Ultrasound-Guided Percutaneous Transhepatic Cholangioscopic Lithotripsy for the Treatment of Common Bile Duct Stones and Analysis of Risk Factors for Recurrence.

BACKGROUND: As a minimally invasive treatment for common bile duct (CBD) stones, ultrasound-guided percutaneous transhepatic cholangioscopic lithotripsy (PTCSL) is gaining attention and recognition from the medical community. METHODS: A retrospective analysis was conducted on patients with CBD stones treated in our hospital from January 2016 to April 2022. Patients were divided into three groups: 77 treated with PTCSL, 93 with endoscopic retrograde cholangiopancreatography (ERCP), and 103 with laparoscopic common bile duct exploration (LCBDE). Their clinical data, perioperative indicators, and complications were analyzed comparatively. Then, risk factors for the post-PTCSL recurrence of CBD stones were analyzed by logistic regressions. Finally, the receiver operating characteristic curve was drawn. RESULTS: All perioperative indicators of the PTCSL group were better than the LCBDE group (P < 0.001). The incidences of cholangitis, hemobilia, and incisional infection after surgery were lower in the PTCSL group than in the LCBDE group (P < 0.05). Pancreatitis, reflux esophagitis, and papillary stenosis occurred less frequently in the PTCSL group than in the ERCP group (P < 0.05). Logistic regression analysis indicated that gallstones and family history were independent risk factors. The AUC for recurrent CBD stones predicted by multi-indicators was 0.895 (95% CI 0.792-0.999, P < 0.001) with a sensitivity of 96.7% and specificity of 68.8%. CONCLUSIONS: Ultrasound-guided PTCSL is a safe and effective treatment for CBD stones. Patients recovered quickly with fewer postoperative complications. It can be a first-line treatment for CBD stones. Gallstones and family history are independent risk factors for recurrent CBD stones, which provide a reference for clinicians in identifying the high-risk population needing close follow-up.

Semi-Supervised Crowd Counting via Multiple Representation Learning.

There has been a growing interest in counting crowds through computer vision and machine learning techniques in recent years. Despite that significant progress has been made, most existing methods heavily rely on fully-supervised learning and require a lot of labeled data. To alleviate the reliance, we focus on the semi-supervised learning paradigm. Usually, crowd counting is converted to a density estimation problem. The model is trained to predict a density map and obtains the total count by accumulating densities over all the locations. In particular, we find that there could be multiple density map representations for a given image in a way that they differ in probability distribution forms but reach a consensus on their total counts. Therefore, we propose multiple representation learning to train several models. Each model focuses on a specific density representation and utilizes the count consistency between models to supervise unlabeled data. To bypass the explicit density regression problem, which makes a strong parametric assumption on the underlying density distribution, we propose an implicit density representation method based on the kernel mean embedding. Extensive experiments demonstrate that our approach outperforms state-of-the-art semi-supervised methods significantly.

Robust and Rotation-Equivariant Contrastive Learning.

Contrastive learning (CL) methods achieve great success by learning the invariant representation from various transformations. However, rotation transformations are considered harmful to CL and are rarely used, which results in failure when the objects show unseen orientations. This article proposes a representation focus shift network (RefosNet), which adds the rotation transformations to CL methods to improve the robustness of representation. First, the RefosNet constructs the rotation-equivariant mapping between the features of the original image and the rotated ones. Then, the RefosNet learns semantic-invariant representations (SIRs) based on explicitly decoupling the rotation-invariant features and the rotation-equivariant features. Moreover, an adaptive gradient passivation strategy is introduced to gradually shift the representation focus to invariant representations. This strategy can prevent catastrophic forgetting of the rotation equivariance, which is beneficial to the generalization of representations in both seen and unseen orientations. We adapt the baseline methods (i.e.", SimCLR" and "momentum contrast (MoCo) v2") to work with RefosNet to verify the performance. Extensive experimental results show that our method achieves significant improvements on the task of recognition. On ObjectNet-13 with unseen orientations, RefosNet gains 7.12% in terms of classification accuracy compared with SimCLR. On datasets in seen orientation, the performance improves by 5.5% on ImageNet-100, 7.29% on STL10, and 1.93% on CIFAR10. In addition, RefosNet has strong generalization on Place205, PASCAL VOC, and Caltech 101. Our method has also achieved satisfactory results in image retrieval tasks.

