Hand skeletal features of children and adolescents with different growth statuses and periods.

Background: The hand skeletal features of children and adolescents at different growth statuses and development periods, and the correlation between these skeletal features and hand asymmetric force are currently unclear. Thus, this study sought to investigate the hand skeletal features of children and adolescents at different growth statuses and at different periods of development, and the correlation between these skeletal features and asymmetric force in hands. Methods: A retrospective study was performed on subjects aged 4-20 years with good growth status (group A) or short stature (group B). Additional subjects aged 4-20, 21-40, and >40 years were enrolled in groups C, D, and E, respectively. All the subjects underwent left-hand posteroanterior X-ray radiography. Brachymesophalangia-V (BMP-V), conical epiphysis, epiphysis/metaphysis symmetry of the proximal phalanx (ESP), and the angle of the metacarpal-phalangeal axis were analyzed. Results: Of the 654 children and teenagers aged 4-20 years (median: 11 years) enrolled in the study, 432 were allocated to group A, of whom 237 (54.9%) were male and 195 (45.1%) were female, and 222 matched cases were allocated to group B, of whom 112 (50.5%) were male and 110 (49.5%) were female. The first to third ESPs were significantly (P<0.05) greater in group A than in group B, while the first to third angles of the metacarpal-phalangeal axis were significantly (P<0.05) smaller in group A than in group B. The correlation analysis revealed a highly significant (P<0.01) negative correlation between the ESP and angle of the metacarpal-phalangeal axis (r=-0.948, -0.926, -0.940, -0.885, and -0.848, respectively). The incidence of BMP-V was 15.4% in all patients, while that of conical epiphysis was 19.5%. The incidence of BMP-V and conical epiphysis was significantly (P<0.05) smaller in group A than in group B (11.1% vs. 23.8% for BMP-V and 16.6% vs. 25.2% for conical epiphysis, respectively). Additionally, 216 subjects were enrolled in group C (108 male and 108 female), 185 subjects were enrolled in in group D (93 male and 92 female), and 176 subjects were enrolled in in group E (104 male and 72 female). The second to fifth ESPs in group C were significantly (P<0.05) smaller than those in both groups D and E, while the second to fifth angles of the metacarpal-phalangeal axis were significantly (P<0.05) larger in group C than in both groups D and E. A BMP-V was present in 35 (16.2%) patients in group C, 8 (4.3%) in group D, and 2 (1.1%) in group E, and the difference among the three groups was statistically significant (P<0.05). Conclusions: The epiphyseal symmetry of the proximal phalanges is poor in short stature children and adolescents, and the angle between the metacarpal and phalangeal axes is larger in children and adolescents with short stature than those with normal height and good growth status. A negative correlation was found between the epiphyseal symmetry of the proximal phalanges and asymmetrical stress.

Analysis of Breast Cancer Screening Results and Influencing Factors of Breast Cancer in Guangdong Province from 2017 to 2021.

BACKGROUNDS: Breast cancer screening plays an important role in the early detection, diagnosis and treatment of breast cancer. The aim of this study was to evaluate the screening results and explore the influencing factors of breast cancer detection rate in Guangdong. METHODS: This cross-sectional study was conducted among 2,024,960 women aged 35-64 in Guangdong Province during 2017-2021. The data about breast cancer screening information were collected from the Guangdong maternal and child health information system. Descriptive statistical analysis was used to explain demographic characteristics and results of breast cancer screening. The generalized linear regression model was applied to analyze the related influencing factors of breast cancer detection rate. RESULTS: The estimated detection rate of breast cancer in Guangdong Province is 70.32/105, with an early diagnosis rate of 82.06%. After adjusting covariates, those women with older age (45-55 [OR (95% CI) 2.174 (1.872, 2.526)], 55-65 [OR (95% CI) 2.162 (1.760, 2.657)]), education for high school ([OR (95% CI) 1.491 (1.254, 1.773)]) and older age at first birth ([OR (95% CI) 1.632 (1.445, 1.844)]) were more likely to have higher detection rate of breast cancer. No history of surgery or biopsy ([OR (95% CI) 0.527 (0.387, 0.718)]), no history of breast cancer check ([OR (95% CI) 0.873 (0.774, 0.985)]) and no family history of breast cancer ([OR (95% CI) 0.255 (0.151, 0.432)]) women were more likely to screen negative for breast cancer (P < 0.05). CONCLUSION: The detection rate of breast cancer in screening showed an increasing trend year by year in Guangdong Province. Older age, education for high school and older age at first birth were risk factors for breast cancer detection rate, while no surgery or biopsy history, no family history of breast cancer and no history of breast cancer check were protective factors.

