Multimodal risk prediction with physiological signals, medical images and clinical notes.

The broad adoption of electronic health record (EHR) systems brings us a tremendous amount of clinical data and thus provides opportunities to conduct data-based healthcare research to solve various clinical problems in the medical domain. Machine learning and deep learning methods are widely used in the medical informatics and healthcare domain due to their power to mine insights from raw data. When adapting deep learning models for EHR data, it is essential to consider its heterogeneous nature: EHR contains patient records from various sources including medical tests (e.g. blood test, microbiology test), medical imaging, diagnosis, medications, procedures, clinical notes, etc. Those modalities together provide a holistic view of patient health status and complement each other. Therefore, combining data from multiple modalities that are intrinsically different is challenging but intuitively promising in deep learning for EHR. To assess the expectations of multimodal data, we introduce a comprehensive fusion framework designed to integrate temporal variables, medical images, and clinical notes in EHR for enhanced performance in clinical risk prediction. Early, joint, and late fusion strategies are employed to combine data from various modalities effectively. We test the model with three predictive tasks: in-hospital mortality, long length of stay, and 30-day readmission. Experimental results show that multimodal models outperform uni-modal models in the tasks involved. Additionally, by training models with different input modality combinations, we calculate the Shapley value for each modality to quantify their contribution to multimodal performance. It is shown that temporal variables tend to be more helpful than CXR images and clinical notes in the three explored predictive tasks.

Stable clinical risk prediction against distribution shift in electronic health records.

The availability of large-scale electronic health record datasets has led to the development of artificial intelligence (AI) methods for clinical risk prediction that help improve patient care. However, existing studies have shown that AI models suffer from severe performance decay after several years of deployment, which might be caused by various temporal dataset shifts. When the shift occurs, we have access to large-scale pre-shift data and small-scale post-shift data that are not enough to train new models in the post-shift environment. In this study, we propose a new method to address the issue. We reweight patients from the pre-shift environment to mitigate the distribution shift between pre- and post-shift environments. Moreover, we adopt a Kullback-Leibler divergence loss to force the models to learn similar patient representations in pre- and post-shift environments. Our experimental results show that our model efficiently mitigates temporal shifts, improving prediction performance.

Multimodal Risk Prediction with Physiological Signals, Medical Images and Clinical Notes.

The broad adoption of electronic health records (EHRs) provides great opportunities to conduct healthcare research and solve various clinical problems in medicine. With recent advances and success, methods based on machine learning and deep learning have become increasingly popular in medical informatics. Combining data from multiple modalities may help in predictive tasks. To assess the expectations of multimodal data, we introduce a comprehensive fusion framework designed to integrate temporal variables, medical images, and clinical notes in Electronic Health Record (EHR) for enhanced performance in downstream predictive tasks. Early, joint, and late fusion strategies were employed to effectively combine data from various modalities. Model performance and contribution scores show that multimodal models outperform uni-modal models in various tasks. Additionally, temporal signs contain more information than CXR images and clinical notes in three explored predictive tasks. Therefore, models integrating different data modalities can work better in predictive tasks.

A fair and interpretable network for clinical risk prediction: a regularized multi-view multi-task learning approach.

In healthcare domain, complication risk profiling which can be seen as multiple clinical risk prediction tasks is challenging due to the complex interaction between heterogeneous clinical entities. With the availability of real-world data, many deep learning methods are proposed for complication risk profiling. However, the existing methods face three open challenges. First, they leverage clinical data from a single view and then lead to suboptimal models. Second, most existing methods lack an effective mechanism to interpret predictions. Third, models learned from clinical data may have inherent pre-existing biases and exhibit discrimination against certain social groups. We then propose a multi-view multi-task network (MuViTaNet) to tackle these issues. MuViTaNet complements patient representation by using a multi-view encoder to exploit more information. Moreover, it uses a multi-task learning to generate more generalized representations using both labeled and unlabeled datasets. Last, a fairness variant (F-MuViTaNet) is proposed to mitigate the unfairness issues and promote healthcare equity. The experiments show that MuViTaNet outperforms existing methods for cardiac complication profiling. Its architecture also provides an effective mechanism for interpreting the predictions, which helps clinicians discover the underlying mechanism triggering the complication onsets. F-MuViTaNet can also effectively mitigate the unfairness with only negligible impact on accuracy.

