Microstructural imaging enables researchers to visualise changes in the arterial wall, allowing for (i) a deeper understanding of the role of specific components in arterial mechanics, (ii) the observation of cellular responses, (iii) insights into pathological alterations in tissue microstructure, and/or (iv) advancements in tissue engineering aimed at replicating healthy native tissue. In this prospective review, we present various imaging modalities spanning from ex vivo to in vivo applications within arterial tissue. The pros, cons, and sensitivities of these modalities are highlighted. By consolidating the latest advancements in microstructural imaging of arterial tissue, the authors aim for this paper to serve as a guide for researchers designing experiments at various stages. Furthermore, the integration of non-invasive, non-destructive imaging techniques into studies provides an additional layer of microstructural information, enhancing scientific findings, improving our understanding of disease, and potentially enabling earlier or more effective diagnostic capabilities. STATEMENT OF SIGNIFICANCE: Imaging the specific microstructural components of the arterial wall provides critical insights into vascular biology, mechanics, and pathology. It enables the visualisation of key structural components and their roles in arterial function, supports the analysis of cell-matrix interactions, and reveals microarchitectural changes associated with disease progression. This level of specificity also informs the design of biomimetic materials and scaffolds in tissue engineering, facilitating the replication of native arterial properties. By synthesising recent developments in microstructural imaging techniques, this paper serves as a reference for investigators designing experiments across a range of vascular research applications. Moreover, the incorporation of non-invasive, non-destructive imaging methods offers a means to acquire detailed microstructural data without compromising tissue integrity. This enhances the interpretability and translational potential of findings, deepens our understanding of vascular disease mechanisms, and may ultimately contribute to the development of earlier and more precise diagnostic approaches.

Hepatocyte cytokeratin 7 (CK7) is a reliable marker for evaluating the severity of cholestasis in chronic cholestatic cholangiopathies. However, there is currently no noninvasive test available to assess the status of hepatocyte CK7 in pancreaticobiliary maljunction patients.

We aimed to develop a deep learning radiomics nomogram using magnetic resonance images (MRIs) to preoperatively identify the hepatocyte CK7 status and assess cholestasis progression in patients with pancreaticobiliary maljunction.

In total, 180 pancreaticobiliary maljunction patients were retrospectively enrolled and were randomly divided into a training cohort (n = 144) and a validation cohort (n = 36). CK7 status was determined through immunohistochemical analysis. Pyradiomics and pretrained ResNet50 were used to extract radiomics and deep learning features, respectively. To construct the radiomics and deep learning signature, feature selection methods including the minimum redundancy-maximum relevance and least absolute shrinkage and selection operator were employed. The integrated deep learning radiomics nomogram model was constructed by combining the imaging signatures and valuable clinical feature.

The deep learning signature exhibited superior predictive performance compared with the radiomics signature, as evidenced by the higher area under the curve (AUC) values in validation cohort (0.92 vs. 0.81). Further, the deep learning radiomics nomogram, which incorporated the radiomics signature, deep learning signature, and Komi classification, demonstrated excellent predictive ability for CK7 expression, with AUC value of 0.95 in the validation cohort.

The proposed deep learning radiomics nomogram exhibits promising performance in accurately identifying hepatic CK7 expression, thus facilitating prediction of cholestasis progression and perhaps earlier initiation of treatment in pancreaticobiliary maljunction children.

Bone taphonomy and diagenesis contribute to anthropological analysis in forensic investigations by attempting to reconstruct the relationship between human cadaveric remains and their postmortem depositional environment. The rare aquatic taphonomic experiments have been delivering conflicting results on the influence of time and the environment on the decay of bone and teeth, especially considering that the main diagenetic processes can lead to fragmentation, progressive dissolution or fossilization. The aim of this experimental, quantitative, randomized and controlled 2-year study was to analyse the taphonomy and diagenesis of submerged terrestrial mammalian bones to achieve a more accurate estimation of both the post-mortem interval (PMI) and the post-mortem submersion interval (PMSI) in the short term. Three parameters of bone diagenesis, the Oxford Histological Index (OHI), the total porosity and the collagen content of cortical bone were analysed by MicroCT Scan, bright-field Light Microscopy (Picrosirius Red stain), Scanning Electron Microscopy (SEM) and Laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) on 75 sheep femurs and tibias placed in four distinct types of environment (natural saltwater, natural freshwater, an artificial seawater solution and exposed to the air) vs. non-exposed controls. LA-ICP-MS was soon discontinued because no measurable changes of the elemental profiles could be detected. Multivariate statistical analysis was applied to the collected data. The macroscopical preservation was consistently excellent (OHI=5). The total porosity and the degradation of collagen were greater underwater than in subaerial exposure, whereas demineralization zones and bioerosion tunnelling appeared after 12 months in the air-exposed samples only. Underwater, the continuous movement, the correlated abrasion by sand and sediment and the constant alkaline pH (≥ 8) can explain the progressive removal of the mineral component and the subsequent exposure of collagen to bioeroders and chemical hydrolysis. On land, the same process occurs at a slower rate on account of the seasonality of the water flow, however, the action of the more abundant and diversified species of bioeroding microorganisms appears more efficient. Despite some limitations, this study indicates that three parameters of bone diagenesis can predict the depositional environment of terrestrial mammalian bone characterized by a PMI and/or PMSI of at least 12 months.

As a transcription factor, NR2F1 regulates spatiotemporal gene expression in the nucleus particularly during development. Aberrant NR2F1 causes the rare neurodevelopmental disorder Bosch-Boonstra-Schaaf Optic Atrophy Syndrome. In addition, altered NR2F1 expression is frequently observed in various cancers and is considered a prognostic marker or potential therapeutic target. NR2F1 has been found in both the nucleus and nucleoli, suggesting a non-canonical and direct role in the latter compartment. Hence, we studied this phenomenon employing various in vitro and in vivo models using different antibody-dependent approaches. Examination of seven commonly used anti-NR2F1 antibodies in different human cancer and stem cells as well as in wild type and null mice revealed that NR2F1 nucleolar localization is artificial and has no functional role. Our subsequent comparative analysis demonstrated which anti-NR2F1 antibody best fits which approach. The data allow for correct data interpretation and underline the need to optimize any antibody-mediated technique.

