Inhibition mechanism of theaflavins on matrix metalloproteinase-2: inhibition kinetics, multispectral analysis, molecular docking and molecular dynamics simulation.

Dental caries is a chronic and destructive disease and matrix metalloproteinase-2 (MMP-2) plays a major role in caries. The inhibitory mechanisms of theaflavins [theaflavin (TF1), theaflavin-3-gallate (TF2A), theaflavin-3'-gallate (TF2B), and theaflavin-3,3'-digallate (TF3)] on MMP-2 were investigated using techniques such as enzyme inhibition kinetics, multi-spectral methods, molecular docking, and molecular dynamics simulations. The results showed that TF1, TF2A, TF2B, and TF3 all competitively and reversibly inhibited MMP-2 activity. Fluorescence spectra and molecular docking indicated that four theaflavins spontaneously bind to MMP-2 through noncovalent interactions, driven by hydrogen bonds and hydrophobic interactions, constituting a static quenching mechanism and resulting in an altered tryptophan residue environment around MMP-2. Molecular dynamic simulations demonstrated that four theaflavins can form stable, compact complexes with MMP-2. In addition, the order of theaflavins' ability to inhibit MMP-2 was found to be TF1 > TF2B > TF2A > TF3. Interestingly, the order of binding capacity between MMP-2 and TF1, TF2A, TF2B, and TF3 was consistent with the order of inhibitory capacity, and was opposite to the order of steric hindrance of theaflavins. This may be due to the narrow space of the active pocket of MMP-2, and the smaller the steric hindrance of theaflavins, the easier it is to enter the active pocket and bind to MMP-2. This study provided novel insights into theaflavins as functional components in the exploration of natural MMP-2 inhibitors.

DAU-Net: A medical image segmentation network combining the Hadamard product and dual scale attention gate.

Medical image segmentation has an important application value in the modern medical field, it can help doctors accurately locate and analyze the tissue structure, lesion areas, and organ boundaries in the image, which provides key information support for clinical diagnosis and treatment, but there are still a large number of problems in the accuracy of the segmentation, so in this paper, we propose a medical image segmentation network combining the Hadamard product and dual-scale attention gate (DAU-Net). First, the Hadamard product is introduced in the structure of the fifth layer of the codec for element-by-element multiplication, which can generate feature representations with more representational capabilities. Second, in the jump connection module, we propose a dual scale attention gating (DSAG), which can highlight more valuable features and achieve more efficient jump connections. Finally, in the decoder feature structure, the final segmentation result is obtained by aggregating the feature information provided by each part, and decoding is achieved by up-sampling operation. Through experiments on two public datasets, Luna and Isic2017, DAU-Net is able to extract feature information more efficiently using different modules and has better segmentation results compared to classical segmentation models such as U-Net and U-Net++, and also verifies the effectiveness of the model.

Biomedical image segmentation algorithm based on dense atrous convolution.

Biomedical images have complex tissue structures, and there are great differences between images of the same part of different individuals. Although deep learning methods have made some progress in automatic segmentation of biomedical images, the segmentation accuracy is relatively low for biomedical images with significant changes in segmentation targets, and there are also problems of missegmentation and missed segmentation. To address these challenges, we proposed a biomedical image segmentation method based on dense atrous convolution. First, we added a dense atrous convolution module (DAC) between the encoding and decoding paths of the U-Net network. This module was based on the inception structure and atrous convolution design, which can effectively capture multi-scale features of images. Second, we introduced a dense residual pooling module to detect multi-scale features in images by connecting residual pooling blocks of different sizes. Finally, in the decoding part of the network, we adopted an attention mechanism to suppress background interference by enhancing the weight of the target area. These modules work together to improve the accuracy and robustness of biomedical image segmentation. The experimental results showed that compared to mainstream segmentation networks, our segmentation model exhibited stronger segmentation ability when processing biomedical images with multiple-shaped targets. At the same time, this model can significantly reduce the phenomenon of missed segmentation and missegmentation, improve segmentation accuracy, and make the segmentation results closer to the real situation.

A global optimization generation method of stitching dental panorama with anti-perspective transformation.

