NAP1L1 Promotes Endometrial Cancer Progression via EP300-Mediated DDX5 Promoter Acetylation.

Endometrial cancer (EC) is one of the predominant tumors of the female reproductive system. In this current study, we investigated the functions and related mechanisms of NAP1L1/DDX5 in EC. This retrospective study analyzed the medical records of EC patients, collected tissue samples for NAP1L1 and DDX5 staining, and conducted survival analysis using the Kaplan-Meier method. To evaluate the impact of NAP1L1 and/or DDX5 on cellular processes in EC cells, several techniques were employed. These included CCK-8 assay, wound healing assay, Transwell assay, as well as overexpression or knockdown of target gene expression. Additionally, ChIP, dual luciferase reporter gene, Co-IP assay were utilized to confirm the interaction between NAP1L1, EP300 and DDX5. Furthermore, qRT-PCR, western blot and Co-IP assay were performed to analyze the modulation of NAP1L1/DDX5 in Wnt/ß-catenin. NAP1L1 and DDX5 expression were upregulated in EC tissues, and correlated with poor prognosis. NAP1L1/DDX5 promoted EC cell proliferation, migration and invasion. NAP1L1 promotes acetylation and transcription by recruiting EP300 to the DDX5 promoter. DDX5 could activate Wnt/ß-catenin signal by binding to ß-catenin. In animal models, knockdown of NAP1L1 inhibits EC tumor growth and lung metastasis. To sum up, our study demonstrated that NAP1L1 promoted the malignant phenotypes of EC cells via recruiting EP300 to promote DDX5 acetylation, thus activating the Wnt/ß-catenin signaling pathway. Implications: Our research findings indicate that targeting the NAP1L1/EP300/DX5 axis might be a new potential treatment option for endometrial cancer.

The Simpler the Better: Ultrafast Air-Plasma Synthesis of 3D Crosslinked Graphene Nanoscroll-Nanosheet Aerogels at Room Temperature for Capacitive Deionization.

Graphene nanoscroll (GNS) is an important 1D tubular form of graphene-derivative materials, which has garnered widely attention. However, conventional fabrication methods commonly suffer from complex processing and time-consuming. Herein, with graphene oxide (GO) as a precursor, the study puts forward a facile air-plasma synthesis strategy to fabricate 3D graphene nanoscroll-nanosheet aerogels (GSSA). It is demonstrated that without using any chemical additives, a highly efficient reduction-exfoliation-scrolling process can be achieved all-in-one at room temperature within 1 s. The GNSs "grew" from 2D graphene sheets and firmly cross-linked them together, and they not only provide a shortcut path for electron transport but also act as intrinsic spacers to prevent restacking of graphene sheets. When using as an electrode material for capacitive deionization (CDI), GSSA exhibits excellent merits of salt-removal performance. These findings open a new pathway to large-scale synthesis of high-quality and high-purity GNS-based materials with promising applications in CDI and beyond.

High-aspect-ratio dielectric pillar with nanocavity backed by metal substrate in the infrared range.

We investigated absorption and field enhancements of shallow nanocavities on top of high-aspect-ratio dielectric pillars in the infrared range. The structure includes a high-aspect-ratio nanopillar array of high refractive index, with nano-cavities on top of the pillars, and a metal plane at the bottom. The enhancement factor of electric field intensity reaches 3180 in the nanocavities and peak absorption reaches 99%. We also investigated the finite-size effect of the presented structure to simulate real experiments. Due to its narrow absorption bandwidth 3.5 nm, it can work as a refractive index sensor with sensitivity 297.5 nm/RIU and figure of merit 85. This paves the way to directly control light field at the nanoscales in the infrared light range. The investigated nanostructure will find applications in multifunctional photonics devices such as chips for culturing cells, refractive index sensors, biosensors of single molecule detection and nonlinear sensors.

NOTCH Pathway Genes in Ovarian Cancer: Clinical Significance and Associations with Immune Cell Infiltration.

