Characterization of cytotoxic Citrobacter braakii isolated from human stomach.

Citrobacter braakii (C. braakii) is an anaerobic, gram-negative bacterium that has been isolated from the environment, food, and humans. Infection by C. braakii has been associated with acute mucosal inflammation in the intestine, respiratory tract, and urinary tract. However, the pathogenesis of C. braakii in the gastric mucosa has not yet been clarified. In this study, the bacterium was detected in 35.5% (61/172) of patients with chronic gastritis (CG) and was closely associated with the severity of mucosal inflammation. Citrobacter braakii P1 isolated from a patient with CG exhibited urease activity and acid resistance. It contained multiple secretion systems, including a complete type I secretion system (T1SS), T5aSS and T6SS. We then predicted the potential pilus-related adhesins. Citrobacter braakii P1 diffusely adhered to AGS cells and significantly increased lactate dehydrogenase (LDH) release; the adhesion rate and LDH release were much lower in HEp-2 cells. Strain P1 also induced markedly increased mRNA and protein expression of IL-8 and TNF-α in AGS cells, and the fold increase was much higher than that in HEp-2 cells. Our results demonstrate proinflammatory and cytotoxic role of C. braakii in gastric epithelial cells, indicating the bacterium is potentially involved in inducing gastric mucosa inflammation.

Feature interaction network based on hierarchical decoupled convolution for 3D medical image segmentation.

Manual image segmentation consumes time. An automatic and accurate method to segment multimodal brain tumors using context information rich three-dimensional medical images that can be used for clinical treatment decisions and surgical planning is required. However, it is a challenge to use deep learning to achieve accurate segmentation of medical images due to the diversity of tumors and the complex boundary interactions between sub-regions while limited computing resources hinder the construction of efficient neural networks. We propose a feature fusion module based on a hierarchical decoupling convolution network and an attention mechanism to improve the performance of network segmentation. We replaced the skip connections of U-shaped networks with a feature fusion module to solve the category imbalance problem, thus contributing to the segmentation of more complicated medical images. We introduced a global attention mechanism to further integrate the features learned by the encoder and explore the context information. The proposed method was evaluated for enhance tumor, whole tumor, and tumor core, achieving Dice similarity coefficient metrics of 0.775, 0.900, and 0.827, respectively, on the BraTS 2019 dataset and 0.800, 0.902, and 0.841, respectively on the BraTS 2018 dataset. The results show that our proposed method is inherently general and is a powerful tool for brain tumor image studies. Our code is available at: https://github.com/WSake/Feature-interaction-network-based-on-Hierarchical-Decoupled-Convolution.

Prognostic risk factors for T1 thoracic esophageal cancer: a retrospective cohort study.

Background: Esophageal cancer has a poor overall prognosis and a high incidence of post-treatment complications. This study aimed to analyze the common surgical methods for treating T1 thoracic esophageal cancer and explore its prognostic risk factors to provide a basis for appropriate treatment selection. Methods: In this population-based retrospective cohort study, data of patients diagnosed with T1 thoracic esophageal cancer from 2010 to 2016 were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. Patients were divided according to those who had surgery and those who had radiotherapy. Survival curves were generated using the Kaplan-Meier method and validated by the log-rank test. Cox's regression model was used to analyze the independent prognostic risk factors. Results: Overall, 2,027 eligible patients, including 824 and 1,203 patients in the surgical and non-surgical groups, respectively, were analyzed. There was no significant difference in survival between the surgical and non-surgical groups (P=0.79). In subgroup analysis, the Cox regression analysis showed that radiotherapy was a significant prognostic factor (P=0.00059). Conclusions: The impact of surgery on patients with T1 thoracic esophageal cancer was insignificant; however, radiotherapy was an independent prognostic risk factor. These results provide a reliable basis for clinical treatment of patients with T1 thoracic esophageal cancer.

The driving factors and future changes of CO2 emission in China's nonferrous metal industry.

As an energy-intensive industry in China, it is critical to promote energy conservation and carbon emission reduction in the nonferrous metal industry (NMI). This study first applies the Tapio decoupling model to explore the relationships between the industrial output and CO2 emission in China's NMI. Then, the Generalized Divisia Index Model (GDIM) is adopted to uncover the factors driving the changes in CO2 emission from 2000 to 2019, and based on the decomposition results, scenario analysis is used to predict potential CO2 emission during 2021-2035. The results show that (1) the CO2 emission in China's NMI increases by 397.93 million tons (Mt) during 2000-2019, and the decoupling state between the industrial output and CO2 emission is characterized by the weak decoupling status; (2) overall, the output scale is the dominant factor promoting the CO2 emissions increase, followed by the investment scale and energy consumption scale, while the carbon intensity of output and the carbon intensity of investment are the two most important abatement factors; (3) the scenario analysis indicates that the CO2 emission from NMI will peak around 2030 under the low-carbon scenario while 2026 under the enhanced low-carbon scenario. Policy suggestions are further put forward for carbon emission reduction in China's NMI.

