A novel diagnostic nomogram based on serological and ultrasound findings for preoperative prediction of malignancy in patients with ovarian masses.

OBJECTIVE: To develop a novel diagnostic nomogram model to predict malignancy in patients with ovarian masses. METHODS: In total, 1277 patients with ovarian masses were retrospectively analyzed. Receiver operating characteristic (ROC) analysis was performed to identify valuable predictive factors. Univariate and multivariate logistic regression analyses were used to identify risk factors for ovarian cancer. Subsequently, a predictive nomogram model was developed. The performance of the nomogram model was assessed by its calibration and discrimination in a validation cohort. Decision curve analysis (DCA) was applied to assess the clinical net benefit of the model. RESULTS: Overall, 496 patients (38.8%) had ovarian cancer. Eighteen parameters were significantly different between the malignant and benign groups. Five parameters were identified as being most optimal for predicting malignancy, including age, carbohydrate antigen 125, fibrinogen-to-albumin ratio, monocyte-to-lymphocyte ratio, and ultrasound result. These parameters were incorporated to establish a nomogram model, and this model exhibited an area under the ROC curve (AUC) of 0.937 (95% confidence interval [CI], 0.920-0.954). The model was also well calibrated in the validation cohort and showed an AUC of 0.925 (95%CI, 0.896-0.953) at the cut-off point of 0.298. DCA confirmed that the nomogram model achieved the best clinical utility with almost the entire range of threshold probabilities. The model has demonstrated superior efficacy in predicting malignancy compared to currently available models, including the risk of ovarian malignancy algorithm, copenhagen index, and the risk of malignancy index. More importantly, the nomogram established here showed potential value in identification of early-stage ovarian cancer. CONCLUSION: The cost-effective and easily accessible nomogram model exhibited favorable accuracy for preoperative prediction of malignancy in patients with ovarian masses, even at early stages.

[Psychosis speech recognition algorithm based on deep embedded sparse stacked autoencoder and manifold ensemble].

Speech feature learning is the core and key of speech recognition method for mental illness. Deep feature learning can automatically extract speech features, but it is limited by the problem of small samples. Traditional feature extraction (original features) can avoid the impact of small samples, but it relies heavily on experience and is poorly adaptive. To solve this problem, this paper proposes a deep embedded hybrid feature sparse stack autoencoder manifold ensemble algorithm. Firstly, based on the prior knowledge, the psychotic speech features are extracted, and the original features are constructed. Secondly, the original features are embedded in the sparse stack autoencoder (deep network), and the output of the hidden layer is filtered to enhance the complementarity between the deep features and the original features. Third, the L1 regularization feature selection mechanism is designed to compress the dimensions of the mixed feature set composed of deep features and original features. Finally, a weighted local preserving projection algorithm and an ensemble learning mechanism are designed, and a manifold projection classifier ensemble model is constructed, which further improves the classification stability of feature fusion under small samples. In addition, this paper designs a medium-to-large-scale psychotic speech collection program for the first time, collects and constructs a large-scale Chinese psychotic speech database for the verification of psychotic speech recognition algorithms. The experimental results show that the main innovation of the algorithm is effective, and the classification accuracy is better than other representative algorithms, and the maximum improvement is 3.3%. In conclusion, this paper proposes a new method of psychotic speech recognition based on embedded mixed sparse stack autoencoder and manifold ensemble, which effectively improves the recognition rate of psychotic speech.

CHD1L promotes EOC cell invasiveness and metastasis via the regulation of METAP2.

Chromodomain helicase DNA binding protein 1-like (CHD1L) gene has been proposed to play an oncogenic role in human hepatocellular carcinoma. Previously we reported that CHD1L overexpression is significantly associated with the metastasis proceeding of epithelial ovarian cancer (EOC), and may predict a poor prognosis in EOC patients. However, the potential oncogenic mechanisms by which CHD1L acts in EOC remain unclear. To elucidate the oncogenic function of CHD1L, we carried out a series of in vitro assays, with effects of CHD1L ectogenic overexpression and silencing being determined in EOC cell lines (HO8910, A2780 and ES2). Real-time PCR and Western blotting analyses were used to identify potential downstream targets of CHD1L in the process of EOC invasion and metastasis. In ovarian carcinoma HO8910 cell lines, ectopic overexpression of CHD1L substantially induced the invasive and metastasis ability of the cancer cells in vitro. In contrast, knockdown of CHD1L using shRNA inhibited cell invasion in vitro in ovarian carcinoma A2780 and ES2 cell lines. We also demonstrated that methionyl aminopeptidase 2 (METAP2) was a downstream target of CHD1L in EOC, and we found a significant, positive correlation between the expression of CHD1L and METAP2 in EOC tissues (P<0.05). Our findings indicate that CHD1L plays a potential role in the inducement of EOC cancer cell invasion and/or metastasis via the regulation of METAP2 expression and suggests that CHD1L inhibition may provide a potential target for therapeutic intervention in human EOC.

Dualistic classification of high grade serous ovarian carcinoma has its root in spatial heterogeneity.

