CH4/NH3 Flame Structure and Extinction Limit under Flame-Flame Interactions.

Ammonia is considered to play an important role in replacing traditional fossil fuels in future energy systems. In the experimental study, CH4/NH3 flame was lit by applying a double-nozzle burner to gain insight into the structure, and the laminar diffusion flame structure, CH*/OH* intensity maximum, and flame size were analyzed by an ICCD camera. In addition, the extinction limit (lower limit) of the CH4/NH3 flame under different conditions was also studied. The results showed that with the increase of burner pitch, the two diffusion flames showed four states of merged flames, merging flames, inclining separated flames, and independent flames in turn. In the process of flame separation, the continuous pitch between merging flames was short. At this point, higher syngas flow could help increase the continuous pitch to keep merging form. The paper investigated the flame structure and found that the flame size would decrease when the NH3 content in the fuel was high. The flame stability also decreased with an increase of the NH3 content in the fuel. These findings provided experimental proof and a theoretical basis for future studies on the stability of CH4/NH3 co-firing.

Visualization of Endogenous Hypochlorite in Drug-Induced Liver Injury Mice via a Bioluminescent Probe Combined with Firefly Luciferase mRNA-Loaded Lipid Nanoparticles.

Drug-induced liver injury (DILI) is a common liver disease with a high rate of morbidity, and its pathogenesis is closely associated with the overproduction of highly reactive hypochlorite (ClO-) in the liver. However, bioluminescence imaging of endogenous hypochlorite in nontransgenic natural mice remains challenging. Herein, to address this issue, we report a strategy for imaging ClO- in living cells and DILI mice by harnessing a bioluminescent probe formylhydrazine luciferin (ClO-Luc) combined with firefly luciferase (fLuc) mRNA-loaded lipid nanoparticles (LNPs). LNPs could efficiently deliver fLuc mRNA into living cells and in vivo, expressing abundant luciferase in the cytoplasm in situ. In the presence of ClO-, probe ClO-Luc locked by formylhydrazine could release cage-free d-luciferin through oxidation and follow-up hydrolysis reactions, further allowing for bioluminescence imaging. Moreover, based on the luciferase-luciferin system, it was able to sensitively and selectively detect ClO- in vitro with a limit of detection of 0.59 µM and successfully monitor the endogenous hypochlorite generation in the DILI mouse model for the first time. We postulate that this work provides a new method to elucidate the roles of ClO- in related diseases via bioluminescence imaging.

Electron-Withdrawing Substituents Allow Boosted NIR-II Fluorescence in J-Type Aggregates for Bioimaging and Information Encryption.

Developing molecular fluorophores with enhanced fluorescence in aggregate state for the second near-infrared (NIR-II) imaging is highly desirable but remains a tremendous challenge due to the lack of reliable design guidelines. Herein, we report an aromatic substituent strategy to construct highly bright NIR-II J-aggregates. Introduction of electron-withdrawing substituents at 3,5-aryl and meso positions of classic boron dipyrromethene (BODIPY) skeleton can promote slip-stacked J-type arrangement and further boost NIR-II fluorescence of J-aggregates via increased electrostatic repulsion and intermolecular hydrogen bond interaction. Notably, NOBDP-NO2 with three nitro groups (-NO2 ) shows intense NIR-II fluorescence at 1065 nm and high absolute quantum yield of 3.21 % in solid state, which can be successfully applied in bioimaging, high-level encoding encryption, and information storage. Moreover, guided by this electron-withdrawing substituent strategy, other skeletons (thieno-fused BODIPY, aza-BODIPY, and heptamethine cyanine) modified with -NO2 are converted into J-type aggregates with enhanced NIR-II fluorescence, showing great potential to convert aggregation caused emission quenching (ACQ) dyes into brilliant J-aggregates. This study provides a universal method for construction of strong NIR-II emissive J-aggregates by rationally manipulating molecular packing and establishing relationships among molecular structures, intermolecular interactions, and fluorescence properties.

Non-Solvatochromic Cell Membrane-Targeted NIR Fluorescent Probe for Visualization of Polarity Abnormality in Drug-Induced Liver Injury Mice.