Comprehensive genomic analysis of puerarin in inhibiting bladder urothelial carcinoma cell proliferation and migration.

BACKGROUND: Bladder urothelial carcinoma (BUC) ranks second in the incidence of urogenital system tumors, and the treatment of BUC needs to be improved. Puerarin, a traditional Chinese medicine (TCM), has been shown to have various effects such as anti-cancer effects, the promotion of angiogenesis, and anti-inflammation. This study investigates the effects of puerarin on BUC and its molecular mechanisms. METHODS: Through GeneChip experiments, we obtained differentially expressed genes (DEGs) and analyzed these DEGs using the Ingenuity® Pathway Analysis (IPA®), Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) pathway enrichment analyses. The Cell Counting Kit 8 (CCK8) assay was used to verify the inhibitory effect of puerarin on the proliferation of BUC T24 cells. String combined with Cytoscape® was used to create the Protein-Protein Interaction (PPI) network, and the MCC algorithm in cytoHubba plugin was used to screen key genes. Gene Set Enrichment Analysis (GSEA®) was used to verify the correlation between key genes and cell proliferation. RESULTS: A total of 1617 DEGs were obtained by GeneChip. Based on the DEGs, the IPA® and pathway enrichment analysis showed they were mainly enriched in cancer cell proliferation and migration. CCK8 experiments proved that puerarin inhibited the proliferation of BUC T24 cells, and its IC50 at 48 hours was 218µmol/L. Through PPI and related algorithms, 7 key genes were obtained: ITGA1, LAMA3, LAMB3, LAMA4, PAK2, DMD, and UTRN. GSEA showed that these key genes were highly correlated with BUC cell proliferation. Survival curves showed that ITGA1 upregulation was associated with poor prognosis of BUC patients. CONCLUSION: Our findings support the potential antitumor activity of puerarin in BUC. To the best of our knowledge, bioinformatics investigation suggests that puerarin demonstrates anticancer mechanisms via the upregulation of ITGA1, LAMA3 and 4, LAMB3, PAK2, DMD, and UTRN, all of which are involved in the proliferation and migration of bladder urothelial cancer cells.

scGCC: Graph Contrastive Clustering With Neighborhood Augmentations for scRNA-Seq Data Analysis.

Single-cell RNA sequencing (scRNA-seq) has rapidly emerged as a powerful technique for analyzing cellular heterogeneity at the individual cell level. In the analysis of scRNA-seq data, cell clustering is a critical step in downstream analysis, as it enables the identification of cell types and the discovery of novel cell subtypes. However, the characteristics of scRNA-seq data, such as high dimensionality and sparsity, dropout events and batch effects, present significant computational challenges for clustering analysis. In this study, we propose scGCC, a novel graph self-supervised contrastive learning model, to address the challenges faced in scRNA-seq data analysis. scGCC comprises two main components: a representation learning module and a clustering module. The scRNA-seq data is first fed into a representation learning module for training, which is then used for data classification through a clustering module. scGCC can learn low-dimensional denoised embeddings, which is advantageous for our clustering task. We introduce Graph Attention Networks (GAT) for cell representation learning, which enables better feature extraction and improved clustering accuracy. Additionally, we propose five data augmentation methods to improve clustering performance by increasing data diversity and reducing overfitting. These methods enhance the robustness of clustering results. Our experimental study on 14 real-world datasets has demonstrated that our model achieves extraordinary accuracy and robustness. We also perform downstream tasks, including batch effect removal, trajectory inference, and marker genes analysis, to verify the biological effectiveness of our model.

Ovarian cyst torsion in Prader-Willi Syndrome.