Ultramicroscopy has offered new avenues into the visualisation of tissues within animal models, providing three-dimensional visualisation through the use of light sheet fluorescence microscopy. This study aimed to develop and apply an optical tissue clearing method to investigate the application of light sheet fluorescence microscopy to image late-stage chicken embryos, and compare anatomical visualisation to traditional histological staining. Seventeen-day old specific pathogen free embryos were collected, fixed, and sectioned. Haematoxylin and eosin stained sections were prepared for histology, while light sheet imaging required the tissues to be optically clear. For this, an ethyl cinnamate-based method was utilised, allowing for acquisition of clear, unobstructed three-dimensional images of significant organ structures and systems using only autofluorescence. The use of established histological techniques provided anatomical mapping of structures between familiar histology images and the three-dimensional light sheet images. Rendering of organs using light sheet imaging provided contextual insights into the surrounding tissues and physiological architecture of major organ structures and systems. This was most apparent through the identification of the pulmonary vein and rendering of a volumetric projection of the vasculature branching within the lung and the subsequent merging of vasculature into the left side of the heart. Overall, the visualisation of the chicken embryo was enhanced by combining traditional histology with the information gained by three-dimensional light sheet fluorescence microscopy.

Invasion and metastasis are well-known hallmarks of cancer, with metastatic disease accounting for 60% to 90% of cancer-related deaths [...].

New bio-mimetic approaches are needed to develop effective delivery systems for inflammation regulation in chronic diseases like ulcerative colitis, avoiding fast clearance by immune system. The cell membrane-coated nanoparticle with a therapeutic payload has been considered as a promising delivery system to address the requirement.

Here, Glibenclamide (GLY)-loaded PLGA nanoparticles (NPs) were constructed by a single emulsion procedure and camouflaged by a layer of monocyte membrane using the extrusion technique to fabricate bio-mimetic nanoghosts (NGs), followed by physiochemical and biological characterizations.

Upon coating the NPs by the membrane, the hydrodynamic size and zeta potential of NGs was changed. The formation of the shell compartment with diameter of about 15.5nm around NP core was confirmed by TEM. The expression levels of NLRP3, IL-1β, IL-18, caspase-1, TNF-α and IL-6 were decreased upon the NGs treatment. The lower cellular internalization of the NGs exhibited potential for improved circulation stability against macrophage phagocytosis. Treatment of acetic acid-induced UC with NGs exhibited healing of the mucosal lining in the colon tissue.

The monocyte membrane-coated NPs with a sulfonylurea derivatives payload can be considered as an excellent biologically inspired candidate for management of inflammatory diseases like UC via inflammation regulation.

Changes in the density and organization of fibrous biological tissues often accompany the progression of serious diseases ranging from fibrosis to neurodegenerative diseases, heart disease and cancer. However, challenges in cost, complexity, or precision faced by existing imaging methodologies and materials pose barriers to elucidating the role of tissue microstructure in disease. Here, we leverage the intrinsic optical anisotropy of the Morpho butterfly wing and introduce Morpho-Enhanced Polarized Light Microscopy (MorE-PoL), a stain- and contact-free imaging platform that enhances and quantifies the birefringent material properties of fibrous biological tissues. We develop a mathematical model, based on Jones calculus, which describes fibrous tissue density and organization. As a representative example, we analyzed collagen-dense and collagen-sparse human breast cancer tissue sections and leverage our technique to assess the microstructural properties of distinct regions of interest. We compare our results with conventional Hematoxylin and Eosin (H&E) staining procedures and second harmonic generation (SHG) microscopy for fibrillar collagen detection. Our findings demonstrate that our MorE-PoL technique provides a robust, quantitative, and accessible route toward analyzing biological tissue microstructures, with great potential for application to a broad range of biological materials.

Peripheral nerve stimulation is approved by the US Food and Drug Administration for treating various disorders, but it is often limited by side effects, highlighting the need for a clear understanding of fascicular and fiber organization to design selective therapies. Micro-CT imaging of contrast-stained nerves enables the visualization of tissue microstructures, such as the fascicular perineurium and vasculature. In this work, we evaluated phosphotungstic acid (PTA) as a contrast agent and assessed its compatibility with downstream histology.

Human vagus nerve samples were collected from three embalmed cadavers and subjected to three different staining methods, followed by micro-CT imaging: Lugol's iodine, osmium tetroxide, and PTA. Contrast ratios of adjacent tissue microstructures (perineurium, interfascicular epineurium, and fascicle) were quantified for each stain and compared. We further developed a pipeline to optimize micro-CT scan acquisition parameters based on objective metrics for sharpness, noise, and pixel saturation. The PTA-stained samples underwent subsequent histological processing and staining with hematoxylin and eosin, Masson's trichrome, and immunohistochemistry and were assessed for tissue degradation.

PTA enhanced the visualization of perineurium, providing high contrast ratios compared to iodine and osmium tetroxide. Optimized scanning parameters for PTA-stained nerves (55 kV and 109 µA) effectively balanced noise and sharpness. While we found that PTA is generally nondestructive for downstream histology, higher concentrations and longer exposure could alter the optical density of nuclei and affect stain differentiation in special stains.

PTA serves as a valuable micro-CT contrast agent for nerve imaging, effective in visualizing the perineurium with minimal impact on histological integrity.

High-grade serous ovarian carcinoma (HGSOC) is one of the deadliest malignancies in female population and the cause of 70% of all ovarian cancer-related deaths. Among its hallmarks, the fluid accumulation in the peritoneal cavity, or ascites, is a peculiar pathological sign during late stages and in recurrent patients. Besides cancer cells, ascitic fluids contain a heterogeneous cellular composition, representing a precious source to dissect molecular mechanisms underlying invasion and metastatization or find new biomarkers to predict therapy response. However, malignant cells are often a minority population in ascites making the detection and analysis of cancer cells a challenge. Here we propose a combinatorial approach for the detection of malignant cells in OC ascites based on TP53 deep sequencing and PAX8 cytological staining. In addition, we improve the procedure by implementing a cancer cell enrichment step, increasing the sensitivity in the detection of neoplastic fraction and potentiating downstream research and diagnostics applications.