BACKGROUND: Activation of the NOTCH signaling pathway is associated with tumorigenesis. The aim of this study was to investigate NOTCH pathway gene functions and regulatory mechanisms in ovarian cancer (OC). METHODS: We conducted a bioinformatics analysis of publicly available datasets in order to identify potential NOTCH-related mechanisms, associated genes, biological pathways, and their relation to immune function. RESULTS: Significant differential expression of the NOTCH pathway genes DLL1, DLL3, DLL4, HES1, HEY1, JAG1, NOTCH2, NOTCH3, and NOTCH4 was observed between OC samples and normal controls. Low expression of DLL4 and of NOTCH4 in OC patients was associated with International Federation of Gynecology and Obstetrics (FIGO) stage (p <0.001 and p = 0.036, respectively), while high expression of NOTCH3 was associated with race (p = 0.039) and age (p = 0.044). JAG2 and NOTCH1 expression were significantly associated with progression-free interval (PFI) (p = 0.011 and p = 0.039, respectively). DLL1 (Hazard Ratio (HR): 2.096; 95% CI: 1.522-2.886, p < 0.001) and NOTCH1 (HR: 0.711; 95% CI: 0.514-0.983, p = 0.039) expression were independently associated with PFI in multivariate analysis. DLL1, DLL3, JAG1, JAG2, NOTCH3 and NOTCH4 expression could significantly differentiate OC from non-cancer samples. Genes associated with the NOTCH pathway were mainly enriched in five signaling pathways: the NOTCH signaling pathway, breast cancer, endocrine resistance, Th1 and Th2 cell differentiation, and oxidative phosphorylation. The expression of NOTCH pathway genes was significantly associated with immune cell infiltration. CONCLUSIONS: NOTCH pathway genes appear to play an important role in the progression of OC by regulating immune cells, endocrine resistance, Th1 and Th2 cell differentiation, and oxidative phosphorylation. JAG2 and NOTCH1 are potential biomarkers and therapeutic targets for the treatment of OC.

PAXIP1-AS1 is associated with immune infiltration and predicts poor prognosis in ovarian cancer.

The long non-coding RNA (LncRNA) PAXIP1 antisense RNA 1 (PAXIP1-AS1) was found to promote proliferation, migration, EMT, and apoptosis of ovarian cancer (OC) cells in OC cell lines, but the relationship between PAXIP1-AS1 expression and clinical characteristics, prognosis, and immune infiltration of OC patients and its regulatory network are unclear. 379 OC tissues were collected from The Cancer Genome Atlas (TCGA) database. 427 OC tissues and 88 normal ovarian tissues were collected from GTEx combined TCGA database. 130 OC samples were collected from GSE138866. Kruskal-Wallis test, Wilcoxon sign-rank test, logistic regression, Kaplan-Meier method, Cox regression analysis, Gene set enrichment analysis (GSEA), and immuno-infiltration analysis were used to evaluate the relationship between clinical characteristics and PAXIP1-AS1 expression, prognostic factors, and determine the significant involvement of PAXIP1-AS1 in function. QRT-PCR was used to validate the expression of PAXIP1-AS1 in OC cell lines. Low PAXIP1-AS1 expression in OC was associated with age (P = 0.045), histological grade (P = 0.011), and lymphatic invasion (P = 0.004). Low PAXIP1-AS1 expression predicted a poorer overall survival (OS) (HR: 0.71; 95% CI: 0.55-0.92; P = 0.009), progression free interval (PFS) (HR: 1.776; 95% CI: 1.067-2.955; P = 0.001) and disease specific survival (DSS) (HR: 0.67; 95% CI: 0.51-0.89; P = 0.006). PAXIP1-AS1 expression (HR: 0.711; 95% CI: 0.542-0.934; P = 0.014) was independently correlated with PFS in OC patients. GSEA demonstrated that neutrophil degranulation, signaling by Interleukins, GPCR-ligand binding, G alpha I signaling events, VEGFAVEGFR-2 signaling pathway, naba secreted factors, Class A 1 Rhodopsin-Like Receptors, PI3K-Akt signaling pathway, and Focal Adhesion-PI3K-Akt-mTOR-signaling pathway were differentially enriched in PAXIP1-AS1 high expression phenotype. PAXIP1-AS1 was significantly downregulated in OC cell lines compared with IOSE29 cell line. The expression of PAXIP1-AS1 was associated with immune infiltration. low expression of PAXIP1-AS1 was correlated with poor OS (HR: 0.52; 95% CI: 0.34-0.80; P = 0.003) from GSE138866. There were some genomic variations between the PAXIP1-AS1 high and low expression groups. Low expression of PAXIP1-AS1 was significantly associated with poor survival and immune infiltration in OC. PAXIP1-AS1 could be a promising prognosis biomarker and response to immunotherapy for OC.

