The purpose of this research was to examine the effects of continuous care model of information-based hospital-family integration on colostomy patients. Miles’ operation is a major operative method ...for treating low rectal cancer, and this operation results in a permanent colostomy. It is difficult for patients to adapt to their colostomy. Previous studies have applied generally conventional nursing models to colostomy patients. This was a single-blind randomized controlled trial study. The sample of 155 patients who met the inclusion criteria was randomly assigned to either the experimental (
n
= 81) or control group (
n
= 74). The control group was provided with a routine standard of care. The experimental group was provided with an experimental treatment that consisted of an information-based (WeChat, blog, QQ, telephone, etc.) hospital-family integration continuous care model. Study variables were collected and instruments were selected as follows: basic information, State-Trait Anxiety Inventory (STAI), a self-efficacy scale, a colostomy complication assessment table, a quality of life scale, and a table of the degree of satisfaction. No statistically significant differences were found in demographic information between the experimental and control groups. In comparison with the control group, subjects in the experimental group had less anxiety and could better cope with anxiety, had a better self-efficacy and quality of life scores, and had fewer complications. The patients in the experimental group were shown to be more satisfied with the care model. In addition, the most useful and popular service is the online social tools WeChat and QQ, because they can communicate with video, and they are more real-time, efficient, and cheap. The continuous care model of information-based hospital-family integration significantly strengthened patients’ self-efficacy and confidence, which decreased colostomy complications, ultimately improving the quality of life.
The outbreak of coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has been a global challenge since December 2019. Although most patients ...with COVID-19 exhibit mild clinical manifestations, in approximately 5% of these patients, the disease eventually progresses to severe lung injury or even multiorgan dysfunction. This situation represents various challenges to hepatology. In the context of liver injury in patients with COVID-19, several key problems need to be solved. For instance, it is important to determine whether SARS-CoV-2 can directly invade liver, especially when ACE2 appears to be negligibly expressed on hepatocytes. In addition, the mechanisms underlying liver dysfunction in COVID-19 patients are not fully understood, which are likely multifactorial and related to hyperinflammation, dysregulated immune responses, abnormal coagulation and drugs. Here, we systematically describe the potential pathogenesis of COVID-19-associated liver injury and propose several hypotheses about its etiopathogenesis.
In this paper, we propose an accurate and effective method for detecting abnormal behavior. We consider the video as a series of frame sequences; in the training phase, our deep learning framework is ...used to extract appearance features and learn the relationship between historical features and current features in the normal video. In the testing phase, the predicted features that differ from the actual features are considered as abnormal. Our model is designed as a feature prediction framework with a new temporal attention mechanism. In the feature extraction stage, we transform a pre-trained Vgg16 network into a fully convolutional neural network and used the third pooling layer output as the appearance feature extraction to effectively capture static appearance features. Then, a new temporal attention mechanism is introduced to learn the contribution of different historical appearance features at the same position to the current features, thereby solving the problem of representing dynamic motion features. Finally, the LSTM network is used to decode the historical feature sequences with temporal attention to predict the features at the current moment. Those actual features that differ from the predicted features are considered as abnormal features. Using upsampling for the abnormal features locates abnormal behavior on the original frames. Experiments on two benchmark datasets demonstrate the competitive performance of our method with the state-of-the-art methods.
Human action recognition is a challenging task in the field of computer vision, where deep learning-based methods have made significant progress. Existing methods often use uniform or random ...sampling, which results in behaviorally irrelevant frames that contain redundant irrelevant features, which in turn leads to misclassification and high computational cost. To solve the problem of misclassification caused by unsuitable sampling methods and to reduce the computational cost, we propose a novel framework named Multi-Stream network based on Key Frame Sampling for human action recognition (MS-KFS). Specifically, we first introduce self-attention to associate deep information between different regions. On this basis, a key frame sampling module will be trained based on rewards and pseudo-labels to extract the moments where key actions are performed. Finally, the novel difference feature as well as the appearance and motion feature is developed to enhance feature characteristic in terms of both depth and timing, and thus, the features will be put into classifier and accomplish the task. A series experiment results validate that the MS-KFS outperforms the state-of-art methods.
Human-Object Interaction (HOI) Detection is a new genre of human-centric visual relationship detection task, which is significant to deep understanding of visual scenes. Due to the complexity of the ...visual scene in the image, HOI detection is still a challenging task, the most critical part of which is feature extraction and representation. Some existing approaches rely solely on local region information for HOI detection without using global contextual information, but global contextual information contributes to this task in some HOI categories. Other approaches incorporate global contextual information for HOI detection while losing local region information. In this work, we propose a multi-stream neural network architecture composed of three special module that employs both local region information and global contextual information for HOI detection. This model can detect not only the HOI categories based on local region information but also on global contextual information. Our model more fully considers all HOI categories in the dataset. Compared with other existing approaches, the proposed model shows improved performance on V-COCO and HICO-DET benchmark datasets, especially when predicting rare HOI categories.
Optical flow is widely used in human action recognition. However, the influence of complex background on optical flow often leads to low recognition efficiency. To deal with this issue, an optical ...flow-based physical feature-driven action recognition framework is proposed in this paper. We first calculate the original dense optical flow field. Then, for reducing computational burden, joint action relevance that can eliminate the pseudo-optical flow in complex background is developed. After that, a more state flow field is obtained by local spatial–temporal thermal diffusion processing. On this basis, we design a feature descriptor that takes the divergence, curl and gradient features of flow field into consideration. Finally, we adopt Fisher vector to encode descriptors for classification. Experimental on HMDB51, KTH and UCF101 datasets proves that actions in complex background can be recognized accurately by the proposed framework, which outperforms the already developed methods.