Microscopic inspection of histologically stained tissue is considered as the gold standard for cancer diagnosis. This research is inspired by the practices of pathologists who analyze diagnostic samples by zooming in and out. We propose a dual-encoder model that simultaneously evaluates two views of the tissue at different levels of magnification. The lower magnification view provides contextual information for a target area, while the higher magnification view provides detailed information. The model consists of two encoder branches that consider both detail and context resolutions of the target area concurrently for binary pixel-wise segmentation. We introduce a unique weight initialization for the cross-attention between the context and detail feature tensors, allowing the model to incorporate contextual information. Our design is evaluated using the Camelyon16 dataset of sentinel lymph node tissue and cancer. The results demonstrate the benefit of including context regions when segmenting for cancer, with an improvement in AUC ranging from 0.31 to 0.92% and an improvement in cancer Dice score ranging from 4.09% to 6.81% compared to single detailed input models.

Bone tissue, with its complex structure, often necessitates decalcification of the hard tissue for ex vivo morphological studies. The choice of a suitable decalcification method plays a crucial role in preserving desired features and ensuring compatibility with diverse imaging techniques. The search for a universal decalcification method that is suitable for a range of biophotonic analyses remains an ongoing challenge. In this study, we systematically assessed five standard bone decalcification protocols, encompassing strong mineralic acids (3% and 5% nitric acid), a commercially available formulation of hydrochloric and formic acid), as well as weak organic acids (5% trichloroacetic acid and 8% formic acid), and a chelating agent (25% ethylenediamine-tetraacetic acid) with varying decalcification durations, using mouse long bones as our experimental model. Our imaging analysis panel included classical histological staining (Hematoxylin and Eosin, H&E), immunofluorescence staining, and label-free Raman microspectroscopic imaging. We used cryosections instead of paraffin sections since paraffin interferes with tissue Raman signals. This approach is not as commonly used as it is more prone to handling artifacts, but is the preferred method for subsequent Raman analysis. Decalcification efficacy was evaluated based on various qualitative and some quantitative imaging parameters by 2-3 independent observers. Our systematic approach revealed that the chelating agent, when used for 24 h, optimally preserved bone features and, thus, would be the ideal decalcifying agent for comprehensive subsequent analysis. However, the choice of decalcifier and the ideal decalcification duration may vary depending on the type and thickness of bone, necessitating tailored adjustments to meet specific experimental requirements.

PSD95 is an abundant scaffolding protein that assembles multiprotein complexes controlling synaptic physiology and behavior. Confocal microscopy has previously shown that PSD95 is enriched in the postsynaptic terminals of excitatory synapses and two-dimensional (2D) super-resolution microscopy further revealed that it forms nanoclusters. In this study, we utilized three-dimensional (3D) super-resolution microscopy to examine the nanoarchitecture of PSD95 in the mouse brain, characterizing the spatial arrangement of over 8 million molecules. While we were able to identify molecular arrangements that have been previously reported, imaging in 3D allowed us to classify these with higher accuracy. Furthermore, 3D super-resolution microscopy enabled the quantification of protein levels, revealing that an abundance of PSD95 molecules existed outside of synapses as a diffuse population of supercomplexes, containing multiple copies of PSD95. Further analysis of the supercomplexes containing two units identified two populations: one that had PSD95 molecules separated by 39 ± 2 nm, and a second with a separation of 94 ± 27 nm. The finding that there exists supercomplexes containing two PSD95 units outside of the synapse suggests that supercomplexes containing multiple protein copies assemble outside the synapse and then integrate into the synapse to form a supramolecular nanocluster architecture.

Due to the challenges of direct in vivo measurements in humans, previous studies of cochlear tonotopy primarily utilized human cadavers and animal models. This study uses cochlear implant electrodes as a tool for intracochlear recordings of acoustically evoked responses to achieve two primary goals: (1) to map the in vivo tonotopy of the human cochlea, and (2) to assess the impact of sound intensity and the creation of an artificial "third window" on this tonotopic map.

Fifty patients with hearing loss received cochlear implant electrode arrays. Postimplantation, pure-tone acoustic stimuli (0.25 to 4 kHz) were delivered, and electrophysiological responses were recorded from all 22 electrode contacts. The analysis included fast Fourier transformation to determine the amplitude of the first harmonic, indicative of predominantly outer hair cell activity, and tuning curves to identify the best frequency (BF) electrode. These measures, coupled with postoperative imaging for precise electrode localization, facilitated the construction of an in vivo frequency-position function. The study included a specific examination of 2 patients with auditory neuropathy spectrum disorder (ANSD), with preserved cochlear function as assessed by present distortion-product otoacoustic emissions, to determine the impact of sound intensity on the frequency-position map. In addition, the electrophysiological map was recorded in a patient undergoing a translabyrinthine craniotomy for vestibular schwannoma removal, before and after creating an artificial third window, to explore whether an experimental artifact conducted in cadaveric experiments, as was performed in von Békésy landmark experiments, would produce a shift in the frequency-position map.

A significant deviation from the Greenwood model was observed in the electrophysiological frequency-position function, particularly at high-intensity stimulations. In subjects with hearing loss, frequency tuning, and BF location remained consistent across sound intensities. In contrast, ANSD patients exhibited Greenwood-like place coding at low intensities (~40 dB SPL) and a basal shift in BF location at higher intensities (~70 dB SPL or greater). Notably, creating an artificial "third-window" did not alter the frequency-position map.

This study successfully maps in vivo tonotopy of human cochleae with hearing loss, demonstrating a near-octave shift from traditional frequency-position maps. In patients with ANSD, representing more typical cochlear function, intermediate intensity levels (~70 to 80 dB SPL) produced results similar to high-intensity stimulation. These findings highlight the influence of stimulus intensity on the cochlear operational point in subjects with hearing loss. This knowledge could enhance cochlear implant programming and improve auditory rehabilitation by more accurately aligning electrode stimulation with natural cochlear responses.

