Identification of the role of DAB2 and CXCL8 in uterine spiral artery remodeling in early-onset preeclampsia.

Aberrant remodeling of uterine spiral arteries (SPA) is strongly associated with the pathogenesis of early-onset preeclampsia (EOPE). However, the complexities of SPA transformation remain inadequately understood. We conducted a single-cell RNA sequencing analysis of whole placental tissues derived from patients with EOPE and their corresponding controls, identified DAB2 as a key gene of interest and explored the mechanism underlying the communication between Extravillous trophoblast cells (EVTs) and decidual vascular smooth muscle cells (dVSMC) through cell models and a placenta-decidua coculture (PDC) model in vitro. DAB2 enhanced the motility and viability of HTR-8/SVneo cells. After exposure to conditioned medium (CM) from HTR-8/SVneoshNC cells, hVSMCs exhibited a rounded morphology, indicative of dedifferentiation, while CM-HTR-8/SVneoshDAB2 cells displayed a spindle-like morphology. Furthermore, the PDC model demonstrated that CM-HTR-8/SVneoshDAB2 was less conducive to vascular remodeling. Further in-depth mechanistic investigations revealed that C-X-C motif chemokine ligand 8 (CXCL8, also known as IL8) is a pivotal regulator governing the dedifferentiation of dVSMC. DAB2 expression in EVTs is critical for orchestrating the phenotypic transition and motility of dVSMC. These processes may be intricately linked to the CXCL8/PI3K/AKT pathway, underscoring its central role in intricate SPA remodeling.

Teacher-student collaborated multiple instance learning for pan-cancer PDL1 expression prediction from histopathology slides.

Programmed cell death ligand 1 (PDL1), as an important biomarker, is quantified by immunohistochemistry (IHC) with few established histopathological patterns. Deep learning aids in histopathological assessment, yet heterogeneity and lacking spatially resolved annotations challenge precise analysis. Here, we present a weakly supervised learning approach using bulk RNA sequencing for PDL1 expression prediction from hematoxylin and eosin (H&E) slides. Our method extends the multiple instance learning paradigm with the teacher-student framework, which assigns dynamic pseudo-labels for intra-slide heterogeneity and retrieves unlabeled instances using temporal ensemble model distillation. The approach, evaluated on 12,299 slides across 20 solid tumor types, achieves a weighted average area under the curve of 0.83 on fresh-frozen and 0.74 on formalin-fixed specimens for 9 tumors with PDL1 as an established biomarker. Our method predicts PDL1 expression patterns, validated by IHC on 20 slides, offering insights into histologies relevant to PDL1. This demonstrates the potential of deep learning in identifying diverse histological patterns for molecular changes from H&E images.

Transcutaneous carbon dioxide suppresses skeletal muscle atrophy in a mouse model of oral squamous cell carcinoma.

Cancer cachexia causes skeletal muscle atrophy, impacting the treatment and prognosis of patients with advanced cancer, but no treatment has yet been established to control cancer cachexia. We demonstrated that transcutaneous application of carbon dioxide (CO2) could improve local blood flow and reduce skeletal muscle atrophy in a fracture model. However, the effects of transcutaneous application of CO2 in cancer-bearing conditions are not yet known. In this study, we calculated fat-free body mass (FFM), defined as the skeletal muscle mass, and evaluated the expression of muscle atrophy markers and uncoupling protein markers as well as the cross-sectional area (CSA) to investigate whether transcutaneous application of CO2 to skeletal muscle could suppress skeletal muscle atrophy in cancer-bearing mice. Human oral squamous cell carcinoma was transplanted subcutaneously into the upper dorsal region of nude mice, and 1 week later, CO2 gas was applied to the legs twice a week for 4 weeks and FFM was calculated by bioimpedance spectroscopy. After the experiment concluded, the quadriceps were extracted, and muscle atrophy markers (muscle atrophy F-box protein (MAFbx), muscle RING-finger protein 1 (MuRF-1)) and uncoupling protein markers (uncoupling protein 2 (UCP2) and uncoupling protein 3 (UCP3)) were evaluated by real-time polymerase chain reaction and immunohistochemical staining, and CSA by hematoxylin and eosin staining. The CO2-treated group exhibited significant mRNA and protein expression inhibition of the four markers. Furthermore, immunohistochemical staining showed decreased MAFbx, MuRF-1, UCP2, and UCP3 in the CO2-treated group. In fact, the CSA in hematoxylin and eosin staining and the FFM revealed significant suppression of skeletal muscle atrophy in the CO2-treated group. We suggest that transcutaneous application of CO2 to skeletal muscle suppresses skeletal muscle atrophy in a mouse model of oral squamous cell carcinoma.

