Cellular Details Research Articles

Optimized Methyl methacrylate embedding of small and large undecalcified bones.

Methyl methacrylate (MMA) plastic embedding has been long established as a technique for the processing and histological assessment of bones. It provides the added benefit over paraffin in that it does not require decalcification of the tissue in order visualize the cellular detail, thus preserving vital information about the amount of unmineralized osteoid present in addition to the degree of mineralization in the bone. It also allows for the incorporation of dynamic histomorphometric analysis through the retention of fluorescent labels incorporated into the bone. Efficient infiltration of hard tissue is essential to the processing of bones and producing quality slides suitable for achieving usable quantifiable histology out the other end. This technique:• Updates previously published MMA embedding protocols to reflect utilization of stabilized acrylamides (over the unstabilized reagents of the past)• Outlines the techniques that are important for embedding both small (mus), medium (rattus), and large (porcine, lagomorph, human) histological samples.• Updates the clearing and infiltration processes utilized and validates quality of the sample preparation though histological staining to confirm preservation of cellular detail, mineralization information, and enzymatic activity

Unveiling the Power of Model-Agnostic Multiscale Analysis for Enhancing Artificial Intelligence Models in Breast Cancer Histopathology Images.

Developing AI models for digital pathology has traditionally relied on single-scale analysis of histopathology slides. However, a whole slide image is a rich digital representation of the tissue, captured at various magnification levels. Limiting our analysis to a single scale overlooks critical information, spanning from intricate high-resolution cellular details to broad low-resolution tissue structures. In this study, we propose a model-agnostic multiresolution feature aggregation framework tailored for the analysis of histopathology slides in the context of breast cancer, on a multicohort dataset of 2038 patient samples. We have adapted 9 state-of-the-art multiple instance learning models on our multi-scale methodology and evaluated their performance on grade prediction, TP53 mutation status prediction and survival prediction. The results prove the dominance of the multiresolution methodology, and specifically, concatenating or linearly transforming via a learnable layer the feature vectors of image patches from a high (20x) and low (10x) magnification factors achieve improved performance for all prediction tasks across domain-specific and imagenet-based features. On the contrary, the performance of uniresolution baseline models was not consistent across domain-specific and imagenet-based features. Moreover, we shed light on the inherent inconsistencies observed in models trained on whole-tissue-sections when validated against biopsy-based datasets. Despite these challenges, our findings underscore the superiority of multiresolution analysis over uniresolution methods. Finally, cross-scale analysis also benefits the explainability aspects of attention-based architectures, since one can extract attention maps at the tissue- and cell-levels, improving the interpretation of the model's decision.

Label-free spatiotemporal decoding of single-cell fate via acoustic driven 3D tomography

Label-free three-dimensional imaging plays a crucial role in unraveling the complexities of cellular functions and interactions in biomedical research. Conventional single-cell optical tomography techniques offer affordability and the convenience of bypassing laborious cell labelling protocols. However, these methods are encumbered by restricted illumination scanning ranges on abaxial plane, resulting in the loss of intricate cellular imaging details. The ability to fully control cellular rotation across all angles has emerged as an optimal solution for capturing comprehensive structural details of cells. Here, we introduce a label-free, cost-effective, and readily fabricated contactless acoustic-induced vibration system, specifically designed to enable multi-degree-of-freedom rotation of cells, ultimately attaining stable in-situ rotation. Furthermore, by integrating this system with advanced deep learning technologies, we perform 3D reconstruction and morphological analysis on diverse cell types, thus validating groups of high-precision cell identification. Notably, long-term observation of cells reveals distinct features associated with drug-induced apoptosis in both cancerous and normal cells populations. This methodology, based on deep learning-enabled cell 3D reconstruction, charts a novel trajectory for groups of real-time cellular visualization, offering promising advancements in the realms of drug screening and post-single-cell analysis, thereby addressing potential clinical requisites.