The tyrosine phosphatase SHP2 promotes proliferation and oxaliplatin resistance of colon cancer cells through AKT and ERK.

The SH2 domain-containing phosphatase 2 (SHP2) is a widely expressed protein tyrosine phosphatase, and it is proposed to act as an oncogenic protein. SHP2 is also engaged in drug resistance of a variety of cancers. However, the role of SHP2 in the proliferation and drug resistance of colon cancer cells remains elusive. In this work we determined the effect of SHP2 expression on colon cancer cell proliferation and resistance to oxaliplatin (L-OHP), a commonly used drug in the clinic. Our results show that knockdown of SHP2 decreased and overexpression of SHP2 increased the proliferation of SW480 cells, respectively. Knockdown of SHP2 increased, and overexpression of SHP2 decreased apoptosis of the cells. We selected oxaliplatin-resistant SW480(SW480/L-OHP) and HCT116(HCT116/L-OHP) cells and found that the SHP2 protein level was raised in these drug-resistant cells. The upregulated SHP2 contributed to oxaliplatin resistance of the cells, as knockdown of SHP2 decreased the IC50 of oxaliplatin and abated proliferation and survival of SW480/L-OHP and HCT116/L-OHP cells in the presence of oxaliplatin. Also, SW480/L-OHP and HCT116/L-OHP cells had increased phosphorylation of AKT and ERK. Inhibition of AKT, ERK, or SHP2 sensitized SW480/L-OHP and HCT116/L-OHP cells to oxaliplatin. Our results indicate that SHP2 contributes oxaliplatin resistance through AKT and ERK. These results also suggest that SHP2-targeting is a potential strategy for overcoming oxaliplatin resistance of colon cancer cells.

Computational and Experimental Approaches to Decipher the Binding Mechanism of General Odorant-Binding Protein 2 from Athetis lepigone to Chlorpyrifos and Phoxim.

Insect resistance to insecticides is an increasingly serious problem, and the resistant mechanisms are complicated. The resistance research based on the chemosensory pathway is one of the hot problems at present, but the specific binding mechanism of chemosensory genes and insecticides remains elusive. The binding mechanism of AlepGOBP2 (belong to insect chemosensory gene) with two insecticides was investigated by computational and experimental approaches. Our calculation results indicated that four key residues (Phe12, Ile52, Ile94, and Phe118) could steadily interact with these two insecticides and be assigned as hotspot sites responsible for their binding affinities. The significant alkyl-π and hydrophobic interactions involved by these four hotspot residues were found to be the driving forces for their binding affinities, especially for two residues (Phe12 and Ile94) that significantly contribute to the binding of chlorpyrifos, which were also validated by our binding assay results. Furthermore, we also found that the AlepGOBP2-chlorpyrifos/phoxim complexes can be more efficiently converged in the residue-specific force field-(RSFF2C) and its higher accuracy and repeatability in protein dynamics simulation, per-residue free energy decomposition, and computational alanine scanning calculations have also been achieved in this paper. These findings provided useful insights for efficient and reliable calculation of the binding mechanism of relevant AlepGOBPs with other insecticides, facilitating to develop new and efficient insecticides targeting the key sites of AlepGOBP2.

Combining a Pharmacological Network Model with a Bayesian Signal Detection Algorithm to Improve the Detection of Adverse Drug Events.

Introduction: Improving adverse drug event (ADE) detection is important for post-marketing drug safety surveillance. Existing statistical approaches can be further optimized owing to their high efficiency and low cost. Objective: The objective of this study was to evaluate the proposed approach for use in pharmacovigilance, the early detection of potential ADEs, and the improvement of drug safety. Methods: We developed a novel integrated approach, the Bayesian signal detection algorithm, based on the pharmacological network model (ICPNM) using the FDA Adverse Event Reporting System (FAERS) data published from 2004 to 2009 and from 2014 to 2019Q2, PubChem, and DrugBank database. First, we used a pharmacological network model to generate the probabilities for drug-ADE associations, which comprised the proper prior information component (IC). We then defined the probability of the propensity score adjustment based on a logistic regression model to control for the confounding bias. Finally, we chose the Side Effect Resource (SIDER) and the Observational Medical Outcomes Partnership (OMOP) data to evaluate the detection performance and robustness of the ICPNM compared with the statistical approaches [disproportionality analysis (DPA)] by using the area under the receiver operator characteristics curve (AUC) and Youden's index. Results: Of the statistical approaches implemented, the ICPNM showed the best performance (AUC, 0.8291; Youden's index, 0.5836). Meanwhile, the AUCs of the IC, EBGM, ROR, and PRR were 0.7343, 0.7231, 0.6828, and 0.6721, respectively. Conclusion: The proposed ICPNM combined the strengths of the pharmacological network model and the Bayesian signal detection algorithm and performed better in detecting true drug-ADE associations. It also detected newer ADE signals than a DPA and may be complementary to the existing statistical approaches.