Deconfounding Actor-Critic Network with Policy Adaptation for Dynamic Treatment Regimes.

Despite intense efforts in basic and clinical research, an individualized ventilation strategy for critically ill patients remains a major challenge. Recently, dynamic treatment regime (DTR) with reinforcement learning (RL) on electronic health records (EHR) has attracted interest from both the healthcare industry and machine learning research community. However, most learned DTR policies might be biased due to the existence of confounders. Although some treatment actions non-survivors received may be helpful, if confounders cause the mortality, the training of RL models guided by long-term outcomes (e.g., 90-day mortality) would punish those treatment actions causing the learned DTR policies to be suboptimal. In this study, we develop a new deconfounding actor-critic network (DAC) to learn optimal DTR policies for patients. To alleviate confounding issues, we incorporate a patient resampling module and a confounding balance module into our actor-critic framework. To avoid punishing the effective treatment actions non-survivors received, we design a short-term reward to capture patients' immediate health state changes. Combining short-term with long-term rewards could further improve the model performance. Moreover, we introduce a policy adaptation method to successfully transfer the learned model to new-source small-scale datasets. The experimental results on one semi-synthetic and two different real-world datasets show the proposed model outperforms the state-of-the-art models. The proposed model provides individualized treatment decisions for mechanical ventilation that could improve patient outcomes.

Predicting Age-Related Macular Degeneration Progression with Contrastive Attention and Time-Aware LSTM.

Age-related macular degeneration (AMD) is the leading cause of irreversible blindness in developed countries. Identifying patients at high risk of progression to late AMD, the sight-threatening stage, is critical for clinical actions, including medical interventions and timely monitoring. Recently, deep-learning-based models have been developed and achieved superior performance for late AMD prediction. However, most existing methods are limited to the color fundus photography (CFP) from the last ophthalmic visit and do not include the longitudinal CFP history and AMD progression during the previous years' visits. Patients in different AMD subphenotypes might have various speeds of progression in different stages of AMD disease. Capturing the progression information during the previous years' visits might be useful for the prediction of AMD progression. In this work, we propose a Contrastive-Attention-based Time-aware Long Short-Term Memory network (CAT-LSTM) to predict AMD progression. First, we adopt a convolutional neural network (CNN) model with a contrastive attention module (CA) to extract abnormal features from CFPs. Then we utilize a time-aware LSTM (T-LSTM) to model the patients' history and consider the AMD progression information. The combination of disease progression, genotype information, demographics, and CFP features are sent to T-LSTM. Moreover, we leverage an auto-encoder to represent temporal CFP sequences as fixed-size vectors and adopt k-means to cluster them into subphenotypes. We evaluate the proposed model based on real-world datasets, and the results show that the proposed model could achieve 0.925 on area under the receiver operating characteristic (AUROC) for 5-year late-AMD prediction and outperforms the state-of-the-art methods by more than 3%, which demonstrates the effectiveness of the proposed CAT-LSTM. After analyzing patient representation learned by an auto-encoder, we identify 3 novel subphenotypes of AMD patients with different characteristics and progression rates to late AMD, paving the way for improved personalization of AMD management. The code of CAT-LSTM can be found at GitHub.

Brain Atlas Guided Attention U-Net for White Matter Hyperintensity Segmentation.

White Matter Hyperintensities (WMH) are the most common manifestation of cerebral small vessel disease (cSVD) on the brain MRI. Accurate WMH segmentation algorithms are important to determine cSVD burden and its clinical con-sequences. Most of existing WMH segmentation algorithms require both fluid attenuated inversion recovery (FLAIR) images and T1-weighted images as inputs. However, T1-weighted images are typically not part of standard clinical scans which are acquired for patients with acute stroke. In this paper, we propose a novel brain atlas guided attention U-Net (BAGAU-Net) that leverages only FLAIR images with a spatially-registered white matter (WM) brain atlas to yield competitive WMH segmentation performance. Specifically, we designed a dual-path segmentation model with two novel connecting mechanisms, namely multi-input attention module (MAM) and attention fusion module (AFM) to fuse the information from two paths for accurate results. Experiments on two publicly available datasets show the effectiveness of the proposed BAGAU-Net. With only FLAIR images and WM brain atlas, BAGAU-Net outperforms the state-of-the-art method with T1-weighted images, paving the way for effective development of WMH segmentation. Availability: https://github.com/Ericzhang1/BAGAU-Net.

