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Objective: To review and analyze the airway and anesthesia management methods for patients who underwent endoscopic closure of tracheoesophageal fistula (TEF) and to summarize the experience of intraoperative airway management. Method: We searched the anesthesia information system of the First Affiliated Hospital of Nanjing Medical University for anesthesia cases of TEF from July 2020 to July 2023 and obtained a total of 34 anesthesia records for endoscopic TEF occlusion. The intraoperative airway management methods and vital signs were recorded, and the patients' disease course and follow-up records were analyzed and summarized. Results: The airway management strategies used for TEF occlusion patients included nasal catheter oxygen (NCO, n = 5), high-flow nasal cannula oxygen therapy (HFNC, n = 4) and tracheal intubation (TI, n = 25). The patients who underwent tracheal intubation with an inner diameter of 5.5 mm had stable hemodynamics and oxygenation status during surgery, while intravenous anesthesia without intubation could not effectively inhibit the stress response caused by occluder implantation, which could easily cause hemodynamic fluctuations, hypoxemia, and carbon dioxide accumulation. Compared with those in the TI group, the NCO group and the HFNC group had significantly longer surgical times, and the satisfaction score of the endoscopists was significantly lower. In addition, two patients in the NCO group experienced postoperative hypoxemia. Conclusion: During the anesthesia process for TEF occlusions, a tracheal catheter with an inner diameter of 5.5 mm can provide a safe and effective airway management method. [ABSTRACT FROM AUTHOR]

Background: Integrative analysis of multi-omics data is becoming increasingly important to unravel functional mechanisms of complex diseases. However, the currently available multi-omics datasets inevitably suffer from missing values due to technical limitations and various constrains in experiments. These missing values severely hinder integrative analysis of multi-omics data. Current imputation methods mainly focus on using single omics data while ignoring biological interconnections and information imbedded in multi-omics data sets. Results: In this study, a novel multi-omics imputation method was proposed to integrate multiple correlated omics datasets for improving the imputation accuracy. Our method was designed to: 1) combine the estimates of missing value from individual omics data itself as well as from other omics, and 2) simultaneously impute multiple missing omics datasets by an iterative algorithm. We compared our method with five imputation methods using single omics data at different noise levels, sample sizes and data missing rates. The results demonstrated the advantage and efficiency of our method, consistently in terms of the imputation error and the recovery of mRNA-miRNA network structure. Conclusions: We concluded that our proposed imputation method can utilize more biological information to minimize the imputation error and thus can improve the performance of downstream analysis such as genetic regulatory network construction. [ABSTRACT FROM AUTHOR]

As an important subtype of structural variations, chromosomal translocation is associated with various diseases, especially cancers, by disrupting gene structures and functions. Traditional methods for identifying translocations are time consuming and have limited resolutions. Recently, a few studies have employed next-generation sequencing (NGS) technology for characterizing chromosomal translocations on human genome, obtaining high-throughput results with high resolutions. However, these studies are mainly focused on mechanism-specific or site-specific translocation mapping. In this study, we conducted a comprehensive genomewide analysis on the characterization of human chromosomal material exchange with regard to the chromosome translocations. Using NGS data of 1,481 subjects from the 1000 Genomes Project, we identified 15,349,092 translocated DNA fragment pairs, ranging from65 to 1,886 bp and with an average size of approximately 102 bp. On average, each individual genome carried about 10,364 pairs, covering approximately 0.069%of the genome. We identified 16 translocation hot regions, amongwhich tworegions did not contain repetitive fragments. Results of our study overlapped with a majority of previous results, containing approximately 79%of approximately 2,340 translocations characterized in three available translocation databases. In addition, our study identified five novel potential recurrent chromosomal material exchange regions with greater than 20% detection rates. Our results will be helpful for an accurate characterization of translocations in human genomes, and contribute as a resource for future studies of the roles of translocations in human disease etiology and mechanisms. [ABSTRACT FROM AUTHOR]