To address the limitation of narrow field-of-view in local oral cavity images that fail to capture large-area targets at once, this paper designs a method for generating natural dental panoramas based on oral endoscopic imaging that consists of two main stages: the anti-perspective transformation feature extraction and the coarse-to-fine global optimization matching. In the first stage, we increase the number of matched pairs and improve the robustness of the algorithm to viewpoint transformation by normalizing the anti-affine transformation region extracted from the Gaussian scale space and using log-polar coordinates to compute the gradient histogram of the octagonal region to obtain the set of perspective transformation resistant feature points. In the second stage, we design a coarse-to-fine global optimization matching strategy. Initially, we incorporate motion smoothing constraints and improve the Fast Library for Approximate Nearest Neighbors (FLANN) algorithm by utilizing neighborhood information for coarse matching. Then, we eliminate mismatches via homography-guided Random Sample Consensus (RANSAC) and further refine the matching using the Levenberg-Marquardt (L-M) algorithm to reduce cumulative errors and achieve global optimization. Finally, multi-band blending is used to eliminate the ghosting due to unalignment and make the image transition more natural. Experiments show that the visual effect of dental panoramas generated by the proposed method is significantly better than that of other methods, addressing the problems of sparse splicing discontinuities caused by sparse keypoints, ghosting due to parallax, and distortion caused by the accumulation of errors in multi-image splicing in oral endoscopic image stitching.

Intelligent crowd sensing pickpocketing group identification using remote sensing data for secure smart cities.

As a public infrastructure service, remote sensing data provided by smart cities will go deep into the safety field and realize the comprehensive improvement of urban management and services. However, it is challenging to detect criminal individuals with abnormal features from massive sensing data and identify groups composed of criminal individuals with similar behavioral characteristics. To address this issue, we study two research aspects: pickpocketing individual detection and pickpocketing group identification. First, we propose an IForest-FD pickpocketing individual detection algorithm. The IForest algorithm filters the abnormal individuals of each feature extracted from ticketing and geographic information data. Through the filtered results, the factorization machines (FM) and deep neural network (DNN) (FD) algorithm learns the combination relationship between low-order and high-order features to improve the accuracy of identifying pickpockets composed of factorization machines and deep neural networks. Second, we propose a community relationship strength (CRS)-Louvain pickpocketing group identification algorithm. Based on crowdsensing, we measure the similarity of temporal, spatial, social and identity features among pickpocketing individuals. We then use the weighted combination similarity as an edge weight to construct the pickpocketing association graph. Furthermore, the CRS-Louvain algorithm improves the modularity of the Louvain algorithm to overcome the limitation that small-scale communities cannot be identified. The experimental results indicate that the IForest-FD algorithm has better detection results in Precision, Recall and F1score than similar algorithms. In addition, the normalized mutual information results of the group division effect obtained by the CRS-Louvain pickpocketing group identification algorithm are better than those of other representative methods.

Research on user recruitment algorithms based on user trajectory prediction with sparse mobile crowd sensing.

Sparse mobile crowd sensing saves perception cost by recruiting a small number of users to perceive data from a small number of sub-regions, and then inferring data from the remaining sub-regions. The data collected by different people on their respective trajectories have different values, and we can select participants who can collect high-value data based on their trajectory predictions. In this paper, we study two aspects of user trajectory prediction and user recruitment. First, we propose an STGCN-GRU user trajectory prediction algorithm, which uses the STGCN algorithm to extract features related to temporal and spatial information from the trajectory map, and then inputs the feature sequences into GRU for trajectory prediction, and this algorithm improves the accuracy of user trajectory prediction. Second, an ADQN (action DQN) user recruitment algorithm is proposed.The ADQN algorithm improves the objective function in DQN on the idea of reinforcement learning. The action with the maximum input value is found from the Q network, and then the output value of the objective function of the corresponding action Q network is found. This reduces the overestimation problem that occurs in Q networks and improves the accuracy of user recruitment. The experimental results show that the evaluation metrics FDE and ADE of the STGCN-GRU algorithm proposed in this paper are better than other representative algorithms. And the experiments on two real datasets verify the effectiveness of the ADQN user selection algorithm, which can effectively improve the accuracy of data inference under budget constraints.