Noninvasive visualization of liver polarity by using fluorescence imaging technology is helpful to better understand drug-induced liver injury (DILI). However, cell membrane-targeted polarity-sensitive near-infrared (NIR) fluorescent probes are still scarce. Herein, we report a non-solvatochromic cell membrane-targeted NIR small molecular probe (N-BPM-C10) for monitoring the polarity changes on cell membranes in living cells and in vivo. N-BPM-C10 exhibits polarity-dependent fluorescence around 655 nm without an obvious solvatochromic effect, which endows it with good capability for the in vivo imaging study. Moreover, it can rapidly and selectively light up the cell membranes as well as distinguish tumor cells from normal cells due to its excellent polarity-sensitive ability. More importantly, N-BPM-C10 has been successfully applied to visualize liver polarity changes in vivo, revealing the reduction of liver polarity in DILI mice. We believe that N-BPM-C10 provides a new way for the diagnosis of DILI.

Multi-scale discriminative network for prostate cancer lesion segmentation in multiparametric MR images.

PURPOSE: The accurate and reliable segmentation of prostate cancer (PCa) lesions using multiparametric magnetic resonance imaging (mpMRI) sequences, is crucial to the image-guided intervention and treatment of prostate disease. For PCa lesion segmentation, it is essential to reliably combine local and global information to retain the features of small targets at multiple scales. Therefore, this study proposes a multi-scale segmentation network with a cascading pyramid convolution module (CPCM) and a double-input channel attention module (DCAM) for the automated and accurate segmentation of PCa lesions using mpMRI. METHODS: First, the region of interest was extracted from the data by clipping to enlarge the target region and reduce the background noise interference. Next, four CPCMs with large convolution kernels in their skip connection paths were designed to improve the feature extraction capability of the network for small targets. At the same time, a convolution decomposition was applied to reduce the computational complexity. Finally, the DCAM was adopted in the decoder to provide bottom-up semantic discriminative guidance; it can use the semantic information of the network's deep features to guide the shallow output of features with a higher discriminant ability. A residual refinement module (RRM) was also designed to strengthen the recognition ability of each stage. The feature maps of the skip connection and the decoder all go through the RRM. RESULTS: For the Initiative for Collaborative Computer Vision Benchmarking (I2CVB) dataset, our proposed model achieved a Dice similarity coefficient (DSC) of 79.31% and an average boundary distance (ABD) of 4.15 mm. For the Prostate Multiparametric MRI (PROMM) dataset, our method greatly improved the DSC to 82.11% and obtained an ABD of 3.64 mm. CONCLUSIONS: The experimental results of two different mpMRI prostate datasets demonstrate that our model is more accurate and reliable on small targets. In addition, it outperforms other state-of-the-art methods.

MA-Net:Mutex attention network for COVID-19 diagnosis on CT images.

COVID-19 is an infectious pneumonia caused by 2019-nCoV. The number of newly confirmed cases and confirmed deaths continues to remain at a high level. RT-PCR is the gold standard for the COVID-19 diagnosis, but the computed tomography (CT) imaging technique is an important auxiliary diagnostic tool. In this paper, a deep learning network mutex attention network (MA-Net) is proposed for COVID-19 auxiliary diagnosis on CT images. Using positive and negative samples as mutex inputs, the proposed network combines mutex attention block (MAB) and fusion attention block (FAB) for the diagnosis of COVID-19. MAB uses the distance between mutex inputs as a weight to make features more distinguishable for preferable diagnostic results. FAB acts to fuse features to obtain more representative features. Particularly, an adaptive weight multiloss function is proposed for better effect. The accuracy, specificity and sensitivity were reported to be as high as 98.17%, 97.25% and 98.79% on the COVID-19 dataset-A provided by the Affiliated Medical College of Qingdao University, respectively. State-of-the-art results have also been achieved on three other public COVID-19 datasets. The results show that compared with other methods, the proposed network can provide effective auxiliary information for the diagnosis of COVID-19 on CT images.

FERGCN: facial expression recognition based on graph convolution network.