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

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

DeepDOF-SE: affordable deep-learning microscopy platform for slide-free histology.

Histopathology plays a critical role in the diagnosis and surgical management of cancer. However, access to histopathology services, especially frozen section pathology during surgery, is limited in resource-constrained settings because preparing slides from resected tissue is time-consuming, labor-intensive, and requires expensive infrastructure. Here, we report a deep-learning-enabled microscope, named DeepDOF-SE, to rapidly scan intact tissue at cellular resolution without the need for physical sectioning. Three key features jointly make DeepDOF-SE practical. First, tissue specimens are stained directly with inexpensive vital fluorescent dyes and optically sectioned with ultra-violet excitation that localizes fluorescent emission to a thin surface layer. Second, a deep-learning algorithm extends the depth-of-field, allowing rapid acquisition of in-focus images from large areas of tissue even when the tissue surface is highly irregular. Finally, a semi-supervised generative adversarial network virtually stains DeepDOF-SE fluorescence images with hematoxylin-and-eosin appearance, facilitating image interpretation by pathologists without significant additional training. We developed the DeepDOF-SE platform using a data-driven approach and validated its performance by imaging surgical resections of suspected oral tumors. Our results show that DeepDOF-SE provides histological information of diagnostic importance, offering a rapid and affordable slide-free histology platform for intraoperative tumor margin assessment and in low-resource settings.

The Effects of Different Storage Conditions on Leukocytes in Human Breast Milk.

Objectives: Breast milk is the best baby food because it contains various nutrients and important factors for the baby's immune system, including leukocytes. This study aimed to determine the effects on morphology, number of cells and breast milk leukocytes count of various ways of storing breast milk based on different temperatures and storage durations. Methods: This study was conducted at the Biochemistry Laboratory, Integrated Laboratory and the Histology Laboratory, Universitas Indonesia, Jakarta, Indonesia from September 2022 to February 2023. Transitional breast milk samples from 7 breastfeeding mothers were utilised in the study. A total of 50 mL was divided into 4 tubes of 12.5 mL each and treated based on temperature, storage time and method of thawing frozen breast milk based on the Centers for Disease Control and Prevention's (CDC) recommendations for breast milk storage. The breast milk cells were isolated to calculate the cell number and leukocyte population. Subsequently, the breast milk cells were stained with haematoxylin and eosin to analyse the number and morphology of leukocytes. Results: The findings showed a significant decrease in the breast milk's total number and population and changes in the morphology of breast milk leukocytes after storage. Conclusion: This study indicates that CDC storage recommendations do not affect the quantity of the CD45+ leukocyte population; however, there is a decrease in the total number of leukocytes and alterations in their microscopic morphology. Thus, additional research is recommended to determine whether these modifications influence the function of the breast milk cells.

[Effect of recombinant human thrombopoietin on platelets in rats with D-galactosamine salt-induced acute liver failure].