CXCL14 inhibits the growth and promotes apoptosis of hepatocellular carcinoma cells via suppressing Akt/mTOR pathway.

C-X-C motif chemokine ligand 14 (CXCL14) has antitumor effect. Kinase B (Akt)/mammalian target of rapamycin (mTOR) pathway is activated in various tumors. The relationship between CXCL14 and Akt/mTOR pathway in hepatocellular carcinoma (HCC) remained elusive. Therefore, this paper aimed to examine their interaction in HCC. First, CXCL14 expression was determined to be low-expressed in HCC tissues and cells (SNU-423, SNU-182, SNU-387, PLC/PRF/5, HuH7, and HCCLM3). Then, CXCL14 was overexpressed in HuH7 cells and inhibited in HCCLM3 cells to help investigate the function of CXCL14 on cell viability, growth and apoptosis. Akt activator (SC79) and inhibitor (AZD5363) were used to examine the involvement of Akt pathway in hepatocellular carcinoma. Overexpressed CXCL14 suppressed cell viability and growth, but promoted the apoptosis by upregulated Bax and cleaved(C) caspase-3, donwregulated Bcl-2 and the inhibition of Akt and mTOR phosphorylation. Meanwhile, knockdown of CXCL14 imposed an opposite effect to overexpressed CXCL14. SC79 partially mitigated the functions of overexpressed CXCL14, while AZD5363 mitigated the functions of CXCL14 knockdown. To conclude, CXCL14 inhibited growth but promoted apoptosis of HCC cells via suppressing Akt/mTOR pathway, thus, CXCL14 might be a potential target for HCC treatment in clinical practice.

Prediction of RNA secondary structure based on stem region replacement using the RSRNA algorithm.

RNA functions, including the regulation of various cellular activities, seem to be closely related to its structure. However, accurately predicting RNA secondary structures can be difficult. Structural prediction can be achieved by selecting stem areas that are suitable and compatible from stem pools. Here, we propose a method for predicting the secondary structure of non-coding RNA based on stem region substitution, which we named RSRNA. This method is compatible with nested RNA secondary structures, while reducing any randomness. Our algorithm had higher performance and prediction accuracy than other algorithms, which deems it more effective for future RNA structure studies.

DSARna: RNA Secondary Structure Alignment Based on Digital Sequence Representation.

BACKGROUND: With increasing applications and development of high-throughput sequencing, knowledge of the primary structure of RNA has expanded exponentially. Moreover, the function of RNA is determined by the secondary or higher RNA structure, and similar structures are related to similar functions, such as the secondary clover structure of tRNA. Therefore, RNA structure alignment is an important subject in computational biology and bioinformatics to predict function accurately. However, the traditional RNA structure alignment algorithms have some drawbacks such as high complexity and easy loss of secondary structure information. OBJECTIVE: To study R,,NA secondary structure alignment according to the shortcomings of existing secondary structure alignment algorithms and the characteristics of RNA secondary structure. METHODS: We propose a new digital sequence RNA structure representation algorithm named "DSARna". Then based on a dynamic programming algorithm, the scoring matrix and binary path matrix are simultaneously constructed. The backtracking path is identified in the path matrix, and the optimal result is predicted according to the path length. CONCLUSIONS: Upon comparison with the existing SimTree algorithm through experimental analysis, the proposed method showed higher accuracy and could ensure that the structural information is not easily lost in terms of improved specificity, sensitivity, and the Matthews correlation coefficient.

miR-21 promotes proliferation and inhibits apoptosis of hepatic stellate cells through targeting PTEN/PI3K/AKT pathway.