A survival model generalized to regression learning algorithms.

Survival prediction is an important problem that is encountered widely in industry and medicine. Despite the explosion of artificial intelligence technologies, no uniformed method allows the application of any type of regression learning algorithm to a survival prediction problem. Here, we present a statistical modeling method that is generalized to all types of regression learning algorithm, including deep learning. We present its empirical advantage when it is applied to traditional survival problems. We demonstrate its expanded applications in different types of regression learning algorithm, such as gradient boosted trees, convolutional neural networks and recurrent neural networks. Additionally, we demonstrate its application in clinical informatic data, pathological images and the hardware industry. We expect that this algorithm will be widely applicable for diverse types of survival data, including discrete data types and those suitable for deep learning such as those with time or spatial continuity.

An interpretable deep-learning model for early prediction of sepsis in the emergency department.

Sepsis is a life-threatening condition with high mortality rates and expensive treatment costs. Early prediction of sepsis improves survival in septic patients. In this paper, we report our top-performing method in the 2019 DII National Data Science Challenge to predict onset of sepsis 4 h before its diagnosis on electronic health records of over 100,000 unique patients in emergency departments. A long short-term memory (LSTM)-based model with event embedding and time encoding is leveraged to model clinical time series and boost prediction performance. Attention mechanism and global max pooling techniques are utilized to enable interpretation for the deep-learning model. Our model achieved an average area under the curve of 0.892 and was selected as one of the winners of the challenge for both prediction accuracy and clinical interpretability. This study paves the way for future intelligent clinical decision support, helping to deliver early, life-saving care to the bedside of septic patients.

LncRNA Malat-1 From MSCs-Derived Extracellular Vesicles Suppresses Inflammation and Cartilage Degradation in Osteoarthritis.

Purpose: Extracellular Vesicles (EVs) derived from hMSCs, have the potential to alleviate cartilage damage and inflammation. We aimed to explore the effects of EVs derived from lncRNA malat-1-overexpressing human mesenchymal stem cells (hMSCs) on chondrocytes. Material and Methods: hMSCs-derived Extracellular Vesicles (hMSCs-EVs) were identified by transmission electron microscopy and western blot. We used a Sprague-Dawley (SD) rat model of CollagenaseⅡ-induced osteoarthritis (OA) as well as IL-1ß-induced OA chondrocytes. Lentiviral vectors were used to overexpress lncRNA malat-1 in hMSCs. Chondrocyte proliferation, inflammation, extracellular matrix degradation, and cell migration were measured by Edu staining, ELISA, western blot analysis, and transwell assay. Chondrocyte apoptosis was evaluated by flow cytometry, Hoechst 33342/PI Staining, and western blot. Safranine O-fast green (S-O) staining and HE staining were used to assess morphologic alterations of the rat knee joint. Results: hMSCsmalat-1-EVs decreased MMP-13, IL-6, and Caspase-3 expression in IL-1ß-induced OA chondrocytes. Moreover, hMSCsmalat-1-EVs promoted chondrocyte proliferation and migration, suppressed apoptosis, and attenuated IL-1ß-induced chondrocyte injury. Our animal experiments suggested that hMSCsmalat-1-EVs were sufficient to prevent cartilage degeneration. Conclusion: Our findings show that lncRNA malat-1from hMSCs-delivered EVs can promote chondrocyte proliferation, alleviate chondrocyte inflammation and cartilage degeneration, and enhance chondrocyte repair. Overall, hMSCsmalat-1-EVs might be a new potential therapeutic option for patients with OA.

An interpretable risk prediction model for healthcare with pattern attention.