OBJECTIVE: To evaluate the effects of recombinant human thrombopoietin (rhTPO) on platelet count (PLT) and liver function in acute liver failure (ALF) rats by observing the dynamic changes of PLT, thrombopoietin (TPO) and liver function during ALF. METHODS: Twenty-four male Sprague-Dawley (SD) rats were divided into model group, TPO group and interleukin-11 (IL-11) group using a random number table method, with eight rats in each group. All rats were intraperitoneally injected with D-galactosamine (D-GalN, 1 500 mg/kg, dosed within 72 hours) to induce the ALF model. After modeling, rats in TPO group was received subcutaneous injection of 15 µg/kg of rhTPO for 5 days, and rats in IL-11 group was received subcutaneous injection of 0.45 mg/kg of IL-11 for 5 days. Venous blood samples were collected before and at 1, 3, 5, 7 and 12 days after molding for whole blood cell detection. The level of TPO in serum was detected by enzyme-linked immunosorbent assay (ELISA). Liver function indexes including serum alanine aminotransferase (ALT), aspartate aminotransferase (AST), total bilirubin (TBil) and albumin (ALB) were measured before and at 1, 3 and 5 days after modeling. The rats were sacrificed 12 days after the modeling, and the pathological changes of liver tissue were observed by hematoxylin-eosin (HE) staining. RESULTS: Two rats in each group died within 24-48 hours after modeling. HE staining showed that all three groups of ALF rats showed large flake necrosis of hepatocytes, disorder of hepatic lobular structure, mesh scaffold collapse, hepatic sinus congestion and hemorrhage, and flake infiltration of inflammatory cells on day 12 after modeling. The levels of serum ALT, AST and TBil of rats in each group were significantly increased 1 day after modeling and then decreased. The level of ALB decreased significantly on the first day after modeling and then increased, but there was no significant difference in the trend of liver function indexes among the three groups. PLT in the three groups decreased rapidly on day 1 after modeling, and then recovered gradually with the improvement of liver function. The PLT of the TPO group rose to the peak value 7 days after molding and was significantly higher than that of the model group [PLT (×109/L): 1 673.3±347.5 vs. 855.3±447.0, P < 0.05], while there was no significant difference between the IL-11 group and the model group [PLT (×109/L): 1 350.3±386.6 vs. 855.3±447.0, P > 0.05]. The level of serum TPO of the three groups increased significantly on day 1 after modeling, then decreased, and dropped to the lowest value on day 5, but there was no significant difference in the trend of serum TPO level among the three groups. CONCLUSIONS: PLT in ALF rats decreased rapidly in the early stage and recovered gradually with the improvement of liver function, and the serum TPO level increased first and then decreased. Injection of rhTPO can significantly increase PLT in ALF rats, but has no significant effect on liver function and survival rate.

Construction and validation of artificial intelligence pathomics models for predicting pathological staging in colorectal cancer: Using multimodal data and clinical variables.

OBJECTIVE: This retrospective observational study aims to develop and validate artificial intelligence (AI) pathomics models based on pathological Hematoxylin-Eosin (HE) slides and pathological immunohistochemistry (Ki67) slides for predicting the pathological staging of colorectal cancer. The goal is to enable AI-assisted accurate pathological staging, supporting healthcare professionals in making efficient and precise staging assessments. METHODS: This study included a total of 267 colorectal cancer patients (training cohort: n = 213; testing cohort: n = 54). Logistic regression algorithms were used to construct the models. The HE image features were used to build the HE model, the Ki67 image features were used for the Ki67 model, and the combined model included features from both the HE and Ki67 images, as well as tumor markers (CEA, CA724, CA125, and CA242). The predictive results of the HE model, Ki67 model, and tumor markers were visualized through a nomogram. The models were evaluated using ROC curve analysis, and their clinical value was estimated using decision curve analysis (DCA). RESULTS: A total of 260 deep learning features were extracted from HE or Ki67 images. The AUC for the HE model and Ki67 model in the training cohort was 0.885 and 0.890, and in the testing cohort, it was 0.703 and 0.767, respectively. The combined model and nomogram in the training cohort had AUC values of 0.907 and 0.926, and in the testing cohort, they had AUC values of 0.814 and 0.817. In clinical DCA, the net benefit of the Ki67 model was superior to the HE model. The combined model and nomogram showed significantly higher net benefits compared to the individual HE model or Ki67 model. CONCLUSION: The combined model and nomogram, which integrate pathomics multi-modal data and clinical-pathological variables, demonstrated superior performance in distinguishing between Stage I-II and Stage III colorectal cancer. This provides valuable support for clinical decision-making and may improve treatment strategies and patient prognosis. Furthermore, the use of immunohistochemistry (Ki67) slides for pathomics modeling outperformed HE slide, offering new insights for future pathomics research.

CellViT: Vision Transformers for precise cell segmentation and classification.

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

Development and validation of a Radiopathomics model based on CT scans and whole slide images for discriminating between Stage I-II and Stage III gastric cancer.