miR-149-3p Is a Potential Prognosis Biomarker and Correlated with Immune Infiltrates in Uterine Corpus Endometrial Carcinoma.

Background: Endocrine disruption is an important factor in the development of endometrial cancer. Expression of miR-149-3p is observed in some cancer types, while its role in uterine corpus endometrial carcinoma (UCEC) is unclear. Methods: The clinical and genomic data and prognostic information on UCEC were obtained for patients from the TCGA database. The Kruskal-Wallis test, Wilcoxon signed-rank test, and logistic regression were used to analyze the relationship between clinical characteristics and miR-149-3p expression. Kaplan-Meier survival curve analysis was used to study the influence of miR-149-3p expression and miR-149-3p target genes on the prognosis of UCEC patients. The TargetScan, PicTar, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were used to determine the involvement of miR-149-3p target genes in function. Immune infiltration analysis was used to analyze the functional involvement of miR-149-3p. QRT-PCR was used to validate the expression of miR-149-3p in UCEC cell lines. Results: High expression of miR-149-3p in UCEC was significantly associated with age (P < 0.001), histological type (P < 0.001), histological grade (P < 0.001), tumor invasion (P=0.014), and radiation therapy (P=0.011). High miR-149-3p expression predicted poorer overall survival (OS) (HR: 2.56; 95% CI: 1.64-4.00; P < 0.001), progression-free interval (PFI) (HR: 1.85; 95% CI: 1.29-2.65; P=0.001), and disease-specific survival (DSS) (HR: 2.33; 95% CI: 1.37-3.99; P=0.002). Low expressions of miR-149-3p target genes, including ADCYAP1R1, CGNL1, CHST3, CYGB, DNAH9, ESR1, HHIP, HIC1, HOXD11, IGF1, INMT, LSP1, MTMR10, NFIC, PLCE1, RARA, SNTN, SPRYD3, and ZBTB7A, were associated with poor OS in UCEC. MiR-149-3p may be involved in the occurrence and development of UCEC via pathways including PI3K-Akt signaling pathway, Ras signaling pathway, AGE-RAGE signaling pathway in diabetic complications, focal adhesion, and MAPK signaling pathway. miR-149-3p may inhibit the function of CD8 T cells, cytotoxic cells, eosinophils, iDC, mast cells, neutrophils, NK CD56bright cells, NK CD56dim cells, pDC, T cells, T helper cells, TFH, Th17 cells, and Treg. miR-149-3p was significantly upregulated in UCEC cell lines compared with endometriotic stromal cells. Conclusion: High expression of miR-149-3p was significantly associated with poor survival in UCEC patients. It may be a promising biomarker of prognosis and response to immunotherapy for UCEC patients.

MEDAS: an open-source platform as a service to help break the walls between medicine and informatics.