A novel 3D imaging approach for quantification of GLUT4 levels across the intact myocardium.

Cellular heterogeneity is a well-accepted feature of tissues, and both transcriptional and metabolic diversity have been revealed by numerous approaches, including optical imaging. However, the high magnification objective lenses needed for high-resolution imaging provides information from only small layers of tissue, which can result in poor cell statistics. There is therefore an unmet need for an imaging modality that can provide detailed molecular and cellular insight within intact tissue samples in 3D. Using GFP-tagged GLUT4 as proof of concept, we present here a novel optical mesoscopy approach that allows precise measurement of the spatial location of GLUT4 within specific anatomical structures across the myocardium in ultrathick sections (5 mm×5 mm×3 mm) of intact mouse heart. We reveal distinct GLUT4 distribution patterns across cardiac walls and highlight specific changes in GLUT4 expression levels in response to high fat diet-feeding, and we identify sex-dependent differences in expression patterns. This method is applicable to any target that can be labelled for light microscopy, and to other complex tissues when organ structure needs to be considered simultaneously with cellular detail.

Bioactive Materials That Promote the Homing of Endogenous Mesenchymal Stem Cells to Improve Wound Healing.

Endogenous stem cell homing refers to the transport of endogenous mesenchymal stem cells (MSCs) to damaged tissue. The paradigm of using well-designed biomaterials to induce resident stem cells to home in to the injured site while coordinating their behavior and function to promote tissue regeneration is known as endogenous regenerative medicine (ERM). ERM is a promising new avenue in regenerative therapy research, and it involves the mobilizing of endogenous stem cells for homing as the principal means through which to achieve it. Comprehending how mesenchymal stem cells home in and grasp the influencing factors of mesenchymal stem cell homing is essential for the understanding and design of tissue engineering. This review summarizes the process of MSC homing, the factors influencing the homing process, analyses endogenous stem cell homing studies of interest in the field of skin tissue repair, explores the integration of endogenous homing promotion strategies with cellular therapies and details tissue engineering strategies that can be used to modulate endogenous homing of stem cells. In addition to providing more systematic theories and ideas for improved materials for endogenous tissue repair, this review provides new perspectives to explore the complex process of tissue remodeling to enhance the rational design of biomaterial scaffolds and guide tissue regeneration strategies.

Impact of Partial Substitution of Soybean Meal by Cottonseed Meal with Iron Sulfate Supplementation in Nile Tilapia Diet

Aquatic meals participate in food security and nutrition. The economic dimension greatly influences the sustainability of the aquaculture industry. This study aimed to assess the effects of partial substitution of soybean meal (SBM) with cottonseed meal (CSM) supplemented with ferrous sulfate (FS) on the growth performance and health status of Nile tilapia. One hundred and fifty Nile tilapia (Oreochromis niloticus) were split equally into five group (triplicated). Each group was fed one of the following isonitrogenous isolipid diet; diet1 (0% CSM), diet2 (20% CSM), Diets3 (20% CSM with 580 mg/kg (FS), diet4 (50% CSM), and diet5 (50% CSM with 580 mg/kg FS) of total protein for two months. In comparison to the control group, Statistically significant (p<0.05) improvements in growth performance, hemogram, and leukogram, as well as, the levels of ALT, AST, urea, creatinine, total protein, MDA, and GSH-px were observed in the third treatment . Histopathologically, it showed the highest values of villus height, width, and surface absorption area with normal architecture and cellular details. On the other hand, fish fed diet4 had the lowest growth rat with microcytic normochromic anemia and decreased villus height and width. The groups that fed CSM showed lower cholesterol levels. In conclusion, CSM inclusion with FS supplementation enhanced growth and health parameters.

Survival outcome prediction of breast carcinomas on whole-slide histopathology images using deep learning.

