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.

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.

Nextflow pipeline for Visium and H&E data from patient-derived xenograft samples.

We designed a Nextflow DSL2-based pipeline, Spatial Transcriptomics Quantification (STQ), for simultaneous processing of 10x Genomics Visium spatial transcriptomics data and a matched hematoxylin and eosin (H&E)-stained whole-slide image (WSI), optimized for patient-derived xenograft (PDX) cancer specimens. Our pipeline enables the classification of sequenced transcripts for deconvolving the mouse and human species and mapping the transcripts to reference transcriptomes. We align the H&E WSI with the spatial layout of the Visium slide and generate imaging and quantitative morphology features for each Visium spot. The pipeline design enables multiple analysis workflows, including single or dual reference genome input and stand-alone image analysis. We show the utility of our pipeline on a dataset from Visium profiling of four melanoma PDX samples. The clustering of Visium spots and clustering of H&E imaging features reveal similar patterns arising from the two data modalities.

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.

Morphological Evaluation and Immunohistochemical Analysis of the Reparative Potential of the Buccal Fat Pad.

Background and Objectives: There are many surgical techniques for oroantral communication treatment, one of which is the buccal fat pad. Of particular interest is the high reparative potential of the buccal fat pad, which may be contributed to by the presence of mesenchymal stem cells. The purpose of this work is to evaluate the reparative potential of BFP cells using morphological and immunohistochemical examination. Materials and Methods: 30 BFP samples were provided by the Clinic of Maxillofacial and Plastic Surgery of the Russian University of Medicine (Moscow, Russia) from 28 patients. Morphological examination of 30 BFP samples was performed at the Institute of Clinical Morphology and Digital Pathology of Sechenov University. Hematoxylin-eosin, Masson trichrome staining and immunohistochemical examination were performed to detect MSCs using primary antibodies CD133, CD44 and CD10. Results: During staining with hematoxylin-eosin and Masson's trichrome, we detected adipocytes of white adipose tissue united into lobules separated by connective tissue layers, a large number of vessels of different calibers, as well as the general capsule of BFP. The thin connective tissue layers contained neurovascular bundles. Statistical processing of the results of the IHC examination of the samples using the Mann-Whitney criterion revealed that the total number of samples in which the expression of CD44, CD10 and CD133 antigens was confirmed was statistically significantly higher than the number of samples where the expression was not detected (p < 0.05). Conclusions: During the morphological study of the BFP samples, we revealed statistically significant signs of MSCs presence (p < 0.05), including in the brown fat tissue, which proves the high reparative potential of this type of tissue and can make the BFP a choice option among other autogenous donor materials when eliminating OAC and other surgical interventions in the maxillofacial region.

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.

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.

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.

Prediction of MET Overexpression in Lung Adenocarcinoma from Hematoxylin and Eosin Images.

Mesenchymal epithelial transition (MET) protein overexpression is a targetable event in non-small cell lung cancer and is the subject of active drug development. Challenges in identifying patients for these therapies include lack of access to validated testing, such as standardized immunohistochemistry assessment, and consumption of valuable tissue for a single gene/protein assay. Development of prescreening algorithms using routinely available digitized hematoxylin and eosin (H&E)-stained slides to predict MET overexpression could promote testing for those who will benefit most. Recent literature reports a positive correlation between MET protein overexpression and RNA expression. In this work, a large database of matched H&E slides and RNA expression data were leveraged to train a weakly supervised model to predict MET RNA overexpression directly from H&E images. This model was evaluated on an independent holdout test set of 300 overexpressed and 289 normal patients, demonstrating a receiver operating characteristic area under curve of 0.70 (95th percentile interval: 0.66 to 0.74) with stable performance characteristics across different patient clinical variables and robust to synthetic noise on the test set. These results suggest that H&E-based predictive models could be useful to prioritize patients for confirmatory testing of MET protein or MET gene expression status.

[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.

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.

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.

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.

Use of Two Novel Dyes to Enhance Visualization of Cut Ends of the Vessel in Microvascular Anastomosis-An Experimental Study of 45 Rats.

BACKGROUND: Good visualization is a prerequisite for performing microvascular anastomosis. The most commonly used dye, methylene blue, has several limitations: it is washed off quickly and stains all the vessel layers. The objective of our study is to use 2 new novel dyes for improving visualization. METHODS: After ethical committee approval, 2 Dyes (2% cresyl violet, 1% eosin) were studied in 3 groups, 20 rats in each group and 5 rats in the combined group. End-to-side anastomosis was performed in the classic fashion in 45 rats. After venotomy, the dye was applied to the raw surface of the vessels and subsequently, anastomosis was performed. The improvement in visualization was judged by 3 blinded experts and nonexperts in 4 groups on a scale of 1-10. Scores were statistically analyzed. After 2 weeks, animals were re-explored to check the delayed patency, and segments were harvested for histopathologic analysis. RESULTS: The immediate and delayed patency rates were 100% (45/45) and 97% (33/34), respectively. In statistical analysis, the combined group (P = 0.005)was judged statistically significant because of the contrast in color. All the layers were stained by both dyes, staining lasted until the end of the surgery. Visibility of the cut ends was better in cresyl violet. All histopathologic findings suggested normal changes at the anastomotic site. CONCLUSIONS: This study showed that the use of these 2 dyes was not only feasible but highly efficacious. Even though all the layers were stained by both the dyes, the visibility of the cut ends was better. In both dyes, staining lasted until the end of surgery. To the best of our knowledge, this is the first study that has used these 2 novel dyes to improve visualization in microvascular anastomosis in an experimental setting.