Due to the problems of occlusion, pose change, illumination change, and image blur in the wild facial expression dataset, it is a challenging computer vision problem to recognize facial expressions in a complex environment. To solve this problem, this paper proposes a deep neural network called facial expression recognition based on graph convolution network (FERGCN), which can effectively extract expression information from the face in a complex environment. The proposed FERGCN includes three essential parts. First, a feature extraction module is designed to obtain the global feature vectors from convolutional neural networks branch with triplet attention and the local feature vectors from key point-guided attention branch. Then, the proposed graph convolutional network uses the correlation between global features and local features to enhance the expression information of the non-occluded part, based on the topology graph of key points. Furthermore, the graph-matching module uses the similarity between images to enhance the network's ability to distinguish different expressions. Results on public datasets show that our FERGCN can effectively recognize facial expressions in real environment, with RAF-DB of 88.23%, SFEW of 56.15% and AffectNet of 62.03%.

3D gray density coding feature for benign-malignant pulmonary nodule classification on chest CT.

PURPOSE: Early detection is significant to reduce lung cancer-related death. Computer-aided detection system (CADs) can help radiologists to make an early diagnosis. In this paper, we propose a novel 3D gray density coding feature (3D GDC) and fuse it with extracted geometric features. The fusion feature and random forest are used for benign-malignant pulmonary nodule classification on Chest CT. METHODS: First, a dictionary model is created to acquire codebook. It is used to obtain feature descriptors and includes 3D block database (BD) and distance matrix clustering centers. 3D BD is balanced and randomly selecting from benign and malignant pulmonary nodules of training data. Clustering centers is got by clustering the distance matrix, which is the distance between every two blocks in 3D BD. Then, feature descriptor is obtained by coding the pulmonary nodule with codebook, and 3D GDC feature is the result of histogram statistics on feature descriptor. Second, geometric features are extracted for fusion feature. Finally, random forest is performed for benign-malignant pulmonary nodule classification with fusion feature of the 3D gray density coding feature and the geometric features. RESULTS: We verify the effectiveness of our method on the public LIDC-IDRI dataset and the private ZSHD dataset. For LIDC-IDRI dataset, compared with other state-of-the-art methods, we achieve more satisfactory results with 93.17 ± 1.94% for accuracy and 97.53 ± 1.62% for AUC. As for private ZSHD dataset, it contains a total of 238 lung nodules from 203 patients. The accuracy and AUC achieved by our method are 90.0% and 93.15%. CONCLUSIONS: The results show that our method can provide doctors with more accurate results of benign-malignant pulmonary nodule classification for auxiliary diagnosis, and our method is more interpretable than 3D CNN methods, which can provide doctors with more auxiliary information.

Images denoising for COVID-19 chest X-ray based on multi-resolution parallel residual CNN.

Chest X-ray (CXR) is a medical imaging technology that is common and economical to use in clinical. Recently, coronavirus (COVID-19) has spread worldwide, and the second wave is rebounding strongly now with the coming winter that has a detrimental effect on the global economy and health. To make pre-diagnosis of COVID-19 as soon as possible, and reduce the work pressure of medical staff, making use of deep learning networks to detect positive CXR images of infected patients is a critical step. However, there are complex edge structures and rich texture details in the CXR images susceptible to noise that can interfere with the diagnosis of the machines and the doctors. Therefore, in this paper, we proposed a novel multi-resolution parallel residual CNN (named MPR-CNN) for CXR images denoising and special application for COVID-19 which can improve the image quality. The core of MPR-CNN consists of several essential modules. (a) Multi-resolution parallel convolution streams are utilized for extracting more reliable spatial and semantic information in multi-scale features. (b) Efficient channel and spatial attention can let the network focus more on texture details in CXR images with fewer parameters. (c) The adaptive multi-resolution feature fusion method based on attention is utilized to improve the expression of the network. On the whole, MPR-CNN can simultaneously retain spatial information in the shallow layers with high resolution and semantic information in the deep layers with low resolution. Comprehensive experiments demonstrate that our MPR-CNN can better retain the texture structure details in CXR images. Additionally, extensive experiments show that our MPR-CNN has a positive impact on CXR images classification and detection of COVID-19 cases from denoised CXR images.

COVID-19 lesion discrimination and localization network based on multi-receptive field attention module on CT images.

