Circular RNAs (circRNAs) have received increasing attention in human tumor research. However, there are still a large number of unknown circRNAs that need to be deciphered. The aim of this study is ...to unearth novel circRNAs as well as their action mechanisms in hepatocellular carcinoma (HCC).
A combinative strategy of big data mining, reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and computational biology was employed to dig HCC-related circRNAs and to explore their potential action mechanisms. A connectivity map (CMap) analysis was conducted to identify potential therapeutic agents for HCC.
Six differently expressed circRNAs were obtained from three Gene Expression Omnibus microarray datasets (GSE78520, GSE94508 and GSE97332) using the RobustRankAggreg method. Following the RT-qPCR corroboration, three circRNAs (hsa_circRNA_102166, hsa_circRNA_100291 and hsa_circRNA_104515) were selected for further analysis. miRNA response elements of the three circRNAs were predicted. Five circRNA-miRNA interactions including two circRNAs (hsa_circRNA_104515 and hsa_circRNA_100291) and five miRNAs (hsa-miR-1303, hsa-miR-142-5p, hsa-miR-877-5p, hsa-miR-583 and hsa-miR-1276) were identified. Then, 1424 target genes of the above five miRNAs and 3278 differently expressed genes (DEGs) on HCC were collected. By intersecting the miRNA target genes and the DEGs, we acquired 172 overlapped genes. A protein-protein interaction network based on the 172 genes was established, with seven hubgenes (JUN, MYCN, AR, ESR1, FOXO1, IGF1 and CD34) determined from the network. The Gene Oncology, Kyoto Encyclopedia of Genes and Genomes and Reactome enrichment analyses revealed that the seven hubgenes were linked with some cancer-related biological functions and pathways. Additionally, three bioactive chemicals (decitabine, BW-B70C and gefitinib) based on the seven hubgenes were identified as therapeutic options for HCC by the CMap analysis.
Our study provides a novel insight into the pathogenesis and therapy of HCC from the circRNA-miRNA-mRNA network view.
This study aimed to develop and validate an artificial intelligence radiopathological model using preoperative CT scans and postoperative hematoxylin and eosin (HE) stained slides to predict the ...pathological staging of gastric cancer (stage I-II and stage III).
This study included a total of 202 gastric cancer patients with confirmed pathological staging (training cohort: n = 141; validation cohort: n = 61). Pathological histological features were extracted from HE slides, and pathological models were constructed using logistic regression (LR), support vector machine (SVM), and NaiveBayes. The optimal pathological model was selected through receiver operating characteristic (ROC) curve analysis. Machine learnin algorithms were employed to construct radiomic models and radiopathological models using the optimal pathological model. Model performance was evaluated using ROC curve analysis, and clinical utility was estimated using decision curve analysis (DCA).
A total of 311 pathological histological features were extracted from the HE images, including 101 Term Frequency-Inverse Document Frequency (TF-IDF) features and 210 deep learning features. A pathological model was constructed using 19 selected pathological features through dimension reduction, with the SVM model demonstrating superior predictive performance (AUC, training cohort: 0.949; validation cohort: 0.777). Radiomic features were constructed using 6 selected features from 1834 radiomic features extracted from CT scans via SVM machine algorithm. Simultaneously, a radiopathomics model was built using 17 non-zero coefficient features obtained through dimension reduction from a total of 2145 features (combining both radiomics and pathomics features). The best discriminative ability was observed in the SVM_radiopathomics model (AUC, training cohort: 0.953; validation cohort: 0.851), and clinical decision curve analysis (DCA) demonstrated excellent clinical utility.
The radiopathomics model, combining pathological and radiomic features, exhibited superior performance in distinguishing between stage I-II and stage III gastric cancer. This study is based on the prediction of pathological staging using pathological tissue slides from surgical specimens after gastric cancer curative surgery and preoperative CT images, highlighting the feasibility of conducting research on pathological staging using pathological slides and CT images.
This study aims to develop and validate an innovative radiopathomics model that combines radiomics and pathomics features to effectively differentiate between stages I-II and stage III gastric cancer ...(pathological staging).
