A study on the impact of marital status on the survival status of prostate cancer patients based on propensity score matching.

Marital status is an independent prognostic factor for survival in many types of cancers, but its prognostic impact on patients with prostate cancer (PCa) has not been established. The aim of this study was to explore the independent prognostic factors of PCa and to investigate the effect of marital status on survival outcomes in patients with different stratified by PCa. Using the surveillance, epidemiology, and end results (SEER) database, we collected data on 584,655 PCa patients diagnosed between 1975 and 2019. Marital status was classified as married, divorced, widowed, and single. We used the Kaplan-Meier analysis and single multivariate Cox proportional hazards regression analysis to determine the effect of marital status on overall survival (OS) and cancer-specific survival (CSS). In addition, we performed subgroup analyses for different ages, Gleason score and PSA values, and performed a 1:1 propensity score matching (PSM) to reduce the impact of confounding factors to obtain more accurate matching results. According to our findings, marital status was an independent prognostic factor for the survival of PCa patients and a better prognosis of married patients. Moreover, we also found that factors such as age, TNM stage, Gleason score, and PSA concentration were also considered as important predictors for the prognosis of PCa. The above findings can facilitate early detection and treatment of high-risk PCa patients, prolong their life and reduce family burden.

Identifying Cancer Driver Pathways Based on the Mouth Brooding Fish Algorithm.

Identifying the driver genes of cancer progression is of great significance in improving our understanding of the causes of cancer and promoting the development of personalized treatment. In this paper, we identify the driver genes at the pathway level via an existing intelligent optimization algorithm, named the Mouth Brooding Fish (MBF) algorithm. Many methods based on the maximum weight submatrix model to identify driver pathways attach equal importance to coverage and exclusivity and assign them equal weight, but those methods ignore the impact of mutational heterogeneity. Here, we use principal component analysis (PCA) to incorporate covariate data to reduce the complexity of the algorithm and construct a maximum weight submatrix model considering different weights of coverage and exclusivity. Using this strategy, the unfavorable effect of mutational heterogeneity is overcome to some extent. Data involving lung adenocarcinoma and glioblastoma multiforme were tested with this method and the results compared with the MDPFinder, Dendrix, and Mutex methods. When the driver pathway size was 10, the recognition accuracy of the MBF method reached 80% in both datasets, and the weight values of the submatrix were 1.7 and 1.89, respectively, which are better than those of the compared methods. At the same time, in the signal pathway enrichment analysis, the important role of the driver genes identified by our MBF method in the cancer signaling pathway is revealed, and the validity of these driver genes is demonstrated from the perspective of their biological effects.

Prediction Models for Sleep Quality Among College Students During the COVID-19 Outbreak: Cross-sectional Study Based on the Internet New Media.

BACKGROUND: COVID-19 has been reported to affect the sleep quality of Chinese residents; however, the epidemic's effects on the sleep quality of college students during closed-loop management remain unclear, and a screening tool is lacking. OBJECTIVE: This study aimed to understand the sleep quality of college students in Fujian Province during the epidemic and determine sensitive variables, in order to develop an efficient prediction model for the early screening of sleep problems in college students. METHODS: From April 5 to 16, 2022, a cross-sectional internet-based survey was conducted. The Pittsburgh Sleep Quality Index (PSQI) scale, a self-designed general data questionnaire, and the sleep quality influencing factor questionnaire were used to understand the sleep quality of respondents in the previous month. A chi-square test and a multivariate unconditioned logistic regression analysis were performed, and influencing factors obtained were applied to develop prediction models. The data were divided into a training-testing set (n=14,451, 70%) and an independent validation set (n=6194, 30%) by stratified sampling. Four models using logistic regression, an artificial neural network, random forest, and naïve Bayes were developed and validated. RESULTS: In total, 20,645 subjects were included in this survey, with a mean global PSQI score of 6.02 (SD 3.112). The sleep disturbance rate was 28.9% (n=5972, defined as a global PSQI score >7 points). A total of 11 variables related to sleep quality were taken as parameters of the prediction models, including age, gender, residence, specialty, respiratory history, coffee consumption, stay up, long hours on the internet, sudden changes, fears of infection, and impatient closed-loop management. Among the generated models, the artificial neural network model proved to be the best, with an area under curve, accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of 0.713, 73.52%, 25.51%, 92.58%, 57.71%, and 75.79%, respectively. It is noteworthy that the logistic regression, random forest, and naive Bayes models achieved high specificities of 94.41%, 94.77%, and 86.40%, respectively. CONCLUSIONS: The COVID-19 containment measures affected the sleep quality of college students on multiple levels, indicating that it is desiderate to provide targeted university management and social support. The artificial neural network model has presented excellent predictive efficiency and is favorable for implementing measures earlier in order to improve present conditions.

Link Security Situation Identification Method Based on the Ad Hoc Network of Medical Units.

In order to strengthen the management and security status monitoring of the internal network of medical units and make up for security vulnerabilities in time, an ad hoc network link security situation identification method is proposed. According to the architecture of the ad hoc network, it is analyzed that it has the advantages of strong persistence and its own protocol. Combined with the data of detection equipment and security log, the hierarchical acquisition model is used to obtain the situation elements such as port scanning attack and flood attack. The transmission rate factor, forwarding rate factor, dispersion factor, and node aggregation factor are regarded as eigenvectors. We determine the relationship between identity, difference, and opposition, identify the security situation through the description of the node state, and conduct quantitative processing to obtain the final identification result. The experimental results show that the weight value of this method is the same as the standard weight, which can identify the security situation level, obtain the specific situation value, and present a more intuitive identification result.

An Effective Graph Clustering Method to Identify Cancer Driver Modules.