BACKGROUND: Prader-Willi syndrome (PWS) is a genetic disorder involving multiple systems, with an incidence of about 1/10000-25000. Ovarian torsion (OT) is not commonly found in children. Ovarian cyst acts as one of the primary factors resulting in OT. While ovarian cyst torsion with Prader-Willi Syndrome has not been reported before. CASE PRESENTATION: A 12-years old female was admitted to Emergency Department of our hospital with the chief complaint of abdominal pain. The outcomes of physical examination revealed the height of 150 cm, weight of 103 kg, BMI of 45.77 kg/m2. The patient manifested the special facial features, an obese body, with the abdomen distended into a spherical shape. The fat accumulation in the abdomen significantly embarrassed the palpation. The abdominal CT scan indicated a huge cystic mass in the abdominal cavity, sized about 138 mm × 118 mm. According to medical history, the patient was born with low crying and hypotonia, who has developed the uncontrollable eating behavior since 3-years old. These abnormalities led to a speculation of PWS syndrome, so a genetic test was performed and finally confirmed it, concluding a torsion of ovarian cyst with PWS. With the multidisciplinary consultation, a careful treatment strategy containing the control of blood pressure and blood sugar, coenzyme Q10 was administrated to nourish the myocardium and the application of Growth Hormone was developed. All the above preoperative treatments have brought great benefits to patients. Thus promising the successful completion of operation. The postoperative follow-up till now indicated that the abdominal incision was well healed, without operative complications. CONCLUSIONS: This may be the first case report. In the treatment of ovarian cyst torsion, PWS syndrome requires fully consideration, as the latter can lead to multisystem abnormalities, especially the relation to perioperative management, and even fatalities. Genetic testing should be conducted early when PWS was suspected, accompanied with adequate preparation for the perioperative period, the follow-ups of patients should be maintained for a long time after surgery.

Potential circRNA-Disease Association Prediction Using DeepWalk and Nonnegative Matrix Factorization.

Circular RNAs (circRNAs) are a category of noncoding RNAs that exist in great numbers in eukaryotes. They have recently been discovered to be crucial in the growth of tumors. Therefore, it is important to explore the association of circRNAs with disease. This paper proposes a new method based on DeepWalk and nonnegative matrix factorization (DWNMF) to predict circRNA-disease association. Based on the known circRNA-disease association, we calculate the topological similarity of circRNA and disease via the DeepWalk-based method to learn the node features on the association network. Next, the functional similarity of the circRNAs and the semantic similarity of the diseases are fused with their respective topological similarities at different scales. Then, we use the improved weighted K-nearest neighbor (IWKNN) method to preprocess the circRNA-disease association network and correct nonnegative associations by setting different parameters K1 and K2 in the circRNA and disease matrices. Finally, the L2,1-norm, dual-graph regularization term and Frobenius norm regularization term are introduced into the nonnegative matrix factorization model to predict the circRNA-disease correlation. We perform cross-validation on circR2Disease, circRNADisease, and MNDR. The numerical results show that DWNMF is an efficient tool for forecasting potential circRNA-disease relationships, outperforming other state-of-the-art approaches in terms of predictive performance.

Convolution Neural Networks Using Deep Matrix Factorization for Predicting Circrna-Disease Association.

CircRNAs have a stable structure, which gives them a higher tolerance to nucleases. Therefore, the properties of circular RNAs are beneficial in disease diagnosis. However, there are few known associations between circRNAs and disease. Biological experiments identify new associations is time-consuming and high-cost. As a result, there is a need of building efficient and achievable computation models to predict potential circRNA-disease associations. In this paper, we design a novel convolution neural networks framework(DMFCNNCD) to learn features from deep matrix factorization to predict circRNA-disease associations. Firstly, we decompose the circRNA-disease association matrix to obtain the original features of the disease and circRNA, and use the mapping module to extract potential nonlinear features. Then, we integrate it with the similarity information to form a training set. Finally, we apply convolution neural networks to predict the unknown association between circRNAs and diseases. The five-fold cross-validation on various experiments shows that our method can predict circRNA-disease association and outperforms state of the art methods.

Conjunctival congestion after laparoscopic operation in children: A retrospective case series in a single-centre children's medical centre.