The visualization of soft tissues, like the meniscus, through X-ray micro-computed tomography (micro-CT), requires the use of contrast agents (CAs). While other studies have investigated CA diffusion in fibrocartilagineous tissues, this work aimed to optimize iodine staining protocols for meniscal tissue that improve their visualization by micro-CT. Specific objectives included evaluating the diffusion of CAs within meniscal samples over time, assessing volume changes due to staining, and identifying the iodine ions absorbed by the tissue.

Water-based and PBS-based Lugol solutions (KI3) were used to stain sheep and pig menisci for 24 days. Samples were scanned using micro-CT at different time points (0, 1, 4, 8, 12, 16, 20, and 24 days) to monitor CA diffusion and volume changes. Micro-CT provided three-dimensional (3D) visualization of iodine distribution and quantification of volume changes and radiodensity in the menisci. Additionally, UV-visible spectroscopy (UV-vis) analyses were performed to determine the uptake of iodine ions by the meniscus.

Results indicated volumetric shrinkage and increased radiodensity within the first days of staining, with diffusion primarily occurring from the periphery of the meniscus. UV-visible spectroscopy identified two iodide ions in the CA solution (I- and I3 -) and revealed a preferential absorption of the triiodide ion (I3 -).

This study demonstrated the utility of iodine-based CAs and micro-CT technique for visualizing and investigating the spatial and temporal iodine diffusion within the meniscal tissue of sheep and pigs. The findings of this study have important implications for using iodine-based CAs in imaging analyses of the meniscus and offer potentially valuable insights into the diffusion patterns of iodine in fibrocartilagineous tissues.

The detection of residual nuclei in decellularized extracellular matrix (dECM) biomaterials is critical for ensuring their quality and biocompatibility. However, current evaluation methods have limitations in addressing impurity interference and providing intelligent analysis. In this study, we utilized four staining techniques-hematoxylin-eosin staining, acetocarmine staining, the Feulgen reaction and 4',6-diamidino-2-phenylindole staining-to detect residual nuclei in dECM biomaterials. Each staining method was quantitatively evaluated across multiple parameters, including area, perimeter and grayscale values, to establish a semi-quantitative scoring system for residual nuclei. These quantitative data were further employed as learning indicators in machine learning models designed to automatically identify residual nuclei. The experimental results demonstrated that no single staining method alone could accurately differentiate between nuclei and impurities. In this study, a semi-quantitative scoring table was developed. With this table, the accuracy of determining whether a single suspicious point is a cell nucleus has reached over 98%. By combining four staining methods, false positives caused by impurity contamination were eliminated. The automatic recognition model trained based on nuclear parameter features reached the optimal index of the model after several iterations of training in 172 epochs. The trained artificial intelligence model achieved a recognition accuracy of over 90% for detecting residual nuclei. The use of multidimensional parameters, integrated with machine learning, significantly improved the accuracy of identifying nuclear residues in dECM slices. This approach provides a more reliable and objective method for evaluating dECM biomaterials, while also increasing detection efficiency.

Developing artificial intelligence (AI) models for digital pathology requires large datasets from multiple sources. However, without careful implementation, AI models risk learning confounding site-specific features in datasets instead of clinically relevant information, leading to overestimated performance, poor generalizability to real-world data, and potential misdiagnosis.

Whole-slide images (WSIs) from The Cancer Genome Atlas (TCGA) colon (COAD), and stomach adenocarcinoma datasets were selected for inclusion in this study. Patch embeddings were obtained using three feature extraction models, followed by ComBat harmonization. Attention-based multiple instance learning models were trained to predict tissue-source site (TSS), as well as clinical and genetic attributes, using raw, Macenko normalized, and Combat-harmonized patch embeddings.

TSS prediction achieved high accuracy (AUROC > 0.95) with all three feature extraction models. ComBat harmonization significantly reduced the AUROC for TSS prediction, with mean AUROCs dropping to approximately 0.5 for most models, indicating successful mitigation of batch effects (e.g., CCL-ResNet50 in TCGA-COAD: Pre-ComBat AUROC = 0.960, Post-ComBat AUROC = 0.506, p < 0.001). Clinical attributes associated with TSS, such as race and treatment response, showed decreased predictability post-harmonization. Notably, the prediction of genetic features like MSI status remained robust after harmonization (e.g., MSI in TCGA-COAD: Pre-ComBat AUROC = 0.667, Post-ComBat AUROC = 0.669, p=0.952), indicating the preservation of true histological signals.

ComBat harmonization of deep learning-derived histology features effectively reduces the risk of AI models learning confounding features in WSIs, ensuring more reliable performance estimates. This approach is promising for the integration of large-scale digital pathology datasets.

In vitro studies with cultured cells are often conducted as an important part of basic research. Adherent cells are typically cultivated in flasks or trays, for which cell staining and subsequent visualization become impractical. We here present a simple step-by-step method for growing adherent cells directly on glass microscope slides, using low-cost equipment readily available in most laboratories. Most parameters such as type of microscope slide (e.g. surface coating), cell seeding concentrations and incubation times can be adjusted according to cell line characteristics and experimental aims, reflecting the methods' flexibility. Through our experiments, microscope slides proved to provide an acceptable surface for cell adhesion and growth of the tested cell lines, as well as being robust and functional with respect to downstream procedures. The method can potentially be combined with different techniques for visualization of experimental effects, such as histological staining methods, fluorescent staining, and immunochemistry. In our method development we have successfully cultivated three different cell lines directly on microscope slides - Atlantic salmon kidney cells (ASK), rainbow trout gill cells (RTgill-W1), and human cancerous lung cells (A549) - and subjected them to various experimental treatments. Finally, as proof-of-concept we provide examples of successful histological staining of the fixed cells. Experimental design in short:•Cultivate cells and calculate cell concentration•Seed a small volume of growth medium with an appropriate number of cells on microscope slide in an area confined by hydrophobic marker•Let cells adhere over night before adding more growth medium or directly conducting experiments and fixing cells for downstream applications.