Brain Cognition-Inspired Dual-Pathway CNN Architecture for Image Classification.

Inspired by the global-local information processing mechanism in the human visual system, we propose a novel convolutional neural network (CNN) architecture named cognition-inspired network (CogNet) that consists of a global pathway, a local pathway, and a top-down modulator. We first use a common CNN block to form the local pathway that aims to extract fine local features of the input image. Then, we use a transformer encoder to form the global pathway to capture global structural and contextual information among local parts in the input image. Finally, we construct the learnable top-down modulator where fine local features of the local pathway are modulated by global representations of the global pathway. For ease of use, we encapsulate the dual-pathway computation and modulation process into a building block, called the global-local block (GL block), and a CogNet of any depth can be constructed by stacking a necessary number of GL blocks one after another. Extensive experimental evaluations have revealed that the proposed CogNets have achieved the state-of-the-art performance accuracies on all the six benchmark datasets and are very effective for overcoming the "texture bias" and the "semantic confusion" problems faced by many CNN models.

Model Behavior Preserving for Class-Incremental Learning.

Deep models have shown to be vulnerable to catastrophic forgetting, a phenomenon that the recognition performance on old data degrades when a pre-trained model is fine-tuned on new data. Knowledge distillation (KD) is a popular incremental approach to alleviate catastrophic forgetting. However, it usually fixes the absolute values of neural responses for isolated historical instances, without considering the intrinsic structure of the responses by a convolutional neural network (CNN) model. To overcome this limitation, we recognize the importance of the global property of the whole instance set and treat it as a behavior characteristic of a CNN model relevant to model incremental learning. On this basis: 1) we design an instance neighborhood-preserving (INP) loss to maintain the order of pair-wise instance similarities of the old model in the feature space; 2) we devise a label priority-preserving (LPP) loss to preserve the label ranking lists within instance-wise label probability vectors in the output space; and 3) we introduce an efficient derivable ranking algorithm for calculating the two loss functions. Extensive experiments conducted on CIFAR100 and ImageNet show that our approach achieves the state-of-the-art performance.

Deep Class-Incremental Learning From Decentralized Data.

In this article, we focus on a new and challenging decentralized machine learning paradigm in which there are continuous inflows of data to be addressed and the data are stored in multiple repositories. We initiate the study of data-decentralized class-incremental learning (DCIL) by making the following contributions. First, we formulate the DCIL problem and develop the experimental protocol. Second, we introduce a paradigm to create a basic decentralized counterpart of typical (centralized) CIL approaches, and as a result, establish a benchmark for the DCIL study. Third, we further propose a decentralized composite knowledge incremental distillation (DCID) framework to transfer knowledge from historical models and multiple local sites to the general model continually. DCID consists of three main components, namely, local CIL, collaborated knowledge distillation (KD) among local models, and aggregated KD from local models to the general one. We comprehensively investigate our DCID framework by using a different implementation of the three components. Extensive experimental results demonstrate the effectiveness of our DCID framework. The source code of the baseline methods and the proposed DCIL is available at https://github.com/Vision-Intelligence-and-Robots-Group/DCIL.

Combined Treatment of Tanshinone I and Epirubicin Revealed Enhanced Inhibition of Hepatocellular Carcinoma by Targeting PI3K/AKT/HIF-1α.