Research on rainy day traffic sign recognition algorithm based on PMRNet.

The recognition of traffic signs is of great significance to intelligent driving and traffic systems. Most current traffic sign recognition algorithms do not consider the impact of rainy weather. The rain marks will obscure the recognition target in the image, which will lead to the performance degradation of the algorithm, a problem that has yet to be solved. In order to improve the accuracy of traffic sign recognition in rainy weather, we propose a rainy traffic sign recognition algorithm. The algorithm in this paper includes two modules. First, we propose an image deraining algorithm based on the Progressive multi-scale residual network (PMRNet), which uses a multi-scale residual structure to extract features of different scales, so as to improve the utilization rate of the algorithm for information, combined with the Convolutional long-short term memory (ConvLSTM) network to enhance the algorithm's ability to extract rain mark features. Second, we use the CoT-YOLOv5 algorithm to recognize traffic signs on the recovered images. In this paper, in order to improve the performance of YOLOv5 (You-Only-Look-Once, YOLO), the 3 × 3 convolution in the feature extraction module is replaced by the Contextual Transformer (CoT) module to make up for the lack of global modeling capability of Convolutional Neural Network (CNN), thus improving the recognition accuracy. The experimental results show that the deraining algorithm based on PMRNet can effectively remove rain marks, and the evaluation indicators Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) are better than the other representative algorithms. The mean Average Precision (mAP) of the CoT-YOLOv5 algorithm on the TT100k datasets reaches 92.1%, which is 5% higher than the original YOLOv5.

Antioxidant activity of delphinidin and pelargonidin: Theory and practice.

The quantum chemical density functional theory and in vitro chemical-based antioxidant assays were used to research the reaction mechanism of delphinidin/pelargonidin with free radicals including superoxide anion radicals (O2 - ∙) and hydroperoxide radicals (OOH∙). The geometric configuration, bond dissociation energy, PCM (polarizable continuum model) solvent model reaction enthalpy changes were studied to explain the transition states, and the reaction enthalpy change value was calculated to determine the active site. From the results of spatial configuration, delphinidin showed a stronger conjugation effect than that of pelargonidin. The dihedral angle between the three rings of delphinidin was almost 180°, and the angle between the B and C rings was only -2.81868°. Both coplanar and antioxidant activity of delphinidin was better than pelargonidin. The consequences of reaction enthalpy change in PCM were consistent with the bond dissociation energy. The phenolic hydroxyl bond dissociation energy of delphinidin was slightly smaller than that of pelargonidin. Moreover, the C4' site of delphinidin and the C3 site of pelargonidin were the active sites for scavenging free radicals. The free radical scavenging ability of delphinidin was marginally higher than that of pelargonidin. On the other hand, in vitro antioxidant results proved the scavenging ability of delphinidin and pelargonidin on superoxide anions, DPPH, and ABTS∙+ free radicals. It was shown that the chemical-based antioxidant activity was consistent with the theoretical calculation results, with delphinidin showing greater antioxidant activity. These results could explain the antioxidant mechanism of delphinidin/pelargonidin in scavenging free radicals from chemical reactions. PRACTICAL APPLICATIONS: This manuscript explained the antioxidant mechanism of delphinidin/pelargonidin in scavenging free radicals through the analysis of the geometric configuration of delphinidin/pelargonidin and the theoretical calculation of the reaction transition state. It could also speculate on the possible reaction sites, and provide a basis for judging how to efficiently select antioxidants with great antioxidant activity.

An improved pix2pix model based on Gabor filter for robust color image rendering.

In recent years, with the development of deep learning, image color rendering method has become a research hotspot once again. To overcome the detail problems of color overstepping and boundary blurring in the robust image color rendering method, as well as the problems of unstable training based on generative adversarial networks, we propose an color rendering method using Gabor filter based improved pix2pix for robust image. Firstly, the multi-direction/multi-scale selection characteristic of Gabor filter is used to preprocess the image to be rendered, which can retain the detailed features of the image while preprocessing to avoid the loss of features. Moreover, among the Gabor texture feature maps with 6 scales and 4 directions, the texture map with the scale of 7 and the direction of 0° has the comparable rendering performance. Finally, by improving the loss function of pix2pix model and adding the penalty term, not only the training can be stabilized, but also the ideal color image can be obtained. To reflect image color rendering quality of different models more objectively, PSNR and SSIM indexes are adopted to evaluate the rendered images. The experimental results of the proposed method show that the robust image rendered by this method has better visual performance and reduces the influence of light and noise on the image to a certain extent.