1070 Background: Breast cancer is the most common cancer among women with 2 million new cases and 627,000 deaths in 2020. Early diagnosis and effective treatment are crucial for improved outcomes. Prognosis mainly depends on histopathological features, among them grade. As whole slide histopathology image (WSI) of tumor tissue contains a huge amount of hidden morphological features unexploited by pathologists, we investigate the potential of Artificial Intelligence (AI)-based analysis on WSI to predict prognosis in terms of 5-year overall survival in breast cancer patients. Methods: We used a novel deep neural network (DNN), DiaDeepBreastPRS, designed specifically for predicting a survival risk score in breast cancer patients based on H&E-stained whole slide images (WSI) of tumor tissues. The model incorporates two distinct viewpoints, one capturing cellular details and the other the tissue-level information. DiaDeepBreastPRS was trained and evaluated on a multi-centric discovery cohort of 1,027 patients (1,095 H&E WSI) from the TCGA-BRCA dataset using a cross-testing and cross-validation technique. It was evaluated on an external cohort, comprising 232 patients (247 H&E WSI). A statistical analysis with a multivariate Cox regression model was carried out on clinico-pathological data. AI scores and the concordance index (c-index) serves as the metric for assessing the performance. The predicted risk scores were used for effective risk stratification of breast carcinomas. Results: On the TCGA-BRCA dataset, the model achieved an average c-index of 67%, which reaches 78% by adding the pTNM stage and age at diagnosis. On the external dataset, the model achieved a c-index of 66% and 75% when including some histopathological prognosis factors (pTNM stage, age at diagnosis, HER2 and HR status and mitosis). The AI score was independently associated with the survival of breast cancer patients with the highest hazard ratio (HR 2.46, p<0.005). Furthermore, the model was able to significantly discriminate between the 2 groups of patients, with a good and a poor prognosis in terms of overall survival (p<0.005). Conclusions: In this study, we showcased that the algorithm was able to instantly extract prognostic morphological features from H&E whole slide images (WSI) and could be included in the pathology report. This could potentially enhance clinical decision-making, elevating the standard of care. Compared to commonly used molecular signatures, the AI algorithm enables a reduction in response time and cost savings. However, further investigations using additional independent cohorts are essential to consolidate the algorithm's performance and allow its generalizability, establish its superiority over existing prognostic markers, and provide insights into its interpretability.

Novel Chromogens for Immunohistochemistry in Spatial Biology.

Spatial relations between tumor cells and host-infiltrating cells are increasingly important in both basic science and clinical research. In this study, we have tested the feasibility of using standard methods of immunohistochemistry (IHC) in a multiplex staining system using a newly developed set of chromogenic substrates for the peroxidase and alkaline phosphatase enzymes. Using this approach, we have developed a set of chromogens characterized by (1) providing fine cellular detail, (2) non-overlapping spectral profiles, (3) an absence of interactions between chromogens, (4) stability when stored, and (5) compatibility with current standard immunohistochemistry practices. When viewed microscopically under brightfield illumination, the chromogens yielded the following colors: red, black, blue, yellow, brown, and green. By selecting compatible color combinations, we have shown feasibility for four-color multiplex staining. Depending on the particular type of analysis being performed, visual analysis, without the aid of computer-assisted image analysis, was sufficient to differentiate up to four different markers.

Abstract LB392: Transformative pathomics in oncology: Harnessing FFPE tissue slide imagery for clinically relevant gene expression prediction in invasive breast carcinoma

