A novel clinical tool and risk stratification system for predicting the event-free survival of neuroblastoma patients: A TARGET-based study.

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. In this study, 763 patients primarily diagnosed with NB in the Therapeutically Applicable Research to Generate Effective Treatments (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. Finally, a new risk stratification system was established for NB patients based on the nomogram. The univariate Cox analysis demonstrated that NB patients with age at diagnosis >318 days, International Neuroblastoma Staging System (INSS) stage 4, DNA diploidy, MYCN amplification status, and children oncology group (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 area under the curve (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 the developed nomogram had a 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. NB patients' EFS could be predicted more accurately and easily through the constructed nomogram and event-occurrence risk stratification system, allowing clinicians to better differentiate NB patients and establish individualized treatment plans to maximize patient benefits.

Isoproterenol pre-treatment improve the therapeutic efficacy of cardiosphere-derived cells transplantation for myocardial infarction.

Background: This study aimed to investigate the effect of isoproterenol pre-treatment on the therapeutic efficacy of cardiosphere-derived cells (CDCs) transplantation for myocardial infarction (MI). Methods: Thirty 8-week-old male Sprague-Dawley (SD) rat model of MI was generated by ligation of the left anterior descending artery. The MI rats were treated with PBS (MI group, n=8), CDCs (MI + CDC group, n=8) and isoproterenol pre-treated CDCs (MI + ISO-CDC group, n=8), respectively. In the MI + ISO-CDC group, CDCs were pre-treated by 10-6 M isoproterenol and the cultured for additional 72 h, then injected to the myocardial infraction area like other groups. At 3 weeks after the operation, echocardiographic, hemodynamic, histological assessments and Western blot were performed to compare the CDCs differentiation degree and therapeutic effect. Results: Isoproterenol treatment (10-6 M) simultaneously inhibited proliferation and induced apoptosis of CDCs, up-regulated proteins of vimentin, cTnT, α-sarcomeric actin and connexin 43, and down-regulated c-Kit proteins (all P<0.05). The echocardiographic and hemodynamic analysis demonstrated that the MI rats in the two CDCs transplantation groups had significantly better recovery of cardiac function than the MI group (all P<0.05). MI + ISO-CDC group had better recovery of cardiac function than the MI + CDC group, although the differences did not reach significant. Immunofluorescence staining showed that the MI + ISO-CDC group had more EdU-positive (proliferating) cells and cardiomyocytes in the infarct area than the MI + CDC group. MI + ISO-CDC group had significantly higher protein levels of c-Kit, CD31, cTnT, α-sarcomeric actin and α-SMA in the infarct area than the MI + CDC group. Conclusions: These results suggested that in CDCs transplantation, isoproterenol pre-treated CDCs can provide a better protective effect against MI than the untreated CDCs.

Construction of a novel clinical nomogram to predict cancer-specific survival in patients with primary malignant adrenal tumors: a large population-based retrospective study.

Background: Primary malignant adrenal tumors were rare and had a poor prognosis. This investigation aimed to create a useful clinical prediction nomogram to anticipate cancer-specific survival (CSS) of patients with a primary malignant adrenal tumor. Method: This study included 1748 patients with malignant adrenal tumor diagnoses subjects from 2000 to 2019. These subjects were allocated randomly into training (70%) and validation (30%) cohorts. Patients with adrenal tumors underwent univariate and multivariate Cox regression analyses to identify the CSS-independent predictive biomarkers. Therefore, a nomogram was created depending on those predictors, and calibration curves, receiver operating characteristic (ROC) curves, and decision curve analysis (DCA) were used to assess the calibration capacity of the nomogram, discriminative power, and clinical efficiency, respectively. Afterward, a risk system for categorizing patients with adrenal tumors was established. Result: The univariate and multivariate Cox analysis demonstrated the CSS-independent predictive factors, including age, tumor stage, size, histological type, and surgery. As a result, a nomogram was developed using these variables. For the 3-, 5-, and 10-year CSS of this nomogram, the values of the area under the curve (AUC) of the ROC curves were 0.829, 0.827, and 0.822, respectively. Furthermore, the AUC values of the nomogram were higher than those of the individual independent prognostic components of CSS, indicating that the nomogram had stronger prognostic prediction reliability. A novel risk stratification method was created to further improve patient stratification and give clinical professionals a better reference for clinical decision-making. Conclusion: Through the developed nomogram and risk stratification method, the CSS of patients with malignant adrenal tumors could be predicted more precisely, assisting physicians to differentiate patients better and creating personalized treatment strategies to optimize patient benefits.

[Modulation of gut microbiota during alleviation of antibiotic-associated diarrhea with Zingiberis Rhizoma].