OBJECTIVE: This study aimed to develop and validate an artificial intelligence radiopathological model using preoperative CT scans and postoperative hematoxylin and eosin (HE) stained slides to predict the pathological staging of gastric cancer (stage I-II and stage III). METHODS: This study included a total of 202 gastric cancer patients with confirmed pathological staging (training cohort: n = 141; validation cohort: n = 61). Pathological histological features were extracted from HE slides, and pathological models were constructed using logistic regression (LR), support vector machine (SVM), and NaiveBayes. The optimal pathological model was selected through receiver operating characteristic (ROC) curve analysis. Machine learnin algorithms were employed to construct radiomic models and radiopathological models using the optimal pathological model. Model performance was evaluated using ROC curve analysis, and clinical utility was estimated using decision curve analysis (DCA). RESULTS: A total of 311 pathological histological features were extracted from the HE images, including 101 Term Frequency-Inverse Document Frequency (TF-IDF) features and 210 deep learning features. A pathological model was constructed using 19 selected pathological features through dimension reduction, with the SVM model demonstrating superior predictive performance (AUC, training cohort: 0.949; validation cohort: 0.777). Radiomic features were constructed using 6 selected features from 1834 radiomic features extracted from CT scans via SVM machine algorithm. Simultaneously, a radiopathomics model was built using 17 non-zero coefficient features obtained through dimension reduction from a total of 2145 features (combining both radiomics and pathomics features). The best discriminative ability was observed in the SVM_radiopathomics model (AUC, training cohort: 0.953; validation cohort: 0.851), and clinical decision curve analysis (DCA) demonstrated excellent clinical utility. CONCLUSION: The radiopathomics model, combining pathological and radiomic features, exhibited superior performance in distinguishing between stage I-II and stage III gastric cancer. This study is based on the prediction of pathological staging using pathological tissue slides from surgical specimens after gastric cancer curative surgery and preoperative CT images, highlighting the feasibility of conducting research on pathological staging using pathological slides and CT images.

Pregnancy-Specific Beta-1-Glycoprotein 1 Increases HTR-8/SVneo Cell Migration through the Orai1/Akt Signaling Pathway.

The impaired invasion ability of trophoblast cells is related to the occurrence of preeclampsia (PE). We previously found that pregnancy-specific beta-1-glycoprotein 1 (PSG1) levels were decreased in the serum of individuals with early-onset preeclampsia (EOPE). This study investigated the effect of PSG1 on Orai1-mediated store-operated calcium entry (SOCE) and the Akt signaling pathway in human trophoblast cell migration. An enzyme-linked immunosorbent assay (ELISA) was used to determine the level of PSG1 in the serum of pregnant women with EOPE. The effects of PSG1 on trophoblast proliferation and migration were examined using cell counting kit-8 (CCK8) and wound healing experiments, respectively. The expression levels of Orai1, Akt, and phosphorylated Akt (p-Akt) were determined through Western blotting. The results confirmed that the serum PSG1 levels were lower in EOPE women than in healthy pregnant women. The PSG1 treatment upregulated the protein expression of Orai1 and p-Akt. The selective inhibitor of Orai1 (MRS1845) weakened the migration-promoting effect mediated by PSG1 via suppressing the Akt signaling pathway. Our findings revealed one of the mechanisms possibly involved in EOPE pathophysiology, which was that downregulated PSG1 may reduce the Orai1/Akt signaling pathway, thereby inhibiting trophoblast migration. PSG1 may serve as a potential target for the treatment and diagnosis of EOPE.

Evaluation of serum levels of soluble vascular endothelial (sVE)-Cadherin in early- and late-onset preeclampsia serum sVE-Cadherin levels in preeclampsia.

OBJECTIVE: Endothelial dysfunction is a major feature of preeclampsia. sVE-cadherin plays a role in the preservation and regulation of the endothelial barrier. For that reason, to evaluation of sVE-cadherin may help elucidate the disease pathophysiology of preeclampsia. METHODS: The sample size was calculated as a minimum of 46 pregnant women for each group based on serum sVE-Cadherin levels in a pilot study of 10 preeclamptic and 10 control groups. Hundred-twenty pregnancies complicated with early-onset (n = 60) and late-onset (n = 60) preeclampsia were compared with 120 gestational-age (GA)-matched (±1 week) uncomplicated pregnancies. The venous blood sampling was performed upon preeclampsia diagnosis prior to the onset of the labor in the preeclampsia group and the matching (±1 week) pregnancy week in the control group. Demographic and biochemical parameters were evaluated. RESULTS: Mean serum sVE-Cadherin was significantly higher in women with EOPE compared to that of the GA-matched control group (5.86 ± 1.57 ng/mL vs. 2.28 ± 0.80 ng/mL, p < 0.001), in women with LOPE compared to that of the GA-matched control group (3.11 ± 0.97 ng/mL vs. 1.69 ± 0.87 ng/mL, p < 0.001), and in women with EOPE compared to that of LOPE group (5.86 ± 1.57 ng/mL vs. 3.11 ± 0.97 ng/mL, p < 0.001) after correction for GA. Serum sVE-Cadherin positively correlated with systolic and diastolic blood pressure and a negative correlation with gestational age at sampling. CONCLUSION: The serum level of sVE-Cadherin was higher in women with preeclampsia than that of GA-matched healthy pregnant women, in women with EOPE compared to that of LOPE. sVE-Cadherin is an important marker in early-onset pre-eclampsia with severe clinical findings.