Background: Epirubicin (EADM) is a common chemotherapeutic agent in hepatocellular carcinoma (HCC). The accumulation of hypoxia-inducible factor-1α (HIF-1α) is an important cause of drug resistance to EADM in HCC. Tanshinone I (Tan I) is an agent with promising anti-cancer effects alone or with other drugs. Some tanshinones mediate HIF-1α regulation via PI3K/AKT. However, the role of Tan I combined with EADM to reduce the resistance of HCC to EADM has not been investigated. Therefore, this study aimed to investigate the combined use of Tan I and EADM in HCC and the underlying mechanism of PI3K/AKT/HIF-1α. Methods: HCC cells were treated with Tan I, EADM, or the combined treatment for 48 hrs. Cell transfection was used to construct HIF-1α overexpression HCC stable cells. Cell viability, colony formation, and flow cytometric assays were used to detect the viability, proliferation, and apoptosis in HCC cells. Synergism between Tan I and EADM were tested by calculating the Bliss synergy score, positive excess over bliss additivism (EOBA), and the combination index (CI). Western blotting analyses were used to detect the levels of ß-actin, HIF-1α, PI3K p110α, p-Akt Thr308, Cleaved Caspase-3, and Cleaved Caspase-9. Toxicity parameters were used to evaluate the safety of the combination in mice. The xenograft model of mice was built by HCC stable cell lines, which was administrated with Tan I, EADM, or a combination of them for 8 weeks. Immunohistochemistry staining (IHC) was used to assess tumor apoptosis in mouse models. Results: Hypoxia could upregulate HIF-1α to induce drug resistance in HCC cancer cells. The combination of Tan I and EADM was synergistic. Although Tan I or EADM alone could inhibit HCC cancer cells, the combination of them could further enhance the cytotoxicity and growth inhibition by targeting the PI3K/AKT/HIF-1α signaling pathway. Furthermore, Tan I and EADM synergistically reversed HIF-1α-mediated drug resistance to inhibit HCC. The results of toxicity parameters showed that the combination was safe in mice. Meanwhile, animal models showed that Tan I not only improved the anti-tumor effect of EADM, but also reduced the drug reactions of EADM-induced weight loss. Conclusion: Our results suggested that Tan I could effectively improve the anti-tumor effect of EADM, and synergize EADM to reverse HIF-1α mediated resistance via targeting PI3K/AKT/HIF-1α signaling pathway.

Identity-Quantity Harmonic Multi-Object Tracking.

The data association problem of multi-object tracking (MOT) aims to assign IDentity (ID) labels to detections and infer a complete trajectory for each target. Most existing methods assume that each detection corresponds to a unique target and thus cannot handle situations when multiple targets occur in a single detection due to detection failure in crowded scenes. To relax this strong assumption for practical applications, we formulate the MOT as a Maximizing An Identity-Quantity Posterior (MAIQP) problem on the basis of associating each detection with an identity and a quantity characteristic and then provide solutions to tackle two key problems arising. Firstly, a local target quantification module is introduced to count the number of targets within one detection. Secondly, we propose an identity-quantity harmony mechanism to reconcile the two characteristics. On this basis, we develop a novel Identity-Quantity HArmonic Tracking (IQHAT) framework that allows assigning multiple ID labels to detections containing several targets. Through extensive experimental evaluations on five benchmark datasets, we demonstrate the superiority of the proposed method.

Efficacy of Near-Infrared Fluorescence-Guided Hepatectomy for the Detection of Colorectal Liver Metastases: A Randomized Controlled Trial.

BACKGROUND: The application of indocyanine green fluorescence-guided hepatectomy for liver metastases from colorectal cancer is in the preliminary stage of clinical practice; thus, its efficacy needs to be determined. This study compared the number of intrahepatic colorectal liver metastases detected intraoperatively and postoperative recovery data between patients who underwent traditional hepatectomy (nonindocyanine green group) and traditional hepatectomy plus intraoperative indocyanine green fluorescence imaging (indocyanine green group). STUDY DESIGN: Between January 2018 and March 2020, patients with potentially resectable colorectal liver metastases were randomly assigned to the nonindocyanine green or indocyanine green group. The number of intrahepatic colorectal liver metastases identified intraoperatively and based on postoperative recovery data were compared between both groups. RESULTS: Overall, we recruited 80 patients, among whom 72 eligible patients were randomly assigned. After allocation, 64 patients, comprising 32 in each group, underwent the allocated intervention and follow-up. Compared with the nonindocyanine green group, the mean number of intrahepatic colorectal liver metastases identified intraoperatively in the indocyanine green group was significantly greater (mean [standard deviation], 3.03 [1.58] vs 2.28 [1.35]; p = 0.045), the postoperative hospital stay was shorter (p = 0.012) and the 1-year recurrence rate was also lower (p = 0.017). Postoperative complications and 90-day mortality were comparable, with no statistical differences. CONCLUSIONS: Indocyanine green fluorescence imaging significantly increases the number of intrahepatic colorectal liver metastases identified and reduces postoperative hospital stay and 1-year recurrence rate without increasing hepatectomy-related complications and mortality rates.

Disentangling Task-Oriented Representations for Unsupervised Domain Adaptation.