Objective: In the present study, we report a retrospective analysis of 23 cases of conjunctival congestion after laparoscopic operation in children and try to explore the causes and intervention measures. Methods and Results: This is a retrospective, single-centre and observational study, and all patients with conjunctival congestion after laparoscopic operation admitted to our institution between August 2021 and December 2021 were included in this study. Records of 23 patients including 16 male patients and 7 female patients were retrospectively analysed. These patients were in the age group of 2-12 years. Their primary symptom was different degrees of conjunctival congestion, and the symptom onset was between 2 and 7 days after laparoscopic operation, including laparoscopic inguinal hernia repair, laparoscopic appendectomies, laparoscopic Meckel's diverticulectomy, laparoscopic removal of foreign body ingestions and laparoscopic choledochal cystectomy, and the duration of operations varies from 20 min to 255 min. The symptom disappeared from 5 to 21 days after the operation, and the duration of the symptom ranged from 2 to 14 days. A total of 1718 operations were performed, of which 461 were laparoscopic and 1257 were general operations, the incidence of conjunctival congestion after laparoscopic surgery was 23/461, and compared with 0/1257 after ordinary surgery, there was a significant difference between them. Of these 23 patients, 5 patients received no treatment and the other 18 patients were intervened with steroid-containing eye drops. Although eye drops containing steroids can significantly relieve eye discomfort, the duration of conjunctival congestion between the two groups (i.e. steroid-containing eye drop treated vs. non-steroid-containing eye drop treated) did not differ significantly. All patients recovered well. In the follow-up till the end of February 2022, no serious complications had occurred. Conclusion: Conjunctival congestion after laparoscopic operation is extremely rare in children, and the underlying causes are still unclear. We speculate that the pressure of pneumoperitoneum may be the main cause of this phenomenon. Symptoms may be self-limiting, and steroid-containing eye drops can relieve effectively the discomfort.

GCAEMDA: Predicting miRNA-disease associations via graph convolutional autoencoder.

microRNAs (miRNAs) are small non-coding RNAs related to a number of complicated biological processes. A growing body of studies have suggested that miRNAs are closely associated with many human diseases. It is meaningful to consider disease-related miRNAs as potential biomarkers, which could greatly contribute to understanding the mechanisms of complex diseases and benefit the prevention, detection, diagnosis and treatment of extraordinary diseases. In this study, we presented a novel model named Graph Convolutional Autoencoder for miRNA-Disease Association Prediction (GCAEMDA). In the proposed model, we utilized miRNA-miRNA similarities, disease-disease similarities and verified miRNA-disease associations to construct a heterogeneous network, which is applied to learn the embeddings of miRNAs and diseases. In addition, we separately constructed miRNA-based and disease-based sub-networks. Combining the embeddings of miRNAs and diseases, graph convolutional autoencoder (GCAE) was utilized to calculate association scores of miRNA-disease on two sub-networks, respectively. Furthermore, we obtained final prediction scores between miRNAs and diseases by adopting an average ensemble way to integrate the prediction scores from two types of subnetworks. To indicate the accuracy of GCAEMDA, we applied different cross validation methods to evaluate our model whose performances were better than the state-of-the-art models. Case studies on a common human diseases were also implemented to prove the effectiveness of GCAEMDA. The results demonstrated that GCAEMDA was beneficial to infer potential associations of miRNA-disease.

ILPMDA: Predicting miRNA-Disease Association Based on Improved Label Propagation.

MicroRNAs (miRNAs) are small non-coding RNAs that have been demonstrated to be related to numerous complex human diseases. Considerable studies have suggested that miRNAs affect many complicated bioprocesses. Hence, the investigation of disease-related miRNAs by utilizing computational methods is warranted. In this study, we presented an improved label propagation for miRNA-disease association prediction (ILPMDA) method to observe disease-related miRNAs. First, we utilized similarity kernel fusion to integrate different types of biological information for generating miRNA and disease similarity networks. Second, we applied the weighted k-nearest known neighbor algorithm to update verified miRNA-disease association data. Third, we utilized improved label propagation in disease and miRNA similarity networks to make association prediction. Furthermore, we obtained final prediction scores by adopting an average ensemble method to integrate the two kinds of prediction results. To evaluate the prediction performance of ILPMDA, two types of cross-validation methods and case studies on three significant human diseases were implemented to determine the accuracy and effectiveness of ILPMDA. All results demonstrated that ILPMDA had the ability to discover potential miRNA-disease associations.