Most embedding media for live and fixed samples were not designed for microscopy and have issues including long polymerization times, peak of toxicity toward the sample during the sol-gel transition, and irreversibility of this transition. Gels derived from biological sources are widely used in microscopy, but their precise composition is ill-defined and can vary between batches. Non-physiological temperatures and/or specific enzymatic solutions are often needed to revert the gel back to the sol state to allow sample recovery. Recovering the sample undamaged is important for multiple purposes, from the ability to release a living organism back into its environment and re-observe it at a later stage, to interrogating the sample once freed from the gel after imaging. We describe a supramolecular hydrogel that enables the observation of small living organisms using light microscopy, with simple sample recovery through vigorous pipetting with water. The organisms can be recovered alive and capable of further development into adulthood, which represents a significant advancement, as most other matrices require release conditions such as heating, the addition of chemicals, or mechanical disruption, which can damage or kill the embedded organisms. Furthermore, the gel is compatible with super-resolution multi-colour STED nanoscopy.

Histopathological investigation of metastasis in core needle axillary lymph node (ALN) biopsies is crucial for the prognosis and treatment planning of breast cancer patients. Biopsies are typically sliced and evaluated as two-dimensional (2D) images. Biopsy sampling errors and the limited view provided by 2D histology are leading factors contributing to false-negative results in the preoperative detection of metastatic lymph nodes and underestimation of metastatic foci.In this proof-of-concept study, we aim to explore the technical feasibility and the potential capacities of tridimensional (3D) X-ray micro-computed tomography imaging to expedite error detection, enhancement of histopathological accuracy, and precise measurement of metastatic lesion on ALN core needle biopsies of two breast cancer patients. Our self-developed micro-CT protocol uses eosin for the first time, a common histological dye, to enhance 3D architecture of ALNs. Performed analysis on the images of the ALN biopsies involves cancer tissue segmentation, swift biopsy evaluation, and measurement of the metastatic longest diameter and deposit volume.The eosin micro-CT protocol shows potential for an improved tumor deposit estimates, offering additional clinical value compared to standard 2D histology, however, further studies for validating this method are needed.

Necrosis is a common finding in human and animal neoplasms. The percentage of tumor necrosis is included in tumor grading schemes in veterinary oncology; however, evaluation methods are often overlooked. Different studies have assessed the prognostic value of tumor necrosis in feline mammary tumors with contradictory results, which could be related to methodologic variability. In this study, a comprehensive evaluation of tumor necrosis in feline mammary tumors (FMTs) was conducted, by applying a semi-quantitative and a quantitative methodology for assessing necrosis. The interobserver agreement, the relationship with clinicopathological characteristics and the prognostic value of necrosis were analyzed in 154 FMT cases. Although subjectivity in the assessment of necrosis existed, an almost perfect agreement (weighted quadratic k = 0.851) between two observers was obtained. Furthermore, there was a significant positive correlation between the semi-quantitative and quantitative methods. Necrosis was more common and more extensive in malignant tumors than in their benign counterparts. Despite the non-significant results in the survival analysis, extensive necrosis was significantly associated with aggressive clinicopathological features, such as higher histological grade, high mitotic count and lymphovascular invasion. Our results support the potential relevance of necrosis in FMT.

As the spatial resolution of positron emission tomography (PET) scanners improves, understanding of radiotracer distributions in tissues at high resolutions is important. Hence, we propose a method for co-registration of high-resolution ex vivo specimen PET images, combined with computed tomography (CT) images, and the corresponding specimen histopathology.

We applied our co-registration method to breast cancer (BCa) specimens of patients who were preoperatively injected with 0.8 MBq/kg [18 F]fluorodeoxyglucose ([18F]FDG). The method has two components. First, we used an image acquisition scheme that minimises and tracks tissue deformation: (1) We acquired sub-millimetre (micro)-PET-CT images of ±2 mm-thick lamellas of the fresh specimens, enclosed in tissue cassettes. (2) We acquired micro-CT images of the same lamellas after formalin fixation to visualise tissue deformation. (3) We obtained 1 hematoxylin and eosin (H&E) stained histopathology section per lamella of which we captured a digital whole slide image (WSI). Second, we developed an automatic co-registration algorithm to improve the alignment between the micro-PET-CT images and WSIs, guided by the micro-CT of the fixated lamellas. To estimate the spatial co-registration error, we calculated the distance between corresponding microcalcifications in the micro-CTs and WSIs. The co-registered images allowed to study standardised uptake values (SUVs) of different breast tissues, as identified on the WSIs by a pathologist.

We imaged 22 BCa specimens, 13 cases of invasive carcinoma of no special type (NST), 6 of invasive lobular carcinoma (ILC), and 3 of ductal carcinoma in situ (DCIS). While the cassette framework minimised tissue deformation, the best alignment between the micro-PET-CT images and WSIs was achieved after deformable co-registration. We found an overall average co-registration error of 0.74 ± 0.17 mm between the micro-PET images and WSIs. (Pre)malignant tissue (including NST, ILC, and DCIS) generally showed higher SUVs than healthy tissue (including healthy glandular, connective, and adipose tissue). As expected, inflamed tissue and skin also showed high uptake.

We developed a method to co-register micro-PET-CT images of surgical specimens and WSIs with an accuracy comparable to the spatial resolution of the micro-PET images. While currently, we only applied this method to BCa specimens, we believe this method is applicable to a wide range of specimens and radiotracers, providing insight into distributions of (new) radiotracers in human malignancies at a sub-millimetre resolution.

Histopathology is a gold standard for cancer diagnosis. It involves extracting tissue specimens from suspicious areas to prepare a glass slide for a microscopic examination. However, histological tissue processing procedures result in the introduction of artifacts, which are ultimately transferred to the digitized version of glass slides, known as whole slide images (WSIs). Artifacts are diagnostically irrelevant areas and may result in wrong predictions from deep learning (DL) algorithms. Therefore, detecting and excluding artifacts in the computational pathology (CPATH) system is essential for reliable automated diagnosis.

In this paper, we propose a mixture of experts (MoE) scheme for detecting five notable artifacts, including damaged tissue, blur, folded tissue, air bubbles, and histologically irrelevant blood from WSIs. First, we train independent binary DL models as experts to capture particular artifact morphology. Then, we ensemble their predictions using a fusion mechanism. We apply probabilistic thresholding over the final probability distribution to improve the sensitivity of the MoE. We developed four DL pipelines to evaluate computational and performance trade-offs. These include two MoEs and two multiclass models of state-of-the-art deep convolutional neural networks (DCNNs) and vision transformers (ViTs). These DL pipelines are quantitatively and qualitatively evaluated on external and out-of-distribution (OoD) data to assess generalizability and robustness for artifact detection application.

