Effect of acacetin on inhibition of apoptosis in Helicobacter pylori-infected gastric epithelial cell line.

BACKGROUND: Helicobacter pylori (H. pylori) infection can cause extensive apoptosis of gastric epithelial cells, serving as a critical catalyst in the progression from chronic gastritis, gastrointestinal metaplasia, and atypical gastric hyperplasia to gastric carcinoma. Prompt eradication of H. pylori is paramount for ameliorating the pathophysiological conditions associated with chronic inflammation of the gastric mucosa and the primary prevention of gastric cancer. Acacetin, which has multifaceted pharmacological activities such as anti-cancer, anti-inflammatory, and antioxidative properties, has been extensively investigated across various domains. Nevertheless, the impact and underlying mechanisms of action of acacetin on H. pylori-infected gastric mucosal epithelial cells remain unclear. AIM: To explore the defensive effects of acacetin on apoptosis in H. pylori-infected GES-1 cells and to investigate the underlying mechanisms. METHODS: GES-1 cells were treated with H. pylori and acacetin in vitro. Cell viability was assessed using the CCK-8 assay, cell mortality rate via lactate dehydrogenase assay, alterations in cell migration and healing capacities through the wound healing assay, rates of apoptosis via flow cytometry and TUNEL staining, and expression levels of apoptosis-associated proteins through western blot analysis. RESULTS: H. pylori infection led to decreased GES-1 cell viability, increased cell mortality, suppressed cell migration, increased rate of apoptosis, increased expressions of Bax and cle-caspase3, and decreased Bcl-2 expression. Conversely, acacetin treatment enhanced cell viability, mitigated apoptosis induced by H. pylori infection, and modulated the expression of apoptosis-regulatory proteins by upregulating Bcl-2 and downregulating Bax and cleaved caspase-3. CONCLUSION: Acacetin significantly improved GES-1 cell viability and inhibited apoptosis in H. pylori-infected GES-1 cells, thereby exerting a protective effect on gastric mucosal epithelial cells.

[Clinical Characteristic, Diagnosis and Treatment of Acute Lymphoblastic Leukemia Combined with Pneumocystis Carinii Pneumonia in Children].

OBJECTIVE: To investigate the clinical characteristics and treatment of pneumocystis carinii pneumonia (PCP) in children with acute lymphoblastic leukemia (ALL), in order to improve the early diagnosis and effective treatment. METHODS: Clinical data of five children with ALL developing PCP in the post-chemotherapy granulocyte deficiency phase were analyzed retrospectively. The clinical manifestations, laboratory tests, imaging findings, treatment methods and effect were summarized. RESULTS: The male-to-female ratio of the five children was 1∶4, and the median age was 5.5 (2.9-8) years old. All patients developed PCP during granulocyte deficiency phase after induction remission chemotherapy. The clinical manifestations were generally non-specific, including high fever, tachypnea, dyspnea, non-severe cough, and rare rales in two lungs (wet rales in two patients). Laboratory tests showed elevated C-reactive protein (CRP), serum procalcitonin (PCT), (1,3)-ß-D-glucan (BDG), lactate dehydrogenase (LDH) and inflammatory factors including IL-2R, IL-6 and IL-8. Chest CT showed diffuse bilateral infiltrates with patchy hyperdense shadows. Pneumocystis carinii(PC) was detected in bronchoalveolar lavage fluid (BALF) or induced sputum by high-throughput sequencing in all patients. When PCP was suspected, chemotherapy was discontinued immediately, treatment of trimethoprim-sulfame thoxazole (TMP-SMX) combined with caspofungin against PC was started, and adjunctive methylprednisolone was used. Meanwhile, granulocyte-stimulating factor and gammaglobulin were given as the supportive treatment. All patients were transferred to PICU receiving mechanical ventilation due to respiratory distress during treatment. Four children were cured and one died. CONCLUSION: PCP should be highly suspected in ALL children with high fever, dyspnea, increased LDH and BDG, and diffuse patchy hyperdense shadow or solid changes in lung CT. The pathogen detection of respiratory specimens should be improved as soon as possible. TMP/SMZ is the first-line drug against PCP, and the combination of Caspofungin and TMP/SMZ treatment for NH-PCP may have a better efficacy. Patients with moderate and severe NH-PCP may benefit from glucocorticoid.

Deep Learning-Based Mapping of Tumor Infiltrating Lymphocytes in Whole Slide Images of 23 Types of Cancer.