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A field experiment was conducted using Hanfu apple grafted on 10 dwarfing interstocks to evaluate plant growth, yield, and fruit quality of different apple-interstock combinations in Hulu-dao, Liaoning Province, in order to select the most suitable apple-interstock combinations in the study area. The results showed that tree vigor, yield, and fruit quality were largely affected by the interstocks. Among the interstocks evaluated here, plant height, branch numbers, leaf fresh weight, branch fresh weight, root fresh weight, total root volume, average root diameter and total root tip numbers were the largest on GM256, while the highest trunk diameter, branch length, total root length and total root surface area were observed on CX5, the highest fruit firmness, soluble solids and soluble sugar contents were observed on M26, and the highest fruit vertical diameter, transverse diameter and single fruit weight were observed on GM256. The fuzzy evaluation results showed that the comprehensive character scores decreased in order of GM256, CX5, B9, JM7, CG80, Liaozhen 2, M26, Qingzhen 3, SH38 and MD001. Our results indicated that interstock GM256 and CX5 was better for the cold climate region.

Identifiable Temporal Feature Selection via Horizontal Visibility Graph Towards Smart Medical Applications.

With the proliferation of IoMT (Internet of Medical Things), billions of connected medical devices are constantly producing oceans of time series sensor data, dubbed as time series for short. Considering these time series reflect various functional states of the human body, how to effectively detect the corresponding abnormalities is of great significance for smart healthcare. Accordingly, we develop a horizontal visibility graph-based temporal classification model for disease diagnosis. We conduct extensive comparison experiments on the benchmark datasets to justify the superiority of our method in term of accuracy and efficiency. Besides, we have released the codes and parameters to facilitate the community research. We propose an identifiable temporal feature selection via horizontal visibility graph for time series classification (TSC) based disease diagnosis. We conduct comparison experiments on the benchmark datasets to justify the superiority of our method in term of accuracy and efficiency. As a side contribution, we have released the codes and parameters to facilitate the community research ( https://github.com/sdujicun/SSVG ).

A novel consciousness emotion recognition method using ERP components and MMSE.

Objective.Electroencephalogram (EEG) based emotion recognition mainly extracts traditional features from time domain and frequency domain, and the classification accuracy is often low for the complex nature of EEG signals. However, to the best of our knowledge, the fusion of event-related potential (ERP) components and traditional features is not employed in emotion recognition, and the ERP components are only identified and analyzed by the psychology professionals, which is time-consuming and laborious.Approach.In order to recognize the consciousness and unconsciousness emotions, we propose a novel consciousness emotion recognition method using ERP components and modified multi-scale sample entropy (MMSE). Firstly, ERP components such as N200, P300 and N300 are automatically identified and extracted based on shapelet technique. Secondly, variational mode decomposition and wavelet packet decomposition are utilized to process EEG signals for obtaining different levels of emotional variational mode function (VMF), namelyVMFß+γ, and then nonlinear feature MMSE of eachVMFß+γare extracted. At last, ERP components and nonlinear feature MMSE are fused to generate a new feature vector, which is fed into random forest to classify the consciousness and unconsciousness emotions.Main results.Experimental results demonstrate that the average classification accuracy of our proposed method reach 94.42%, 94.88%, and 94.95% for happiness, horror and anger, respectively.Significance.Our study indicates that the fusion of ERP components and nonlinear feature MMSE is more effective for the consciousness and unconsciousness emotions recognition, which provides a new research direction and method for the study of nonlinear time series.

Multi-layer Representation Learning and Its Application to Electronic Health Records.