INTRODUCTION: Widespread intra-peritoneal metastases is a main feature of high grade serous ovarian carcinoma (HGSOC). Recently, the extent of tumour heterogeneity was used to evaluate the cancer genomes among multi-regions in HGSOC. However, there is no consensus on the effect of tumour heterogeneity on the evolution of the tumour metastasis process in HGSOC. OBJECTIVES: We performed whole-exome sequencing in multiple regions of matched primary and metastatic HGSOC specimens to reveal the genetic mechanisms of ovarian tumourigenesis and malignant progression. METHODS: 63 samples (including ovarian carcinoma, omentum metastasis, and normal tissues) were used. We analyzed the genomic heterogeneity, traced the subclone dissemination and establishment history and compared the different genetic characters of cancer evolutionary models in HGSOC. RESULTS: We found that HGSOC had substantial intra-tumour heterogeneity (median 54.2, range 0 âˆ¼ 106.7), high inter-patient heterogeneity (P < 0.001), but relatively limited intra-patient heterogeneity (P = 0.949). Two COSMIC mutational signatures were identified in HGSOCs: signature 3 was related to homologous recombination, and signature 1 was associated with aging. Two scenarios were identified by phylogenetic reconstruction in our study: 3 cases (33.3 %) showed star topology, and the other 6 cases (66.7 %) displayed tree topology. Compared with star topology group, more driver events were identified in tree topology group (P < 0.001), and occurred more frequently in early stage than in late stage of clonal evolution (P < 0.001). Moreover, compared with the star topology group, the tree topology group showed higher rate of intra-tumour heterogeneity (P = 0.045). CONCLUSION: A dualistic classification model was proposed for the classification of HGSOC based on spatial heterogeneity, which may contribute to better managing patients and providing individual treatment for HGSOC patients.

An aptamer-based shear horizontal surface acoustic wave biosensor with a CVD-grown single-layered graphene film for high-sensitivity detection of a label-free endotoxin.

The thickness of the sensitive layer has an important influence on the sensitivity of a shear horizontal surface acoustic wave (SH-SAW) biosensor with a delay-line structure and lower layer numbers of graphene produce better sensitivity for biological detection. Therefore, a label-free and highly sensitive SH-SAW biosensor with chemical vapor deposition (CVD-)-grown single-layered graphene (SLG) for endotoxin detection was developed in this study. With this methodology, SH-SAW biosensors were fabricated on a 36° Y-90° X quartz substrate with a base frequency of 246.2 MHz, and an effective detection cell was fabricated using acrylic material. To increase the surface hydrophilicity, chitosan was applied to the surface of the SLG film. Additionally, the aptamer was immobilized on the surface of the SLG film by cross-linking with glutaraldehyde. Finally, the sensitivity was verified by endotoxin detection with a linear detection ranging from 0 to 100 ng/mL, and the detection limit (LOD) was as low as 3.53 ng/mL. In addition, the stability of this type of SH-SAW biosensor from the air phase to the liquid phase proved to be excellent and the specificity was tested and verified by detecting the endotoxin obtained from Escherichia coli (E. coli), the endotoxin obtained from Pseudomonas aeruginosa (P. aeruginosa), and aflatoxin. Therefore, this type of SH-SAW biosensor with a CVD-grown SLG film may offer a promising approach to endotoxin detection, and it may have great potential in clinical applications.

A Self-Powered Engine Health Monitoring System Based on L-Shaped Wideband Piezoelectric Energy Harvester.

Engine health monitoring is very important to maintain the life of engines, and the power supply to sensor nodes is a key issue that needs to be solved. The piezoelectric vibration energy harvester has attracted much attention due to its obvious advantages in configuration, electromechanical conversion efficiency, and output power. However, the narrow bandwidth has restricted its practical application. A self-powered engine health monitoring system was proposed in this paper, and an L-shaped wideband piezoelectric energy harvester was designed and implemented. The L-shaped beam-mass structure and the piezoelectric bimorph cantilever beam structure was combined to form the wideband piezoelectric energy harvester configuration, which realized effective output at both resonance points. The theoretical model and finite element simulation analysis of the wideband piezoelectric energy harvester were proposed and the parameters were optimized based on that to meet the requirement of the vibration frequency of the engine. The experimental results show that the proposed energy harvester can be applied into the automobile engine health monitoring system. Engine signal analysis results also demonstrate that the proposed system can be used for engine health monitoring.

Investigation of surface acoustic waves propagating in ZnO-SiO2-Si multilayer structure.

The investigations of the propagation of the SAW in the multilayered structures have been of great interest since the combination of the surface acoustic wave (SAW) technology with the microelectromechanical system (MEMS) technology comes true. In this paper, the recursive asymptotic method (RAM) is applied to analysis the propagation of the SAW in the ZnO-SiO(2)-Si multilayered structure. The influences of the ZnO layer thickness and the SiO(2) layer thickness to the phase velocity and the electro-mechanical coupling coefficient for the Rayleigh wave and Love wave are discussed. The Love mode wave is found to be predominantly generated since the c-axis of the ZnO film is generally perpendicular to the substrate. In order to prove the theoretical results, a series of Love mode SAW devices based on the ZnO-SiO(2)-Si multilayered structure is fabricated by micromachining, and their frequency responses are detected. The experimental results are found to be mainly consistent with the theoretical ones except the little larger velocities induced by the residual stresses produced in the fabrication process of the films. The deviation of the experimental results from the theoretical ones is reduced by thermal annealing.

Synthesis of Ba-doped CeO(2) nanowires and their application as humidity sensors.

Ba-doped CeO(2) nanowires were obtained from CeO(2) particles through a facile composite-hydroxide-mediated (CHM) route. The products were characterized by x-ray diffraction (XRD), field emission scanning electron microscopy (FE-SEM), transmission electron microscopy (TEM) and high-resolution transmission electron microscope (HRTEM). The formation process of the product was discussed. Humidity sensors based on the source material CeO(2) particles, Ba-doped CeO(2) nanowires grown for 12 and 72 h, were fabricated. The responses to humidity for static and dynamic testing proved that both doping Ba into CeO(2) and converting morphology into a nanowire can improve the humidity sensitivity. The resistance changes from 465 to 3.9 MΩ as the relative humidity (RH) increases from 25% to 88%, indicating promising applications of Ba-doped CeO(2) nanowires in environmental monitoring.