Image super-resolution reconstruction for secure data transmission in Internet of Things environment.

The image super-resolution reconstruction method can improve the image quality in the Internet of Things (IoT). It improves the data transmission efficiency, and is of great significance to data transmission encryption. Aiming at the problem of low image quality in image super-resolution using neural networks, a self-attention-based image reconstruction method is proposed for secure data transmission in IoT environment. The network model is improved, and the residual network structure and sub-pixel convolution are used to extract the feature of the image. The self-attention module is used extract detailed information in the image. Using generative confrontation method and image feature perception method to improve the image reconstruction effect. The experimental results on the public data set show that the improved network model improves the quality of the reconstructed image and can effectively restore the details of the image.

Neural Network-Based Mapping Mining of Image Style Transfer in Big Data Systems.

Image style transfer can realize the mutual transfer between different styles of images and is an essential application for big data systems. The use of neural network-based image data mining technology can effectively mine the useful information in the image and improve the utilization rate of information. However, when using the deep learning method to transform the image style, the content information is often lost. To address this problem, this paper introduces L1 loss on the basis of the VGG-19 network to reduce the difference between image style and content and adds perceptual loss to calculate the semantic information of the feature map to improve the model's perceptual ability. Experiments show that the proposal in this paper improves the ability of style transfer, while maintaining image content information. The stylization of the improved model can better meet people's requirements for stylization, and the evaluation indexes of structural similarity, cosine similarity, and mutual information value have increased by 0.323%, 0.094%, and 3.591%, respectively.

A Multiscale Constraints Method Localization of 3D Facial Feature Points.

It is an important task to locate facial feature points due to the widespread application of 3D human face models in medical fields. In this paper, we propose a 3D facial feature point localization method that combines the relative angle histograms with multiscale constraints. Firstly, the relative angle histogram of each vertex in a 3D point distribution model is calculated; then the cluster set of the facial feature points is determined using the cluster algorithm. Finally, the feature points are located precisely according to multiscale integral features. The experimental results show that the feature point localization accuracy of this algorithm is better than that of the localization method using the relative angle histograms.

ORAOV1 overexpression in esophageal squamous cell carcinoma and esophageal dysplasia: a possible biomarker of progression and poor prognosis in esophageal carcinoma.

Oral cancer overexpressed 1 (ORAOV1) has been reported to exhibit high amplification levels in esophageal squamous cell cancer (ESCC) and in premalignant lesions. However, ORAOV1 protein expression levels in ESCC and esophageal squamous intraepithelial neoplasia (ESIN) have not yet been reported. We have explored the relationship of ORAOV1 protein expression with ESCC and ESIN by immunohistochemically analyzing tissue microarrays containing esophageal samples from patients with various clinical features and prognoses. The percentage of ESCC, high-grade ESIN (HGESIN), low-grade ESIN (LGESIN), and nontumoral control patients overexpressing ORAOV1 were 70.63% (101/143), 77.36% (41/53), 48.96% (47/96), and 5.79% (7/121), respectively. ORAOV1 overexpression also appears to be significantly higher in ESCC, HGESIN, and LGESIN than in the controls (all P < .001), and the levels observed for ESCC and HGESIN were also significantly higher than that in LGESIN (both P = .001). These results corresponded to high sensitivity and specificity values in ESCC, HGESIN, and LGESIN tissues. Furthermore, the increased expression of ORAOV1 is significantly associated with lymph node metastasis (P = .001) and an advanced TNM stage (III + IV) (P = .014), and patients with ORAOV1 overexpression experienced shorter overall survival time compared with those with lower ORAOV1 (χ(2) = 11.505, P = .001). This study provides the first evidence of ORAOV1 overexpression in ESCC and ESIN and demonstrates a potential role in tumor progression and metastasis. ORAOV1 overexpression could, therefore, be used as a novel biomarker of poor prognosis in patients with ESCC.

