Search

Background: Neuroblastoma (NB), considered the most common non-intracranial solid tumor in children, accounts for nearly 8% of pediatric malignancies. This study aimed to develop a simple and practical nomogram to predict event-free survival (EFS) in NB patients and establish a new risk stratification system. Method: In this study, 763 patients primarily diagnosed with neuroblastoma in the TARGET database were included and randomly assigned to a training set (70%) and a validation set (30%) in a 7:3 ratio. First, the independent prognostic factors of EFS for NB patients were identified through univariate and multivariate Cox regression analyses. Second, a nomogram was created based on these factors and was validated for calibration capability, discriminative and clinical significance by C-curves, receiver operating characteristic (ROC) curves, and decision curve analysis (DCA). Finally, we developed a new risk stratification system for NB patients based on the nomogram. Result: The univariate Cox analysis demonstrated that NB patients with age at diagnosis > 318 days, INSS stage 4, DNA diploidy, MYCN amplification status, and COG high-risk group had a relatively poor prognosis. However, according to the multivariate Cox regression analysis, only age, INSS stage, and DNA ploidy were independent predictive factors in NB patients regarding EFS, and a nomogram was created based on these factors. The AUC values of the ROC curves for the 3-, 5-, and 10-year EFS of this nomogram were 0.681, 0.706, and 0.720, respectively. Additionally, the AUC values of individual independent prognostic factors of EFS were lower than those of the nomogram, suggesting that our nomogram had higher predictive reliability for prognosis. In addition, a new risk stratification system was developed to better stratify NB patients and provide clinical practitioners with a better reference for clinical decision-making. Conclusion: NB patients’ EFS could be predicted more accurately and easily through the constructed nomogram and event-occurrence risk stratification system, helping clinicians to better differentiate NB patients and develop individualized treatment plans to maximize patient benefits.

Aims Previous reports indicated that the Slit2–Robo signalling pathway is involved in embryonic heart development and fibrosis in other solid organs, but its function in adult cardiac fibrosis has not been investigated. Here, we investigate the role of the Slit2–Robo1 signalling pathway in cardiac fibrosis. Methods and results The right atrial tissue samples were obtained from patients with valvular heart disease complicated by atrial fibrillation during heart valve surgery and from healthy heart donors. The fibrotic animal model is created by performing transverse aortic constriction (TAC) surgery. The Robo1, Slit2, TGF‐β1, and collagen I expression levels in human and animal samples were evaluated by immunohistochemistry and western blot analysis. Echocardiography measured the changes in heart size and cardiac functions of animals. Angiotensin II (Ang II), Slit2‐siRNA, TGF‐β1‐siRNA, recombinant Slit2, and recombinant TGF‐β1 were transfected to cardiac fibroblasts (CFs) respectively to observe their effects on collagen I expression level. The right atrial appendage of patients with valvular heart disease complicated by atrial fibrillation found significantly up‐regulated Slit2, Robo1, TGF‐β1, and collagen I expression levels. TAC surgery leads to heart enlargement, cardiac fibrosis, and up‐regulation of Slit2, Robo1, TGF‐β1, and collagen I expression levels in animal model. Robo1 antagonist R5 and TGF‐β1 antagonist SB431542 suppressed cardiac fibrosis in TAC mice. Treatment with 100 nM Ang II in CFs caused significantly increased Slit2, Robo1, Smad2/3, TGF‐β1, collagen I, PI3K, and Akt expression levels. Transfecting Slit2‐siRNA and TGF‐β1‐siRNA, respectively, into rat CFs significantly down‐regulated Smad2/3 and collagen I expression, inhibiting the effects of Ang II. Recombinant Slit2 activated the TGF‐β1/Smad signalling pathway in CFs and up‐regulated Periostin, Robo1, and collagen I expression. Conclusions The Slit2–Robo1 signalling pathway interfered with the TGF‐β1/Smad pathway and promoted cardiac fibrosis. Blockade of Slit2–Robo1 might be a new treatment for cardiac fibrosis.

Background: To provide prognostic multivariate models for severe complications prediction after heart valvular surgery, such as low cardiac output syndrome (LOCS), acute kidney injury requiring hemodialysis (AKI-rH) and multiple organ dysfunction syndrome (MODS).Methods: We developed multivariate logistic regression models to predict the severe complications after heart valvular surgery using 930 patients collected retrospectively from the first affiliated hospital of Sun Yat-Sen University from January 2014 to December 2015. The validation was conducted using a retrospective dataset of 713 patients from the same hospital from January 2016 to March 2017. We considered two kinds of prognostic models: the PRF models which were built by using the preoperative risk factors only, and the PIRF models which were built by using both of the preoperative and intraoperative risk factors. The Least absolute shrinkage selector operator was used for developing the models. We assessed and compared the discriminative abilities for both of the PRF and PIRF models via the receiver operating characteristic curve. Results: Compared with the PRF models, the PIRF modes selected additional intraoperative factors, such as auxiliary cardiopulmonary bypass time and combined tricuspid valve replacement. The area under the curves (AUCs) of the PRF models for predicting LOCS, AKI-rH and MODS are 0.565 (0.466, 0.664), 0.688 (0.62, 0.757) and 0.657 (0.563, 0.751), respectively. As a comparison, the AUCs of the PIRF models for predicting LOCS, AKI-rH and MODS are 0.821 (0.747, 0.896), 0.78 (0.717, 0.843) and 0.774 (0.7, 0.847), respectively. Conclusions: Adding the intraoperative factors can increase the predictive power of the prognostic models for severe complications prediction after heart valvular surgery.