We extensively evaluated the proposed MoE and multiclass models. DCNNs-based MoE and ViTs-based MoE schemes outperformed simpler multiclass models and were tested on datasets from different hospitals and cancer types, where MoE using (MobileNet) DCNNs yielded the best results. The proposed MoE yields 86.15 % F1 and 97.93% sensitivity scores on unseen data, retaining less computational cost for inference than MoE using ViTs. This best performance of MoEs comes with relatively higher computational trade-offs than multiclass models. Furthermore, we apply post-processing to create an artifact segmentation mask, a potential artifact-free RoI map, a quality report, and an artifact-refined WSI for further computational analysis. During the qualitative evaluation, field experts assessed the predictive performance of MoEs over OoD WSIs. They rated artifact detection and artifact-free area preservation, where the highest agreement translated to a Cohen Kappa of 0.82, indicating substantial agreement for the overall diagnostic usability of the DCNN-based MoE scheme.

The proposed artifact detection pipeline will not only ensure reliable CPATH predictions but may also provide quality control. In this work, the best-performing pipeline for artifact detection is MoE with DCNNs. Our detailed experiments show that there is always a trade-off between performance and computational complexity, and no straightforward DL solution equally suits all types of data and applications. The code and HistoArtifacts dataset can be found online at Github and Zenodo , respectively.

Lung cancer is a major cause accounting for cancer-related mortalities, with lung adenocarcinoma (LUAD) being the most prevalent subtype. Given the high clinical and cellular heterogeneities of LUAD, accurate diagnosis and prognosis are crucial to avoid overdiagnosis and overtreatment. Taking full advantage of scRNA-Seq data to resolve the tumor heterogeneities, we explored the overall landscape of LUAD microenvironment. Utilizing the stage-specific tumor cell markers, we have developed highly accurate diagnostic and prognostic models with elevated sensitivity and specificity. The diagnostic model, developed through random forest algorithms with a thirteen-gene signature, achieved an accuracy of 96.4% and an AUC of 0.993. These metrics were further demonstrated by benchmarking with available models and scoring systems in independent cohorts. Concurrently, the prognostic model, formulated via Cox regression with a six-gene signature, effectively predicted overall survival, with elevated risk scores associated with increased fractions of cancer-associated fibroblasts, and higher likelihood of immune escape and T-cell exclusion. Subsequently, two nomograms were developed to predict survival and drug responses, facilitating their integration into clinical practice. Overall, this study underscores the potential of our models for efficient, rapid, and cost-effective diagnosis and prognosis of LUAD, adaptable to multiple expression profiling platforms and quantification methods.

Our purpose is to develop a computer vision approach to quantify intra-arterial thickness on digital pathology images of kidney biopsies as a computational biomarker of arteriosclerosis.

The severity of the arteriosclerosis was scored (0 to 3) in 753 arteries from 33 trichrome-stained whole slide images (WSIs) of kidney biopsies, and the outer contours of the media, intima, and lumen were manually delineated by a renal pathologist. We then developed a multi-class deep learning (DL) framework for segmenting the different intra-arterial compartments (training dataset: 648 arteries from 24 WSIs; testing dataset: 105 arteries from 9 WSIs). Subsequently, we employed radial sampling and made measurements of media and intima thickness as a function of spatially encoded polar coordinates throughout the artery. Pathomic features were extracted from the measurements to collectively describe the arterial wall characteristics. The technique was first validated through numerical analysis of simulated arteries, with systematic deformations applied to study their effect on arterial thickness measurements. We then compared these computationally derived measurements with the pathologists' grading of arteriosclerosis.

Numerical validation shows that our measurement technique adeptly captured the decreasing smoothness in the intima and media thickness as the deformation increases in the simulated arteries. Intra-arterial DL segmentations of media, intima, and lumen achieved Dice scores of 0.84, 0.78, and 0.86, respectively. Several significant associations were identified between arteriosclerosis grade and pathomic features using our technique (e.g., intima-media ratio average [ τ = 0.52 , p < 0.0001 ]) through Kendall's tau analysis.

We developed a computer vision approach to computationally characterize intra-arterial morphology on digital pathology images and demonstrate its feasibility as a potential computational biomarker of arteriosclerosis.

Most bone metastases are caused by primary breast or prostate cancer cells settling in the bone microenvironment, affecting normal bone physiology and function and reducing 5-year survival rates to 10% and 6%, respectively. To expedite clinical availability of novel and effective bone metastases treatments, reliable and predictive in vitro models are urgently required to screen for novel therapies as current in vitro 2D planar mono-culture models do not accurately predict the clinical efficacy. We herein engineered a novel human in vitro 3D co-culture model based on spheroids to study dynamic cellular quantities of (breast or prostate) cancer cells and human bone marrow stromal cells and screen chemotherapeutic efficacy and specificity of the common anticancer drug cisplatin. Bone metastatic spheroids (BMSs) were formed rapidly within 24 h, while the morphology of breast versus prostate cancer BMS differed in terms of size and circularity upon prolonged culture periods. Prestaining cell types prior to BMS formation enabled confocal imaging and quantitative image analysis of in-spheroid cellular dynamics for up to 7 days of BMS culture. We found that cancer cells in BMS proliferated faster and were less susceptible to cisplatin treatment compared to 2D control cultures. Based on these findings and the versatility of our methodology, BMS represent a feasible 3D in vitro model for screening of new bone cancer metastases therapies.

The incorporation of retromolar bone grafts used for alveolar ridge augmentation is not well understood. This prospective observational study aims to supply histomorphometrical data from bone graft biopsies taken at the time of retrieval and after a 3-month healing period using patient-matched biopsies.

In 17 patients, trephine biopsies of the graft were acquired at the time of graft retrieval and after a 3-month healing period. The biopsies were compared histomorphometrically regarding the number of osteocytes, appearance of osteocyte lacunae, quantity, surface area, and activity of the Haversian canals.