Unclassified renal cell carcinoma: a clinicopathological, comparative genomic hybridization, and whole-genome exon sequencing study.

Unclassified renal cell carcinoma (URCC) is a rare variant of RCC, accounting for only 3-5% of all cases. Studies on the molecular genetics of URCC are limited, and hence, we report on 2 cases of URCC analyzed using comparative genome hybridization (CGH) and the genome-wide human exon GeneChip technique to identify the genomic alterations of URCC. Both URCC patients (mean age, 72 years) presented at an advanced stage and died within 30 months post-surgery. Histologically, the URCCs were composed of undifferentiated, multinucleated, giant cells with eosinophilic cytoplasm. Immunostaining revealed that both URCC cases had strong p53 protein expression and partial expression of cluster of differentiation-10 and cytokeratin. The CGH profiles showed chromosomal imbalances in both URCC cases: gains were observed in chromosomes 1p11-12, 1q12-13, 2q20-23, 3q22-23, 8p12, and 16q11-15, whereas losses were detected on chromosomes 1q22-23, 3p12-22, 5p30-ter, 6p, 11q, 16q18-22, 17p12-14, and 20p. Compared with 18 normal renal tissues, 40 mutated genes were detected in the URCC tissues, including 32 missense and 8 silent mutations. Functional enrichment analysis revealed that the missense mutation genes were involved in 11 different biological processes and pathways, including cell cycle regulation, lipid localization and transport, neuropeptide signaling, organic ether metabolism, and ATP-binding cassette transporter signaling. Our findings indicate that URCC may be a highly aggressive cancer, and the genetic alterations identified herein may provide clues regarding the tumorigenesis of URCC and serve as a basis for the development of targeted therapies against URCC in the future.

Overexpression of GEFT, a Rho family guanine nucleotide exchange factor, predicts poor prognosis in patients with rhabdomyosarcoma.

Rhabdomyosarcoma (RMS) is one of the most common soft-tissue sarcomas in children and adolescents with poor prognosis. Yet, there is lack of effective prognostic biomarkers for RMS. The present study, therefore, aimed to explore potential biomarkers for RMS based on our previous findings using array comparative genomic hybridization. We investigated guanine nucleotide exchange factor, GEFT, at expression level in 45 RMS patients and 36 normal striated muscle controls using immunohistochemistry using tissue microarrays. The expression rate of GEFT in RMS samples (42/45, 93.33%) was significantly higher (P<0.05) than that in normal controls (5/36, 13.89%). Moreover, the overexpression rate of GEFT in RMS (31/45, 68.89%) was also significantly higher (P<0.05) than that in normal controls (0/36, 0.00%). Increased expression of GEFT correlated significantly with advanced disease stages (stages III/IV) (P=0.001), lymph node metastasis (P=0.019), and distant metastasis (P=0.004), respectively, in RMS patients. In addition, RMS patients having overexpressed GEFT experienced worse overall survival (OS) than those having low levels of GEFT (P=0.001). GEFT overexpression was determined to be an independent prognostic factor for poor OS in RMS patients (hazard ratio: 3.491, 95% confidence interval: 1.121-10.871, P=0.004). In conclusion, these observations provide the first evidence of GEFT overexpression in RMS and its correlations with disease aggressiveness and metastasis. These findings suggest that GEFT may serve as a promising biomarker predicting poor prognosis in RMS patients, thus implying its potential as a therapeutic target.

Xp11 translocation renal cell carcinoma in adults: a clinicopathological and comparative genomic hybridization study.

