Do as Sonographers Think: Contrast-enhanced Ultrasound for Thyroid Nodules Diagnosis via Microvascular Infiltrative Awareness.

Dynamic contrast-enhanced ultrasound (CEUS) imaging can reflect the microvascular distribution and blood flow perfusion, thereby holding clinical significance in distinguishing between malignant and benign thyroid nodules. Notably, CEUS offers a meticulous visualization of the microvascular distribution surrounding the nodule, leading to an apparent increase in tumor size compared to gray-scale ultrasound (US). In the dual-image obtained, the lesion size enlarged from gray-scale US to CEUS, as the microvascular appeared to be continuously infiltrating the surrounding tissue. Although the infiltrative dilatation of microvasculature remains ambiguous, sonographers believe it may promote the diagnosis of thyroid nodules. We propose a deep learning model designed to emulate the diagnostic reasoning process employed by sonographers. This model integrates the observation of microvascular infiltration on dynamic CEUS, leveraging the additional insights provided by gray-scale US for enhanced diagnostic support. Specifically, temporal projection attention is implemented on time dimension of dynamic CEUS to represent the microvascular perfusion. Additionally, we employ a group of confidence maps with flexible Sigmoid Alpha Functions to aware and describe the infiltrative dilatation process. Moreover, a self-adaptive integration mechanism is introduced to dynamically integrate the assisted gray-scale US and the confidence maps of CEUS for individual patients, ensuring a trustworthy diagnosis of thyroid nodules. In this retrospective study, we collected a thyroid nodule dataset of 282 CEUS videos. The method achieves a superior diagnostic accuracy and sensitivity of 89.52% and 93.75%, respectively. These results suggest that imitating the diagnostic thinking of sonographers, encompassing dynamic microvascular perfusion and infiltrative expansion, proves beneficial for CEUS-based thyroid nodule diagnosis.

MSEF-Net: Multi-scale edge fusion network for lumbosacral plexus segmentation with MR image.

Nerve damage of spine areas is a common cause of disability and paralysis. The lumbosacral plexus segmentation from magnetic resonance imaging (MRI) scans plays an important role in many computer-aided diagnoses and surgery of spinal nerve lesions. Due to the complex structure and low contrast of the lumbosacral plexus, it is difficult to delineate the regions of edges accurately. To address this issue, we propose a Multi-Scale Edge Fusion Network (MSEF-Net) to fully enhance the edge feature in the encoder and adaptively fuse multi-scale features in the decoder. Specifically, to highlight the edge structure feature, we propose an edge feature fusion module (EFFM) by combining the Sobel operator edge detection and the edge-guided attention module (EAM), respectively. To adaptively fuse the multi-scale feature map in the decoder, we introduce an adaptive multi-scale fusion module (AMSF). Our proposed MSEF-Net method was evaluated on the collected spinal MRI dataset with 89 patients (a total of 2848 MR images). Experimental results demonstrate that our MSEF-Net is effective for lumbosacral plexus segmentation with MR images, when compared with several state-of-the-art segmentation methods.

Multi-Instance Multi-Task Learning for Joint Clinical Outcome and Genomic Profile Predictions From the Histopathological Images.

With the remarkable success of digital histopathology and the deep learning technology, many whole-slide pathological images (WSIs) based deep learning models are designed to help pathologists diagnose human cancers. Recently, rather than predicting categorical variables as in cancer diagnosis, several deep learning studies are also proposed to estimate the continuous variables such as the patients' survival or their transcriptional profile. However, most of the existing studies focus on conducting these predicting tasks separately, which overlooks the useful intrinsic correlation among them that can boost the prediction performance of each individual task. In addition, it is sill challenge to design the WSI-based deep learning models, since a WSI is with huge size but annotated with coarse label. In this study, we propose a general multi-instance multi-task learning framework (HistMIMT) for multi-purpose prediction from WSIs. Specifically, we firstly propose a novel multi-instance learning module (TMICS) considering both common and specific task information across different tasks to generate bag representation for each individual task. Then, a soft-mask based fusion module with channel attention (SFCA) is developed to leverage useful information from the related tasks to help improve the prediction performance on target task. We evaluate our method on three cancer cohorts derived from the Cancer Genome Atlas (TCGA). For each cohort, our multi-purpose prediction tasks range from cancer diagnosis, survival prediction and estimating the transcriptional profile of gene TP53. The experimental results demonstrated that HistMIMT can yield better outcome on all clinical prediction tasks than its competitors.