Open-top Bessel beam two-photon light sheet microscopy for three-dimensional pathology.

Nondestructive pathology based on three-dimensional (3D) optical microscopy holds promise as a complement to traditional destructive hematoxylin and eosin (H&E) stained slide-based pathology by providing cellular information in high throughput manner. However, conventional techniques provided superficial information only due to shallow imaging depths. Herein, we developed open-top two-photon light sheet microscopy (OT-TP-LSM) for intraoperative 3D pathology. An extended depth of field two-photon excitation light sheet was generated by scanning a nondiffractive Bessel beam, and selective planar imaging was conducted with cameras at 400 frames/s max during the lateral translation of tissue specimens. Intrinsic second harmonic generation was collected for additional extracellular matrix (ECM) visualization. OT-TP-LSM was tested in various human cancer specimens including skin, pancreas, and prostate. High imaging depths were achieved owing to long excitation wavelengths and long wavelength fluorophores. 3D visualization of both cells and ECM enhanced the ability of cancer detection. Furthermore, an unsupervised deep learning network was employed for the style transfer of OT-TP-LSM images to virtual H&E images. The virtual H&E images exhibited comparable histological characteristics to real ones. OT-TP-LSM may have the potential for histopathological examination in surgical and biopsy applications by rapidly providing 3D information.

Virtual formalin-fixed and paraffin-embedded staining of fresh brain tissue via stimulated Raman CycleGAN model.

Intraoperative histology is essential for surgical guidance and decision-making. However, frozen-sectioned hematoxylin and eosin (H&E) staining suffers from degraded accuracy, whereas the gold-standard formalin-fixed and paraffin-embedded (FFPE) H&E is too lengthy for intraoperative use. Stimulated Raman scattering (SRS) microscopy has shown rapid histology of brain tissue with lipid/protein contrast but is challenging to yield images identical to nucleic acid-/protein-based FFPE stains interpretable to pathologists. Here, we report the development of a semi-supervised stimulated Raman CycleGAN model to convert fresh-tissue SRS images to H&E stains using unpaired training data. Within 3 minutes, stimulated Raman virtual histology (SRVH) results that matched perfectly with true H&E could be generated. A blind validation indicated that board-certified neuropathologists are able to differentiate histologic subtypes of human glioma on SRVH but hardly on conventional SRS images. SRVH may provide intraoperative diagnosis superior to frozen H&E in both speed and accuracy, extendable to other types of solid tumors.

TR4 worsen urosepsis by regulating GSDMD.

BACKGROUND: Urosepsis is a life-threatening organ disease in which pathogenic microorganisms in the urine enter the blood through the vessels, causing an imbalance in the immune response to infection. The aim of this study was to elucidate the role of testicular orphan receptor 4 (TR4) in urosepsis. METHODS: The role of TR4 in the progression and prognosis of urosepsis was confirmed by analyzing data from online databases and clinical human samples. To mimic urosepsis, we injected E. coli bacteria into the renal pelvis of mice to create a urosepsis model. Hematoxylin and eosin staining was used to observe histopathological changes in urosepsis. The effects of the upregulation or downregulation of TR4 on macrophage pyroptosis were verified in vitro. Chromatin immunoprecipitation assay was used to verify the effect of TR4 on Gasdermin D (GSDMD) transcription. RESULTS: TR4 was more highly expressed in the nonsurviving group than in the surviving group. Furthermore, overexpressing TR4 promoted inflammatory cytokine expression, and knocking down TR4 attenuated inflammatory cytokine expression. Mechanistically, TR4 promoted pyroptosis by regulating the expression of GSDMD in urosepsis. Furthermore, we also found that TR4 knockdown protected mice from urosepsis induced by the E. coli. CONCLUSIONS: TR4 functions as a key regulator of urosepsis by mediating pyroptosis, which regulates GSDMD expression. Targeting TR4 may be a potential strategy for urosepsis treatment.