To study the clinicopathological and genomic characteristics of Xp11.2 translocation renal cell carcinoma (Xp11.2 RCC) in adults, we analyzed 9 Xp11.2 RCCs, confirmed by transcription factor E3 (TFE3) immunohistochemistry, in patients aged ≥20 years. TFE3 expression was also determined in 12 cases of alveolar soft part sarcoma (ASPS) served as a positive control. Comparative genomic hybridization (CGH) was used to investigate genomic imbalances in all Xp11.2 RCC cases. Most of our Xp11.2 RCC patients (5/9) presented with TNM stages 3-4, and 6 patients died 10 months to 7 years after their operation. Histologically, Xp11.2 RCC was composed of a mixed papillary nested/alveolar growth pattern (8/9). Immunostaining showed that all Xp11.2 RCC and ASPS cases had strong TFE3 expression and high positive ratios for p53 and vimentin. However, there were significant differences in the expression of AMACR (p<0.001), AE1/AE3 (p=0.002), and CD10 (p=0.024) between the 2 diseases. CGH profiles showed chromosomal imbalances in all 9 Xp11.2 RCC cases; gains were observed in chromosomes Xp11 (6/9), 7q20-25, 12q25-31 (5/9), 7p16-24 (4/9), 8p12-13, 8q20-21, 16q20-22, 17q25-26, 20q22-23 (4/9), and losses occurred frequently on chromosomes 3p12-16, 9q31-32, 14q22-24 (4/9). Our Conclusions show Xp11.2 RCC that occur in adults may be aggressive cancers, the expressions of AMACR, CD10, AE1/AE3 are helpful in the differential diagnosis between Xp11.2 RCC and ASPS, and CGH assay is a useful complementary method for confirming the diagnosis of Xp11.2 RCC.

Analysis of face stability during excavation of Double-O-Tube shield tunnel.

This paper focuses on the face stability analysis of Double-O-Tube shield tunnel. This kind of analysis is significant to ensure the safety of workers and reduce the influence on the surrounding environment. The key point of the stability analysis is to determine the supporting pressure applied to the face by the shield. A collapse failure will occur when the supporting pressure is not sufficient to prevent the movement of the soil mass towards the tunnel. A three-dimensional collapse failure mechanism was presented in this paper. Based on the mechanism of a single circular shield tunnel, the mechanism of Double-O-Tube shield tunnel was established by using the fact that both of the mechanisms are symmetrical. Then by means of the kinematic theorem of limit analysis, the numerical results were obtained, and a design chart was provided. The finite difference software FLAC3D was applied to investigate the face failure mechanism of DOT shield tunnel established in this paper; the critical supporting pressures of the collapse failure mechanism in different strata (sand and silt) were calculated. Through comparative analysis, the theoretical values were very close to the numerical values. This shows that the face failure mechanism of DOT shield tunnel is reasonable, and it can be applied to the sand and silt strata.

Analysis of PTEN methylation patterns in soft tissue sarcomas by MassARRAY spectrometry.

Soft tissue sarcomas (STSs) are a rare and fascinating group of diseases that can be subdivided into specific reciprocal translocations in STSs (SRTSs) and nonspecific reciprocal translocations in STSs (NRTSs). PTEN mutations are rare in STSs, suggesting that PTEN expression may be lost by alternative mechanisms such as methylation. In order to reveal whether aberrant PTEN methylation occurs in STSs, MassARRAY Spectrometry was carried to detect methylation patterns of PTEN in STSs. We evaluated methylation levels in 41 CpG sites from -2,515 to -2,186 bp (amplicon A) and -1,786 to -1,416 bp (amplicon B) relative to the translation initiation site in 110 different cases (46 cases of SRTSs, 40 cases of NRTSs, and 24 cases of normal controls). In addition, immunohistochemistry (IHC) was used to detect the loss of PTEN to determine whether PTEN alterations were responsible for decreased PTEN expression. Our data showed that expression of PTEN was diminished in 49 (57%) STSs, whereas the remaining cases (43%) were classified as high expression. Our previous results found that only 2 of 86 cases (2.3%) had a PTEN mutation suggesting that PTEN may be mainly downregulated in STSs by methylation, but not by mutation of PTEN itself. We observed that amplicon A was hypermethylated in STSs with low PTEN expression, whereas normal controls had low methylation levels (P<0.0001), which was not present in amplicon B (P>0.05), nor were there significant differences in the methylation levels in PTEN between SRTS and NRTS cases. The majority of individual CpG units within two amplicons was demonstrated to be hypermethylated. These findings indicate that PTEN hypermethylation is a common event in STSs suggesting that the inactivation of PTEN may be due to hypermethylation in the promoter of PTEN. The aberrant methylation of the CpG sites within PTEN promoter may serve as a potential candidate biomarker for STSs.