Our study included 200 patients with well-defined stages of gastric cancer divided into a training cohort (n = 140) and a test cohort (n = 60). Radiomics features were extracted from contrast-enhanced CT images using PyRadiomics, while pathomics features were obtained from whole slide images of pathological specimens through a fine-tuned deep learning model (ResNet-18). After rigorous feature dimensionality reduction and selection, we constructed radiomics models (SVM_rad, LR_rad, and MLP_rad) and pathomics models (SVM_path, LR_path, and MLP_path) utilizing support vector machine (SVM), logistic regression (LR), and multilayer perceptron (MLP) algorithms. The optimal radiomics and pathomics models were chosen based on comprehensive evaluation criteria such as ROC curves, Hosmer‒Lemeshow tests, and calibration curve tests. Feature patterns extracted from the best-performing radiomics model (MLP_rad) and pathomics model (SVM_rad) were integrated to create a powerful radiopathomics nomogram.
From a pool of 1834 radiomics features extracted from CT images, 14 were selected to construct radiomics models. Among these, the MLP_rad model exhibited the most robust predictive performance (AUC, training cohort: 0.843; test cohort: 0.797). Likewise, 10 pathomics features were chosen from 512 extracted from whole slide images to build pathomics models, with the SVM_path model demonstrating the highest predictive efficiency (AUC, training cohort: 0.937; test cohort: 0.792). The combined radiopathomics nomogram model exhibited optimal discriminative ability (AUC, training cohort: 0.951; test cohort: 0.837), as confirmed by decision curve analysis (DCA), which indicated superior clinical effectiveness.
This study presents a cutting-edge radiopathomics nomogram model designed to predict pathological staging in gastric cancer, distinguishing between stages I-II and stage III. Our research leverages preoperative CT images and histopathological slides to forecast gastric cancer staging accurately, potentially facilitating the estimation of staging before radical gastric cancer surgery in the future.
Meiosis initiation is a crucial step for the production of haploid gametes, which occurs from anterior to posterior in fetal ovaries. The asynchrony of the transition from mitosis to meiosis results ...in heterogeneity in the female germ cell populations, which limits the studies of meiosis initiation and progression at a higher resolution level. To dissect the process of meiosis initiation, we investigated the transcriptional profiles of 19 363 single germ cells collected from E12.5, E14.5, and E16.5 mouse fetal ovaries. Clustering analysis identified seven groups and defined dozens of corresponding transcription factors, providing a global view of cellular differentiation from primordial germ cells toward meiocytes. Furthermore, we explored the dynamics of gene expression within the developmental trajectory with special focus on the critical state of meiosis. We found that meiosis initiation occurs as early as E12.5 and the cluster of oogonia_4 is the critical state between mitosis and meiosis. Our data provide key insights into the transcriptome features of peri‐meiotic female germ cells, which offers new information not only on meiosis initiation and progression but also on screening pathogenic mutations in meiosis‐associated diseases.
AZD1208, a pan‐inhibitor that can effectively inhibit PIM kinase, is used for the treatment of advanced solid tumors and malignant lymphomas. Numerous studies have proved its curative effects while ...its potential cellular toxicity on reproduction was still little known. In this study, we investigated the toxic effects of AZD1208 on mouse oocytes. The results showed that AZD1208 treatment did not affect meiotic resumption, but postponed oocyte maturation as indicated by delayed first polar body extrusion. Further mechanistic study showed that AZD1208 treatment delayed spindle assembly. In addition, we found that oocytes treated with AZD1208 showed mitochondrial dysfunction. Abnormal mitochondrial clusters with decreased mitochondrial membrane potential were observed in oocytes during incubation in vitro. Moreover, increased oxidative stress was observed by testing the level of reactive oxygen species. In summary, our results suggest that AZD1208 treatment influences oocyte meiotic progression by causing mitochondrial dysfunctions and subsequent delayed spindle assembly.
AZD1208 treatment influences oocyte meiotic progression by causing mitochondrial dysfunctions and subsequent delayed spindle assembly.