Background: The emergence of high-throughput genomic datasets from different sources and platforms (e.g., gene expression, single nucleotide polymorphisms (SNP), and copy number variation (CNV)) has greatly enhanced our understandings of the interplay of these genomic factors as well as their influences on the complex diseases. It is challenging to explore the relationship between these different types of genomic data sets. In this paper, we focus on a multivariate statistical method, canonical correlation analysis (CCA) method for this problem. Conventional CCA method does not work effectively if the number of data samples is significantly less than that of biomarkers, which is a typical case for genomic data (e.g., SNPs). Sparse CCA (sCCA) methods were introduced to overcome such difficulty, mostly using penalizations with l-1 norm (CCA-l1) or the combination of l-1and l-2 norm (CCA-elastic net). However, they overlook the structural or group effect within genomic data in the analysis, which often exist and are important (e.g., SNPs spanning a gene interact and work together as a group). Results: We propose a new group sparse CCA method (CCA-sparse group) along with an effective numerical algorithm to study the mutual relationship between two different types of genomic data (i.e., SNP and gene expression). We then extend the model to a more general formulation that can include the existing sCCA models. We apply the model to feature/variable selection from two data sets and compare our group sparse CCA method with existing sCCA methods on both simulation and two real datasets (human gliomas data and NCI60 data). We use a graphical representation of the samples with a pair of canonical variates to demonstrate the discriminating characteristic of the selected features. Pathway analysis is further performed for biological interpretation of those features. Conclusions: The CCA-sparse group method incorporates group effects of features into the correlation analysis while performs individual feature selection simultaneously. It outperforms the two sCCA methods (CCA-l1 and CCAgroup) by identifying the correlated features with more true positives while controlling total discordance at a lower level on the simulated data, even if the group effect does not exist or there are irrelevant features grouped with true correlated features. Compared with our proposed CCA-group sparse models, CCA-l1 tends to select less true correlated features while CCA-group inclines to select more redundant features. [ABSTRACT FROM AUTHOR]

Current microarray studies primarily focus on identifying individual genes with differential expression levels across different conditions or classes. A potential problem is that they may disregard multidimensional information hidden in gene interactions. In this study, we propose an approach to detect gene interactions related to study phenotypes through identifying gene pairs with correlations that appear to be class or condition specific. In addition, we explore the effects of ignoring class-specific correlations (CSC) on correlation-based gene-clustering analyses. Our simulation studies show that ignoring CSC can significantly decrease the accuracy of gene clustering and increase the dissimilarity within clusters. Our results from a DLBCL (distinct types of diffuse large B cell lymphoma) data set illustrate that CSC are clearly present and have great adverse effects on gene-clustering results if ignored. Meanwhile, interesting biological interpretations may be derived from studying gene pairs with CSC. This study demonstrates that our algorithm is simple and computationally efficient and has the ability to detect gene pairs with CSC that are informative for uncovering interesting regulation patterns. [ABSTRACT FROM AUTHOR]

Tight oil reservoirs are characterized by low porosity, low permeability, small seepage channels (micro-nano-scale pore development), and low pressure coefficient. The existing development technology can not solve the problems of low output, fast decline, difficulty in replenishing energy, and low recovery ratio. In this paper, the T2 spectrum of saturated oil samples is detected by NMR analyzer, and the spontaneous imbibition and pressurized imbibition experiments are carried out with deuterium water. By scanning the T2 spectrum of nuclear magnetic resonance, the peak value of T2 spectrum decreases with the imbibition process, which indicates that the oil in the core is gradually imbibed, and the change trend of spontaneous imbibition quality is basically consistent with that of the spectral area. The experimental results show that the effects of soaking wells on the oil recovery of different wells are obviously different. There are many factors that affect the effect of well soaking, including porosity, permeability, mineral composition, and so on, which together affect the final effect of the well soaking. From the experimental results, the clay minerals in the core, especially the illite-montmorillonite mixed layer, have an obvious influence on the imbibition rate. The higher the content of the illite-montmorillonite mixed layer, the faster the imbibition rate. This study is of great significance for the development of the tight oil. [ABSTRACT FROM AUTHOR]