Since discovered in Hubei, China in December 2019, Corona Virus Disease 2019 named COVID-19 has lasted more than one year, and the number of new confirmed cases and confirmed deaths is still at a high level. COVID-19 is an infectious disease caused by SARS-CoV-2. Although RT-PCR is considered the gold standard for detection of COVID-19, CT plays an important role in the diagnosis and evaluation of the therapeutic effect of COVID-19. Diagnosis and localization of COVID-19 on CT images using deep learning can provide quantitative auxiliary information for doctors. This article proposes a novel network with multi-receptive field attention module to diagnose COVID-19 on CT images. This attention module includes three parts, a pyramid convolution module (PCM), a multi-receptive field spatial attention block (SAB), and a multi-receptive field channel attention block (CAB). The PCM can improve the diagnostic ability of the network for lesions of different sizes and shapes. The role of SAB and CAB is to focus the features extracted from the network on the lesion area to improve the ability of COVID-19 discrimination and localization. We verify the effectiveness of the proposed method on two datasets. The accuracy rate of 97.12%, specificity of 96.89%, and sensitivity of 97.21% are achieved by the proposed network on DTDB dataset provided by the Beijing Ditan Hospital Capital Medical University. Compared with other state-of-the-art attention modules, the proposed method achieves better result. As for the public COVID-19 SARS-CoV-2 dataset, 95.16% for accuracy, 95.6% for F1-score and 99.01% for AUC are obtained. The proposed network can effectively assist doctors in the diagnosis of COVID-19 CT images.

Cu2+ -selective naked-eye 'off-on' fluorescent probe with multisignals: chromaticity, fluorescence, electrochemistry.

In this study, a rhodamine-acetylferrocene conjugate of RBFc was synthesized and then characterized using spectroscopy and single-crystal analysis. The chemosensor RBFc exhibited a marked colour change from colourless to pink after binding to Cu2+ ions. Importantly, under the presence of the other competing cations in aqueous solution, only Cu2+ ions caused spirolactam ring opening in rhodamine B in RBFc, resulting in an enhanced absorbance of ultraviolet light spectra and fluorescence spectra, as well as obvious shifts in cyclic voltammetry curves and differential pulsed voltammetry curves. The novel probe described in this manuscript provides an attractive approach for detecting Cu2+ in the presence of other multisignals.

Maternal 25-Hydroxyvitamin D Deficiency Promoted Metabolic Syndrome and Downregulated Nrf2/CBR1 Pathway in Offspring.

Metabolic syndrome is a disorder of energy use and storage, which is characterized by central obesity, dyslipidemia, and raised blood pressure and blood sugar levels. Maternal 25-hydroxyvitamin D deficiency is known to cause metabolic changes, chronic disease, and increased adiposity in adulthood. However, the underlying mechanism of induced metabolic syndrome (MetS) in the offspring in vitamin D deficient pregnant mothers remains unclear. We identified that maternal 25-hydroxyvitamin D deficiency enhances oxidative stress, which leads to the development of MetS in the mother and her offspring. Further, immunohistochemical, Western blotting, and qRT-PCR analyses revealed that maternal 25-hydroxyvitamin D deficiency inhibited the activation of the Nrf2/carbonyl reductase 1 (CBR1) pathway in maternal placenta, liver, and pancreas, as well as the offspring's liver and pancreas. Further analyses uncovered that application of 25-hydroxyvitamin D activated the Nrf2/CBR1 pathway, relieving the oxidative stress in BRL cells, suggesting that 25-hydroxyvitamin D regulates oxidative stress in offspring and induces the activation of the Nrf2/CBR1 pathway. Taken together, our study finds that maternal 25-hydroxyvitamin D deficiency is likely to result in offspring's MetS probably via abnormal nutrition transformation across placenta. Depression of the Nrf2/CBR1 pathway in both mothers and their offspring is one of the causes of oxidative stress leading to MetS. This study suggests that 25-hydroxyvitamin D treatment may relieve the offspring's MetS.

MSD-Net: Multi-Scale Discriminative Network for COVID-19 Lung Infection Segmentation on CT.