A Generalized Full-to-Partial Registration Framework of 3D Point Sets for Computer-Aided Orthopedic Surgery.

OBJECTIVE: The precise alignment of full and partial 3D point sets is a crucial technique in computer-aided orthopedic surgery, but remains a significant challenge. This registration process is complicated by the partial overlap between the full and partial 3D point sets, as well as the susceptibility of 3D point sets to noise interference and poor initialization conditions. METHODS: To address these issues, we propose a novel full-to-partial registration framework for computer-aided orthopedic surgery that utilizes reinforcement learning. Our proposed framework is both generalized and robust, effectively handling the challenges of noise, poor initialization, and partial overlap. Moreover, this framework demonstrates exceptional generalization capabilities for various bones, including the pelvis, femurs, and tibias. RESULTS: Extensive experimentation on several bone datasets has demonstrated that the proposed method achieves a superior C.D. error of 8.211 e-05 and our method consistently outperforms state-of-the-art registration techniques. CONCLUSION AND SIGNIFICANCE: Hence, our proposed method is capable of achieving precise bone alignments for computer-aided orthopedic surgery.

Deep Semi-Supervised Ultrasound Image Segmentation by Using a Shadow Aware Network With Boundary Refinement.

Accurate ultrasound (US) image segmentation is crucial for the screening and diagnosis of diseases. However, it faces two significant challenges: 1) pixel-level annotation is a time-consuming and laborious process; 2) the presence of shadow artifacts leads to missing anatomy and ambiguous boundaries, which negatively impact reliable segmentation results. To address these challenges, we propose a novel semi-supervised shadow aware network with boundary refinement (SABR-Net). Specifically, we add shadow imitation regions to the original US, and design shadow-masked transformer blocks to perceive missing anatomy of shadow regions. Shadow-masked transformer block contains an adaptive shadow attention mechanism that introduces an adaptive mask, which is updated automatically to promote the network training. Additionally, we utilize unlabeled US images to train a missing structure inpainting path with shadow-masked transformer, which further facilitates semi-supervised segmentation. Experiments on two public US datasets demonstrate the superior performance of the SABR-Net over other state-of-the-art semi-supervised segmentation methods. In addition, experiments on a private breast US dataset prove that our method has a good generalization to clinical small-scale US datasets.

Anatomy preserving GAN for realistic simulation of intraoperative liver ultrasound images.

In ultrasound-guided liver surgery, the lack of large-scale intraoperative ultrasound images with important anatomical structures remains an obstacle hindering the successful application of AI to ultrasound guidance. In this case, intraoperative ultrasound (iUS) simulation should be conducted from preoperative magnetic resonance (pMR), which not only helps doctors understand the characteristics of iUS in advance, but also expands the iUS dataset from various imaging positions, thereby promoting the automatic iUS analysis in ultrasound guidance. Herein, a novel anatomy preserving generative adversarial network (ApGAN) framework was proposed to generate simulated intraoperative ultrasound (Sim-iUS) of liver with precise structure information from pMR. Specifically, the low-rank factors based bimodal fusion was first established focusing on the effective information of hepatic parenchyma. Then, a deformation field based correction module was introduced to learn and correct the slight structural distortion from surgical operations. Meanwhile, the multiple loss functions were designed to constrain the simulation of the content, structures, and style. Empirical results of clinical data showed that the proposed ApGAN obtained higher Structural Similarity (SSIM) of 0.74 and Fr´echet Inception Distance (FID) of 35.54 compared to existing methods. Furthermore, the average Hausdorff Distance (HD) error of the liver capsule structure was less than 0.25 mm, and the average relative (Euclidean Distance) ED error for polyps was 0.12 mm, indicating the high-level precision of this ApGAN in simulating the anatomical structures and focal areas.