Unsupervised domain adaptation (UDA) aims to address the domain-shift problem between a labeled source domain and an unlabeled target domain. Many efforts have been made to eliminate the mismatch between the distributions of training and testing data by learning domain-invariant representations. However, the learned representations are usually not task-oriented, i.e., being class-discriminative and domain-transferable simultaneously. This drawback limits the flexibility of UDA in complicated open-set tasks where no labels are shared between domains. In this paper, we break the concept of task-orientation into task-relevance and task-irrelevance, and propose a dynamic task-oriented disentangling network (DTDN) to learn disentangled representations in an end-to-end fashion for UDA. The dynamic disentangling network effectively disentangles data representations into two components: the task-relevant ones embedding critical information associated with the task across domains, and the task-irrelevant ones with the remaining non-transferable or disturbing information. These two components are regularized by a group of task-specific objective functions across domains. Such regularization explicitly encourages disentangling and avoids the use of generative models or decoders. Experiments in complicated, open-set scenarios (retrieval tasks) and empirical benchmarks (classification tasks) demonstrate that the proposed method captures rich disentangled information and achieves superior performance.

Evidence of SARS-CoV-2 infection in gallbladder and aggravating cholecystitis to septic shock: a case report.

Coronavirus disease 2019 (COVID-19) has threatened human health worldwide and could lead to multiple organs injury. However, the impact on the virus infecting the biliary system, especially the gallbladder, has remained unclear and no pathological evidence has been reported yet. A case of SARS-CoV-2 infection in a gallbladder with cholecystitis, which progressed rapidly to sepsis and required an emergency operation was investigated and reported. Clinical specimens of the COVID-19 patient including serum, oropharyngeal swabs, sputum, bile, abdominal drainage fluid, urine, stool, and gallbladder tissue were collected and tested for SARS-CoV-2 RNA using a quantitative polymerase chain reaction (qPCR) assay. Fresh normal gallbladder tissue and gangrenous gallbladder tissue were also collected for further research including hematoxylin and eosin (HE), immunohistochemistry (IHC), and immunofluorescent (IF) staining, and compared with the gallbladder from the COVID-19 patient. The bile, as well as the serum, oropharyngeal swabs, sputum, abdominal drainage fluid, urine, and rectal swabs were consecutively negative for SARS-CoV-2 RNA. The viral host receptor angiotensin-converting enzyme 2 (ACE2) was highly expressed in gallbladder epithelial cells, and viral nucleocapsid protein (NP) was visualized in the cytoplasm of gallbladder epithelial cells. Immune cells including CD2, CD3, CD4, CD8, CD20, CD38, CD68, and MPO were positive in gangrenous gallbladder tissues without SARS-CoV-2 infection, and were relatively downregulated in SARS-CoV-2 infective gallbladder tissue. This study provided evidence of SARS-CoV-2 infection in the gallbladder and verified that the gallbladder was one of the target organs that SARS-CoV-2 could attack and damage using ACE2 as a cell receptor. Due to the immune dysregulation involved, more vigilant management and early assessment is needed for COVID-19 patients with the comorbidity of cholecystitis.

Selenite-induced ROS/AMPK/FoxO3a/GABARAPL-1 signaling pathway modulates autophagy that antagonize apoptosis in colorectal cancer cells.

Previous studies have shown that selenium possessed chemotherapeutic effect against multiple malignant cancers, inducing diverse stress responses including apoptosis and autophagy. Selenite was previously shown to induce apoptosis and autophagy in colorectal cancer cells. However, the relationship between selenite-induced apoptosis and autophagy was not fully understood. Our results revealed a pro-survival role of selenite-induced autophagy against apoptosis in colorectal cancer cells. Real-time PCR array of autophagy-related genes showed that GABARAPL-1 was significantly upregulated in colorectal cancer cells, which was confirmed by western blot and immunofluorescence results. Knockdown of GABARAPL-1 significantly inhibited selenite-induced autophagy and enhanced apoptosis. Furthermore, we found that selenite-induced upregulation of GABARAPL-1 was caused by upregulated p-AMPK and FoxO3a level. Their interaction was correlated with involved in regulation of GABARAPL-1. We observed that activation and inhibition of AMPK influenced both autophagy and apoptosis level via FoxO3a/ GABARAPL-1 signaling, implying the pro-survival role of autophagy against apoptosis. Importantly, we corroborated these findings in a colorectal cancer xenograft animal model with immunohistochemistry and western blot results. Collectively, these results show that sodium selenite could induce ROS/AMPK/FoxO3a/GABARAPL-1-mediated autophagy and downregulate apoptosis in both colorectal cancer cells and colon xenograft model. These findings help to explore sodium selenite as a potential anti-cancer drug in clinical practices.