Since the first patient reported in December 2019, 2019 novel coronavirus disease (COVID-19) has become global pandemic with more than 10 million total confirmed cases and 500 thousand related deaths. Using deep learning methods to quickly identify COVID-19 and accurately segment the infected area can help control the outbreak and assist in treatment. Computed tomography (CT) as a fast and easy clinical method, it is suitable for assisting in diagnosis and treatment of COVID-19. According to clinical manifestations, COVID-19 lung infection areas can be divided into three categories: ground-glass opacities, interstitial infiltrates and consolidation. We proposed a multi-scale discriminative network (MSD-Net) for multi-class segmentation of COVID-19 lung infection on CT. In the MSD-Net, we proposed pyramid convolution block (PCB), channel attention block (CAB) and residual refinement block (RRB). The PCB can increase the receptive field by using different numbers and different sizes of kernels, which strengthened the ability to segment the infected areas of different sizes. The CAB was used to fusion the input of the two stages and focus features on the area to be segmented. The role of RRB was to refine the feature maps. Experimental results showed that the dice similarity coefficient (DSC) of the three infection categories were 0.7422,0.7384,0.8769 respectively. For sensitivity and specificity, the results of three infection categories were (0.8593, 0.9742), (0.8268,0.9869) and (0.8645,0.9889) respectively. The experimental results demonstrated that the network proposed in this paper can effectively segment the COVID-19 infection on CT images. It can be adopted for assisting in diagnosis and treatment of COVID-19.

Blockade of Cyclophilin D Attenuates Oxidative Stress-Induced Cell Death in Human Dental Pulp Cells.

Pathological stimuli, such as bacterial activity, dental bleaching, and nonpolymerized resin monomers, can cause death of dental pulp cells (DPCs) through oxidative stress- (OS-) induced mitochondrial dysfunction. However, the crucial molecular mechanisms that mediate such a phenomenon remain largely unknown. OS is characterized by the overproduction of reactive oxygen species (ROS), e.g., H2O2, O2 -, and ·OH. Mitochondria are a major source of ROS and the principal attack target of ROS. Cyclophilin D (CypD), as the only crucial protein for mitochondrial permeability transition pore (mPTP) induction, facilitates the opening of mPTP and causes mitochondrial dysfunction, leading to cell death. In the present study, we hypothesized that CypD-mediated mitochondrial molecular pathways were closely involved in the process of OS-induced death of human DPCs (HDPCs). We tested the phenotypic and molecular changes of HDPCs in a well-established OS model-H2O2 treatment. We showed that H2O2 dramatically reduced the viability and increased the death of HDPCs in a time- and dose-dependent manner by performing MTT, flow cytometry, and TUNEL assays and quantifying the expression changes of Bax and Bcl-2 proteins. H2O2 also induced mitochondrial dysfunction, as reflected by the increased mitochondrial ROS, reduced ATP production, and activation of mPTP (decreased mitochondrial membrane potential and enhanced intracellular Ca2+ level). An antioxidant (N-acetyl-L-cysteine) effectively preserved mitochondrial function and significantly attenuated H2O2-induced cytotoxicity and death. Moreover, H2O2 treatment markedly upregulated the CypD protein level in HDPCs. Notably, genetic or pharmacological blockade of CypD significantly attenuated H2O2-induced mitochondrial dysfunction and cell death. These findings provided novel insights into the role of a CypD-dependent mitochondrial pathway in the H2O2-induced death in HDPCs, indicating that CypD may be a potential therapeutic target to prevent OS-mediated injury in dental pulp.

SLC25A22 promotes proliferation and metastasis by activating MAPK/ERK pathway in gallbladder cancer.

BACKGROUND: SLC25A22, a member of mitochondrial carrier system (MCS) family encoding a mitochondrial glutamate transporter, has been reported to have vital roles in promoting proliferation and migration in cancer. Gallbladder cancer (GBC) is the most common biliary tract malignancy and has a poor prognosis. We aimed to determine the expression and function of SLC25A22 in GBC. METHODS: Immunohistochemistry (IHC) staining analysis and quantitative real-time PCR (qRT-PCR) were conducted to determine the expression of SLC25A22 in GBC tissues. Human NOZ and GBC-SD cells were used to perform the experiments. The protein expression was detected by western-blot analysis. Cell viability was evaluated via CCK-8 assay and colony formation assay. Cell migration and invasion in vitro were investigated by wound healing and transwell assay. Annexin V/PI staining assay for apoptosis were measured by flow cytometry. The effect of SLC25A22 in vivo was conducted with subcutaneous xenograft. RESULTS: We indicated that the expression of SLC25A22 was significantly upregulated in GBC tumor tissues as well as cell lines. Downregulation of SLC25A22 inhibited GBC cell growth and proliferation in vitro and in vivo and also had an effect on metastasis of GBC cells through the EMT processes. In addition, inhibition of SLC25A22 promoted mitochondrial apoptosis via downregulating BCL-2 and upregulating cleaved PARP, Cytochrome-c, and BAX mediated by MAPK/ERK pathway. CONCLUSIONS: Our study identified that SLC25A22 promoted development of GBC activating MAPK/ERK pathway. SLC25A22 has a potential to be used as a target for cancer diagnosis of GBC and related therapies.