Abstract Purpose: Pathologists routinely analyze H&E-stained FFPE tissue slides microscopically to arrive at diagnoses for patients. Pathomics, the study of quantitative imaging from such samples, aims to elevate diagnostic accuracy by revealing intricate tissue and cellular details. Our research evaluates how pathomic features from tissue imagery can predict gene expression, as determined by RNA sequencing, offering advancements in the molecular profiling and targeted therapy for invasive breast carcinoma (IBC). Method: We conducted an analysis of 90 regions of interest (ROIs) on FFPE tissue slide images from the TCGA registry for patients with IBC. Using HistomicsTK software, we extracted nearly 300 pathomic features depicting cell morphometry, intensity, and gradient from these regions of interest (ROIs). We then assessed the intra-correlation of the pathomic features within the ROIs of the same tissue slide to identify the most heterogeneous and highly correlating features (n=20) across categories—morphometry, intensity, and gradient—with a Pearsonʼs Correlation coefficient (r) greater than 0.9 and a FDR adjusted p-value less than 0.05. Subsequently, gene expression data in the form of FPKM were obtained for the corresponding tissues from TCGA. We trained a Multitask Elastic Net (MTEN) model on these pathomic features using an 80:20 split for the training and testing sets, with a three-fold cross-validation applied to the training set utilizing ImaGene software. The model's performance was evaluated by measuring the AUC and R² value for the predicted gene expression on the testing set. Result and Biological significance: The testing of the MTEN model using a testing set predicted the expression of three genes identified in the literature as prognostic markers in breast cancer, namely ALDH1L2, MFAP5, and MXRA8, with an AUC greater than 0.8 and an R² above 0.5 at a p-value of less than 0.002. According to the literature, expression of genes from the ALDH1 superfamily in breast cancer correlates with the stage of the disease, triple-negative status, and response to neoadjuvant therapy. The upregulation of MFAP5 in invasive breast carcinomas has been associated with high risk prognostic features, such as higher tumor grade and stage and increased angiogenesis, and poorer outcomes, such as lymph node metastasis. Furthermore, MXRA8 is involved in modulating the progression of human triple-negative breast cancer, likely through its influence on the interactions of tumor cells with their microenvironment. Conclusion: The present study predicts the expression of clinically relevant genes in IBC using a heterogeneous set of pathomic features extracted from FFPE tissue slide imagery. By linking cell and tissue-based morphometric, intensity, and gradient features with gene expression, researchers can gain insights into the molecular mechanisms underlying disease progression. Digital pathology and pathomics may reduce the need for additional genetic testing in critical patient-cases by providing predictive information from routinely acquired pathology slides. Bridging the gap between phenotypic tissue data and molecular data, the predictive capabilities of pathomic features represent a significant advancement in the field of precision medicine. Citation Format: Shrey S. Sukhadia, Digvijay Yadav, Kristen E. Muller. Transformative pathomics in oncology: Harnessing FFPE tissue slide imagery for clinically relevant gene expression prediction in invasive breast carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB392.

Adipose-derived stem cells can alleviate RHDV2 induced acute liver injury in rabbits

Rabbit hemorrhagic disease virus (RHDV) can cause fatal fulminant hepatitis, which is very similar to human acute liver failure. The aim of this study was to investigate whether adipose-derived stem cells (ADSCs) could alleviate RHDV2-induced liver injury in rabbits. Twenty 50-day-old rabbits were divided randomly into two groups (RHDV2 group, ADSCs + RHDV2 group). Starting from the 1st day, two groups of rabbits were given 0.5 ml of viral suspensions by subcutaneous injection in the neck. Meanwhile, the ADSCs + RHDV2 group was injected with ADSCs cell suspension (1.5 × 107 cells/ml) via a marginal ear vein, and the RHDV2 group was injected with an equal amount of saline via a marginal ear vein. At the end of the 48 h experiment, the animals were euthanized and gross hepatic changes were observed before liver specimens were collected. Histopathological analysis was performed using hematoxylin-eosin (HE), periodic acid schiff (PAS) and Masson's trichrome staining. For RHDV2 affected rabbits, HE staining demonstrated disorganized hepatic cords, loss of cellular detail, and severe cytoplasmic vacuolation within hepatocytes. Glycogen was not observed with PAS staining, and Masson's Trichrome staining showed increased hepatic collagen deposition. For rabbits treated with ADSCs at the time of inoculation, hepatic pathological changes were significantly less severe, liver glycogen synthesis was increased, and collagen fiber deposition was decreased. For RHDV2 affected rabbits, Tunel and immunofluorescence staining showed that the number of apoptotic cells, TGF-β, and MMP-9 protein expression increased. And that in the ADSC treated group there was less hepatocyte apoptosis. In addition, RHDV2 induces liver inflammation and promotes the expression of IL-1β, IL-6, and TNF-α. In rabbits administered ADSCs at time of inoculation, the expression of inflammatory factors in liver tissue decreased significantly. Our experiments show that ADSCs can protect rabbits from liver injury by RHDV2 and reduce the pathological and inflammatory response of liver. However, the specific protective mechanism needs further study.