The role of tumor infiltrating lymphocytes (TILs) as a biomarker to predict disease progression and clinical outcomes has generated tremendous interest in translational cancer research. We present an updated and enhanced deep learning workflow to classify 50x50 um tiled image patches (100x100 pixels at 20x magnification) as TIL positive or negative based on the presence of 2 or more TILs in gigapixel whole slide images (WSIs) from the Cancer Genome Atlas (TCGA). This workflow generates TIL maps to study the abundance and spatial distribution of TILs in 23 different types of cancer. We trained three state-of-the-art, popular convolutional neural network (CNN) architectures (namely VGG16, Inception-V4, and ResNet-34) with a large volume of training data, which combined manual annotations from pathologists (strong annotations) and computer-generated labels from our previously reported first-generation TIL model for 13 cancer types (model-generated annotations). Specifically, this training dataset contains TIL positive and negative patches from cancers in additional organ sites and curated data to help improve algorithmic performance by decreasing known false positives and false negatives. Our new TIL workflow also incorporates automated thresholding to convert model predictions into binary classifications to generate TIL maps. The new TIL models all achieve better performance with improvements of up to 13% in accuracy and 15% in F-score. We report these new TIL models and a curated dataset of TIL maps, referred to as TIL-Maps-23, for 7983 WSIs spanning 23 types of cancer with complex and diverse visual appearances, which will be publicly available along with the code to evaluate performance. Code Available at: https://github.com/ShahiraAbousamra/til_classification.

Dataset of segmented nuclei in hematoxylin and eosin stained histopathology images of ten cancer types.

The distribution and appearance of nuclei are essential markers for the diagnosis and study of cancer. Despite the importance of nuclear morphology, there is a lack of large scale, accurate, publicly accessible nucleus segmentation data. To address this, we developed an analysis pipeline that segments nuclei in whole slide tissue images from multiple cancer types with a quality control process. We have generated nucleus segmentation results in 5,060 Whole Slide Tissue images from 10 cancer types in The Cancer Genome Atlas. One key component of our work is that we carried out a multi-level quality control process (WSI-level and image patch-level), to evaluate the quality of our segmentation results. The image patch-level quality control used manual segmentation ground truth data from 1,356 sampled image patches. The datasets we publish in this work consist of roughly 5 billion quality controlled nuclei from more than 5,060 TCGA WSIs from 10 different TCGA cancer types and 1,356 manually segmented TCGA image patches from the same 10 cancer types plus additional 4 cancer types.

Utilizing Automated Breast Cancer Detection to Identify Spatial Distributions of Tumor-Infiltrating Lymphocytes in Invasive Breast Cancer.

Quantitative assessment of spatial relations between tumor and tumor-infiltrating lymphocytes (TIL) is increasingly important in both basic science and clinical aspects of breast cancer research. We have developed and evaluated convolutional neural network analysis pipelines to generate combined maps of cancer regions and TILs in routine diagnostic breast cancer whole slide tissue images. The combined maps provide insight about the structural patterns and spatial distribution of lymphocytic infiltrates and facilitate improved quantification of TILs. Both tumor and TIL analyses were evaluated by using three convolutional neural network networks (34-layer ResNet, 16-layer VGG, and Inception v4); the results compared favorably with those obtained by using the best published methods. We have produced open-source tools and a public data set consisting of tumor/TIL maps for 1090 invasive breast cancer images from The Cancer Genome Atlas. The maps can be downloaded for further downstream analyses.

Robust Histopathology Image Analysis: to Label or to Synthesize?

Detection, segmentation and classification of nuclei are fundamental analysis operations in digital pathology. Existing state-of-the-art approaches demand extensive amount of supervised training data from pathologists and may still perform poorly in images from unseen tissue types. We propose an unsupervised approach for histopathology image segmentation that synthesizes heterogeneous sets of training image patches, of every tissue type. Although our synthetic patches are not always of high quality, we harness the motley crew of generated samples through a generally applicable importance sampling method. This proposed approach, for the first time, re-weighs the training loss over synthetic data so that the ideal (unbiased) generalization loss over the true data distribution is minimized. This enables us to use a random polygon generator to synthesize approximate cellular structures (i.e., nuclear masks) for which no real examples are given in many tissue types, and hence, GAN-based methods are not suited. In addition, we propose a hybrid synthesis pipeline that utilizes textures in real histopathology patches and GAN models, to tackle heterogeneity in tissue textures. Compared with existing state-of-the-art supervised models, our approach generalizes significantly better on cancer types without training data. Even in cancer types with training data, our approach achieves the same performance without supervision cost. We release code and segmentation results on over 5000 Whole Slide Images (WSI) in The Cancer Genome Atlas (TCGA) repository, a dataset that would be orders of magnitude larger than what is available today.

Comparison of Different Classifiers with Active Learning to Support Quality Control in Nucleus Segmentation in Pathology Images.

Segmentation of nuclei in whole slide tissue images is a common methodology in pathology image analysis. Most segmentation algorithms are sensitive to input algorithm parameters and the characteristics of input images (tissue morphology, staining, etc.). Because there can be large variability in the color, texture, and morphology of tissues within and across cancer types (heterogeneity can exist even within a tissue specimen), it is likely that a set of input parameters will not perform well across multiple images. It is, therefore, highly desired, and necessary in some cases, to carry out a quality control of segmentation results. This work investigates the application of machine learning in this process. We report on the application of active learning for segmentation quality assessment for pathology images and compare three classification methods, Support Vector Machine (SVM), Random Forest (RF) and Convolutional Neural Network (CNN), for their performance improvement and efficiency.

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images.

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumor-infiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment.

