitaxel resistance in breast cancer cells via targeting the miR-136-5p/YWHAZ axis

BACKGROUND

Breast cancer is one of the most malignant cancers. Increasing evidence suggests that circular RNAs (circRNAs) are involved in breast cancer progression through sponging microRNA (miRNA). However, the underlying molecular mechanisms of circ_0069094 in breast cancer are unclear. This study aimed to reveal the effect of the circ_0069094/miR-136-5p/tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein zeta (YWHAZ) pathway on the malignant progression of breast cancer.

METHODS

The quantitative real-time polymerase chain reaction and western blot were used to assess the expression of circRNA/miRNA/mRNA. The functional effects of circ_0069094 on the cell processes of breast cancer were investigated by cell counting kit-8, colony-forming assay, 5-ethynyl-2'-deoxyuridine (EdU) assay, flow cytometry, and transwell invasion assay. The interactions among circ_0069094, miR-136-5p, and YWHAZ were assessed by dual-luciferase reporter assay. A xenograft experiment was performed to determine the effects of circ_0069094 on tumor formation.

RESULTS

Circ_0069094 was overexpressed in paclitaxel (PTX)-resistant breast cancer tissues and cells, and the silencing of circ_0069094 decreased tumor growth, cell proliferation and cell invasion while increasing PTX sensitivity and cell apoptosis in PTX-resistant cells. In addition, miR-136-5p was a target of circ_0069094, and miR-136-5p inhibition abolished circ_0069094 knockdown-induced effects in PTX-resistant cells. MiR-136-5p expression was decreased in PTX-resistant breast cancer tissues and cells, and the overexpression of miR-136-5p suppressed the malignant behaviors of breast cancer cells by targeting YWHAZ. Importantly, circ_0069094 regulated YWHAZ expression in breast cancer by targeting miR-136-5p.

CONCLUSION

Circ_0069094 silencing improved PTX sensitivity in breast cancer progression through competitively sponging miR-136-5p.

The effect of neural network architecture on virtual H&E staining: Systematic assessment of histological feasibility

Conventional histopathology has relied on chemical staining for over a century. The staining process makes tissue sections visible to the human eye through a tedious and labor-intensive procedure that alters the tissue irreversibly, preventing repeated use of the sample. Deep learning-based virtual staining can potentially alleviate these shortcomings. Here, we used standard brightfield microscopy on unstained tissue sections and studied the impact of increased network capacity on the resulting virtually stained H&E images. Using the generative adversarial neural network model pix2pix as a baseline, we observed that replacing simple convolutions with dense convolution units increased the structural similarity score, peak signal-to-noise ratio, and nuclei reproduction accuracy. We also demonstrated highly accurate reproduction of histology, especially with increased network capacity, and demonstrated applicability to several tissues. We show that network architecture optimization can improve the image translation accuracy of virtual H&E staining, highlighting the potential of virtual staining in streamlining histopathological analysis.

Radioenhancement with the Combination of Docetaxel and Ultrasound Microbubbles: In Vivo Prostate Cancer