BACKGROUND: The availability of massive amount of data enables the possibility of clinical predictive tasks. Deep learning methods have achieved promising performance on the tasks. However, most existing methods suffer from three limitations: (1) There are lots of missing value for real value events, many methods impute the missing value and then train their models based on the imputed values, which may introduce imputation bias. The models' performance is highly dependent on the imputation accuracy. (2) Lots of existing studies just take Boolean value medical events (e.g. diagnosis code) as inputs, but ignore real value medical events (e.g., lab tests and vital signs), which are more important for acute disease (e.g., sepsis) and mortality prediction. (3) Existing interpretable models can illustrate which medical events are conducive to the output results, but are not able to give contributions of patterns among medical events. METHODS: In this study, we propose a novel interpretable Pattern Attention model with Value Embedding (PAVE) to predict the risks of certain diseases. PAVE takes the embedding of various medical events, their values and the corresponding occurring time as inputs, leverage self-attention mechanism to attend to meaningful patterns among medical events for risk prediction tasks. Because only the observed values are embedded into vectors, we don't need to impute the missing values and thus avoids the imputations bias. Moreover, the self-attention mechanism is helpful for the model interpretability, which means the proposed model can output which patterns cause high risks. RESULTS: We conduct sepsis onset prediction and mortality prediction experiments on a publicly available dataset MIMIC-III and our proprietary EHR dataset. The experimental results show that PAVE outperforms existing models. Moreover, by analyzing the self-attention weights, our model outputs meaningful medical event patterns related to mortality. CONCLUSIONS: PAVE learns effective medical event representation by incorporating the values and occurring time, which can improve the risk prediction performance. Moreover, the presented self-attention mechanism can not only capture patients' health state information, but also output the contributions of various medical event patterns, which pave the way for interpretable clinical risk predictions. AVAILABILITY: The code for this paper is available at: https://github.com/yinchangchang/PAVE .

Study on automatic detection and classification of breast nodule using deep convolutional neural network system.

BACKGROUNDS: Conventional ultrasound manual scanning and artificial diagnosis approaches in breast are considered to be operator-dependence, slight slow and error-prone. In this study, we used Automated Breast Ultrasound (ABUS) machine for the scanning, and deep convolutional neural network (CNN) technology, a kind of Deep Learning (DL) algorithm, for the detection and classification of breast nodules, aiming to achieve the automatic and accurate diagnosis of breast nodules. METHODS: Two hundred and ninety-three lesions from 194 patients with definite pathological diagnosis results (117 benign and 176 malignancy) were recruited as case group. Another 70 patients without breast diseases were enrolled as control group. All the breast scans were carried out by an ABUS machine and then randomly divided into training set, verification set and test set, with a proportion of 7:1:2. In the training set, we constructed a detection model by a three-dimensionally U-shaped convolutional neural network (3D U-Net) architecture for the purpose of segment the nodules from background breast images. Processes such as residual block, attention connections, and hard mining were used to optimize the model while strategies of random cropping, flipping and rotation for data augmentation. In the test phase, the current model was compared with those in previously reported studies. In the verification set, the detection effectiveness of detection model was evaluated. In the classification phase, multiple convolutional layers and fully-connected layers were applied to set up a classification model, aiming to identify whether the nodule was malignancy. RESULTS: Our detection model yielded a sensitivity of 91% and 1.92 false positive subjects per automatically scanned imaging. The classification model achieved a sensitivity of 87.0%, a specificity of 88.0% and an accuracy of 87.5%. CONCLUSIONS: Deep CNN combined with ABUS maybe a promising tool for easy detection and accurate diagnosis of breast nodule.

Combining structured and unstructured data for predictive models: a deep learning approach.