In the past decade, deep learning (DL) has achieved unprecedented success in numerous fields, such as computer vision and healthcare. Particularly, DL is experiencing an increasing development in advanced medical image analysis applications in terms of segmentation, classification, detection, and other tasks. On the one hand, tremendous needs that leverage DL's power for medical image analysis arise from the research community of a medical, clinical, and informatics background to share their knowledge, skills, and experience jointly. On the other hand, barriers between disciplines are on the road for them, often hampering a full and efficient collaboration. To this end, we propose our novel open-source platform, i.e., MEDAS-the MEDical open-source platform As Service. To the best of our knowledge, MEDAS is the first open-source platform providing collaborative and interactive services for researchers from a medical background using DL-related toolkits easily and for scientists or engineers from informatics modeling faster. Based on tools and utilities from the idea of RINV (Rapid Implementation aNd Verification), our proposed platform implements tools in pre-processing, post-processing, augmentation, visualization, and other phases needed in medical image analysis. Five tasks, concerning lung, liver, brain, chest, and pathology, are validated and demonstrated to be efficiently realizable by using MEDAS. MEDAS is available at http://medas.bnc.org.cn/.

An Effective Multimodal Image Fusion Method Using MRI and PET for Alzheimer's Disease Diagnosis.

Alzheimer's disease (AD) is an irreversible brain disease that severely damages human thinking and memory. Early diagnosis plays an important part in the prevention and treatment of AD. Neuroimaging-based computer-aided diagnosis (CAD) has shown that deep learning methods using multimodal images are beneficial to guide AD detection. In recent years, many methods based on multimodal feature learning have been proposed to extract and fuse latent representation information from different neuroimaging modalities including magnetic resonance imaging (MRI) and 18-fluorodeoxyglucose positron emission tomography (FDG-PET). However, these methods lack the interpretability required to clearly explain the specific meaning of the extracted information. To make the multimodal fusion process more persuasive, we propose an image fusion method to aid AD diagnosis. Specifically, we fuse the gray matter (GM) tissue area of brain MRI and FDG-PET images by registration and mask coding to obtain a new fused modality called "GM-PET." The resulting single composite image emphasizes the GM area that is critical for AD diagnosis, while retaining both the contour and metabolic characteristics of the subject's brain tissue. In addition, we use the three-dimensional simple convolutional neural network (3D Simple CNN) and 3D Multi-Scale CNN to evaluate the effectiveness of our image fusion method in binary classification and multi-classification tasks. Experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset indicate that the proposed image fusion method achieves better overall performance than unimodal and feature fusion methods, and that it outperforms state-of-the-art methods for AD diagnosis.

Expression, Clinical Significance, and Prospective Pathway Signaling of miR-501-3p in Ovarian Cancer Based on Database and Informatics Analysis.

BACKGROUND: The present study aims to explore the expression, clinical significance, and prospective pathway signaling of miR-501-3p in ovarian cancer (OC) based on database and informatics analysis. METHODS: Kruskal-Wallis test, Wilcoxon sign-rank test, and logistic regression were used to evaluate the relationship between clinical features and miR-501-3p expression. Kaplan-Meier survival curve analysis was used to explore the relationship between miR-501-3p expression and the prognosis of OC patients. The miRNA targets were obtained from databases TargetScan, miRanda, TarBase, miRTarBase, miR2Disease, miRecords, and miRWalk. GO and KEGG analyses were used to analyze the significant involvement of miR-501-3p target genes in function. RESULTS: The low miR-501-3p expression in OC was significantly associated with histologic grade (P=0.015). Low miR-501-3p expression predicted a poorer overall survival (HR: 0.77; 95% CI: 0.61-0.96; P=0.02) and disease-specific survival (HR: 0.77; 95% CI: 0.61-0.99; P=0.038). GO and KEGG analyses demonstrated that miR-501-3p might participate in the development of OC by pathways including one carbon pool by folate, protein digestion and absorption, cell cycle, kaposi sarcoma-associated herpesvirus infection, and viral carcinogenesis. CONCLUSIONS: Low miR-501-3p expression is significantly associated with poor survival in OC patients. It may be a promising prognostic biomarker for OC patients.