Selective oxidative protection leads to tissue topological changes orchestrated by macrophage during ulcerative colitis.

Ulcerative colitis is a chronic inflammatory bowel disorder with cellular heterogeneity. To understand the composition and spatial changes of the ulcerative colitis ecosystem, here we use imaging mass cytometry and single-cell RNA sequencing to depict the single-cell landscape of the human colon ecosystem. We find tissue topological changes featured with macrophage disappearance reaction in the ulcerative colitis region, occurring only for tissue-resident macrophages. Reactive oxygen species levels are higher in the ulcerative colitis region, but reactive oxygen species scavenging enzyme SOD2 is barely detected in resident macrophages, resulting in distinct reactive oxygen species vulnerability for inflammatory macrophages and resident macrophages. Inflammatory macrophages replace resident macrophages and cause a spatial shift of TNF production during ulcerative colitis via a cytokine production network formed with T and B cells. Our study suggests components of a mechanism for the observed macrophage disappearance reaction of resident macrophages, providing mechanistic hints for macrophage disappearance reaction in other inflammation or infection situations.

Characterizing the Survival-Associated Interactions Between Tumor-Infiltrating Lymphocytes and Tumors From Pathological Images and Multi-Omics Data.

The tumor-infiltrating lymphocytes (TILs) and its correlation with tumors have shown significant values in the development of cancers. Many observations indicated that the combination of the whole-slide pathological images (WSIs) and genomic data can better characterize the immunological mechanisms of TILs. However, the existing image-genomic studies evaluated the TILs by the combination of pathological image and single-type of omics data (e.g., mRNA), which is difficulty in assessing the underlying molecular processes of TILs holistically. Additionally, it is still very challenging to characterize the intersections between TILs and tumor regions in WSIs and the high dimensional genomic data also brings difficulty for the integrative analysis with WSIs. Based on the above considerations, we proposed an end-to-end deep learning framework i.e., IMO-TILs that can integrate pathological image with multi-omics data (i.e., mRNA and miRNA) to analyze TILs and explore the survival-associated interactions between TILs and tumors. Specifically, we firstly apply the graph attention network to describe the spatial interactions between TILs and tumor regions in WSIs. As to genomic data, the Concrete AutoEncoder (i.e., CAE) is adopted to select survival-associated Eigengenes from the high-dimensional multi-omics data. Finally, the deep generalized canonical correlation analysis (DGCCA) accompanied with the attention layer is implemented to fuse the image and multi-omics data for prognosis prediction of human cancers. The experimental results on three cancer cohorts derived from the Cancer Genome Atlas (TCGA) indicated that our method can both achieve higher prognosis results and identify consistent imaging and multi-omics bio-markers correlated strongly with the prognosis of human cancers.

FAM3L: Feature-Aware Multi-Modal Metric Learning for Integrative Survival Analysis of Human Cancers.

Survival analysis is to estimate the survival time for an individual or a group of patients, which is a valid solution for cancer treatments. Recent studies suggested that the integrative analysis of histopathological images and genomic data can better predict the survival of cancer patients than simply using single bio-marker, for different bio-markers may provide complementary information. However, for the given multi-modal data that may contain irrelevant or redundant features, it is still challenge to design a distance metric that can simultaneously discover significant features and measure the difference of survival time among different patients. To solve this issue, we propose a Feature-Aware Multi-modal Metric Learning method (FAM3L), which not only learns the metric for distance constraints on patients' survival time, but also identifies important images and genomic features for survival analysis. Specifically, for each modality of data, we firstly design one feature-aware metric that can be decoupled into a traditional distance metric and a diagonal weight for important feature identification. Then, in order to explore the complex correlation across multiple modality data, we apply Hilbert-Schmidt Independence Criterion (HSIC) to jointly learn multiple metrics. Finally, based on the learned distance metrics, we apply the Cox proportional hazards model for prognosis prediction. We evaluate the performance of our proposed FAM3L method on three cancer cohorts derived from The Cancer Genome Atlas (TCGA), the experimental results demonstrate that our method can not only achieve superior performance for cancer prognosis, but also identify meaningful image and genomic features correlating strongly with cancer survival.