Continuous Refinement-based Digital Pathology Image Assistance Scheme in Medical Decision-Making Systems.

Digital pathology images' extensive cellular information provide a trustworthy foundation for tumor diagnosis. With the aid of computer-aided diagnostics, pathologists can locate crucial information more quickly. The cascade structure refines the segmentation results by utilizing its multi-task and multi-stage characteristics. However, cascade-based models require downsampling and cropping of patches during the inference process due to the ultra-high resolution and complex structure of pathology images. This not only increases the cost and computation time but also results in the loss of cellular details and corrupts the global contextual information. This study proposes a Digital Pathology Image Assistance Program (CRSDPI) for medical decision-making systems that is based on continuous improvement. After locating the region of interest using the maximum inter-class variance method, the pictures are preprocessed to account for the impacts of staining inconsistencies and sensitivity variations on the model's performance. Ultimately, we create a two-phase continuously refined segmentation network (TCRNet) by combining an enhanced continuous refinement model with a coarse segmentation network built on a pyramid scene parsing network. The coarse segmentation network introduces an auxiliary loss term to speed up convergence, and the refined model introduces an implicit function to reduce computational cost and reconstruct more details. The TCRNet model refines the target by successively aligning the features without the need to take cascading decoder operations after encoder. Experiments conducted on digital pathology images of breast cancer and osteosarcoma demonstrate the superior prediction accuracy and computational speed of our strategy.

An insight into thymidylate synthase inhibitor as anticancer agents: an explicative review.

Cancer, a widespread challenge to global health, remains a puzzle of intricate molecular dynamics. This review article delves into the mystery of cancer, with a keen focus on understanding the contributory role of thymidylate synthase (TS) in cancer. TS, a vital enzyme in DNA synthesis and repair, emerges as a significant player in the narrative of cancer development. The conversion of deoxyuridine monophosphate (dUMP) to deoxythymidine monophosphate (dTMP) is a major step in producing DNA. Numerous malignancies, including those of the breast, colon, lung, and ovary, have been linked to dysregulation of TS activity. Overexpression or mutations of TS lead to uncontrolled cell proliferation and tumorigenesis molecular interactions and signalling pathways involving TS come under scrutiny, revealing the nuanced connections that propel its involvement in cancer progression. Beyond overexpression and mutations, there emerges a subtle layer of regulation that involves microRNAs (miRNAs). These tiny particles attach to the TS messenger RNA, causing translational repression or its degradation, which in turn affects TS activity. Moving towards the therapeutic realm, thymidylate synthase inhibition acts as a promising anti-cancer strategy. Targeting TS with small-molecule inhibitors could provide a novel approach to treat various cancers. By reducing the number of available nucleotides, TS inhibition would slow down or halt cancer cell division, thus depriving the tumor of the building blocks required for its proliferation and growth. The aim is to assess the viability and effectiveness of targeting TS to halt or slow down cancer progression. There is growing evidence that, in comparison to traditional TS inhibitors, few novel antifolate TS inhibitors are effective against a wider variety of neoplasms, such as lung carcinomas. It has been discovered that TS inhibitors increase cancer tissues' sensitivity to chemotherapy and radiation, increasing their vulnerability to these treatments. This article aims to provide a comprehensive insight into TS, examining its cellular details, detailing the heterocyclic moieties and molecular foundations, and providing a promising future outlook.