Analogy-Detail Networks for Object Recognition.

The human visual system can recognize object categories accurately and efficiently and is robust to complex textures and noises. To mimic the analogy-detail dual-pathway human visual cognitive mechanism revealed in recent cognitive science studies, in this article, we propose a novel convolutional neural network (CNN) architecture named analogy-detail networks (ADNets) for accurate object recognition. ADNets disentangle the visual information and process them separately using two pathways: the analogy pathway extracts coarse and global features representing the gist (i.e., shape and topology) of the object, while the detail pathway extracts fine and local features representing the details (i.e., texture and edges) for determining object categories. We modularize the architecture and encapsulate the two pathways into the analogy-detail block as the CNN building block to construct ADNets. For implementation, we propose a general principle that transmutes typical CNN structures into the ADNet architecture and applies the transmutation on representative baseline CNNs. Extensive experiments on CIFAR10, CIFAR100, street view house numbers, and ImageNet data sets demonstrate that ADNets significantly reduce the test error rates of the baseline CNNs by up to 5.76% and outperform other state-of-the-art architectures. Comprehensive analysis and visualizations further demonstrate that ADNets are interpretable and have a better shape-texture tradeoff for recognizing the objects with complex textures.

SSR2 overexpression associates with tumorigenesis and metastasis of Hepatocellular Carcinoma through modulating EMT.

Background: Hepatocellular carcinoma (HCC) is a common malignancy around the world. The molecular mechanisms underlying HCC tumorigenesis and metastasis are far from clear. Numerous studies have pointed out that signal sequence receptor (SSR) is an endoplasmic reticulum-related protein involved in protein folding and processing of eukaryotic cells. SSR2 is a subunit of SSR protein, but the role of SSR2 in hepatocellular carcinoma is largely unknown and warrants further study. Materials and Methods: Several public databases were data mined to analyze the expression of four subunits of SSR between tumor and its peritumor counterparts. Also, the expression of SSR2 in our own collected tissues from HCC patients were analyzed by IHC and quantitative PCR. Survival analyses were conducted to delineate the prognostic value of SSR2. Clinical data were obtained followed by analysis based on SSR2 expression. Afterwards, cell proliferation, migration and invasion were detected by IncuCyte and trans-well assays, respectively. RNA interference was carried out by transfecting specific siRNA targeting SSR2 into cells using lipo2000. Western blot was applied to validate the knockdown effect and regulation on EMT-related proteins. Results: We examined the expression of SSR and its correlation with recurrence and survival of patients. We discovered that SSR2 overexpression was negatively associated with survival of HCC patients from TCGA databases and the mutation of SSR2 was most among the four subunits of SSR protein. Additionally, in this study, we collected tumor and adjacent tissues from 125 cases of HCC patients. Through constructing tissue microarray, we have identified that SSR2 was highly expressed in HCC tumor tissues compared with adjacent normal tissues of hepatocellular carcinoma patients by immunohistochemistry assays. Furthermore, Kaplan-Meier survival analysis from our collected tissues revealed that the overexpression of SSR2 was inversely correlated with disease free survival and overall survival of HCC patients. We elucidated that SSR2 promotes proliferation, migration and invasion of HCC cells. SSR2 knockdown suppressed epithelial mesenchymal transition (EMT) of HCC cells. Conclusions: These results collectively show that SSR2 is overexpressed in HCC tumor tissues, and it is an important factor in predicting survival of HCC patients. Additionally, it is involved in metastasis of HCC. These findings may help to exploit SSR2 as a novel factor in predicting prognosis and metastasis of HCC.

SRXN1 stimulates hepatocellular carcinoma tumorigenesis and metastasis through modulating ROS/p65/BTG2 signalling.