Joint Segmentation and Differential Diagnosis of Thyroid Nodule in Contrast-Enhanced Ultrasound Images.

OBJECTIVE: Microvascular perfusion can be observed in real time with contrast-enhanced ultrasound (CEUS), which is a novel ultrasound technology for visualizing the dynamic patterns of parenchymal perfusion. Automatic lesion segmentation and differential diagnosis of malignant and benign based on CEUS are crucial but challenging tasks for computer-aided diagnosis of thyroid nodule. METHODS: To tackle these two formidable challenges concurrently, we provide Trans-CEUS, a spatial-temporal transformer-based CEUS analysis model to finish the joint learning of these two challenging tasks. Specifically, the dynamic swin-transformer encoder and multi-level feature collaborative learning are combined into U-net for achieving accurate segmentation of lesions with ambiguous boundary from CEUS. In addition, variant transformer-based global spatial-temporal fusion is proposed to obtain long-distance enhancement perfusion of dynamic CEUS for promoting differential diagnosis. RESULTS: Empirical results of clinical data showed that our Trans-CEUS model achieved not only a good lesion segmentation result with a high Dice similarity coefficient of 82.41%, but also superior diagnostic accuracy of 86.59%. Conclusion & significance: This research is novel since it is the first to incorporate the transformer into CEUS analysis, and it shows promising results on dynamic CEUS datasets for both segmentation and diagnosis tasks of the thyroid nodule.

Dynamic Perfusion Representation and Aggregation Network for Nodule Segmentation Using Contrast-Enhanced US.

Dynamic contrast-enhanced ultrasound (CEUS) imaging has been widely applied in lesion detection and characterization, due to its offered real-time observation of microvascular perfusion. Accurate lesion segmentation is of great importance to the quantitative and qualitative perfusion analysis. In this paper, we propose a novel dynamic perfusion representation and aggregation network (DpRAN) for the automatic segmentation of lesions using dynamic CEUS imaging. The core challenge of this work lies in enhancement dynamics modeling of various perfusion areas. Specifically, we divide enhancement features into the two scales: short-range enhancement patterns and long-range evolution tendency. To effectively represent real-time enhancement characteristics and aggregate them in a global view, we introduce the perfusion excitation (PE) gate and cross-attention temporal aggregation (CTA) module, respectively. Different from the common temporal fusion methods, we also introduce an uncertainty estimation strategy to assist the model to locate the critical enhancement point first, in which a relatively distinguished enhancement pattern is displayed. The segmentation performance of our DpRAN method is validated on our collected CEUS datasets of thyroid nodules. We obtain the mean dice coefficient (DSC) and intersection of union (IoU) of 0.794 and 0.676, respectively. Superior performance demonstrates its efficacy to capture distinguished enhancement characteristics for lesion recognition.

Human umbilical cord mesenchymal stem cells prevent glucocorticoid-induced osteonecrosis of the femoral head by promoting angiogenesis.

The impairment of angiogenesis is an outstanding pathogenic characteristic of glucocorticoid (GC)-induced osteonecrosis of the femoral head (ONFH). Human umbilical cord mesenchymal stem cells (hUC-MSCs) have been used in several diseases models, which were reported to be involved in the angiogenesis. However, whether hUC-MSCs suppress the GC-induced ONFH via promoting angiogenesis is still unclear. hUC-MSCs were isolated from the Wharton's jelly using the explant culture method. A GC-induced ONFH model was established in vitro and in vivo. The angiogenesis, proliferation and migration ability of HMECs were determined using the tube-forming, CCK-8, transwell and scratching assays in vitro. The protective role of hUC-MSCs in GC-induced ONFH was evaluated using micro-CT scanning and histological, immunohistochemical (IHC) and Quantitative reverse transcription-polymerase chain reaction (qRT-PCR) assays in vivo. The results showed that hUC-MSCs treatment improved the tube-forming, proliferation and migration ability of HMECs in vitro. Moreover, hUC-MSCs treatment enhanced the integrity of trabecular bone of the femoral head, and the tube-forming ability in vivo. hUC-MSCs prevent the femoral head against necrosis and damage caused by GCs though promoting angiogenesis.