Electronic Health Records (EHRs) are digital records associated with hospitalization, diagnosis, medications and so on. Secondary use of EHRs can promote the clinical informatics applications and the development of healthcare undertaking. EHRs have the unique characteristic where the patient visits are temporally ordered but the diagnosis codes within a visit are randomly ordered. The hierarchical structure requires a multi-layer network to explore the different relational information of EHRs. In this paper, we propose a Multi-Layer Representation Learning method (MLRL), which is capable of learning effective patient representation by hierarchically exploring the valuable information in both diagnosis codes and patient visits. Firstly, MLRL utilizes the multi-head attention mechanism to explore the potential connections in diagnosis codes, and a linear transformation is implemented to further map the code vectors to non-negative real-valued representations. The initial visit vectors are then obtained by summarizing all the code representations. Secondly, the proposed method combines Bidirectional Long Short-Term Memory with self-attention mechanism to learn the weighted visit vectors which are aggregated to form the patient representation. Finally, to evaluate the performance of MLRL, we apply it to patient's mortality prediction on real EHRs and the experimental results demonstrate that MLRL has a significant improvement in prediction performance. MLRL achieves around 0.915 in Area Under Curve which is superior to the results obtained by baseline methods. Furthermore, compared with raw data and other data representations, the learned representation with MLRL shows its outstanding results and availability on multiple different classifiers.

Effects of resveratrol on the expression of molecules related to the mTOR signaling pathway in pathological scar fibroblasts.

BACKGROUND: We observed the effects of resveratrol on the expression of molecules involved in the mTOR signaling pathway in pathological scar fibroblasts, including PI3K, Akt and mTOR. METHODS: We detected the expression of PI3K, Akt and mTOR in pathological scar and normal skin fibroblasts through immunofluorescence. After being treated with different concentrations of resveratrol, the expression of PI3K, Akt and mTOR mRNA and protein were detected by RT-PCR and Western Blot respectively. RESULTS: Results showed that the expression of PI3K, Akt and mTOR were significantly enhanced in pathological scar fibroblasts and mainly expressed in the nucleus, with no expression in normal skin fibroblasts. Results from RT-PCR and Western Blot tests demonstrated that after Res intervention with different concentrations for pathological scar fibroblasts, the relative expression quantity of PI3K mRNA and protein decreased and showed a dose dependent relationship. Compared to the control group, the differences were statistically significant (P<0.05). However, decrease in the expression of PI3K mRNA and protein was not obvious and there were no significant differences in comparison to the control group (P>0.05). CONCLUSIONS: The mechanism of resveratrol in the inhibition of the proliferation of pathological scar fibroblasts may be related to its down-regulation in the expression of Akt and mTOR, which are the key molecules of mTOR signaling pathway.

Analysis of the mortality trend of female reproductive system cancer in China from 2004 to 2016 / 中华疾病控制杂志

Objective To describe and analyze the level and variation trend of mortality of female genital malignancies in China from 2004 to 2016, so as to provide scientific evidence for the formulation and implementation of cancer prevention and control measures.Methods The mortality data of cervical cancer, corpus cancer and ovarian cancer were selected from the mortality monitoring data of the national disease monitoring system, the crude mortality and standardized mortality rates were calculated respectively, the annual change trend was analyzed by Joinpoint software.Results From 2004 to 2016, the mortality rate of cervical cancer in Chinese women increased significantly. The mortality trend obviously accelerated after 2011(annual percent change, APC = 13.7%), and the mortality rate in rural areas was higher than that in urban areas. The mortality rate of corpus cancer was relatively stable, and showed a downward trend after 2013(APC=-23.5%). Ovarian cancer mortality rate slightly increased, but the death trend was not statistically significant.Conclusions From 2004 to 2016, the mortality of corpus cancer in Chinese women had improved, the mortality of cervical cancer was still relatively serious, and there were differences between urban and rural areas.

Expression of TGF-ß1/mTOR signaling pathway in pathological scar fibroblasts.

The aim of the present study was to detect the expression of the key molecules, including transforming growth factor­ß1 (TGF-ß1), phosphatidylinositol 3-kinase (PI3K), protein kinase B (Akt) of TGF­ß1/mammalian target of rapamycin (mTOR) pathway in pathological scar fibroblasts. Immunofluorescence, reverse transcription­polymerase chain reaction (RT­PCR) and western blot analysis were used to detect the expression of the key molecules TGF­ß1, PI3K, Akt, mTOR in fibroblasts of normal skin tissue and pathological scar tissue. Immunofluorescence showed that the expression of TGF­ß1, PI3K and Akt was significantly enhanced (P<0.05) in pathological scar fibroblasts, and mainly expressed in the cell nucleus, but not in normal skin tissue or fibroblasts. RT­PCR and western blot test results revealed that the TGF­ß1, PI3K, Akt, and mTOR mRNA and protein expression in pathological scar fibroblasts were significantly higher (P<0.05) than in the normal skin tissue. Expression of the TGF­ß1/mTOR signaling pathway in pathological scar fibroblasts was significantly increased. Data suggest that this expression may be an important mechanism for pathological scar formation.