This study was aimed to explore the effect of Zingiberis Rhizoma extract on rats with antibiotic-associated diarrhea(AAD), and reveal the modulation of gut microbiota during alleviation of AAD. AAD rat model was successfully established by exposing rats to appropriate antibiotic mixed solution. Peficon(70 mg·kg~(-1)·d~(-1)) was used as positive control, then rats were treated with 200 mg·kg~(-1)·d~(-1) and 400 mg·kg~(-1)·d~(-1) of Zingiberis Rhizoma extract for low and high dosage groups of Zingiberis Rhizoma extract, respectively. The weight changes of the rats were observed, and the degree of diarrhea were evaluated by fecal score, 120 min fecal weight and fecal water content. Colon tissues for histopathological examination were stained with hematoxylin and eosin(HE), and 16 S rRNA sequencing analysis of gut microbiota was performed. The results showed that compared with the model group, the degree of diarrhea, indicated by fecal water content, fecal score, and 120 min fecal weight of positive control group, Zingiberis Rhizoma low-dose group and Zingiberis Rhizoma high-dose group were significantly ameliorated. And the treatment of Zingiberis Rhizoma could significantly improve the pathological condition of colon tissue in AAD rats, especially the high dose of Zingiberis Rhizoma. In addition, 16 S rRNA sequencing analysis of gut microbiota showed that the diversity and abundance of gut microbiota were significantly improved and the reco-very of gut microbiota was accelerated after given high-dose of Zingiberis Rhizoma, while no significant changes of alterations were observed after given Pefikon. Of note, compared with the pefikon group, the abundance and diversity of gut microbiota in Zingi-beris Rhizoma high-dose group were significantly elevated. At the phylum level, the abundance of Firmicutes in AAD rats increased and the abundance of Proteobacteria was decreased after the Zingiberis Rhizoma intervention. At the genus level, the abundance of Bacillus spp., Lachnoclostridium and Escherichia coli-Shigella were decreased, and the abundance of Lactobacillus spp., Trichophyton spp., and Trichophyton spp., etc., were increased. While compared with the AAD model group, there was no significant difference of gut microbiota after given Peficon. The results showed that Zingiberis Rhizoma exerted beneficial health effects against AAD, and positively affected the microbial environment in the gut of rats with AAD.

Electroencephalogram Emotion Recognition Based on 3D Feature Fusion and Convolutional Autoencoder.

As one of the key technologies of emotion computing, emotion recognition has received great attention. Electroencephalogram (EEG) signals are spontaneous and difficult to camouflage, so they are used for emotion recognition in academic and industrial circles. In order to overcome the disadvantage that traditional machine learning based emotion recognition technology relies too much on a manual feature extraction, we propose an EEG emotion recognition algorithm based on 3D feature fusion and convolutional autoencoder (CAE). First, the differential entropy (DE) features of different frequency bands of EEG signals are fused to construct the 3D features of EEG signals, which retain the spatial information between channels. Then, the constructed 3D features are input into the CAE constructed in this paper for emotion recognition. In this paper, many experiments are carried out on the open DEAP dataset, and the recognition accuracy of valence and arousal dimensions are 89.49 and 90.76%, respectively. Therefore, the proposed method is suitable for emotion recognition tasks.

The multivariable prognostic models for severe complications after heart valve 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.

Study on the neuroprotective effects of Genistein on Alzheimer's disease.

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 ß-protein (Aß), 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.

Crosstalk between the activated Slit2-Robo1 pathway and TGF-ß1 signalling promotes cardiac fibrosis.

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-ß1, 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-ß1-siRNA, recombinant Slit2, and recombinant TGF-ß1 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-ß1, and collagen I expression levels. TAC surgery leads to heart enlargement, cardiac fibrosis, and up-regulation of Slit2, Robo1, TGF-ß1, and collagen I expression levels in animal model. Robo1 antagonist R5 and TGF-ß1 antagonist SB431542 suppressed cardiac fibrosis in TAC mice. Treatment with 100 nM Ang II in CFs caused significantly increased Slit2, Robo1, Smad2/3, TGF-ß1, collagen I, PI3K, and Akt expression levels. Transfecting Slit2-siRNA and TGF-ß1-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-ß1/Smad signalling pathway in CFs and up-regulated Periostin, Robo1, and collagen I expression. CONCLUSIONS: The Slit2-Robo1 signalling pathway interfered with the TGF-ß1/Smad pathway and promoted cardiac fibrosis. Blockade of Slit2-Robo1 might be a new treatment for cardiac fibrosis.

Bioinformatics analysis of molecular mechanisms involved in intervertebral disc degeneration induced by TNF-α and IL-1ß.

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)­1ß. 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 protein­protein 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.