Direct three-dimensional segmentation of prostate glands with nnU-Net.

Significance: In recent years, we and others have developed non-destructive methods to obtain three-dimensional (3D) pathology datasets of clinical biopsies and surgical specimens. For prostate cancer risk stratification (prognostication), standard-of-care Gleason grading is based on examining the morphology of prostate glands in thin 2D sections. This motivates us to perform 3D segmentation of prostate glands in our 3D pathology datasets for the purposes of computational analysis of 3D glandular features that could offer improved prognostic performance. Aim: To facilitate prostate cancer risk assessment, we developed a computationally efficient and accurate deep learning model for 3D gland segmentation based on open-top light-sheet microscopy datasets of human prostate biopsies stained with a fluorescent analog of hematoxylin and eosin (H&E). Approach: For 3D gland segmentation based on our H&E-analog 3D pathology datasets, we previously developed a hybrid deep learning and computer vision-based pipeline, called image translation-assisted segmentation in 3D (ITAS3D), which required a complex two-stage procedure and tedious manual optimization of parameters. To simplify this procedure, we use the 3D gland-segmentation masks previously generated by ITAS3D as training datasets for a direct end-to-end deep learning-based segmentation model, nnU-Net. The inputs to this model are 3D pathology datasets of prostate biopsies rapidly stained with an inexpensive fluorescent analog of H&E and the outputs are 3D semantic segmentation masks of the gland epithelium, gland lumen, and surrounding stromal compartments within the tissue. Results: nnU-Net demonstrates remarkable accuracy in 3D gland segmentations even with limited training data. Moreover, compared with the previous ITAS3D pipeline, nnU-Net operation is simpler and faster, and it can maintain good accuracy even with lower-resolution inputs. Conclusions: Our trained DL-based 3D segmentation model will facilitate future studies to demonstrate the value of computational 3D pathology for guiding critical treatment decisions for patients with prostate cancer.

Multi-omic dataset of patient-derived tumor organoids of neuroendocrine neoplasms.

BACKGROUND: Organoids are 3-dimensional experimental models that summarize the anatomical and functional structure of an organ. Although a promising experimental model for precision medicine, patient-derived tumor organoids (PDTOs) have currently been developed only for a fraction of tumor types. RESULTS: We have generated the first multi-omic dataset (whole-genome sequencing [WGS] and RNA-sequencing [RNA-seq]) of PDTOs from the rare and understudied pulmonary neuroendocrine tumors (n = 12; 6 grade 1, 6 grade 2) and provide data from other rare neuroendocrine neoplasms: small intestine (ileal) neuroendocrine tumors (n = 6; 2 grade 1 and 4 grade 2) and large-cell neuroendocrine carcinoma (n = 5; 1 pancreatic and 4 pulmonary). This dataset includes a matched sample from the parental sample (primary tumor or metastasis) for a majority of samples (21/23) and longitudinal sampling of the PDTOs (1 to 2 time points), for a total of n = 47 RNA-seq and n = 33 WGS. We here provide quality control for each technique and the raw and processed data as well as all scripts for genomic analyses to ensure an optimal reuse of the data. In addition, we report gene expression data and somatic small variant calls and describe how they were generated, in particular how we used WGS somatic calls to train a random forest classifier to detect variants in tumor-only RNA-seq. We also report all histopathological images used for medical diagnosis: hematoxylin and eosin-stained slides, brightfield images, and immunohistochemistry images of protein markers of clinical relevance. CONCLUSIONS: This dataset will be critical to future studies relying on this PDTO biobank, such as drug screens for novel therapies and experiments investigating the mechanisms of carcinogenesis in these understudied diseases.

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

BACKGROUND: Staining 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. METHODS: The 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. RESULTS: Stain 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. CONCLUSION: The 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.

Virtual histological staining of unlabeled autopsy tissue.