Immunohistochemical Index Prediction of Breast Cancer by Using Hybrid Ultrasound Data.

The immunohistochemical index is significant to help the selection of treatment strategy for breast cancer patients. Existing studies that focus on conventional ultrasound features and certain types of immunohistochemistry expressions are limited to correlation exploration, and only few studies have built predictive models. In this study, a Tri-Branch deep learning network is built for prediction of the immunohistochemical HER2 using the hybrid ultrasound data, instead of relying on the invasive and biopsy-based histopathological examination. Specifically, the deep learning model uses the cross-model attention and the interactive learning approaches to obtain the strong complementarity of hybrid data comprising B-mode US, contrast-enhanced ultrasound, and optical flow motion information to enhance accuracy of immunohistochemical HER2 prediction. The proposed prediction model was evaluated using hybrid ultrasound dataset from 335 breast cancer patients. The experimental results indicated that the Tri-Branch model had a high accuracy of 86.23% for HER2 status prediction, and the HER2 status prediction for patients with different pathology grades exhibited some meaningful clinical observations.

Transport-Based Anatomical-Functional Metric Learning for Liver Tumor Recognition Using Dual-View Dynamic CEUS Imaging.

Hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC) are the most common malignancy arising from primary liver cancer (PLC). Liver ultrasound (US) has been the main approach for the early screening and differential diagnosis. Since ultrasonic findings of PLC are closely associated with tumor growth pattern, histological morphology, tumor staging, and other factors, ultrasonic imaging findings overlap partially. Besides, qualitative assessment is highly dependent on expertise. To improve the diagnostic objectiveness, we propose a novel transport-based anatomical-functional metric learning (T-AFML) method to quantify imaging similarity of both the gray-scale US and dynamic contrast-enhanced US view. Considering that the hemodynamic changes vary with individuals, we introduce a temporally regularized optimal transport to align the local enhancement patterns automatically. To sufficiently exploit ultrasonic findings similarity from different modalities, a selector-based metric integration mechanism is adopted to adaptively select a dominant modality accounting for the similarity measure. In this retrospective study, we collected a total of 174 liver cancer patients consists of 105 HCC and 69 ICC, and our method achieves the superior diagnostic accuracy and sensitivity of 88.41% and 86.16%, respectively, demonstrating its efficacy in quantifying multi-modal ultrasonic findings similarity for PLC diagnosis.

Cognition, Aryl Hydrocarbon Receptor Repressor Methylation, and Abstinence Duration-Associated Multimodal Brain Networks in Smoking and Long-Term Smoking Cessation.

Cigarette smoking and smoking cessation are associated with changes in cognition and DNA methylation; however, the neurobiological correlates of these effects have not been fully elucidated, especially in long-term cessation. Cognitive performance, percent methylation of the aryl hydrocarbon receptor repressor (AHRR) gene, and abstinence duration were used as references to supervise a multimodal fusion analysis of functional, structural, and diffusion magnetic resonance imaging (MRI) data, in order to identify associated brain networks in smokers and ex-smokers. Correlations among these networks and with smoking-related measures were performed. Cognition-, methylation-, and abstinence duration-associated networks discriminated between smokers and ex-smokers and correlated with differences in fractional amplitude of low frequency fluctuations (fALFF) values, gray matter volume (GMV), and fractional anisotropy (FA) values. Long-term smoking cessation was associated with more accurate cognitive performance, as well as lower fALFF and more GMV in the hippocampus complex. The methylation- and abstinence duration-associated networks positively correlated with smoking-related measures of abstinence duration and percent methylation, respectively, suggesting they are complementary measures. This analysis revealed structural and functional co-alterations linked to smoking abstinence and cognitive performance in brain regions including the insula, frontal gyri, and lingual gyri. Furthermore, AHRR methylation, a promising epigenetic biomarker of smoking recency, may provide an important complement to self-reported abstinence duration.