[Detection and clinical significance of Notch1 methylation in breast cancer and intraductal proliferative breast lesions].

OBJECTIVE: To explore the relevance between the promoter methylation status of Notch1 gene and the invasive ductal carcinoma and ductal hyperplastic lesions of the breast. METHODS: Methylation status of Notch1 gene in human breast invasive ductal carcinoma (IDC, n = 89), ductal carcinoma in situ (DCIS, n = 20), atypical ductal hyperplasia (ADH, n = 11) and usual ductal hyperplasia (UDH, n = 20) were quantitatively evaluated by MALDI-TOF MS. The expression of Notch1 protein was detected by immunohistochemical stain (SP method). RESULTS: Positive expression rates of Notch1 protein in IDC and DCIS were 91.0% (81/89) and 75.0% (15/20), respectively, which were significantly higher than those of ADH (4/11) and UDH (30.0%, 6/20;P < 0.05). Notch1 protein expression was correlated significantly with lymph node metastasis, pathological grades and TNM stages of IDC. The mean methylation levels of Notch1 gene at CpG_3, CpG_4.5 and CpG_8 significantly decreased in IDC group compared with those of DCIS, ADH and UDH groups (P < 0.0083). In breast carcinomas, the mean methylation rates of Notch1 gene at CpG_4.5, CpG_10.11, and CpG_14.15.16 loci in cases with axillary node metastasis were significantly lower than those without axillary node metastasis (P < 0.05); and the methylation rates at CpG_14.15.16 and CpG_18 loci in stage Iwere lower than that in stage II, further lower than that in stage III (P < 0.05); and that in CpG_1.2, CpG_12.13 loci in grade I (highly-differentiated group) were higher than that in grade II (moderate-differentiated group) and grade III (poorly-differentiated group) (P < 0.05); and the methylation rates at CpG_3, CpG_8 and CpG_14.15.16 loci in ER(+) PR(+) HER2(-) group were lower than that in ER(-) PR(-) HER2(+) group (P < 0.05). CONCLUSIONS: There is an overall hypomethylation of Notch1 gene in breast invasive ductal carcinomas with corresponding over-expression of Notch1 protein. This inverse correlation show that the alteration of protein expression result from hypomethylation oncogene Notch1, and this change may have important significance in breast tumorigenesis and the development. Specific hypomethylation at CpG_3, CpG_ 4.5 and CpG_8 loci of Notch1 gene may play a role in the pathogenesis of breast carcinoma, suggesting the progression and/or malignant transformation from benign glandular lesions of the breast.

Phenotypic changes of human cells in human-rat liver during partial hepatectomy-induced regeneration.

AIM: To examine the human hepatic parenchymal and stromal components in rat liver and the phenotypic changes of human cells in liver of human-rat chimera (HRC) generated by in utero transplantation of human cells during partial hepatectomy (PHx)-induced liver regeneration. METHODS: Human hepatic parenchymal and stromal components and phenotypic changes of human cells during liver regeneration were examined by flow cytometry, in situ hybridization and immunohistochemistry. RESULTS: ISH analysis demonstrated human Alu-positive cells in hepatic parenchyma and stroma of recipient liver. Functional human hepatocytes generated in this model potentially constituted human hepatic functional units with the presence of donor-derived human endothelial and biliary duct cells in host liver. Alpha fetoprotein (AFP)(+), CD34(+) and CD45(+) cells were observed in the chimeric liver on day 10 after PHx-induced liver regeneration and then disappeared in PHx group, but not in non-PHx group, suggesting that dynamic phenotypic changes of human cells expressing AFP, CD34 and CD45 cells may occur during the chimeric liver regeneration. Additionally, immunostaining for human proliferating cell nuclear antigen (PCNA) showed that the number of PCNA-positive cells in the chimeric liver of PHx group was markedly increased, as compared to that of control group, indicating that donor-derived human cells are actively proliferated during PHx-induced regeneration of HRC liver. CONCLUSION: HRC liver provides a tool for investigating human liver regeneration in a humanized animal model.