BACKGROUND: The broad adoption of electronic health records (EHRs) provides great opportunities to conduct health care research and solve various clinical problems in medicine. With recent advances and success, methods based on machine learning and deep learning have become increasingly popular in medical informatics. However, while many research studies utilize temporal structured data on predictive modeling, they typically neglect potentially valuable information in unstructured clinical notes. Integrating heterogeneous data types across EHRs through deep learning techniques may help improve the performance of prediction models. METHODS: In this research, we proposed 2 general-purpose multi-modal neural network architectures to enhance patient representation learning by combining sequential unstructured notes with structured data. The proposed fusion models leverage document embeddings for the representation of long clinical note documents and either convolutional neural network or long short-term memory networks to model the sequential clinical notes and temporal signals, and one-hot encoding for static information representation. The concatenated representation is the final patient representation which is used to make predictions. RESULTS: We evaluate the performance of proposed models on 3 risk prediction tasks (i.e. in-hospital mortality, 30-day hospital readmission, and long length of stay prediction) using derived data from the publicly available Medical Information Mart for Intensive Care III dataset. Our results show that by combining unstructured clinical notes with structured data, the proposed models outperform other models that utilize either unstructured notes or structured data only. CONCLUSIONS: The proposed fusion models learn better patient representation by combining structured and unstructured data. Integrating heterogeneous data types across EHRs helps improve the performance of prediction models and reduce errors.

Marrying Medical Domain Knowledge With Deep Learning on Electronic Health Records: A Deep Visual Analytics Approach.

BACKGROUND: Deep learning models have attracted significant interest from health care researchers during the last few decades. There have been many studies that apply deep learning to medical applications and achieve promising results. However, there are three limitations to the existing models: (1) most clinicians are unable to interpret the results from the existing models, (2) existing models cannot incorporate complicated medical domain knowledge (eg, a disease causes another disease), and (3) most existing models lack visual exploration and interaction. Both the electronic health record (EHR) data set and the deep model results are complex and abstract, which impedes clinicians from exploring and communicating with the model directly. OBJECTIVE: The objective of this study is to develop an interpretable and accurate risk prediction model as well as an interactive clinical prediction system to support EHR data exploration, knowledge graph demonstration, and model interpretation. METHODS: A domain-knowledge-guided recurrent neural network (DG-RNN) model is proposed to predict clinical risks. The model takes medical event sequences as input and incorporates medical domain knowledge by attending to a subgraph of the whole medical knowledge graph. A global pooling operation and a fully connected layer are used to output the clinical outcomes. The middle results and the parameters of the fully connected layer are helpful in identifying which medical events cause clinical risks. DG-Viz is also designed to support EHR data exploration, knowledge graph demonstration, and model interpretation. RESULTS: We conducted both risk prediction experiments and a case study on a real-world data set. A total of 554 patients with heart failure and 1662 control patients without heart failure were selected from the data set. The experimental results show that the proposed DG-RNN outperforms the state-of-the-art approaches by approximately 1.5%. The case study demonstrates how our medical physician collaborator can effectively explore the data and interpret the prediction results using DG-Viz. CONCLUSIONS: In this study, we present DG-Viz, an interactive clinical prediction system, which brings together the power of deep learning (ie, a DG-RNN-based model) and visual analytics to predict clinical risks and visually interpret the EHR prediction results. Experimental results and a case study on heart failure risk prediction tasks demonstrate the effectiveness and usefulness of the DG-Viz system. This study will pave the way for interactive, interpretable, and accurate clinical risk predictions.

[FOXO4 maintains senescence in human umbilical cord mesenchymal stem cells by repressing apoptosis].

The purpose of the present study was to investigate the effects of forkhead box O4 (FOXO4) on the senescence of human umbilical cord-derived mesenchymal stem cells (hUC-MSCs). The hUC-MSCs were induced to senescence by natural passage, and FOXO4 expression was inhibited by lentiviral shRNA transfection. The hallmark of cell senescence was analyzed by ß-galactosidase staining, and the cell viability was assayed by CCK-8 method. Flow cytometry was used to investigate the apoptosis of hUC-MSCs. The expression levels of Bcl-2, Bax, FOXO4, interleukin 6 (IL-6) and cleaved Caspase-3 were detected by qPCR and Western blot. Immunofluorescence staining was used to detect FOXO4 expression. The amount of IL-6 secreted by hUC-MSCs was detected by ELISA. The results showed that, compared with the passage 1, senescent hUC-MSCs showed up-regulated expression levels of Bax and FOXO4, down-regulated expression levels of Bcl-2 and cleaved Caspase-3, and increased IL-6 mRNA expression and secretion. FOXO4 inhibition in senescent hUC-MSCs promoted cell apoptosis, reduced cell viability, and inhibited the mRNA expression and secretion of IL-6. These results suggest that FOXO4 maintains viability and function of senescent hUC-MSCs by repressing their apoptosis response, thus accelerating senescence of the whole cell colony.