Sulfiredoxin 1 (SRXN1) is a pivotal regulator of the antioxidant response in eukaryotic cells. However, the role of SRXN1 in hepatocellular carcinoma (HCC) is far from clear. The present study aims to elucidate whether SRXN1 participates in tumorigenesis and metastasis of HCC and to determine the molecular mechanisms. We found that SRXN1 expression was up-regulated in HCC tissue samples and correlated with poor prognosis in HCC patients. We also observed that SRXN1 knockdown by transient siRNA transfection inhibited HCC cell proliferation, migration and invasion. Overexpression of SRXN1 increased HCC cell migration and invasion. B-cell translocation gene 2 (BTG2) was identified as a downstream target of SRXN1. Mechanistic studies revealed that SRXN1-depleted reactive oxygen species (ROS) modulated migration and invasion of HCC cells. In addition, the ROS/p65/BTG2 signalling hub was found to regulate the epithelial-mesenchymal transition (EMT), which mediates the pro-metastasis role of SRXN1 in HCC cells. In vivo experiments showed SRXN1 promotes HCC tumour growth and metastasis in mouse subcutaneous xenograft and metastasis models. Collectively, our results revealed a novel pro-tumorigenic and pro-metastatic function of SRXN1 in HCC. These findings demonstrate a rationale to exploit SRXN1 as a therapeutic target effectively preventing metastasis of HCC.

3D Skeletal Gesture Recognition via Hidden States Exploration.

Temporal dynamics is an open issue for modeling human body gestures. A solution is resorting to the generative models, such as the hidden Markov model (HMM). Nevertheless, most of the work assumes fixed anchors for each hidden state, which make it hard to describe the explicit temporal structure of gestures. Based on the observation that a gesture is a time series with distinctly defined phases, we propose a new formulation to build temporal compositions of gestures by the low-rank matrix decomposition. The only assumption is that the gesture's "hold" phases with static poses are linearly correlated among each other. As such, a gesture sequence could be segmented into temporal states with semantically meaningful and discriminative concepts. Furthermore, different to traditional HMMs which tend to use specific distance metric for clustering and ignore the temporal contextual information when estimating the emission probability, we utilize the long short-term memory to learn probability distributions over states of HMM. The proposed method is validated on multiple challenging datasets. Experiments demonstrate that our approach can effectively work on a wide range of gestures, and achieve state-of-the-art performance.

A new method of near-infrared fluorescence image-guided hepatectomy for patients with hepatolithiasis: a randomized controlled trial.

BACKGROUND: Hepatectomy is a definitive treatment for hepatolithiasis because it simultaneously removes intrahepatic duct (IHD) stones and biliary tract strictures together with the involved liver region en bloc. Unlike cystic or solid liver tumors, hepatolithiasis is usually associated with alterations of anatomical structures and perihepatic adhesions because of chronic recurrent inflammation. This complicates identification of the target hepatic region and location of biliary strictures. METHODS: To determine the efficacy of near-infrared fluorescence (NIRF) imaging using indocyanine green (ICG), we performed a comparative trial and developed a white-light and near-infrared dual-channel image-guided device (DPM-I) for both open and endoscopic surgery. Forty-four eligible patients were randomly assigned to Group A (NIRF imaging) or Group B (traditional hepatectomy). We injected ICG via peripheral veins for patients in Group A. RESULTS: The NIRF imaging method was associated with less blood loss (OR 1.004, 95% CI 0.999-1.010; P = 0.016), briefer hospitalization (OR 1.336, 95% CI 1.016-1.756; P = 0.001), lower rates of margins with dilated bile ducts (OR 1.278, 95% CI 1.030-1.585; P = 0.023), lower postoperative white blood cell counts (OR 1.262, 95% CI 0.931-1.712; P = 0.038), lower procalcitonin levels (OR 1.316, 95% CI 1.020-1.513; P = 0.002), and lower alanine aminotransferase levels (OR 1.013, 95% CI 1.003-1.023; P = 0.002) compared with traditional hepatectomy. CONCLUSIONS: These data demonstrate the efficacy of NIRF imaging with ICG using DPM-I for treating hepatolithiasis.

Kaempferol inhibits proliferation, migration, and invasion of liver cancer HepG2 cells by down-regulation of microRNA-21.

