Skull base metastases from a malignant solitary fibrous tumor of the liver. A case report and literature review

Solitary fibrous tumors (SFTs) of the liver are rarely described; only 38 cases have been reported in literature, most of which have shown benign clinical characteristics, and only 3 of these cases exhibited malignant variants. In this study, we present a 24-year-old woman with a 1-month history of a rapidly enlarging abdominal mass and a CT showing an exophytic heterogeneous liver mass with a firm parietal bone mass. The patient underwent a transcatheter arterial chemoembolization (TACE) before operation, and an extended right hepatectomy and craniectomy with a negative margin was performed under general anesthesia. The masses showed histological features of oval spindle cells haphazardly arranged in the classic short-storiform or so-called patternless pattern of solitary fibrous tumors. The tumor cells showed positive immunohistochemical reactions to CD34 and bcl-2. The tumor recurred in the residual liver 2 months after operation, metastatic osteoblastic lesions in the thoracic and lumbar vertebrae were identified 3 months after the operation, and lumbar vertebrae metastasis 7 months after operation paralyzed the patient. The patient underwent percutaneous ethanol injection therapy (PEI) and chemotherapy, but the patient died because of the uncontrolled tumor 16 months after the initial operation. To our knowledge, this is the first case of malignant solitary fibrous liver tumors with skeletal metastasis

Antenatal depression is associated with perceived stress, family relations, educational and professional status among women in South of China: a multicenter cross-sectional survey

BackgroundAntenatal depression is a commonly seen mental health concern for women. This study introduced a multicenter cross-sectional survey with a large sample to provide new insights into pregnant women’s depression, its socio-demographic and obstetric characteristics correlates, and its perceived stress among Chinese pregnant women.MethodsThis study conducted an observational survey according to the STROBE checklist. The multicenter cross-sectional survey was performed from August 2020 to January 2021 by distributing paper questionnaires among pregnant women from five tertiary hospitals in South China. The questionnaire included socio-demographic and obstetrics information, the Edinburgh Postnatal Depression Scale, and the 10-item Perceived Stress Scale. For the analyses, the Chi-square test and Multivariate logistic regression were utilized.ResultsAmong 2014 pregnant women in their second/third trimester, the prevalence of antenatal depression was 36.3%. 34.4% of pregnant women reported AD in their second trimester of pregnancy, and 36.9% suffered from AD in third trimester of pregnancy. A multivariate logistic regression model indicated that unemployed women, lower levels of education, poor marital relationships, poor parents-in-law relationships, concerns about contracting COVID-19, and higher perceived stress could aggravate antenatal depression among participants (p<0.05).ConclusionThere is a high proportion of antenatal depression among pregnant women in South China, so integrating depression screening into antenatal care services is worthwhile. Maternal and child health care providers need to evaluate pregnancy-related risk factors (perceived stress), socio-demographic factors (educational and professional status), and interpersonal risk factors (marital relations and relationship with Parents-in-law). In future research, the study also emphasized the importance of providing action and practical support to reduce the experience of antenatal depression among disadvantaged sub-groups of pregnant women

Anxiety, depression, and insomnia among nurses during the full liberalization of COVID-19: a multicenter cross-sectional analysis of the high-income region in China

IntroductionFrontline nurses fighting against the epidemic were under great psychological stress. However, there is a lack of studies assessing the prevalence rates of anxiety, depression, and insomnia among frontline nurses after the full liberalization of COVID-19 in China. This study demonstrates the impact of the full liberalization of COVID-19 on the psychological issues and the prevalence rate and associated factors of depressive symptoms, anxiety, and insomnia among frontline nurses.MethodsA total of 1766 frontline nurses completed a self-reported online questionnaire by convenience sampling. The survey included six main sections: the 9-item Patient Health Questionnaire (PHQ-9), the 7-item Generalized Anxiety Disorder (GAD-7), the 7-item Insomnia Severity Index (ISI), the 10-item Perceived Stress Scale (PSS-10), sociodemographic information, and work information. Multiple logistic regression analyses were applied to identify the potential significantly associated factors for psychological issues. The study methods were compliant with the STROBE checklist.Results90.83% of frontline nurses were infected with COVID-19, and 33.64% had to work while infected COVID-19. The overall prevalence of depressive symptoms, anxiety and insomnia among frontline nurses was 69.20%, 62.51%, and 76.78%, respectively. Multiple logistic analyses revealed that job satisfaction, attitude toward the current pandemic management, and perceived stress were associated with depressive symptoms, anxiety, and insomnia.ConclusionsThis study highlighted that frontline nurses were suffering from varying degrees of depressive symptoms, anxiety, and insomnia during full liberalization of COVID-19. Early detection of mental health issues and preventive and promotive interventions should be implemented according to the associated factors to prevent a more serious psychological impact on frontline nurses

Analysis of lncRNA-Associated ceRNA Network Reveals Potential lncRNA Biomarkers in Human Colon Adenocarcinoma