Real-time Histological Evaluation of Gastric Cancer Tissue by Using a Confocal Laser Endomicroscopic System.

BACKGROUND/AIM: The need for instant histological evaluation of fresh tissue, especially in cancer treatment, remains paramount. The conventional frozen section technique has inherent limitations, prompting the exploration of alternative methods. A recently developed confocal laser endomicroscopic system provides real-time imaging of the tissue without the need for glass slide preparation. Herein, we evaluated its applicability in the histologic evaluation of gastric cancer tissues. MATERIALS AND METHODS: A confocal laser endomicroscopic system (CLES) with a Lissajous pattern laser scanning, was developed. Fourteen fresh gastric cancer tissues and the same number of normal gastric tissues were obtained from advanced gastric cancer patients. Fluorescein sodium was used for staining. Five pathologists interpreted 100 endomicroscopic images and decided their histologic location and the presence of cancer. Following the review of matched hematoxylin and eosin (H&E) slides, their performance was evaluated with another 100 images. RESULTS: CLES images mirrored gastric tissue histology. Pathologists were able to detect the histologic location of the images with 65.7% accuracy and differentiate cancer tissue from normal with 74.7% accuracy. The sensitivity and specificity of cancer detection were 71.9% and 76.1%. Following the review of matched H&E images, the accuracy of identifying the histologic location was increased to 92.8% (p<0.0001), and that of detecting cancer tissue was also increased to 90.9% (p<0.001). The sensitivity and specificity of cancer detection were enhanced to 89.1% and 93.2% (p<0.0001). CONCLUSION: High-quality histological images were immediately acquired by the CLES. The operator training enabled the accurate detection of cancer and histologic location raising its potential applicability as a real-time tissue imaging modality.

High-content stimulated Raman histology of human breast cancer.

Histological examination is crucial for cancer diagnosis, however, the labor-intensive sample preparation involved in the histology impedes the speed of diagnosis. Recently developed two-color stimulated Raman histology could bypass the complex tissue processing to generates result close to hematoxylin and eosin staining, which is one of the golden standards in cancer histology. Yet, the underlying chemical features are not revealed in two-color stimulated Raman histology, compromising the effectiveness of prognostic stratification. Here, we present a high-content stimulated Raman histology (HC-SRH) platform that provides both morphological and chemical information for cancer diagnosis based on un-stained breast tissues. Methods: By utilizing both hyperspectral SRS imaging in the C-H vibration window and sparsity-penalized unmixing of overlapped spectral profiles, HC-SRH enabled high-content chemical mapping of saturated lipids, unsaturated lipids, cellular protein, extracellular matrix (ECM), and water. Spectral selective sampling was further implemented to boost the speed of HC-SRH. To show the potential for clinical use, HC-SRH using a compact fiber laser-based stimulated Raman microscope was demonstrated. Harnessing the wide and rapid tuning capability of the fiber laser, both C-H and fingerprint vibration windows were accessed. Results: HC-SRH successfully mapped unsaturated lipids, cellular protein, extracellular matrix, saturated lipid, and water in breast tissue. With these five chemical maps, HC-SRH provided distinct contrast for tissue components including duct, stroma, fat cell, necrosis, and vessel. With selective spectral sampling, the speed of HC-SRH was improved by one order of magnitude. The fiber-laser-based HC-SRH produced the same image quality in the C-H window as the state-of-the-art solid laser. In the fingerprint window, nucleic acid and solid-state ester contrast was demonstrated. Conclusions: HC-SRH provides both morphological and chemical information of tissue in a label-free manner. The chemical information detected is beyond the reach of traditional hematoxylin and eosin staining and heralds the potential of HC-SRH for biomarker discovery.

[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.

DSFF-GAN: A novel stain transfer network for generating immunohistochemical image of endometrial cancer.

Immunohistochemistry (IHC) is a commonly used histological examination technique. Compared to Hematoxylin and Eosin (H&E) staining, it enables the examination of protein expression and localization in tissues, which is valuable for cancer treatment and prognosis assessment, such as the detection and diagnosis of endometrial cancer. However, IHC involves multiple staining steps, is time-consuming and expensive. One potential solution is to utilize deep learning networks to generate corresponding virtual IHC images from H&E images. However, the similarity of the IHC image generated by the existing methods needs to be further improved. In this work, we propose a novel dual-scale feature fusion (DSFF) generative adversarial network named DSFF-GAN, which comprises a cycle structure-color similarity loss, and DSFF block to constrain the model's training process and enhance its stain transfer capability. In addition, our method incorporates labeling information of positive cell regions as prior knowledge into the network to further improve the evaluation metrics. We train and test our model using endometrial cancer and publicly available breast cancer IHC datasets, and compare it with state-of-the-art methods. Compared to previous methods, our model demonstrates significant improvements in most evaluation metrics on both datasets. The research results show that our method further improves the quality of image generation and has potential value for the future clinical application of virtual IHC images.