MiR-585-3p suppresses tumor proliferation and migration by directly targeting CAPN9 in high grade serous ovarian cancer.

BACKGROUND: Aberrant expression of microRNAs (miRNAs) contributes to the development of high grade serous ovarian cancer (HGSOC). However, the molecular mechanism by which miRNA-585-3p mediates high-grade serous ovarian carcinogenesis is unclear. This study aims to investigate the specific mechanism of action of miR-585-3p in HGSOC. METHODS: Expression of miR-585-3p in HGSOC tissues and cell lines was detected by qRT-PCR. Cell viability and migration were detected using MTT and transwell system. The expression of target genes and target proteins of miR-585-3p was detected by dual luciferase reporter assay and western blot. RESULTS: The expression of miR-585-3p was significantly lower in HGSOC tissues and cells than in normal ovarian tissues and cell lines. In HGSOC tissues, CAPN9 expression was inversely correlated with miR-585-3p expression. MiR-585-3p inhibited the proliferation and migration of HGSOC cells. MiR-585-3p bound to the 3'-untranslated region (UTR) of CAPN9 and inhibits CAPN9 expression. Overexpression of CAPN9 reduced the inhibitory effect of miR-585-3p in HGSOC cells. CONCLUSIONS: miR-585-3p is significantly down-regulated in HGSOC tissues and cell lines. MiR-585-3p inhibits the proliferation and migration of HGSOC cells by targeting CAPN9.

Bioinformatics Analysis of KIF1A Expression and Gene Regulation Network in Ovarian Carcinoma.

BACKGROUND: The study aims to analyze the expression levels of kinesin family member 1A (KIF1A) in ovarian cancer (OC) and explore its clinical significance in the development of OC and its potential regulatory network. METHODS: The Cancer Genome Atlas (TCGA) OC data was used to examine the expression differences between OC and normal tissue and explore the correlation with tumor stage. The relationship between KIF1A expression and prognosis was analyzed using Oncomine and Kaplan-Meier plotter tools. The co-expression network of KIF1A in TCGA OC was analyzed based on the application of cBioPortal, GO cluster, and KEGG analyses were performed based on the co-expression network. Immune-infiltration analysis were used to analyze the significant involvement of KIF1A in function. RESULTS: KIF1A was highly elevated in OC tissues and KIF1A expression was significantly correlated with the FIGO stage (P=0.015) and age (P=0.020). High KIF1A expression of OC predicted the poor prognosis including overall survival (OS) (HR: 1.27; 95% CI: 1.11-1.45; P=0.00046) and post-progression survival (PFS) (HR: 1.18; 95% CI: 1.03-1.35; P=0.015). GO and KEGG analysis showed KIF1A had a potential role in the biological process of ATP-dependent chromatin remodeling, transcription, DNA-templated cytolysis, positive regulation of T cell proliferation, positive regulation of transcription, DNA-templated via cell adhesion molecules (CAMs), primary immunodeficiency, oxidative phosphorylation, NF-kappa B signaling pathway, pathways in cancer and Wnt signaling pathway, and immune infiltrating cells. CONCLUSION: KIF1A was highly expressed and correlated with poor survival and immune infiltration in OC, and it may be a prognostic biomarker in OC.

Multi-Modal Co-Learning for Liver Lesion Segmentation on PET-CT Images.

Liver lesion segmentation is an essential process to assist doctors in hepatocellular carcinoma diagnosis and treatment planning. Multi-modal positron emission tomography and computed tomography (PET-CT) scans are widely utilized due to their complementary feature information for this purpose. However, current methods ignore the interaction of information across the two modalities during feature extraction, omit the co-learning of the feature maps of different resolutions, and do not ensure that shallow and deep features complement each others sufficiently. In this paper, our proposed model can achieve feature interaction across multi-modal channels by sharing the down-sampling blocks between two encoding branches to eliminate misleading features. Furthermore, we combine feature maps of different resolutions to derive spatially varying fusion maps and enhance the lesions information. In addition, we introduce a similarity loss function for consistency constraint in case that predictions of separated refactoring branches for the same regions vary a lot. We evaluate our model for liver tumor segmentation using a PET-CT scans dataset, compare our method with the baseline techniques for multi-modal (multi-branches, multi-channels and cascaded networks) and then demonstrate that our method has a significantly higher accuracy ( ) than the baseline models.