Background: With the advancement of high-throughput technologies and enrichment of popular public databases, more and more research focuses of bioinformatics research have been on computational integration of network and gene expression profiles for extracting context-dependent active subnetworks. Many methods for subnetwork searching have been developed. Scoring and searching algorithms present a range of computational considerations and implementations. The primary goal of present study is to comprehensively evaluate the performance of different subnetwork detection methods. Eleven popular methods were selected for comprehensive comparison. Results: First, taking into account the dependence of genes given a protein-protein interaction (PPI) network, we simulated microarray gene expression data under case and control conditions. Then each method was applied to the simulated data for subnetwork identification. Second, a large microarray data set of prostate cancer was used to assess the practical performance of each method. Using both simulation studies and a real data application, we evaluated the performance of different methods in terms of recall and precision. Conclusions: jActiveModules, PinnacleZ and WMAXC performed well in identifying subnetwork with relative high precision and recall. BioNet performed very well only in precision. As none of methods outperformed other methods overall, users should choose an appropriate method based on the purposes of their studies. [ABSTRACT FROM AUTHOR]

Glioblastoma is rather recalcitrant to existing therapies and effective interventions are needed. Here we report a novel microenvironment-responsive micellar system (ch-K5(s-s)R8-An) for the co-delivery of the radiosensitizer Dbait and the chemotherapeutic doxorubicin (DOX) to glioblastoma. Accordingly, the ch-K5(s-s)R8-An/(Dbait-DOX) micelles plus radiotherapy (RT) treatment resulted in a high degree of apoptosis and DNA damage, which significantly reduced cell viability and proliferation capacity of U251 cells to 64.0% and 16.3%, respectively. The angiopep-2-modified micelles exhibited substantial accumulation in brain-localized U251 glioblastoma xenografts in mice compared to angiopep-2-lacking micelles. The ch-K5(s-s)R8-An/(Dbait-DOX) + RT treatment group exhibited the smallest tumor size and most profound tumor tissue injury in orthotopic U251 tumors, leading to an increase in median survival time of U251 tumor-bearing mice from 26 days to 56 days. The ch-K5(s-s)R8-An/(Dbait-DOX) micelles can be targeted to brain-localized U251 tumor xenografts and sensitize the tumor to chemotherapy and radiotherapy, thereby overcoming the inherent therapeutic challenges associated with malignant glioblastoma. [ABSTRACT FROM AUTHOR]

Background: Hypericum japonicum Thunb. ex Murray is widely used as an herbal medicine for the treatment of hepatitis and tumours in China. However, the molecular mechanisms of its effects are unclear. Our previous research showed that extracts of H. japonicum can induce apoptosis in leukaemia cells. We also previously systematically analysed and isolated the chemical composition of H. japonicum. Methods: The fluorescence polarisation experiment was used to screen for inhibitors of Bcl-2 proteins which are proved as key proteins in apoptosis. The binding mode was modelled by molecular docking. We investigated the proliferation attenuating and apoptosis inducing effects of active compound on cancer cells by MTT assay and flow cytometry analysis. Activation of caspases were tested by Western blot. A broad-spectrum caspase inhibitor Z-VAD-FMK was used to investigate the caspases-dependence. In addition, co-immunoprecipitation was performed to analyse the inhibition of heterodimerization between anti-apoptotic Bcl-2 proteins with pro-apoptotic proteins. Moreover, in vivo activity was tested in a mouse xenograph tumour model. Result: Jacarelhyperol A (Jac-A), a characteristic constituent of H. japonicum, was identified as a potential Bcl-2 inhibitor. Jac-A showed binding affinities to Bcl-xL, Bcl-2, and Mcl-1 with Ki values of 0.46 μM, 0.43 μM, and 1.69 μM, respectively. This is consistent with computational modelling results, which show that Jac-A presents a favorable binding mode with Bcl-xL in the BH3-binding pocket. In addition, Jac-A showed potential growth inhibitory activity in leukaemia cells with IC50 values from 1.52 to 6.92 μM and significantly induced apoptosis of K562 cells by promoting release of cytochrome c and activating the caspases. Jac-A also been proved that its effect is partly caspases-dependent and can disrupt the heterodimerization between anti-apoptotic Bcl-2 proteins with pro-apoptotic proteins. Moreover, Jac-A dose-dependently inhibited human K562 cell growth in a mouse xenograph tumour model with low toxicity. Conclusion: In this study, a characteristic constituent of H. japonicum, Jac-A, was shown to induce apoptosis in leukaemia cells by mediating the Bcl-2 proteins. Therefore, we propose a new lead compound for cancer therapy with a low toxicity, and have provided evidence for using H. japonicum as an anti-cancer herb. [ABSTRACT FROM AUTHOR]