Quantitative assessment of H&E staining for pathology: development and clinical evaluation of a novel system

BackgroundStaining tissue samples to visualise cellular detail and tissue structure is at the core of pathology diagnosis, but variations in staining can result in significantly different appearances of the tissue sample. While the human visual system is adept at compensating for stain variation, with the growth of digital imaging in pathology, the impact of this variation can be more profound. Despite the ubiquity of haematoxylin and eosin staining in clinical practice worldwide, objective quantification is not yet available. We propose a method for quantitative haematoxylin and eosin stain assessment to facilitate quality assurance of histopathology staining, enabling truly quantitative quality control and improved standardisation.MethodsThe stain quantification method comprises conventional microscope slides with a stain-responsive biopolymer film affixed to one side, called stain assessment slides. The stain assessment slides were characterised with haematoxylin and eosin, and implemented in one clinical laboratory to quantify variation levels.ResultsStain assessment slide stain uptake increased linearly with duration of haematoxylin and eosin staining (r = 0.99), and demonstrated linearly comparable staining to samples of human liver tissue (r values 0.98–0.99). Laboratory implementation of this technique quantified intra- and inter-instrument variation of staining instruments at one point in time and across a five-day period.ConclusionThe proposed method has been shown to reliably quantify stain uptake, providing an effective laboratory quality control method for stain variation. This is especially important for whole slide imaging and the future development of artificial intelligence in digital pathology.

PathoGraph: An Attention-Based Graph Neural Network Capable of Prognostication Based on CD276 Labelling of Malignant Glioma Cells.

Computerized methods have been developed that allow quantitative morphological analyses of whole slide images (WSIs), e.g., of immunohistochemical stains. The latter are attractive because they can provide high-resolution data on the distribution of proteins in tissue. However, many immunohistochemical results are complex because the protein of interest occurs in multiple locations (in different cells and also extracellularly). We have recently established an artificial intelligence framework, PathoFusion which utilises a bifocal convolutional neural network (BCNN) model for detecting and counting arbitrarily definable morphological structures. We have now complemented this model by adding an attention-based graph neural network (abGCN) for the advanced analysis and automated interpretation of such data. Classical convolutional neural network (CNN) models suffer from limitations when handling global information. In contrast, our abGCN is capable of creating a graph representation of cellular detail from entire WSIs. This abGCN method combines attention learning with visualisation techniques that pinpoint the location of informative cells and highlight cell-cell interactions. We have analysed cellular labelling for CD276, a protein of great interest in cancer immunology and a potential marker of malignant glioma cells/putative glioma stem cells (GSCs). We are especially interested in the relationship between CD276 expression and prognosis. The graphs permit predicting individual patient survival on the basis of GSC community features. Our experiments lay a foundation for the use of the BCNN-abGCN tool chain in automated diagnostic prognostication using immunohistochemically labelled histological slides, but the method is essentially generic and potentially a widely usable tool in medical research and AI based healthcare applications.

Comparison of liquid based cytology and conventional smears on lymph node aspirates: A cytomorphological study.

In an era of minimally invasive and rapid diagnostic technologies, fine-needle aspiration cytology (FNAC) is most useful when it comes to patients with lymphadenopathies especially of the cervical region. Liquid-based cytology (LBC) is an alternative processing method which is used for both gynecological and non-gynecological samples. Because of the remarkable advantages of LBC smears in gynecological samples, nowadays, many studies have been done to assess its utility in various other lesions. Hereby, with the help of this study, we would like to evaluate the efficiency of LBC smears in comparison to conventional FNAC smears conventional smears (CS) on lymph node aspirates. A retrospective study was done over a 1-year period in which 253 cases of lymph node aspirates were included in the study. The slides were prepared using standard conventional and LBC techniques and compared for adequacy, cellularity, cell architecture, necrosis, background debris, presence of cells in monolayer sheets, and nuclear/cytoplasmic details. Of the total 253 cases, 171 (67.6%) were and 67 (26.5%) were diagnosed as non-neoplastic and malignant, respectively. Although the LBC smears were useful in the diagnosis of malignant cases, they did pose some challenges especially in the non-neoplastic lymph node aspirates due to loss of the background necrosis. In addition, the cellular yield in LBC smears was low in comparison to CS. LBC smears from lymph node aspirates results in better diagnostic accuracy for malignant cases due to better cellular and nuclear details. However, for non-neoplastic etiology, it should not be considered better than CS as loss of the background necrosis and inflammation may result in an incorrect diagnosis.