TPX2 level correlates with cholangiocarcinoma cell proliferation, apoptosis, and EMT.

PURPOSE: The molecular signatures of cholangiocarcinoma are not well characterized. Targeting protein for Xenopus kinesin-like protein 2 (TPX2) has been shown to promote oncogenesis in the context of several cancers; however, its' role in cholangiocarcinoma has not been studied. We evaluated the role of TPX2 in cholangiocarcinoma. METHODS: Expression levels of TPX2 in cholangiocarcinoma were assessed by immunohistochemistry. Potential correlations were assessed by Chi-squared test. Impact of TPX2 expression on cell proliferation, cell cycle, apoptosis, cell invasion and migration was investigated by CCK-8, flow cytometric analysis, and transwell assay, respectively. The expressions of cell-cycle, cell-apoptosis and EMT related target proteins were detected by immunoblotting. RESULTS: TPX2 expression in cholangiocarcinoma tissues was significantly higher than that paracancerous tissue (44.3% vs. 5.7%; P<0.01). Overexpression of TPX2 showed a positive correlation with TNM stage, lymph node metastasis, and prognosis of patients. Knockdown of TPX2 expression induced G2-M arrest, apoptosis and inhibited invasion and migration of cholangiocarcinoma cells. Treatment of cholangiocarcinoma cells with TPX2 siRNA resulted in upregulation of cyclin A1, cyclin B1, p53, Bax, and E-cadherin; while downregulation of cyclin D1, CDK2, Bcl-2, N-cadherin, ß-cadherin MMP-2, MMP-9, Slug, and Twist1. CONCLUSIONS: Collectively, these results indicate that TPX2 may serve as a potential biomarker of prognostic relevance and a potential therapeutic target for cholangiocarcinoma.

Quantitative CT analysis of pulmonary nodules for lung adenocarcinoma risk classification based on an exponential weighted grey scale angular density distribution feature.

BACKGROUND AND OBJECTIVES: To improve lung nodule classification efficiency, we propose a lung nodule CT image characterization method. We propose a multi-directional feature extraction method to effectively represent nodules of different risk levels. The proposed feature combined with pattern recognition model to classify lung adenocarcinomas risk to four categories: Atypical Adenomatous Hyperplasia (AAH), Adenocarcinoma In Situ (AIS), Minimally Invasive Adenocarcinoma (MIA), and Invasive Adenocarcinoma (IA). METHODS: First, we constructed the reference map using an integral image and labelled this map using a K-means approach. The density distribution map of the lung nodule image was generated after scanning all pixels in the nodule image. An exponential function was designed to weight the angular histogram for each component of the distribution map, and the features of the image were described. Then, quantitative measurement was performed using a Random Forest classifier. The evaluation data were obtained from the LIDC-IDRI database and the CT database which provided by Shanghai Zhongshan hospital (ZSDB). In the LIDC-IDRI, the nodules are categorized into three configurations with five ranks of malignancy ("1" to "5"). In the ZSDB, the nodule categories are AAH, AIS, MIA, and IA. RESULTS: The average of Student's t-test p-values were less than 0.02. The AUCs for the LIDC-IDRI database were 0.9568, 0.9320, and 0.8288 for Configurations 1, 2, and 3, respectively. The AUCs for the ZSDB were 0.9771, 0.9917, 0.9590, and 0.9971 for AAH, AIS, MIA and IA, respectively. CONCLUSION: The experimental results demonstrate that the proposed method outperforms the state-of-the-art and is robust for different lung CT image datasets.

KIFC1, a novel potential prognostic factor and therapeutic target in hepatocellular carcinoma.