Identifying the molecular modules that drive cancer progression can greatly deepen the understanding of cancer mechanisms and provide useful information for targeted therapies. Most methods currently addressing this issue primarily use mutual exclusivity without making full use of the extra layer of module property. In this paper, we propose MCLCluster to identity cancer driver modules, which use somatic mutation data, Cancer Cell Fraction (CCF) data, gene functional interaction network and protein-protein interaction (PPI) network to derive the module property on mutual exclusivity, connectivity in PPI network and functionally similarity of genes. We have taken three effective measures to ensure the effectiveness of our algorithm. First, we use CCF data to choose stronger signals and more confident mutations. Second, the weighted gene functional interaction network is used to quantify the gene functional similarity in PPI. The third, graph clustering method based on Markov is exploited to extract the candidate module. MCLCluster is tested in the two TCGA datasets (GBM and BRCA), and identifies several well-known oncogenes driver modules and some modules with functionally associated driver genes. Besides, we compare it with Multi-Dendrix, FSME Cluster and RME in simulated dataset with background noise and passenger rate, MCLCluster outperforming all of these methods.

Proteomics Analysis of Brain Tissue in a Rat Model of Ischemic Stroke in the Acute Phase.

Background: Stroke is a leading health issue, with high morbidity and mortality rates worldwide. Of all strokes, approximately 80% of cases are ischemic stroke (IS). However, the underlying mechanisms of the occurrence of acute IS remain poorly understood because of heterogeneous and multiple factors. More potential biomarkers are urgently needed to reveal the deeper pathogenesis of IS. Methods: We identified potential biomarkers in rat brain tissues of IS using an iTRAQ labeling approach coupled with LC-MS/MS. Furthermore, bioinformatrics analyses including GO, KEGG, DAVID, and Cytoscape were used to present proteomic profiles and to explore the disease mechanisms. Additionally, Western blotting for target proteins was conducted for further verification. Results: We identified 4,578 proteins using the iTRAQ-based proteomics method. Of these proteins, 282 differentiated proteins, comprising 73 upregulated and 209 downregulated proteins, were observed. Further bioinformatics analysis suggested that the candidate proteins were mainly involved in energy liberation, intracellular protein transport, and synaptic plasticity regulation during the acute period. KEGG pathway enrichment analysis indicated a series of representative pathological pathways, including energy metabolite, long-term potentiation (LTP), and neurodegenerative disease-related pathways. Moreover, Western blotting confirmed the associated candidate proteins, which refer to oxidative responses and synaptic plasticity. Conclusions: Our findings highlight the identification of candidate protein biomarkers and provide insight into the biological processes involved in acute IS.

Plasma metabolomics profiles in rats with acute traumatic brain injury.

Traumatic brain injury (TBI) is a major cause of mortality and disability worldwide. We validated the utility of plasma metabolomics analysis in the clinical diagnosis of acute TBI in a rat model of controlled cortical impact (CCI) using gas chromatography/mass spectrometry (GC/MS). Thirty Sprague-Dawley rats were randomly divided into two groups of 15 rats each: the CCI group and sham group. Blood samples were obtained from the rats within the first 24 h after TBI injury. GC/MS measurements were performed to evaluate the profile of acute TBI-induced metabolic changes, resulting in the identification of 45 metabolites in plasma. Principal component analysis, partial least squares-discriminant analysis, orthogonal partial least square discriminant analysis using hierarchical clustering and univariate/multivariate analyses revealed clear differences in the plasma metabolome between the acute CCI group and the sham group. CCI induced distinctive changes in metabolites including linoleic acid metabolism, amino acid metabolism, galactose metabolism, and arachidonic acid metabolism. Specifically, the acute CCI group exhibited significant alterations in proline, phosphoric acid, ß-hydroxybutyric acid, galactose, creatinine, L-valine, linoleic acid and arachidonic acid. A receiver operating characteristic curve analysis showed that the above 8 metabolites in plasma could be used as the potential biomarkers for the diagnosis of acute TBI. Furthermore, this study is the first time to identify the galactose as a biomarker candidate for acute TBI. This comprehensive metabolic analysis complements target screening for potential diagnostic biomarkers of acute TBI and enhances predictive value for the therapeutic intervention of acute TBI.

Highly efficient and energy-conserved flocculation of copper in wastewater by pulse-alternating current.

Copper wastewater from industry is detrimental to plants and environment. There are some problems in the aspects of high efficiency and energy saving during treatment of wastewater. In present work, the novel double iron electrodes technique of pulse-alternating current was applied to flocculate copper in wastewater. The process parameters of the copper removal in wastewater were studied in the developed electrochemical reactors. Scanning electron microscopy, X-ray diffraction, and energy-dispersive spectroscopy were used to characterize the electrocoagulations. The copper residue in the effluent was measured by UV spectrophotometry. The adsorption mechanism was described through the isothermal adsorption curves of copper during flocculation processes. The simulated wastewater containing 100 mg dm-3 Cu2+ and 100 mg dm-3 NaCl as conductive salt was adjusted to pH 7.8-8 with ammonia or sulfuric acid. At room temperature of 20-25 °C, controlling the flow rate of 3 dm3 min-1, and applying pulse-alternating current of 40 µA gFe-1, the copper residue in the effluent passing through four-series reactors was reduced to 0.118 mg dm-3, which was far lower than 0.3 mg dm-3 (from GB20900-2008). The removal rate of copper could reach 99.882%. The removal of copper in the wastewater treated via our electrocoagulation technique was far more efficient than the conventional DC current coagulation and chemical flocculation. The double iron electrodes were used to reduce the concentration polarization and improved the current efficiency. The significant economic and good social benefit will be promisingly produced if our developed technique is applied in the treatment of industrial wastewater.