Different effects of total flavonoids from Arachniodes exilis on human umbilical cord mesenchymal stem cells in vitro.

Traditional Chinese medicines are used in promotion of fractured bone healing and bone diseases. Some studies reported total flavonoids from plant can be used as an auxiliary source of exogenous.Use different methods to identify and verify effects of total flavonoids from Arachniodes exilis (TFAE) on human umbilical cord mesenchymal stem cells (HUCMSCs) in vitro.Concentrations of 1 and 5 µg/mL TFAE significantly increased ALPase activity in HUCMSCs compared to the other concentrations at days 3 and 7 (P < .05). RT-PCR showed that expression levels of osteogenic genes (Col1a1, OPN, Runx2 and Osx) were remarkably enhanced in HUCMSCs following treatment with different concentrations of TFAE for 9 days compared with 0 µg/mL TFAE group (control). The results showed that concentration < 5 µg/mL of TFAE induced osteogenic differentiation in HUCMSCs Alizarin red staining assays revealed that both TFAE and S1191 was significantly decreased (7.80 ±â€Š0.66) compared with the TFAE group (16.00 ±â€Š0.97) (P < .01). ALPase activity on days 3 and 7 was relatively lower in HUCMSCs grown in media supplemented with both S1191 and TFAE than that of in TFAE group only. The results indicated that osteogenic markers (Col1a1, OPN, Runx2 and Osx) were significantly downregulated in the TFAE + S1191 group in comparison to the control group. The expressions of Col1a and OPN in the TFAE + S1191 group decreased significantly (P < .01) by Western blotting.TFAE promotes the odonto/osteogenic differentiation of human UCMSCs via activation of ER.

The bone marrow-derived mesenchymal stem cells (BMSCs) alleviate diabetic peripheral neuropathy induced by STZ via activating GSK-3ß/ß-catenin signaling pathway.

BACKGROUND: Diabetic peripheral neuropathy, a common complication of diabetic mellitus, has brought a threaten on patients' health. The bone marrow-derived mesenchymal stem cells (BMSCs) were reported to play an important role in diverse diseases. Nevertheless, the specific function of BMSCs in diabetic peripheral neuropathy remained uncharacterized. METHODS: A wide range of experiments including RT-qPCR, western blot, H&E staining, oxidative stress assessment, measurement of thermal sensitivity, ELISA, urine protein and CCK-8 assays were implemented to explore the function and mechanism of BMSCs in vivo and vitro. RESULTS: The experimental results displayed that BMSCs improve STZ-induced diabetes symptoms in rats by decreasing blood glucose and urinary protein. Functionally, BMSCs ameliorate oxidative stress, painful diabetic neuropathy, neurotrophic status and angiogenesis in STZ-induced rats. Moreover, BMSCs participate in the regulation of sciatic neuro morphology in diabetic neuropathy rat model. In mechanism, BMSCs alleviate diabetic peripheral neuropathy via activating GSK-3ß/ß-catenin signaling pathway in rats and improve Schwann's cells viability by activating GSK-3ß/ß-catenin signaling pathway under high glucose. CONCLUSIONS: We verified that BMSCs alleviate diabetic peripheral neuropathy of rats induced by STZ via activating GSK-3ß/ß-catenin signaling pathway, which implied a novel biomarker for diabetic peripheral neuropathy treatment.

Silencing of estrogen receptor ß promotes the invasion and migration of osteosarcoma cells through activating Wnt signaling pathway.