Searching new targets for the control of Black Rot: following the role of host factors modulating the infection process of Phyllosticta ampelicida.

Black Rot is a grapevine disease caused by the ascomycete Phyllosticta ampelicida. Neglected so far, this is developing into a pertinent problem in organic viticulture as resistant varieties are still lacking. Here, we follow cellular details of the infection process in the susceptible vinifera variety Müller-Thurgau and screen the ancestral European wild grapevine (V. vinifera sylvestris) for resistance to Black Rot. Using a standardized infection assay, we follow fungal development using LTSEM and quantify key stages on different hosts using fluorescence microscopy. There is considerable variation in susceptibility, which is associated with more rapid leaf maturation. Hyphal growth on different carbon sources shows a preference for pectins over starch, cellulose or xylans. In the resistant sylvestris genotypes Ketsch 16 and Ketsch 18 we find that neither spore attachment nor appressorium formation, but hyphal elongation is significantly inhibited as compared to Müller-Thurgau. Moreover, defence-related oxidative burst and accumulation of phenolic compounds is stimulated in the resistant genotypes. We arrive at a model, where more rapid maturation of the cell wall in these sylvestris genotypes sequesters pectins as major food source and thus block hyphal elongation. This paves the way for introgression of genetic factors responsible for cell wall maturation into V. vinifera to develop Black Rot-resistant varieties of grapevine.

Automated HER2 Scoring in Breast Cancer Images Using Deep Learning and Pyramid Sampling.

Objective and Impact Statement: Human epidermal growth factor receptor 2 (HER2) is a critical protein in cancer cell growth that signifies the aggressiveness of breast cancer (BC) and helps predict its prognosis. Here, we introduce a deep learning-based approach utilizing pyramid sampling for the automated classification of HER2 status in immunohistochemically (IHC) stained BC tissue images. Introduction: Accurate assessment of IHC-stained tissue slides for HER2 expression levels is essential for both treatment guidance and understanding of cancer mechanisms. Nevertheless, the traditional workflow of manual examination by board-certified pathologists encounters challenges, including inter- and intra-observer inconsistency and extended turnaround times. Methods: Our deep learning-based method analyzes morphological features at various spatial scales, efficiently managing the computational load and facilitating a detailed examination of cellular and larger-scale tissue-level details. Results: This approach addresses the tissue heterogeneity of HER2 expression by providing a comprehensive view, leading to a blind testing classification accuracy of 84.70%, on a dataset of 523 core images from tissue microarrays. Conclusion: This automated system, proving reliable as an adjunct pathology tool, has the potential to enhance diagnostic precision and evaluation speed, and might substantially impact cancer treatment planning.

Latent inter-organ mechanism of idiopathic pulmonary fibrosis unveiled by a generative computational approach