Liver cancer is one of the most common and lethal cancers in human digestive system, which kills more than half a million people every year worldwide. This study aimed to investigate the effects of kaempferol, a flavonoid compound isolated from vegetables and fruits, on hepatic cancer HepG2 cell proliferation, migration, invasion, and apoptosis, as well as microRNA-21 (miR-21) expression. Cell viability was detected using cell counting kit-8 (CCK-8) assay. Cell proliferation was measured using 5-bromo-2'-deoxyuridine (BrdU) incorporation assay. Cell apoptosis was assessed using Guava Nexin assay. Cell migration and invasion were determined using two-chamber migration (invasion) assay. Cell transfection was used to change the expression of miR-21. Quantitative reverse transcription polymerase chain reaction (qRT-PCR) was performed to analyze the expressions of miR-21 and phosphatase and tensin homologue (PTEN). Expression of key proteins involved in proliferation, apoptosis, migration, invasion, and phosphatidylinositol 3-kinase/protein kinase 3/mammalian target of rapamycin (PI3K/AKT/mTOR) pathway were evaluated using western blotting. Results showed that kaempferol significantly inhibited HepG2 cell proliferation, migration, and invasion, and induced cell apoptosis. Kaempferol remarkably reduce the expression of miR-21 in HepG2 cells. Overexpression of miR-21 obviously reversed the effects of kaempferol on HepG2 cell proliferation, migration, invasion, and apoptosis. Moreover, miR-21 negatively regulated the expression of PTEN in HepG2 cells. Kaempferol enhanced the expression of PTEN and inactivated PI3K/AKT/mTOR signaling pathway in HepG2 cells. In conclusion, kaempferol inhibited proliferation, migration, and invasion of HepG2 cells by down-regulating miR-21 and up-regulating PTEN, as well as inactivating PI3K/AKT/mTOR signaling pathway.

Knockdown of Urothelial Carcinoma-Associated 1 Suppressed Cell Growth and Migration Through Regulating miR-301a and CXCR4 in Osteosarcoma MHCC97 Cells.

Liver cancer is one of the most common malignancies in the world and a leading cause of cancer-related mortality. Accumulating evidence has highlighted the critical role of long noncoding RNAs (lncRNAs) in various cancers. The present study aimed to explore the role of lncRNA urothelial carcinoma-associated 1 (UCA1) in cell growth and migration in MHCC97 cells and its underlying mechanism. First, we assessed the expression of UCA1 in MHCC97 and three other cell lines by RT-qPCR. Then the expression of UCA1, miR-301a, and CXCR4 in MHCC97 cells was altered by transient transfection. The effects of UCA1 and miR-301 on cell viability, migration, invasion, and apoptosis were assessed. The results revealed that UCA1 expression was relatively higher in MHCC97 cells than in MG63, hFOB1.19, and OS-732 cells. Knockdown of UCA1 reduced cell viability, inhibited migration and invasion, and promoted cell apoptosis. However, the effect of UCA1 knockdown on cell growth and migration was blocked by miR-301a overexpression, whose expression was regulated by UCA1. We also found that miR-301a positively regulated CXCR4 expression. CXCR4 inhibition reversed the effect of miR-301a overexpression on cell growth and migration. Moreover, miR-301a activated the Wnt/ß-catenin and NF-κB pathways via regulating CXCR4. The present study demonstrated that UCA1 inhibition exerted an antigrowth and antimigration role in MHCC97 cells through regulating miR-301a and CXCR4 expression.

Combining LBP difference and feature correlation for texture description.

Effective characterization of texture images requires exploiting multiple visual cues from the image appearance. The local binary pattern (LBP) and its variants achieve great success in texture description. However, because the LBP(-like) feature is an index of discrete patterns rather than a numerical feature, it is difficult to combine the LBP(-like) feature with other discriminative ones by a compact descriptor. To overcome the problem derived from the nonnumerical constraint of the LBP, this paper proposes a numerical variant accordingly, named the LBP difference (LBPD). The LBPD characterizes the extent to which one LBP varies from the average local structure of an image region of interest. It is simple, rotation invariant, and computationally efficient. To achieve enhanced performance, we combine the LBPD with other discriminative cues by a covariance matrix. The proposed descriptor, termed the covariance and LBPD descriptor (COV-LBPD), is able to capture the intrinsic correlation between the LBPD and other features in a compact manner. Experimental results show that the COV-LBPD achieves promising results on publicly available data sets.