Expression of mTOR/70S6K signaling pathway in pathological scar fibroblasts and the effects of resveratrol intervention.

The aim of the study was to examine the expression of mammalian target of rapamycin (mTOR)/70S6K signaling pathway in pathological scar fibroblasts and the effects of resveratrol (Res) intervention. The mTOR and 70S6K in pathological scar and normal skin fibroblasts were detected by immunofluorescence following treatment with different concentrations of Res. RT-PCR and western blot analysis were used to detect the expression of mTOR and 70S6K mRNA and protein, respectively. Immunofluorescence showed that the expression of 70S6K and mTOR was significantly enhanced in pathological scar fibroblasts, and mainly expressed in the nucleus, but not in normal skin fibroblasts. RT-PCR and western blot analysis showed that after different concentrations of Res treatments, the mTOR and 70S6K mRNA and protein expression significantly (P<0.05) decreased in a dose­dependent manner. In conclusion, the expression of mTOR/70S6K signaling pathway in pathological scar fibroblasts was significantly enhanced. Res can downregulate the expression of mTOR and 70S6K to achieve the inhibition of pathological scar fibroblast proliferation.

Effect of Wubeizi ointment aqueous solution on the expression of type I and III procollagen genes in keloid fibroblasts.

We evaluated the effect of Wubeizi (WBZ) ointment on keloids. Keloid-derived fibroblast primary cultures were used to evaluate the effect of the different concentration of WBZ ointment on the expression of type I and III procollagen in keloid fibroblast primary cultures using dot blot assay. Type I and II precollagen cDNA probes labeled with non-radioactive digoxin were used for dot blot. Cell cultures were divided into 4 groups: The large dose group received 1 g/ml of WBZ, middle dose, and small dose groups received 0.5 and 0.25 g/ml of WBZ, respectively. The control group received serum-free medium without WBZ. Our results showed that type I and III procollagen mRNA expression was reduced significantly in the large dose and middle dose groups compared to the control group. Type I and III procollagen mRNA expression level in the small dose group had no statistically significant difference with the control group. However, the difference between the large dose group and the small dose group was statistically significant. We concluded that WBZ ointment aqueous solution restricted keloid fibroblast proliferation by downregulating the expression of type I and III procollagen and therefore reducing collagen deposition in keloid tissue.

BRAF kinase inhibitor exerts anti-tumor activity against breast cancer cells via inhibition of FGFR2.

Most anti-angiogenic therapies currently being evaluated in clinical trials targetvascular endothelial growth factor (VEGF) pathway; however, the tumor vasculature can acquire resistance to VEGF-targeted therapy by shifting to other angiogenesis mechanisms. Therefore, other potential therapeutic agents that block non-VEGF angiogenic pathways need to be evaluated. Here we identified BRAF kinase inhibitor, vemurafenibas an agent with potential anti-angiogenic and anti-breast cancer activities. Vemurafenib demonstrated inhibition of endothelial cell proliferation, migration, and tube formation in response to basic fibroblast growth factor (bFGF). In ex vivo and in vivo angiogenesis assays, vemurafenib suppressed bFGF-induced microvessel sprouting of rat aortic rings and angiogenesis in vivo. To understand the underlying molecular basis, we examined the effects of vemurafenib on different molecular components in treated endothelial cell, and found that vemurafenib suppressed bFGF-triggered activation of FGFR2 and protein kinase B (AKT). Moreover, vemurafenib directly inhibited proliferation and blocked the oncogenic signaling pathways in breast cancer cell. In vivo, using xenograft models of breast cancer cells MDA-MB-231, vemurafenib showed growth-inhibitory activity associated with inhibition of tumor angiogenesis. Taken together, our results indicate that vemurafenib targets the FGFR2-mediated AKT signaling pathway in endothelial cells, leading to the suppression of tumor growth and angiogenesis.