Background/Aims: Long non-coding RNAs (lncRNAs) acting as competing endogenous RNAs (ceRNAs) play significant roles in the development of tumors, but the functions of specific lncRNAs and lncRNA-related ceRNA networks have not been fully elucidated for colon adenocarcinoma (COAD). In this study, we aimed to clarify the lncRNA-microRNA (miRNA)-mRNA ceRNA network and potential lncRNA biomarkers in COAD. Methods: We extracted data from The Cancer Genome Atlas (TCGA) and identified COAD-specific mRNAs, miRNAs, and lncRNAs. The biological processes in Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) were analyzed for COAD-specific mRNAs. We then constructed a ceRNA network of COAD-specific mRNAs, miRNAs and lncRNAs and analyzed the correlation between expression patterns and clinical features of the lncRNAs involved. After identifying potential mRNA targets of 4 lncRNAs related to overall survival (OS), we conducted stepwise analysis of these targets through GO and KEGG. Using tissue samples from our own patients, we also verified certain analytical results using quantitative real-time PCR (qRT-PCR). Results: Data from 521 samples (480 tumor tissue and 41 adjacent non-tumor tissue samples) were extracted from TCGA. A total of 258 specific lncRNAs, 206 specific miRNAs, and 1467 specific mRNAs were identified (absolute log2 [fold change] > 2, false discovery rate < 0.01). Analysis of KEGG revealed that specific mRNAs were enriched in cancer-related pathways. The ceRNA network was constructed with 64 lncRNAs, 18 miRNAs, and 42 mRNAs. Among these lncRNAs involved in the network, 3 lncRNAs (LINC00355, HULC, and IGF2-AS) were confirmed to be associated with certain clinical features and 4 lncRNAs (HOTAIR, LINC00355, KCNQ1OT1, and TSSC1-IT1) were found to be negatively linked to OS (log-rank p < 0.05). KEGG showed that the potential mRNA targets of these 4 lncRNAs may be concentrated in the MAPK pathway. Certain results were validated by qRT-PCR. Conclusion: This study providing novel insights into the lncRNA-miRNA-mRNA ceRNA network and reveals potential lncRNA biomarkers in COAD

A Fault Diagnosis Method of Modular Analog Circuit Based on SVDD and D–S Evidence Theory

In the actual fault diagnosis process of an analog circuit, there is often a problem due to the lack of fault samples, leading to the low-accuracy of diagnostic models. Therefore, using positive samples that are easy to obtain to establish diagnostic models became a research hotspot in the field of analog circuit fault diagnosis. This paper proposes a method based on Support Vector Data Description (SVDD) and Dempster–Shafer evidence theory (D–S evidence theory) for fault diagnosis of modular analog circuit. Firstly, the principle of circuit module partition is proposed to divide the analog circuit under test, and the output port of each module is selected as test point. Secondly, the paper extracts the feature of the time-domain and frequency-domain output signals of the circuit module through Principal Component Analysis (PCA). Thirdly, four state detection models based on SVDD are established to judge the working state of each circuit module, including TSG, TSP, FSG, and FSP state detection model. Finally, the D–S theory is introduced to integrate the test results of each model for locating fault circuit module. To verify the effectiveness of the proposed method, the dual bandpass filter circuit is selected for simulation and hardware experiment. The results show that the proposed method can locate the analog fault effectively and has a higher diagnosis accuracy

Estimation of Cultivated Land Quality Based on Soil Hyperspectral Data

Efficient monitoring of cultivated land quality (CLQ) plays a significant role in cultivated land protection. Soil spectral data can reflect the state of cultivated land. However, most studies have used crop spectral information to estimate CLQ, and there is little research on using soil spectral data for this purpose. In this study, soil hyperspectral data were utilized for the first time to evaluate CLQ. We obtained the optimal spectral variables from dry soil spectral data using a gradient boosting decision tree (GBDT) algorithm combined with the variance inflation factor (VIF). Two estimation algorithms (partial least-squares regression (PLSR) and back-propagation neural network (BPNN)) with 10-fold cross-validation were employed to develop the relationship model between the optimal spectral variables and CLQ. The optimal algorithms were determined by the degree of fit (determination coefficient, R2). In order to estimate CLQ at the regional scale, HuanJing-1A Hyperspectral Imager (HJ-1A HSI) data were transformed into dry soil spectral data using the linkage model of original soil spectral reflectance to dry soil spectral reflectance. This study was conducted in the Guangdong Province, China and the Conghua district within the same province. The results showed the following: (1) the optimal spectral variables selected from the dry soil spectral variables were 478 nm, 502 nm, 614 nm, 872 nm, 966 nm, 1007 nm, and 1796 nm. (2) The BPNN was the optimal model, with an R2(C) of 0.71 and a normalized root mean square error (NRMSE) of 12.20%. (3) The results showed the R2 of the regional-scale CLQ estimation based on the proposed method was 0.05 higher, and the NRMSE was 0.92% lower than that of the CLQ map obtained using the traditional method. Additionally, the NRMSE of the regional-scale CLQ estimation base on dry soil spectral variables from HJ-1A HSI data was 2.00% lower than that of the model base on the original HJ-1A HSI data