Using an in vitro prostate cancer model, we previously demonstrated the significant enhancement of radiotherapy (XRT) with the combined treatment of docetaxel (Taxotere; TXT) and ultrasound-microbubbles (USMB). Here, we extend these findings to an in vivo cancer model. Severe combined immune-deficient male mice were xenografted with the PC-3 prostate cancer cell line in the hind leg and treated with USMB, TXT, radiotherapy (XRT), and their combinations. The tumors were imaged with ultrasound pre-treatment and 24 h post-treatment, following which they were extracted for the histological analysis of the tumor-cell death (D_N; H&E) and apoptosis (D_A; TUNEL). The tumors' growths were assessed for up to ~6 weeks and analysed using the exponential Malthusian tumor-growth model. The tumors' doubling time (V_T) was characterized as growth (positive) or shrinkage (negative). The cellular death and apoptosis increased ~5-fold with the TXT + USMB + XRT (D_n = 83% and D_a = 71%) compared to the XRT alone (D_n = 16% and D_a = 14%), and by ~2-3-fold with the TXT + XRT (D_n = 50% and D_a = 38%) and USMB + XRT (D_n = 45% and D_a = 27%) compared to the XRT. The USMB enhanced the cellular bioeffects of the TXT by ~2-5-fold with the TXT + USMB (D_n = 42% and D_a = 50%), compared with the TXT alone (D_n = 19% and D_a = 9%). The USMB alone caused cell death (D_n = 17% and D_a = 10%) compared to the untreated control (D_n = 0.4% and D_a = 0%). The histological cellular bioeffects were correlated with the changes in the ultrasound RF mid-band-fit data, which were associated with the cellular morphology. The linear regression analysis displayed a positive linear correlation between the mid-band fit and the overall cell death (R² = 0.9164), as well as a positive linear correlation between the mid-band fit and the apoptosis (R² = 0.8530). These results demonstrate a correlation between the histological and spectral measurements of the tissue microstructure and that cellular morphological changes can be detected by ultrasound scattering analysis. In addition, the tumor volumes from the triple-combination treatment were significantly smaller than those from the control, XRT, USMB + XRT, and TXT + XRT, from day 2 onward. The TXT + USMB + XRT-treated tumors shrank from day 2 and at each subsequent time-point measured (V_T ~-6 days). The growth of the XRT-treated tumors was inhibited during the first 16 days, following which the tumors grew (V_T ~9 days). The TXT + XRT and USMB + XRT groups displayed an initial decrease in tumor size (day 1-14; TXT + XRT V_T ~-12 days; USMB + XRT V_T ~-33 days), followed by a growth phase (day 15-37; TXT + XRT V_T ~11 days; USMB + XRT V_T ~22 days). The triple-combination therapy induced tumor shrinkage to a greater extent than any of the other treatments. This study demonstrates the in vivo radioenhancement potential of chemotherapy combined with therapeutic ultrasound-microbubble treatment in inducing cell death and apoptosis, as well as long-term tumor shrinkage.

Combination of Transcriptomics and Proteomics Reveals Differentially Expressed Genes and Proteins in the Skin of EDAR Gene-Targeted and Wildtype Cashmere Goats

Cashmere goats play a pivotal role in the animal hair industry and are economically valuable. Cashmere is produced through the periodic growth of secondary hair follicles. To improve their yield of cashmere, the regulatory mechanisms of cashmere follicle growth and development need to be analysed. Therefore, in this study, EDAR gene-targeted cashmere goats were used as an animal model to observe the phenotypic characteristics of abnormal hair growth and development at the top of the head. Transcriptomic and proteomic techniques were used to screen for differentially expressed genes and proteins. In total, 732 differentially expressed genes were identified, including 395 upregulated and 337 downregulated genes. In addition, 140 differentially expressed proteins were identified, including 69 upregulated and 71 downregulated proteins. These results provide a research target for elucidating the mechanism through which EDAR regulates hair follicle growth in cashmere goats. It also enriches the available data on the regulatory network involved in hair follicle growth.

Detection of Apical Dental Resorption Caused by Endodontic Infection in Mice Using Fluorescence and Bright-Field Microscopy

The aim of this study was to evaluate the sensitivity, specificity, and predictive values of the fluorescence microscopy method in the detection of apical dental reabsorption after induction of apical periodontitis in animal models. Forty-first molars of mice, aged 6 to 8 weeks, had their root canals exposed to the oral environment or were maintained healthy as controls (n = 20). After 14 and 42 days, mice were euthanized and tissues were collected for histological evaluation by means of bright field and fluorescence microscopy. The accuracy of fluorescence microscopy in identifying apical external dental resorption was investigated using a diagnostic validation test based on the sensitivity (S) and specificity (E) properties. Bright-field microscopy revealed a higher number of specimens with scores of 1 to 3 - absence of apical dental resorption (n = 29; 52%), while fluorescence microscopy revealed a higher number of specimens with scores of 4 to 6 - presence of apical dental resorption (n = 37; 66%). Out of 56 specimens, 26 were TP, 11 were FP, and 19 were TN. No FN result was observed. Fluorescence microscopy presented a sensitivity value of 1, similar to the bright-field method, while specificity was lower (0.633). The accuracy of the fluorescent method to detect apical dental resorption was 0.804. Fluorescence microscopy revealed a higher number of false positive apical dental resorption than bright-field microscopy. The detection of apical dental resorption was not impacted by the sensitivity of the method but by its specificity.