Background To provide multivariable prognostic models for severe complications prediction after heart valve surgery, including low cardiac output syndrome (LCOS), acute kidney injury requiring hemodialysis (AKI-rH) and multiple organ dysfunction syndrome (MODS). Methods We developed multivariate logistic regression models to predict severe complications after heart valve surgery using 930 patients collected retrospectively from the first affiliated hospital of Sun Yat-Sen University from January 2014 to December 2015. The validation was conducted using a retrospective dataset of 713 patients from the same hospital from January 2016 to March 2017. We considered two kinds of prognostic models: the PRF models which were built by using the preoperative risk factors only, and the PIRF models which were built by using both of the preoperative and intraoperative risk factors. The least absolute shrinkage selector operator was used for developing the models. We assessed and compared the discriminative abilities for both of the PRF and PIRF models via the receiver operating characteristic (ROC) curve. Results Compared with the PRF models, the PIRF modes selected additional intraoperative factors, such as auxiliary cardiopulmonary bypass time and combined tricuspid valve replacement. Area under the ROC curves (AUCs) of PRF models for predicting LCOS, AKI-rH and MODS are 0.565 (0.466, 0.664), 0.688 (0.62, 0.757) and 0.657 (0.563, 0.751), respectively. As a comparison, the AUCs of the PIRF models for predicting LOCS, AKI-rH and MODS are 0.821 (0.747, 0.896), 0.78 (0.717, 0.843) and 0.774 (0.7, 0.847), respectively. Conclusions Adding the intraoperative factors can increase the predictive power of the prognostic models for severe complications prediction after heart valve surgery.

Along with the aging of the world population, the incidence rate of Alzheimer's disease (AD) has been increasing. At present, AD has become one of the most serious problems faced by modern medicine. Studies have shown that estrogen has a positive effect on AD, but estrogen has the side effect of leading to tumors. Recent in vivo studies have shown that genistein, one of the selective estrogen receptor modulators (SERMs), can improve brain function through the blood–brain barrier (BBB), antagonize the toxicity of amyloid β‐protein (Aβ), that is, to inhibit neurotoxicity due to aggregation of beta amyloid protein, and have neuroprotective effects. In addition, the use of Gen can avoid the risk of endometrial cancer and breast cancer caused by estrogen therapy while exerting an estrogen‐like effect, which has some potential for the delay and treatment of AD., Recent studies have shown that genistein, one of the selective estrogen receptor modulators (SERMs), can improve brain function through the blood–brain barrier (BBB), antagonize the toxicity of amyloid β‐protein (Aβ), and have neuroprotective effects. In addition, the use of Gen can avoid the risk of endometrial cancer and breast cancer caused by estrogen therapy while exerting an estrogen‐like effect, which has some potential for the delay and treatment of AD.

Accurate waterline separation of the sailing area is the base of unmanned surface vehicle (USV) systems moving in a complicated environment. However, shadows resulted from different natural illumination is one of the main error sources. This paper proposes a waterline separation method based on shadow processing with respect to optical images and presents a practical application to a USV system for validation. First, the basic principles of an intrinsic image is used to reduce the intensity influence of different shadows. Then, segmentation and shadow verification based on shadow analysis were introduced to separate and classify shadow regions. Finally, the problem of waterline separation is transformed into an optimization problem of mathematical energy minimum described by differential equations. In the practical application, the waterlines of many images captured by an USV system moving along an inland river are separated with the proposed method, and the experimental results showed that the proposed algorithm is effective and robust.

The present study aimed to explore the molecular mechanisms associated with intervertebral disc degeneration (IDD) induced by tumor necrosis factor (TNF)‑α and interleukin (IL)‑1β. The microarray dataset no. GSE42611 was downloaded from the Gene Expression Omnibus database. The differentially expressed genes (DEGs) between four experimental nucleus pulposus samples and four control nucleus pulposus samples were analyzed. Subsequently, Gene Ontology (GO) and pathway enrichment analyses of DEGs were performed, followed by protein‑protein interaction (PPI) network construction and prediction of a regulatory network of transcription factor (TFs). Finally, the transcriptional regulatory network was integrated into the PPI network to analyze the network modules. A total of 246 upregulated and 290 downregulated DEGs were identified. The upregulated DEGs were mainly associated with GO terms linked with inflammatory response and apoptotic pathways, while the downregulated DEGs were mainly associated with GO terms linked with cell adhesion and pathways of extracellular matrix ‑ receptor interaction. In the PPI network, IL6, COL1A1, NFKB1 and HIF1A were hub genes, and in addition, NFKB1 and HIF1A were TFs. Pathways of apoptosis and extracellular matrix ‑ receptor interaction may have important roles in IDD progression. IL6, COL1A1 and the TFs NFKB1 and HIF1A may be used as biomarkers for IDD diagnosis and treatment.