Annotation-guided encoder-decoder network for bone extraction in ultrasound-assisted orthopedic surgery.

The patients and surgeons are usually exposed in massive ionizing radiation during fluoroscopy-based navigation orthopedic surgery. Comparatively, ultrasound-assisted orthopedic surgery could not only decrease the risk of radiation but also provide rich navigation information. However, due to the artifacts in ultrasound images, the extraction of bone structure from ultrasound sequences can be a particularly difficult task, which leads to some major challenges in ultrasound-assisted orthopedic navigation. In this paper, we propose an annotation-guided encoder-decoder network (AGN) to extract bone structure from the radiation-free ultrasound sequences. Specifically, the variability of the ultrasound probe's pose leads to the change of the ultrasound frame during the acquisition of ultrasound sequences. Therefore, a feature alignment module deployed in the AGN model is used to achieve reliable matching across ultrasound frames. Moreover, inspired by the interactive ultrasound analysis, where user annotated foreground information can help target extraction, our AGN model incorporates the annotation information obtained by Siamese networks. Experimental results validated that the AGN model not only produced better bone surface extraction than state-of-the-art methods (IOU: 0.92 versus. 0.88), but also achieved almost real-time extraction with the speed about 15 frames per second. In addition, the acquired bone surface further provided radiation-free 3D intraoperative bone structure for the intuitive navigation of orthopedic surgery.

[Unsupervised deep learning for identifying the O 6-carboxymethyl guanine by nanopore sequencing].

O 6-carboxymethyl guanine(O 6-CMG) is a highly mutagenic alkylation product of DNA that causes gastrointestinal cancer in organisms. Existing studies used mutant Mycobacterium smegmatis porin A (MspA) nanopore assisted by Phi29 DNA polymerase to localize it. Recently, machine learning technology has been widely used in the analysis of nanopore sequencing data. But the machine learning always need a large number of data labels that have brought extra work burden to researchers, which greatly affects its practicability. Accordingly, this paper proposes a nano-Unsupervised-Deep-Learning method (nano-UDL) based on an unsupervised clustering algorithm to identify methylation events in nanopore data automatically. Specially, nano-UDL first uses the deep AutoEncoder to extract features from the nanopore dataset and then applies the MeanShift clustering algorithm to classify data. Besides, nano-UDL can extract the optimal features for clustering by joint optimizing the clustering loss and reconstruction loss. Experimental results demonstrate that nano-UDL has relatively accurate recognition accuracy on the O 6-CMG dataset and can accurately identify all sequence segments containing O 6-CMG. In order to further verify the robustness of nano-UDL, hyperparameter sensitivity verification and ablation experiments were carried out in this paper. Using machine learning to analyze nanopore data can effectively reduce the additional cost of manual data analysis, which is significant for many biological studies, including genome sequencing.

Recent Advances of Deep Learning for Computational Histopathology: Principles and Applications.

With the remarkable success of digital histopathology, we have witnessed a rapid expansion of the use of computational methods for the analysis of digital pathology and biopsy image patches. However, the unprecedented scale and heterogeneous patterns of histopathological images have presented critical computational bottlenecks requiring new computational histopathology tools. Recently, deep learning technology has been extremely successful in the field of computer vision, which has also boosted considerable interest in digital pathology applications. Deep learning and its extensions have opened several avenues to tackle many challenging histopathological image analysis problems including color normalization, image segmentation, and the diagnosis/prognosis of human cancers. In this paper, we provide a comprehensive up-to-date review of the deep learning methods for digital H&E-stained pathology image analysis. Specifically, we first describe recent literature that uses deep learning for color normalization, which is one essential research direction for H&E-stained histopathological image analysis. Followed by the discussion of color normalization, we review applications of the deep learning method for various H&E-stained image analysis tasks such as nuclei and tissue segmentation. We also summarize several key clinical studies that use deep learning for the diagnosis and prognosis of human cancers from H&E-stained histopathological images. Finally, online resources and open research problems on pathological image analysis are also provided in this review for the convenience of researchers who are interested in this exciting field.