Differential Expression Profiles and Function Prediction of Transfer RNA-Derived Fragments in High-Grade Serous Ovarian Cancer.

BACKGROUND: The present study is aimed at providing systematic insight into the composition and expression of transfer RNA (tRNA) derivative transcription in high-grade serous ovarian cancer (HGSOC). METHODS: tRNA derivative expression profiles in three pairs of HGSOC and adjacent normal ovarian tissues were conducted by tRNA-derived small RNA fragment (tRF) and tRNA half (tiRNA) sequencing. The differentially expressed tRFs and tiRNAs between HGSOC and paired adjacent normal samples were screened. The targeted genes of differentially expressed tRFs and tiRNAs were screened. The Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) of target genes of tRFs and tiRNAs were analyzed. RESULTS: There are a total of 20 significantly upregulated and 15 significantly downregulated tRFs and tiRNAs between the cancer group and the paracarcinoma group. The upregulated tRFs and tiRNAs are mucin-type O-glycan biosynthesis, glycosphingolipid biosynthesis, the glucagon signaling pathway, the AMPK signaling pathway, maturity-onset diabetes of the young, glycosphingolipid biosynthesis, the insulin signaling pathway, insulin resistance, leukocyte transendothelial migration, starch, and sucrose metabolism. The downregulated tRFs and tiRNAs are other glycan degradation, vitamin digestion and absorption, fatty acid elongation, and biosynthesis of unsaturated fatty acids. CONCLUSIONS: There are significantly expressed tRFs and tiRNAs in HGSOC tissues, and these may provide potential diagnostic biomarkers and therapeutic targets for HGSOC.

Serine/threonine Kinases Play Important Roles in Regulating Polyunsaturated Fatty Acid Biosynthesis in Synechocystis sp. PCC6803.

Serine/threonine kinases (STKs) play important roles in prokaryotic cellular functions such as growth, differentiation, and secondary metabolism. When the external environment changes, prokaryotes rely on signal transduction systems, including STKs that quickly sense these changes and alter gene expression to induce the appropriate metabolic changes. In this study, we examined the roles of the STK genes spkD and spkG in fatty acid biosynthesis in the unicellular cyanobacterium Synechocystis sp. PCC6803, using targeted gene knockout. The linoleic acid (C18: 2), γ-linolenic acid (C18: 3n6), α-linolenic acid (C18: 3n3), and stearidonic acid (C18: 4) levels were significantly lower in spkD and spkG gene knockout mutants than in the wild type at a culture temperature of 30°C and a light intensity of 40 µmol⋅m-2⋅s-1. The expression levels of fatty acid desaturases and STK genes differed between the spkD and spkG gene knockout mutants. These observations suggest that spkD and spkG may directly or indirectly affect the fatty acid composition in Synechocystis sp. PCC6803 by regulating the expression of fatty acid desaturases genes. Therefore, the STK genes spkD and spkG play important roles in polyunsaturated fatty acid biosynthesis in Synechocystis sp. PCC6803. These findings could facilitate the development of cyanobacteria germplasm resources that yield high levels of fatty acids. In addition, they provide a theoretical basis for the genetic engineering of cyanobacteria with improved yields of secondary metabolites and increased economic benefits.

Sensing refractive index gradients along dielectric nanopillar metasurfaces.