Kinesin family member C1 (KIFC1, also known as HSET) is a minus end-directed motor protein, which is critical in centrosome clustering. The present study investigated the expression of KIFC1 in paired hepatocellular carcinoma (HCC) tissues and adjacent non-cancerous tissues from 91 patients by immunohistochemical analysis; clinical data were concomitantly collected. KIFC1 was expressed at high levels in HCC tissues, compared with that in peritumoral tissues (54.9 vs. 14.3%; P<0.01), and its expression correlated with tumor emboli, metastasis, recurrence and time of recurrence. Kaplan-Meier analysis showed that the expression of KIFC1 was significantly associated with tumor-free survival rates. In addition, multivariate analyses revealed that the overexpression of KIFC1was an independent predictive marker in patients with HCC. Consistently, data derived from GEPIA was in agreement with the results. In vitro, KIFC1 knockdown effectively decreased HCC cell viability, and induced apoptosis and cell death. KIFC1 knockdown also significantly suppressed tumor cell migration and invasion in vitro. Mechanistically, the apoptosis-related protein, B-cell lymphoma-2 (Bcl-2), was downregulated in KIFC1 small interfering RNA-treated groups, whereas thee levels of Bcl-2-associated X protein and p53 were upregulated. In addition, the expression levels of phosphorylated phosphoinositide 3-kinase and phosphorylated AKT were decreased significantly when KIFC1 was silenced. The epithelial-mesenchymal transition-related proteins, N-cadherin, matrix metalloproteinase-2 (MMP-2), ß-catenin, Slug, and Zinc finger E-box-binding homeobox 1, were downregulated, whereas the expression of E-cadherin was upregulated. The overexpression of KIFC1 was correlated closely with the progression of HCC and poor prognosis, and suggested that the expression levels of KIFC1 are a potential prognostic biomarker and therapeutic target in HCC.

Diabetes Induces Abnormal Ovarian Function via Triggering Apoptosis of Granulosa Cells and Suppressing Ovarian Angiogenesis.

Diabetes triggers abnormal ovarian follicular development and consequently leads to infertility. Here, we established a type 2 diabetes mouse model by feeding with high fat diet (HFD) for 15/20 weeks and assessed the effect of diabetes on follicular development and ovarian angiogenesis. After fed with HFD for 15 weeks, mice had the characteristics of type 2 diabetes, which was much more serious after 20 weeks on HFD. After 20 weeks on HFD, the mice had shown abnormal ovarian morphology with hyaline appearance, much less blood vessel, follicular development arrest and less of granulosa cells (GCs) in mature follicles, but not in ovaries from 15 weeks on HFD. Elevated makers of DNA damage, ER stress and apoptosis of GCs were observed in ovaries from HFD for 20 weeks. Additionally, diabetes significantly suppressed ovarian angiogenesis with the evidence of down-regulation of CD31 via inhibiting HIF1α-VEGF signaling pathway in time-dependent. We concluded that diabetes triggers abnormal ovarian function via inducing GCs apoptosis and suppressing ovarian angiogenesis.

The differential expression of microRNA-143,145 in endometriosis.

BACKGROUND: Recent studies showed that inappropriate expression of microRNAs (miRNAs) is strongly associated with tumor-related processes in humans (2-9,11-17). OBJECTIVE: To understand the changes of miRNAs in endometriosis. MATERIALS AND METHODS: With real-time RT-PCR, we investigated the miR-143 and miR-145 expression in eutopic (EU, n=2) and ectopic endometrium (EC, n=11) (from women with endometriosis) (as well as EU+EC, n=11), along with the normal endometrium (EN, n=22) (from women without endometriosis, but with leiomyoma). RESULTS: We did not find that the expression of miR-143 and/or miR-145 in EN or EC changed with menstrual cycle. But our results showed the miR-143 was up-regulated in EC (p=0.000) compared to EN. The miR-143 was also up-regulated in EU, but the difference did not reach statistically significance (p=0.053). Compared to EU, the expression of miR-143 in EC was up-regulated (p=0.016). MiR-145 had the similar characteristic to miR-143. The miR-145 was up-regulated in both EU (p=0.004) and EC (p=0.000) in compared to EN group. When compared with EU, the miR-145 in EC was up-regulated (p=0.008). CONCLUSION: In conclusion, the miR-143 and miR-145 may play a certain role in the development and progression of endometriosis.