All grafts appeared clinically stable after screw removal and 17 implants were placed. Histomorphometric analysis revealed no significant difference in the number of osteocytes (p = .413), osteocyte lacunae (p = .611), the ratio of filled/empty osteocyte lacunae (p = .467) and active Haversian canals (p = .495) between the biopsies retrieved after a 3-months healing period with those at the time of grafting. The only significant difference was noted in the mean surface area of the Haversian canals (p = .002). Specifically, the grafts post 3-month healing showed a significantly larger mean area (0.069 mm2) compared to the time of grafting (0.029 mm2).

This study demonstrates, compared to other data, a high rate of vital structures in retromolar bone block grafts after 3 months of healing, exhibiting the same histological features in comparison to the biopsies from the native alveolar ridge. Standard histomorphometrical parameters, e.g., the amount of filled or empty osteocyte lacunae for the description of the vitality of the graft need to be reappraised.

The problem of artifacts in whole slide image acquisition, prevalent in both clinical workflows and research-oriented settings, necessitates human intervention and re-scanning. Overcoming this challenge requires developing quality control algorithms, that are hindered by the limited availability of relevant annotated data in histopathology. The manual annotation of ground-truth for artifact detection methods is expensive and time-consuming. This work addresses the issue by proposing a method dedicated to augmenting whole slide images with artifacts. The tool seamlessly generates and blends artifacts from an external library to a given histopathology dataset. The augmented datasets are then utilized to train artifact classification methods. The evaluation shows their usefulness in classification of the artifacts, where they show an improvement from 0.10 to 0.01 AUROC depending on the artifact type. The framework, model, weights, and ground-truth annotations are freely released to facilitate open science and reproducible research.

Perineuronal nets (PNN) are specialized extracellular matrix (ECM) components of the central nervous system, frequently accumulating at the surface of inhibitory GABAergic interneurons. While an altered distribution of PNN has been observed in neurological disorders including Alzheimer's disease, schizophrenia and epilepsy, their anatomical distribution also changes during physiological brain maturation and aging. Such an age-dependent shift was experimentally associated also with hippocampal engram formation during brain maturation. Our aim was to histopathologically assess PNN in the hippocampus of adult and pediatric patients with temporal lobe epilepsy (TLE) compared to age-matched post-mortem control subjects and to compare PNN-related changes with memory impairment observed in our patient cohort.

Sixty-six formalin-fixed and paraffin-embedded tissue specimens of the human hippocampus were retrieved from the European Epilepsy Brain Bank. Twenty-nine patients had histopathologically confirmed hippocampal sclerosis (HS), and eleven patients suffered from TLE without HS. PNN were immunohistochemically visualized using an antibody directed against aggrecan and manually counted from hippocampus subfields and the subiculum.

PNN density increased with age in both human controls and TLE patients. However, their density was significantly higher in all HS patients compared to age-matched controls. Intriguingly, TLE patients presented presurgically with better memory when their hippocampal PNN density was higher (p < 0.05).

Our results were compatible with age-dependent ECM specialization in the human hippocampus and its precocious aging in the epileptic condition. These observations confirm recent experimental animal models and also support the notion that PNN play a role in memory formation in the human brain.

"Perineuronal nets" (PNN) are a specialized compartment of the extracellular matrix (ECM), especially surrounding highly active neurons of the mammalian brain. There is evidence that PNN play a role in memory formation, brain maturation, and in some pathologies like Alzheimer's disease, schizophrenia or epilepsy. In this study, we investigated the role of PNN in patients suffering from drug-resistant focal epilepsy compared to controls. We found that with increasing age, more neurons are surrounded by PNN. Similarly, all epilepsy patients but especially patients with better memory performance also had more PNN. This study raises further interest in studying ECM molecules in the human brain under physiological and pathophysiological conditions.

Myocardial infarction (MI) is a life-threatening medical emergency resulting in coronary microvascular dysregulation and heart muscle damage. One of the primary characteristics of MI is capillary loss, which plays a significant role in the progression of this cardiovascular condition. In this study, we utilized optical coherence tomography angiography (OCTA) to image coronary microcirculation in fixed rat hearts, aiming to analyze coronary microvascular impairment post-infarction. Various angiographic metrics are presented to quantify vascular features, including the vessel area density, vessel complexity index, vessel tortuosity index, and flow impairment. Pathological differences identified from OCTA analysis are corroborated with histological analysis. The quantitative assessments reveal a significant decrease in microvascular density in the capillary-sized vessels and an enlargement for the arteriole/venule-sized vessels. Further, microvascular tortuosity and complexity exhibit an increase after myocardial infarction. The results underscore the feasibility of using OCTA to offer qualitative microvascular details and quantitative metrics, providing insights into coronary vascular network remodeling during disease progression and response to therapy.

Advancements in imaging technologies have revolutionized our ability to deeply profile pathological tissue architectures, generating large volumes of imaging data with unparalleled spatial resolution. This type of data collection, namely, spatial proteomics, offers invaluable insights into various human diseases. Simultaneously, computational algorithms have evolved to manage the increasing dimensionality of spatial proteomics inherent in this progress. Numerous imaging-based computational frameworks, such as computational pathology, have been proposed for research and clinical applications. However, the development of these fields demands diverse domain expertise, creating barriers to their integration and further application. This review seeks to bridge this divide by presenting a comprehensive guideline. We consolidate prevailing computational methods and outline a roadmap from image processing to data-driven, statistics-informed biomarker discovery. Additionally, we explore future perspectives as the field moves toward interfacing with other quantitative domains, holding significant promise for precision care in immuno-oncology.