Human Umbilical Cord Mesenchymal Stem Cells Improve the Necrosis and Osteocyte Apoptosis in Glucocorticoid-Induced Osteonecrosis of the Femoral Head Model through Reducing the Macrophage Polarization.

Background and Objectives: Apoptosis is an outstanding determinant of glucocorticoid (GC)-induced osteonecrosis of the femoral head (ONFH). Human umbilical cord mesenchymal stem cells (hUC-MSCs) have been demonstrated to be associated with apoptosis in diseases models. However, the role of hUC-MSCs in GC-induced ONFH via regulating apoptosis still needs further study. Methods and Results: In the present study, a GC-induced ONFH model was built in vivo through a consecutive injection with lipopolysaccharide (LPS) and methylprednisolone. The necrosis and apoptosis of the femoral head was evaluated by histological and Terminal-deoxynucleoitidyl Transferase Mediated Nick End Labeling (TUNEL) assay. The level of collagen and TRAP positive cells were determined by Masson and TRAP staining, respectively. M1 macrophage polarization was assessed using immunofluorescence assay. The level of proinflammatory cytokines including tumor necrosis factor (TNF)-α, Interleukin (IL)-1ß and IL-6 of femoral head was determined by enzyme-linked immunosorbent assay (ELISA) kits. The protein expression of AKT, mTOR, p-AKT and p-mTOR was detected using western blot assay. The results showed that hUC-MSCs treatment prominently promoted the GC-induced the decrease of the collagen level and the increase of TRAP positive cells. Besides, hUC-MSCs treatment decreased necrosis and apoptosis, macrophage polarization, the level of TNF-α, IL-1ß and IL-6, the protein expression of p-AKT and p-mTOR, and the radio of p-AKT to AKT and p-mTOR to mTOR of femoral head in vivo. Conclusions: Therefore, the present study revealed that hUC-MSCs improved the necrosis and osteocyte apoptosis in GC-induced ONFH model through reducing the macrophage polarization, which was associated with the inhibition of AKT/mTOR signaling pathway.

Development and validation of a set of novel and robust 4-lncRNA-based nomogram predicting prostate cancer survival by bioinformatics analysis.

BACKGROUND AND OBJECTIVE: Accumulating evidence shows that long noncoding RNAs (lncRNAs) possess great potential in the diagnosis and prognosis of prostate cancer (PCa). Therefore, this study aimed to construct an lncRNA-based signature to more accurately predict the prognosis of different PCa patients, so as to improve patient management and prognosis. METHODS: Through univariate and multivariate Cox regression analysis, this study constructed a 4 lncRNAs-based prognosis nomogram for the classification and prediction of survival risk in patients with PCa based on TCGA data. Then we used the data of TCGA and ICGC to verify the performance of our prediction model. The receiver operating characteristic curve was plotted for detecting and validating our prediction model sensitivity and specificity. In addition, Cox regression analysis was conducted to examine whether the signature's prediction ability was independent of additional clinicopathological variables. Possible biological functions for those prognostic lncRNAs were predicted on those 4 protein-coding genes (PCGs) related to lncRNAs. RESULTS: Four lncRNAs (HOXB-AS3, YEATS2-AS1, LINC01679, PRRT3-AS1) were extracted after COX regression analysis for classifying patients into high and low-risk groups by different OS rates. As suggested by ROC analysis, our proposed model showed high sensitivity and specificity. Independent prognostic capability of the model from other clinicopathological factors was indicated through further analysis. Based on functional enrichment, those action sites for prognostic lncRNAs were mostly located in the extracellular matrix and cell membrane, and their functions are mainly associated with the adhesion, activation and transport of the components across the extracellular matrix or cell membrane. CONCLUSION: Our current study successfully identifies a novel candidate, which can provide more convincing evidence for prognosis in addition to the traditional clinicopathological indicators to predict the PCa survival, and laying the foundation for offering potentially novel therapeutic treatment. Additionally, this study sheds more lights on the PCa-related molecular mechanisms.