Metasurfaces exhibit unique optical properties that depend on the ratio of their refractive index and that of their surroundings. As such, they are effective for sensing global changes in refractive index based on the shifts of resonances in their reflectivity spectra. However, when used as a biosensor, the metasurface can be exposed to a spatial distribution of biomolecules that brings about gradients in refractive index along the plane of the metasurface. Such gradients produce complex global reflectivity spectrum but with distinct optical enhancements in localized areas along the metasurface. Here, we propose a unique sensing paradigm that images and maps out the optical enhancements that are correlated with the spatial distribution of the refractive index. Moreover, we designed a metasurface whose resonances can be tuned to detect a range of refractive indices. Our metasurface consists of silicon nanopillars with a cylindrical nanotrench at their centers and a metal plane at the base. To assess its feasibility, we performed numerical simulations to show that the design effectively produces the desired reflectivity spectrum with resonances in the near-infrared. Using an incident light tuned to one of its resonances, our simulations further show that the field enhancements are correlated with the spatial mapping of the gradients of refractive indices along the metasurface.

Expression of PKG2 in ovarian cancer and its effect on epidermal growth factor receptor.

PURPOSE: This research tried to explore the expression level of II cGMP-dependent protein kinase (PKG2) in human ovarian tissue and to clarify the molecular mechanism of EGFR regulation and its clinical significance. METHODS: The expression levels of PKG2 and EGFR in 10 normal ovarian tissues, 14 benign ovarian tumor tissues and 39 epithelial ovarian cancer tissues preserved in the archives of the Affiliated Hospital of Xuzhou Medical University from 2016 to 2018 were detected by real-time fluorescence quantitative (RT-PCR), and the correlation between the expressions of the two genes was analyzed. The expressions of in vitro cultured ovarian cancer cell lines SKOV3, PKG2 and EGFR were detected by RT-PCR and western blot, and the over-expressed PKG2 plasmid and PKG2 small interfering RNA (siRNA) were transfected into the cells, and the protein and phosphorylation of Akt and ERK in EGFR and its downstream signaling pathway were detected by western blot. RESULTS: Compared with normal ovarian tissue, the mRNA and protein expression levels of PKG2 in ovarian cancer tissue and SKOV3 cell line were significantly reduced (p<0.05). However, the mRNA and protein expression levels of EGFR in ovarian cancer tissue and SKOV3 cell line were both high (p<0.05). In addition, after transient transfection of PKG2, the expression changes of PKG2 significantly affected the expression of EGFR, and PKG2 over-expression could significantly inhibit the phosphorylation of Akt and ERK in EGFR and its downstream signaling pathways, thereby affecting cell proliferation. CONCLUSION: PKG2 may play a role in inhibiting EGFR expression in ovarian cancer, but the specific mechanism of its effect on tumor development still needs to be further explored.

Directional Osteo-Differentiation Effect of hADSCs on Nanotopographical Self-Assembled Polystyrene Nanopit Surfaces.

INTRODUCTION: Cells exhibit high sensitivity and a diverse response to the nanotopography of the extracellular matrix, thereby endowing materials with instructive performances formerly reserved for growth factors. This finding leads to opportunities for improvement. However, the interplay between the topographical surface and cell behaviors remains incompletely understood. METHODS: In the present study, we showed nanosurfaces with various dimensions of nanopits (200-750 nm) fabricated by self-assembling polystyrene (PS) nanospheres. Human adipose-derived stem cell behaviors, such as cell morphology, adhesion, cytoskeleton contractility, proliferation, and differentiation, were investigated on the prepared PS nanopit surface. RESULTS: The osteogenic differentiation can be enhanced by nanopits with a diameter of 300-400 nm. DISCUSSION: The present study provided exciting new avenues to investigate cellular responses to well-defined nanoscale topographic features, which could further guide bone tissue engineering and stem cell clinical research. The capability to control developing biomaterials mimicking nanotopographic surfaces promoted functional tissue engineering, such as artificial joint replacement, bone repair, and dental applications.