Histological analysis is the core of follicular thyroid carcinoma (FTC) classification. The histopathological criteria of capsular and vascular invasion define malignancy and aggressiveness of FTC. Analysis of multiple sections is cumbersome and as only a minute tissue fraction is analyzed during histopathology, under-sampling remains a problem. Application of an efficient tool for complete tissue imaging in 3D would speed-up diagnosis and increase accuracy. We show that X-ray propagation-based imaging (XPBI) of paraffin-embedded tissue blocks is a valuable complementary method for follicular thyroid carcinoma diagnosis and assessment. It enables a fast, non-destructive and accurate 3D virtual histology of the FTC resection specimen. We demonstrate that XPBI virtual slices can reliably evaluate capsular invasions. Then we discuss the accessible morphological information from XPBI and their significance for vascular invasion diagnosis. We show 3D morphological information that allow to discern vascular invasions. The results are validated by comparing XPBI images with clinically accepted histology slides revised by and under supervision of two experienced endocrine pathologists.

Despite the major use of mice in biomedical research, little information is available with regard to identifying their postmortem changes and using that information to determine the postmortem interval (PMI), defined as the time after death. Both PMI and environmental conditions influence decomposition (autolysis and putrefaction) and other postmortem changes. Severe decomposition compromises lesion interpretation and disease detection and wastes limited pathology resources. The goal of this study was to assess postmortem changes in mice in room temperature cage conditions and under refrigeration at 4 °C to develop gross criteria for the potential value of further gross and histologic evaluation. We used 108 experimentally naïve C57BL/6 mice that were humanely euthanized and then allocated them into 2 experimental groups for evaluation of postmortem change: room temperature (20 to 22 °C) or refrigeration (4 °C). PMI assessments, including gross changes and histologic scoring, were performed at hours 0, 4, 8, and 12 and on days 1 to 14. Factors such as temperature, humidity, ammonia in the cage, and weight change were also documented. Our data indicates that carcasses held at room temperature decomposed faster than refrigerated carcasses. For most tissues, decomposition was evident by 12 h at room temperature as compared with 5 d under refrigeration. At room temperature, gross changes were present by day 2 as compared with day 7 under refrigeration. Mice at room temperature lost 0.78% of their baseline body weight per day as compared with 0.06% for refrigerated mice (95% CI for difference 0.67% to 0.76%, P < 0.0005). This study supports the consideration of temperature and PMI as important factors affecting the suitability of postmortem tissues for gross and histologic evaluation and indicates that storage of carcasses under refrigeration will significantly slow autolysis.

Quality control (QC) is a crucial step to ensure the reliability and accuracy of the data obtained from RNA sequencing experiments, including spatially-resolved transcriptomics (SRT). Existing QC approaches for SRT that have been adopted from single-nucleus RNA sequencing (snRNA-seq) methods are confounded by spatial biology and are inappropriate for SRT data. In addition, no methods currently exist for identifying histological tissue artifacts unique to SRT. Here, we introduce SpotSweeper, spatially-aware QC methods for identifying local outliers and regional artifacts in SRT. SpotSweeper evaluates the quality of individual spots relative to their local neighborhood, thus minimizing bias due to biological heterogeneity, and uses multiscale methods to detect regional artifacts. Using SpotSweeper on publicly available data, we identified a consistent set of Visium barcodes/spots as systematically low quality and demonstrate that SpotSweeper accurately identifies two distinct types of regional artifacts, resulting in improved downstream clustering and marker gene detection for spatial domains.

To use synchrotron radiation phase-contrast imaging (SR-PCI) to visualize and measure the morphology of the entire cochlear scala tympani (ST) and assess cochlear implant (CI) electrode trajectories.

SR-PCI images were used to obtain geometric measurements of the cochlear scalar diameter and area at 5-degree increments in 35 unimplanted and three implanted fixed human cadaveric cochleae.

The cross-sectional diameter and area of the cochlea were found to decrease from the base to the apex. This study represents a wide variability in cochlear morphology and suggests that even in the smallest cochlea, the ST can accommodate a 0.4 mm diameter electrode up to 720°. Additionally, all lateral wall array trajectories were within the anatomically accommodating insertion zone.

This is the first study to use SR-PCI to visualize and quantify the entire ST morphology, from the round window to the apical tip, and assess the post-operative trajectory of electrodes. These high-resolution anatomical measurements can be used to inform the angular insertion depth that can be accommodated in CI patients, accounting for anatomical variability.

N/A. Laryngoscope, 134:2889-2897, 2024.

The automatic registration of differently stained whole slide images (WSIs) is crucial for improving diagnosis and prognosis by fusing complementary information emerging from different visible structures. It is also useful to quickly transfer annotations between consecutive or restained slides, thus significantly reducing the annotation time and associated costs. Nevertheless, the slide preparation is different for each stain and the tissue undergoes complex and large deformations. Therefore, a robust, efficient, and accurate registration method is highly desired by the scientific community and hospitals specializing in digital pathology.

We propose a two-step hybrid method consisting of (i) deep learning- and feature-based initial alignment algorithm, and (ii) intensity-based nonrigid registration using the instance optimization. The proposed method does not require any fine-tuning to a particular dataset and can be used directly for any desired tissue type and stain. The registration time is low, allowing one to perform efficient registration even for large datasets. The method was proposed for the ACROBAT 2023 challenge organized during the MICCAI 2023 conference and scored 1st place. The method is released as open-source software.

The proposed method is evaluated using three open datasets: (i) Automatic Nonrigid Histological Image Registration Dataset (ANHIR), (ii) Automatic Registration of Breast Cancer Tissue Dataset (ACROBAT), and (iii) Hybrid Restained and Consecutive Histological Serial Sections Dataset (HyReCo). The target registration error (TRE) is used as the evaluation metric. We compare the proposed algorithm to other state-of-the-art solutions, showing considerable improvement. Additionally, we perform several ablation studies concerning the resolution used for registration and the initial alignment robustness and stability. The method achieves the most accurate results for the ACROBAT dataset, the cell-level registration accuracy for the restained slides from the HyReCo dataset, and is among the best methods evaluated on the ANHIR dataset.

The article presents an automatic and robust registration method that outperforms other state-of-the-art solutions. The method does not require any fine-tuning to a particular dataset and can be used out-of-the-box for numerous types of microscopic images. The method is incorporated into the DeeperHistReg framework, allowing others to directly use it to register, transform, and save the WSIs at any desired pyramid level (resolution up to 220k x 220k). We provide free access to the software. The results are fully and easily reproducible. The proposed method is a significant contribution to improving the WSI registration quality, thus advancing the field of digital pathology.