Idiopathic pulmonary fibrosis (IPF) is a chronic and progressive disease characterized by complex lung pathogenesis affecting approximately three million people worldwide. While the molecular and cellular details of the IPF mechanism is emerging, our current understanding is centered around the lung itself. On the other hand, many human diseases are the products of complex multi-organ interactions. Hence, we postulate that a dysfunctional crosstalk of the lung with other organs plays a causative role in the onset, progression and/or complications of IPF. In this study, we employed a generative computational approach to identify such inter-organ mechanism of IPF. This approach found unexpected molecular relatedness of IPF to neoplasm, diabetes, Alzheimer’s disease, obesity, atherosclerosis, and arteriosclerosis. Furthermore, as a potential mechanism underlying this relatedness, we uncovered a putative molecular crosstalk system across the lung and the liver. In this inter-organ system, a secreted protein, kininogen 1, from hepatocytes in the liver interacts with its receptor, bradykinin receptor B1 in the lung. This ligand–receptor interaction across the liver and the lung leads to the activation of calmodulin pathways in the lung, leading to the activation of interleukin 6 and phosphoenolpyruvate carboxykinase 1 pathway across these organs. Importantly, we retrospectively identified several pre-clinical and clinical evidence supporting this inter-organ mechanism of IPF. In conclusion, such feedforward and feedback loop system across the lung and the liver provides a unique opportunity for the development of the treatment and/or diagnosis of IPF. Furthermore, the result illustrates a generative computational framework for machine-mediated synthesis of mechanisms that facilitates and complements the traditional experimental approaches in biomedical sciences.

Estimation of postmortem interval using histological and oxidative biomarkers in human bone marrow.

In forensic medicine, estimating the postmortem interval (PMI) is of great importance for the timeline and the reconstruction of the events surrounding death. Bone marrow (BM) is one of the largest organs in the body, with good resistance to autolysis and contamination. Therefore, the present study aims to correlate different postmortem intervals and bone marrow antioxidant enzyme levels using an Enzyme-linked immunosorbent assay (ELISA). In addition, detection of the changes in the histological structure of human bone marrow in relation to the time passed since death. BM samples from 20 forensic autopsy cadavers were obtained from cases referred to the Department of Forensic Medicine in the Ministry of Justice, Dakahlia Governorate, processed for histopathological examination as well as estimation of reduced glutathione (GSH), glutathione peroxidase (GPX), and glutathione reductase (GRX) using ELISA. Results of ELISA analysis showed a significant decrease in the level of antioxidant enzymes with increasing PMI; regarding histopathological examination, from 6 to > 18h PMI, the changes in morphology after death were gradual, progressive, and regular, indicating great value in PMI determination. Also, 18h of PMI showed loss of cellular details, absence of fat cells, and necrosis of BM with the nucleus dispersed as eosinophilic debris. Estimation of antioxidant enzymes level in human bone marrow using ELISA and detection of the changes in the histological structure of human bone marrow in relation to time passed since the death, either separately or in combination, can be used to estimate PMI accurately.

Identification of Surface Markers and Functional Characterization of Myeloid Derived Suppressor Cell-Like Adherent Cells.

Myeloid-derived suppressor cell (MDSC)-like adherent cells (MLACs) are a recently identified CD11b+ F4/80- myeloid cell subset that can infiltrate tumors early in development and promote their growth. Because of these functions, MLACs play an important role in establishing an immunosuppressive tumor microenvironment (TME). However, the lack of MLAC-specific markers has hampered further characterization of this cell type. This study identifies the gene signature of MLACs by analyzing RNA-sequencing (RNA-seq) and public single-cell RNA-seq data, revealing that MLACs are an independent cell population that are distinct from other intratumoral myeloid cells. After combining proteome analysis of membrane proteins with RNA-seq data, H2-Ab1 and CD11c are indicated as marker proteins that can support the isolation of MLAC subsets from CD11b+ F4/80- myeloid cells by fluorescence-activated cell sorting. The CD11b+ F4/80- H2-Ab1+ and CD11b+ F4/80- CD11c+ MLAC subsets represent approximately half of the MLAC population that is isolated based on their adhesion properties and possess gene signatures and functional properties similar to those of the MLAC population. Additionally, membrane proteome analysis suggests that MLACs express highly heterogeneous surface proteins. This study facilitates an integrated understanding of heterogeneous intratumoral myeloid cells, as well as the molecular and cellular details of the development of an immunosuppressive TME.