Metastasis is the main reason for high mortality in hepatocellular carcinoma (HCC) patients and the molecular mechanism remains unclear. Therefore, it is important to elucidate the mechanism ...underlying HCC metastasis. Here, we report a novel role of SIX homeobox 4 (SIX4), one of the SIX gene family, in promoting HCC metastasis. The elevated expression of SIX4 was positively correlated with loss of tumor encapsulation, microvascular invasion, higher TNM stage, and poor prognosis in human HCC. SIX4 expression was an independent and significant risk factor for the recurrence and survival in HCC patients. Upregulation of SIX4 promoted HCC invasion and metastasis, whereas downregulation of SIX4 decreased HCC invasion and metastasis. SIX4 transactivated Yes1 associated transcriptional regulator (YAP1) and MET proto-oncogene, receptor tyrosine kinase (MET) expression through directly binding to their promoters. Knockdown of YAP1 and c-MET inhibited SIX4-medicated HCC metastasis, while the stable overexpression of YAP1 and c-MET reversed the decreased metastasis induced by SIX4 knockdown. Hepatocyte growth factor (HGF), the specific ligand of c-MET, upregulated SIX4 expression through ERK/NF-κB pathway. Knockdown of SIX4 significantly decreased HGF-enhanced HCC metastasis. In human HCC tissues, SIX4 expression was positively correlated with nuclear YAP1, c-MET and HGF expression. Patients with positive coexpression of SIX4/ nuclear YAP1, SIX4/c-MET or HGF/SIX4 had the poorest prognosis. Moreover, the combination treatment of YAP1 inhibitor Verteporfin and c-MET inhibitor Capmatinib significantly suppressed SIX4-mediated HCC metastasis. In conclusion, SIX4 is a prognostic biomarker in HCC patients and targeting the HGF-SIX4-c-MET positive feedback loop may provide a promising strategy for the treatment of SIX4-driven HCC metastasis.
Sparc/osteonectin, cwcv, and kazal-like domain proteoglycan 1 (SPOCK1) is a matricellular protein which regulates cell proliferation, invasion, and survival but the function of SPOCK1 in liver ...fibrosis is obscure. In this study, we found that SPOCK1 expression increased significantly in fibrotic liver tissues and activated primary rat hepatic stellate cells (R-HSCs). SPOCK1 co-localized with α-smooth muscle actin (α-SMA) in the cytoplasm. Mechanistically, we found platelet-derived growth factor-BB (PDGF-BB) induced SPOCK1 expression by activating the PI3K/Akt/forkhead box M1 (FoxM1) signaling pathway. Intracellular SPOCK1 downregulation decreased the HSC activation, proliferation, and migration induced by PDGF-BB. Furthermore, intracellular SPOCK1 overexpression or recombinant SPOCK1 treatment promoted HSC activation, proliferation, and migration by activating the PI3K/Akt signaling pathway. Co-immunoprecipitation, double immunofluorescence staining indicated that SPOCK1 interacted with integrin α5β1, and neutralization of integrin α5β1 significantly reduced the role of recombinant SPOCK1 in HSCs. In vivo HSC-specific SPOCK1 knockdown following lentivirus administration dramatically ameliorated thioacetamide (TAA)-induced collagen deposition in rat livers. Collectively, our study indicates that SPOCK1 is crucial for hepatic fibrosis and it might be a promising therapeutic target.
Matricellular protein SPOCK1 expression positively correlates with liver fibrosis and hepatic stellate cell activation. SPOCK1 is upregulated by PDGF-BB via the PI3K/Akt/FoxM1 signaling pathway and induces pro-fibrogenic responses. Additionally, it promotes stellate cell pro-fibrogenic responses through the integrin α5β1/PI3K/Akt signaling pathway. SPOCK1 is therefore a promising therapeutic target for liver fibrosis.
Hepatocellular carcinoma is one of the most common highly malignant tumors in humans, as well as the leading cause of cancer-related death worldwide. Growing evidence has indicated that lncRNAs are ...implicated in different molecular mechanisms, including interactions with DNA, RNA, or protein, so that to regulate the gene expression at epigenetic, transcriptional, or posttranscriptional level. Moreover, the mechanism of action of lncRNA is closely related to its subcellular localization. An increasing number of studies have certified that lncRNA plays a significant biological function in the occurrence and development of hepatocellular carcinoma, such as involving in cell proliferation, metastasis, apoptosis, ferroptosis, autophagy, and reprogramming of energy metabolism. As a result, lncRNA has great potential as a novel biomarker for diagnosis or therapeutics of hepatocellular carcinoma. In this review, we highlight the correlation between subcellular localization of lncRNA and its mechanism of action, discuss the biological roles of lncRNA and the latest research advances in hepatocellular carcinoma, and emphasize the potential of lncRNA as a therapeutic target for advanced patients of hepatocellular carcinoma.
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•LncRNA plays crucial roles in hepatocellular carcinoma (HCC).•The function of lncRNA is determined by its subcellular localization•LncRNA mediate HCC cell proliferation, death and metastasis.•LncRNA may serve as promising therapeutic target for patients with advanced HCC.
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