Nitidine chloride (NC) has been demonstrated to have an anticancer effect in hepatocellular carcinoma (HCC). However, the mechanism of action of NC against HCC remains largely unclear. In this study, ...three pairs of NC-treated and NC-untreated HCC xenograft tumour tissues were collected for circRNA sequencing analysis. In total, 297 circRNAs were differently expressed between the two groups, with 188 upregulated and 109 downregulated, among which hsa_circ_0088364 and hsa_circ_0090049 were validated by real-time quantitative polymerase chain reaction. The in vitro experiments showed that the two circRNAs inhibited the malignant biological behaviour of HCC, suggesting that they may play important roles in the development of HCC. To elucidate whether the two circRNAs function as "miRNA sponges" in HCC, we identified circRNA-miRNA and miRNA-mRNA interactions by using the CircInteractome and miRwalk, respectively. Subsequently, 857 miRNA-associated differently expressed genes in HCC were selected for weighted gene co-expression network analysis. Module Eigengene turquoise with 423 genes was found to be significantly related to the survival time, pathology grade and TNM stage of HCC patients. Gene functional enrichment analysis showed that the 423 genes mainly functioned in DNA replication- and cell cycle-related biological processes and signalling cascades. Eighteen hubgenes (SMARCD1, CBX1, HCFC1, RBM12B, RCC2, NUP205, ECT2, PRIM2, RBM28, COPS7B, PRRC2A, GPR107, ANKRD52, TUBA1B, ATXN7L3, FUS, MCM8 and RACGAP1) associated with clinical outcomes of HCC patients were then identified. These findings showed that the crosstalk between hsa_circ_0088364 and hsa_circ_0090049 and their competing mRNAs may play important roles in HCC, providing interesting clues into the potential of circRNAs as therapeutic targets of NC in HCC.
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•Ag2O-doped BiFeO3hetrostructure was fabricated as photo-anode in DSSCs.•High energy-conversion efficiency 4.25% achieved for 8% Ag2O-doped BiFeO3.•High IPCE and dye loading capacity ...was noticed after doping with Ag2O nanoparticles with BFO.•The positive shift in flat-band potential, open-circuit voltage decay observed.
In the present study, hierarchical bismuth ferrite (BFO) and Ag2O-doped bismuth ferrite (Ag2O/BFO) nanostructures are successfully fabricated using hydrothermal methods. The fabricated nanostructures were characterized via several analytical techniques. BiFeO3 (BFO) nanoparticles were found to be 30–50 nm in diameter with pure perovskite phase. The nanocomposites were used for the preparation of photoanode to fabricate the dye-sensitized solar cells (DSSCs). The results revealed that the doping of Ag2O nanoparticles with BFO improves the transportation of electrons and decreased the recombination of photogenerated charges. Because of these advantages, the DSSC based on the Ag2O/BFO photoanode has been fabricated and shows the efficiency of energy conversion about to 4.25%, which indicates>100% development equated to the DSSCs fabricated using pure BFO nanoparticle based photoanode and prepared under the similar conditions. These results indicate that the BFO based nanocomposites doped with silver oxide can potentially be used as working electrode materials in DSSCs.
BiFeO3 was prepared by using a simple one-step hydrothermal synthesis method. The key factor is to rapidly cool the product after the hydrothermal reaction in order to suppress the formation of ...Bi2Fe4O9 and Bi25FeO40 phases. Different cascade structures of TiO2 and BiFeO3 with controllable dopants(Pr, Al) and concentrations were also prepared on the FTO. The J-V test showed that the photoelectric conversion efficiency of the conventional TiO2 photoanode was increased from 2.54% to 3.21%. However, the J-V tests of Bi0.98Pr0.02FeO3, BiFe0.95Al0.05O3 and Bi0.98Pr0.02Fe0.95Al0.05O3 indicated that their photoelectric conversion efficiencies were up to 3.97%, 4.07%, and 4.99% respectively.
It is well known that maternal ageing not only causes increased spontaneous abortion and reduced fertility, but it is also a high genetic disease risk. Although assisted reproductive technologies ...(ARTs) have been widely used to treat infertility, the overall success is still low. The main reasons for age-related changes include reduced follicle number, compromised oocyte quality especially aneuploidy, altered reproductive endocrinology, and increased reproductive tract defect. Various approaches for improving or treating infertility in aged women including controlled ovarian hyperstimulation with intrauterine insemination (IUI), IVF/ICSI-ET, ovarian reserve testing, preimplantation genetic diagnosis and screening (PGD/PGS), oocyte selection and donation, oocyte and ovary tissue cryopreservation before ageing, miscarriage prevention, and caloric restriction are summarized in this review. Future potential reproductive techniques for infertile older women including oocyte and zygote micromanipulations, derivation of oocytes from germ stem cells, ES cells, and iPS cells, as well as through bone marrow transplantation are discussed.
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