Traditional histochemical staining of post-mortem samples often confronts inferior staining quality due to autolysis caused by delayed fixation of cadaver tissue, and such chemical staining procedures covering large tissue areas demand substantial labor, cost and time. Here, we demonstrate virtual staining of autopsy tissue using a trained neural network to rapidly transform autofluorescence images of label-free autopsy tissue sections into brightfield equivalent images, matching hematoxylin and eosin (H&E) stained versions of the same samples. The trained model can effectively accentuate nuclear, cytoplasmic and extracellular features in new autopsy tissue samples that experienced severe autolysis, such as COVID-19 samples never seen before, where the traditional histochemical staining fails to provide consistent staining quality. This virtual autopsy staining technique provides a rapid and resource-efficient solution to generate artifact-free H&E stains despite severe autolysis and cell death, also reducing labor, cost and infrastructure requirements associated with the standard histochemical staining.

[Clinicopathological factors and clinical significance of No.12b lymph node metastasis in gastric antrum cancer].

Objective: To investigate the clinicopathological factors and clinical significance of (micro)metastasis in No.12b lymph node in patients with gastric antrum cancer. Methods: This was a retrospective cohort study of data of 242 patients with gastric adenocarcinoma without distant metastasis, complete follow-up data, and no preoperative anti-tumor therapy or history of other malignancies. All study patients had undergone radical gastrectomy (at least D2 radical range) + No.12b lymph node dissection in the Department of Gastric Surgery of Liaoning Cancer Hospital from January 2007 to December 2012. Immunohistochemical staining with antibody CK8/18 was used to detect micrometastasis to lymph nodes. Patients with positive findings on hematoxylin and eosin stained specimens and/or CK8/18 positivity in No.12b lymph node were diagnosed as having No.12b (micro)metastasis and included in the No.12b positive group. All other patients were classified as 12b negative. We investigated the impact of No.12b (micro)metastasis by comparing the clinicopathological characteristics and recurrence free survival (RFS) of these two groups of patients and subjecting possible risk factors to statistical analysis. Results: Traditional hematoxylin-eosin staining showed that 15/242 patients were positive for No.12b lymph nodes and 227 were negative. A total of 241 negative No. 12b lymph nodes were detected. Immunohistochemical testing revealed that seven of these 241 No.12b lymph nodes (2.9%) were positive for micrometastasis. A further seven positive nodes were identified among the 227 nodes (3.1%) that had been evaluated as negative on hematoxylin-eosin-stained sections. Thus, 22 /242 patients' (9.1%) No.12b nodes were positive for micrometastases, the remaining 220 (90.9%) being negative. Factor analysis showed that No.12b lymph node (micro) metastasis is associated with more severe invasion of the gastric serosa (HR=3.873, 95%CI: 1.676-21.643, P=0.006), T3 stage (HR=1.615, 95%CI: 1.113-1.867, P=0.045), higher N stage (HR=1.768, 95%CI: 1.187-5.654, P=0.019), phase III of TNM stage (HR=2.129, 95%CI: 1.102-3.475, P=0.046), and lymph node metastasis in the No.1/No.8a/No.12a groups (HR=0.451, 95%CI: 0.121-0.552, P=0.035; HR=0.645, 95%CI:0.071-0.886, P=0.032; HR=1.512, 95%CI: 1.381-2.100, P=0.029, respectively). Survival analysis showed that the 5-year RFS of patients in the No.12b positive group was worse than that of those in the No.12b negative group (18.2% vs. 34.5%, P<0.001). Independent predictors of RFS were poorer differentiation of the primary tumor (HR=0.528, 95%CI:0.288-0.969, P=0.039), more severe serous invasion (HR=1.262, 95%CI:1.039-1.534, P=0.019), higher T/N/TNM stage (HR=4.880, 95%CI: 1.909-12.476, P<0.001; HR=2.332, 95%CI: 1.640-3.317, P<0.001; HR=0.139, 95%CI: 0.027-0.713, P=0.018, respectively), and lymph node metastasis in the No.12a/No.12b group(HR=0.698, 95%CI:0.518-0.941, P=0.018; HR=0.341, 95%CI:0.154-0.758,P=0.008, respectively). Conclusion: Detection of micrometastasis can improve the rate of positive lymph nodes. In patients with gastric antrum cancer, dissection of group No.12b lymph nodes may improve the prognosis of those with intraoperative evidence of tumor invasion into the serosa, more than two lymph node metastases, and suspicious lymph nodes in groups No.1 / No.8a / 12a.