Multifunctional red carbon dots: a theranostic platform for magnetic resonance imaging and fluorescence imaging-guided chemodynamic therapy.

In recent years, carbon dots (CDs) with red-emitting wavelengths have received increasing attention in cancer therapy and imaging. Here, we reported a multi-functional CD based platform combining bimodal magnetic resonance/fluorescence (MR/FL) imaging and chemodynamic therapy (CDT) for in vivo imaging of tumor tissues and efficient anticancer treatment. The red-emitting CDs were synthesized via a one-step solvothermal method with p-phenylenediamine as the carbon source. Ethylenediaminetetraacetic acid (EDTA) was covalently coupled to the surface of CDs and then complexed with Fe2+ and Gd3+ to obtain functionalized red CDs (CDs@EDTA@Gd@Fe). CDs@EDTA@Gd@Fe exhibited bright and stable fluorescence and strong T1-weighted MR imaging (MRI) contrast. Moreover, the CDs@EDTA@Gd@Fe showed an excellent anticancer effect both in vitro and in vivo via a Fenton reaction-based CDT by releasing Fe2+ in the tumor. Our study offers a promising strategy for developing multi-functional CDs for cancer theranostics.

Block Level Skip Connections Across Cascaded V-Net for Multi-Organ Segmentation.

Multi-organ segmentation is a challenging task due to the label imbalance and structural differences between different organs. In this work, we propose an efficient cascaded V-Net model to improve the performance of multi-organ segmentation by establishing dense Block Level Skip Connections (BLSC) across cascaded V-Net. Our model can take full advantage of features from the first stage network and make the cascaded structure more efficient. We also combine stacked small and large kernels with an inception-like structure to help our model to learn more patterns, which produces superior results for multi-organ segmentation. In addition, some small organs are commonly occluded by large organs and have unclear boundaries with other surrounding tissues, which makes them hard to be segmented. We therefore first locate the small organs through a multi-class network and crop them randomly with the surrounding region, then segment them with a single-class network. We evaluated our model on SegTHOR 2019 challenge unseen testing set and Multi-Atlas Labeling Beyond the Cranial Vault challenge validation set. Our model has achieved an average dice score gain of 1.62 percents and 3.90 percents compared to traditional cascaded networks on these two datasets, respectively. For hard-to-segment small organs, such as the esophagus in SegTHOR 2019 challenge, our technique has achieved a gain of 5.63 percents on dice score, and four organs in Multi-Atlas Labeling Beyond the Cranial Vault challenge have achieved a gain of 5.27 percents on average dice score.

3D-QSAR, molecular docking, and molecular dynamics simulation study of thieno[3,2-b]pyrrole-5-carboxamide derivatives as LSD1 inhibitors.

Histone Lysine Specific Demethylase 1 (LSD1) is overexpressed in many cancers and becomes a new target for anticancer drugs. In recent years, small molecule inhibitors with various structures targeting LSD1 have been reported. Here we report the binding interaction modes of a series of thieno[3,2-b]pyrrole-5-carboxamide LSD1 inhibitors using molecular docking, and three-dimensional quantitative structure-activity relationships (3D-QSAR). Comparative molecular field analysis (CoMFA q 2 = 0.783, r 2 = 0.944, r pred 2 = 0.851) and comparative molecular similarity indices analysis (CoMSIA q 2 = 0.728, r 2 = 0.982, r pred 2 = 0.814) were used to establish 3D-QSAR models, which had good verification and prediction capabilities. Based on the contour maps and the information of molecular docking, 8 novel small molecules were designed in silico, among which compounds D4, D5 and D8 with high predictive activity were subjected to further molecular dynamics simulations (MD), and their possible binding modes were explored. It was found that Asn535 plays a crucial role in stabilizing the inhibitors. Furthermore, ADME and bioavailability prediction for D4, D5 and D8 were carried out. The results would provide valuable guidance for designing new reversible LSD1 inhibitors in the future.