Center-Focusing Multi-task CNN with Injected Features for Classification of Glioma Nuclear Images.

Classifying the various shapes and attributes of a glioma cell nucleus is crucial for diagnosis and understanding of the disease. We investigate the automated classification of the nuclear shapes and visual attributes of glioma cells, using Convolutional Neural Networks (CNNs) on pathology images of automatically segmented nuclei. We propose three methods that improve the performance of a previously-developed semi-supervised CNN. First, we propose a method that allows the CNN to focus on the most important part of an image-the image's center containing the nucleus. Second, we inject (concatenate) pre-extracted VGG features into an intermediate layer of our Semi-Supervised CNN so that during training, the CNN can learn a set of additional features. Third, we separate the losses of the two groups of target classes (nuclear shapes and attributes) into a single-label loss and a multi-label loss in order to incorporate prior knowledge of inter-label exclusiveness. On a dataset of 2078 images, the combination of the proposed methods reduces the error rate of attribute and shape classification by 21.54% and 15.07% respectively compared to the existing state-of-the-art method on the same dataset.

Patch-based Convolutional Neural Network for Whole Slide Tissue Image Classification.

Convolutional Neural Networks (CNN) are state-of-the-art models for many image classification tasks. However, to recognize cancer subtypes automatically, training a CNN on gigapixel resolution Whole Slide Tissue Images (WSI) is currently computationally impossible. The differentiation of cancer subtypes is based on cellular-level visual features observed on image patch scale. Therefore, we argue that in this situation, training a patch-level classifier on image patches will perform better than or similar to an image-level classifier. The challenge becomes how to intelligently combine patch-level classification results and model the fact that not all patches will be discriminative. We propose to train a decision fusion model to aggregate patch-level predictions given by patch-level CNNs, which to the best of our knowledge has not been shown before. Furthermore, we formulate a novel Expectation-Maximization (EM) based method that automatically locates discriminative patches robustly by utilizing the spatial relationships of patches. We apply our method to the classification of glioma and non-small-cell lung carcinoma cases into subtypes. The classification accuracy of our method is similar to the inter-observer agreement between pathologists. Although it is impossible to train CNNs on WSIs, we experimentally demonstrate using a comparable non-cancer dataset of smaller images that a patch-based CNN can outperform an image-based CNN.

Clinical evaluation of balloon dilation eustachian tuboplasty surgery in adult otitis media with effusion.

CONCLUSION: BDET might be effective for the patients with OME, and proved to be an efficacious and mini-invasive treatment for OME. OBJECTIVES: To evaluate the therapeutic benefits of balloon dilation eustachian tuboplasty (BDET) in the treatment of adult patients with otitis media with effusion (OME) caused by eustachian tube dysfunction (ETD). METHODS: After informed consent, eight adult patients with OME were included in this study. After investigated patients' case history and oto-function, all patients underwent BDET treatment. Then four criteria including tympanic membrane, pure tone audiometry (PTA), tympanometry, and subjective symptoms were adopted to evaluate the therapeutic benefits of BDET. RESULTS: None of the involved patients complained of problems or complications during the post-operative period, or with absence of pain and bleeding after the operation. Prominent post-operative improvement was observed in tympanic membrane and otoscopic appearance. In addition, cure rates after 3 months and 6 months post-operatively were gradually increased.

Alterations of CSF cystatin C levels and their correlations with CSF Αß40 and Αß42 levels in patients with Alzheimer's disease, dementia with lewy bodies and the atrophic form of general paresis.

Immunohistochemical studies have revealed that cystatin C (CysC) co-localizes with amyloid-ß (Αß) in amyloid-laden vascular walls and in the senile plaque cores of amyloid. In vitro and in vivo animal studies suggest that CysC protects against neurodegeneration by inhibition of cysteine proteases, inhibition of Αß aggregation, induction of autophagy and induction of cell division. CysC levels may be altered and may have a potential link with cerebrospinal fluid (CSF) Aß levels in various types of dementia with characteristic amyloid deposits, such as Alzheimer's disease (AD), dementia with Lewy bodies (DLB) and the atrophic form of general paresis (AF-GP). We assessed the serum and CSF levels of CysC and the CSF levels of Aß40 and Aß42 in patients with AD (n = 51), DLB (n = 26) and AF-GP (n = 43) and normal controls (n = 30). Using these samples, we explored the correlation between CSF CysC and CSF Aß levels. We found that in comparison to the normal control group, both CSF CysC and CSF Aß42 levels were significantly lower in all three dementia groups (all p<0.001); serum CysC levels were the same in the AD and DLB groups, and were lower in the AF-GP group (p = 0.008). The CSF CysC levels were positively correlated with both the CSF Aß40 and Aß42 levels in the AD, AF-GP and normal control groups (r = 0.306∼0.657, all p<0.05). Lower CSF CysC levels might be a common feature in dementia with characteristic amyloid deposits. Our results provide evidence for the potential role of CysC involvement in Aß metabolism and suggest that modulation of the CysC level in the brain might produce a disease-modifying effect in dementia with characteristic amyloid deposits.