PURPOSE: This study aimed to evaluate the specific roles of estrogen receptor ß (ERß) on the invasion and migration of osteosarcoma (OS) cells and explore the regulatory mechanisms relating with Wnt signaling pathway. METHODS: The expression of ERß was detected in human OS tissues by quantitative real-time PCR and immunohistochemistry. U2-OS cells were transfected with siRNA-ERß (si-ERß) to downregulate ERß and treated with FH535 to inhibit Wnt signaling. The migration and invasion ability was detected by scratch and transwell assay, respectively. The expression of ß-catenin, MMP-7, and MMP-9 was detected by Western blot. Subcutaneous tumor-bearing model was established by injection of U2-OS cells into mice, and the tumor volumes were measured. Orthotopic transplantation model was established by transplantation of tumor tissues into the liver of mice, and the metastatic tumors were counted. RESULTS: ERß was downregulated in human OS tissues and U2-OS cells. The transfection of si-ERß significantly increased the scratch healing rate; the number of invasion cells; and the expression of ß-catenin, MMP-7, and MMP-9 in U2-OS cells. The injection of si-ERß-transfected U2-OS cells into mice significantly increased the subcutaneous tumor volume; the expression of ß-catenin, MMP-7, and MMP-9; and the number of metastatic tumors in liver tissues. The promoting effects of si-ERß on the invasion and migration of U2-OS cells were significantly reversed by FH535 in vitro and in vivo. CONCLUSION: Silencing of ERß promotes the invasion and migration of OS cells via activating Wnt signaling pathway.

Overexpression of ERß inhibits the proliferation through regulating TNG-ß signaling pathway in osteosarcoma.

The present study aimed to explore the potential anti-tumor effect of ERß overexpression and investigate its related mechanism in osteosarcoma. Cell cycle and apoptosis rates were measured by flow cytometry. Cell proliferation and formation of autophagosome were assessed by 3-(4, 5-dimethylthiazol-2-yl)-2, 5-diphenyltetrazolium bromide (MTT) assay and dansylcadaverine (MDC) staining assay. Cell migration and invasion were detected by wound healing assay and transwell assay. Western blot analysis was designed to detect the protein expressions of surviving, Bax, LC-3 П, Beclin-1, ERß, TßRⅠ, TßRⅡ, Smad2, Smad3 and Smad7. Real-Time fluorogenic PCR was designed to examine the mRNA expressions of surviving, Bax, ERß, TßRⅠ, TßRII, Smad2, Smad3 and Smad7. The results showed that ERß overexpression inhibited cell proliferation, migration and invasion, blocked cell cycle, and induced apoptosis and autophagy. Additionally, ERß overexpression significantly inhibited the expression of surviving, TßRⅠ, TßRⅡ, Smad2 and Smad3. Meanwhile, the expressions of Bax, LC-3 П, Beclin-1 and Smad7 were dramatically upregulated by ERß overexpression. In conclusion, ERß overexpression could inhibit cell proliferation, migration and invasion, block cell cycle, and promote apoptosis and autophagy in OS by downregulating TNG-ß signaling pathway.

17ß-estradiol protects nucleus pulposus cells from serum deprivation-induced apoptosis and regulates expression of MMP-3 and MMP-13 through promotion of autophagy.

Serum deprivation is a likely contributor to intervertebral disc (IVD) degeneration (IVDD).17ß-estradiol (E2) have been noted to protect nucleus pulposus cells (NPCs) against apoptosis. Autophagy and apoptosis play a paramount role in maintaining the homeostasis of IVD. So far, little research has been published on whether autophagy plays a role for the E2 mediated protection of NPCs. The aim of this study is to understand whether autophagy is involved in the protective effect of E2 against serum deprivation-induced cell apoptosis and expression of matrix metalloproteinase (MMP)-3 and MMP-13. mCherry-GFP-LC3-adenovirus transfection is used to monitor autophagy detection. The expression levels of autophagy-related proteins were measured by Western blotting, Apoptosis and MMPs were detected by flow cytometry and Western blotting. Accordingly, Autophagy and apoptosis was detected in NP cells under serum deprivation conditions, the autophagy incidence began to reached a peak value at 48 h, the apoptosis and MMPs incidence began reached a minimum value treat with E2 (10-7 M). Whereas the combined use of E2 and 3-MA led to a dramatic decrease in autophagy, while aberrantly elevated expression levels of apoptotic and MMPs. These data suggest that serum deprivation-induced apoptosis and MMP-3, MMP-13, which was efficiently suppressed by the E2 through promoting autophagy in rat NPCs.