To investigate the effect of microRNA 21 (miR-21) on hepatic stellate cells (HSCs) proliferation and apoptosis, and further to study its potential mechanisms. LX-2 cells were divided into miR-21 mimic group (Mimic), miR-21 mimic negative control group (NM), miR-21 inhibitor group (Inhibitor), miR-21 inhibitor negative control group (NC), and blank control group (Control). The cell proliferation was detected by CCK-8 assay and the cell migration and invasion were detected by scratch and transwell assay. Cell cycle and apoptosis were detected by flow cytometry. The levels of interleukin (IL)-6, tumor necrosis factor (TNF)-α, and transforming growth factor (TGF)-ß1 were detected by enzyme-linked immunosorbent assay (ELISA). Proliferation, apoptosis, and phosphatase and tensin homolog (PTEN)/phosphoinositide 3-kinase (PI3K)/protein kinase B (AKT) signaling pathway related genes and proteins were detected by quantitative real-time polymerase chain reaction (qRT-PCR) and western blot, respectively. The cells proliferation, migration, and invasion were promoted in Mimic group. The levels of IL-6, TNF-α, and TGF-ß1 were increased after miR-21 administration. The expression of α-smooth muscle actin (SMA) and collagen 1 (Colla1) were increased, while Bax/B-cell lymphoma (Bcl)-2 ratio and programed cell death 4 (PDCD4) were reduced after miR­21 treatment. Meanwhile, the mRNA and protein expression of PTEN were reduced and PI3K/AKT pathway been promoted. Our study demonstrated that miR-21 could promote proliferation and inhibit apoptosis of HSCs, and its mechanism may be related to PTEN/PI3K/AKT pathway.

RGRNA: prediction of RNA secondary structure based on replacement and growth of stems.

Owing to their structural diversity, RNAs perform many diverse biological functions in the cell. RNA secondary structure is thus important for predicting RNA function. Here, we propose a new combinatorial optimization algorithm, named RGRNA, to improve the accuracy of predicting RNA secondary structure. Following the establishment of a stempool, the stems are sorted by length, and chosen from largest to smallest. If the stem selected is the true stem, the secondary structure of this stem when combined with another stem selected at random will have low free energy, and the free energy will tend to gradually diminish. The free energy is considered as a parameter and the structure is converted into binary numbers to determine stem compatibility, for step-by-step prediction of the secondary structure for all combinations of stems. The RNA secondary structure can be predicted by the RGRNA method. Our experimental results show that the proposed algorithm outperforms RNAfold in terms of sensitivity, specificity, and Matthews correlation coefficient value.

Identification of rifampin-regulated functional modules and related microRNAs in human hepatocytes based on the protein interaction network.

BACKGROUND: In combination with gene expression profiles, the protein interaction network (PIN) constructs a dynamic network that includes multiple functional modules. Previous studies have demonstrated that rifampin can influence drug metabolism by regulating drug-metabolizing enzymes, transporters, and microRNAs (miRNAs). Rifampin induces gene expression, at least in part, by activating the pregnane X receptor (PXR), which induces gene expression; however, the impact of rifampin on global gene regulation has not been examined under the molecular network frameworks. METHODS: In this study, we extracted rifampin-induced significant differentially expressed genes (SDG) based on the gene expression profile. By integrating the SDG and human protein interaction network (HPIN), we constructed the rifampin-regulated protein interaction network (RrPIN). Based on gene expression measurements, we extracted a subnetwork that showed enriched changes in molecular activity. Using the Kyoto Encyclopedia of Genes and Genomes (KEGG), we identified the crucial rifampin-regulated biological pathways and associated genes. In addition, genes targeted by miRNAs that were significantly differentially expressed in the miRNA expression profile were extracted based on the miRNA-gene prediction tools. The miRNA-regulated PIN was further constructed using associated genes and miRNAs. For each miRNA, we further evaluated the potential impact by the gene interaction network using pathway analysis. RESULTS AND DISCCUSSION: We extracted the functional modules, which included 84 genes and 89 interactions, from the RrPIN, and identified 19 key rifampin-response genes that are associated with seven function pathways that include drug response and metabolism, and cancer pathways; many of the pathways were supported by previous studies. In addition, we identified that a set of 6 genes (CAV1, CREBBP, SMAD3, TRAF2, KBKG, and THBS1) functioning as gene hubs in the subnetworks that are regulated by rifampin. It is also suggested that 12 differentially expressed miRNAs were associated with 6 biological pathways. CONCLUSIONS: Our results suggest that rifampin contributes to changes in the expression of genes by regulating key molecules in the protein interaction networks. This study offers valuable insights into rifampin-induced biological mechanisms at the level of miRNAs, genes and proteins.

PSRna: Prediction of small RNA secondary structures based on reverse complementary folding method.

Prediction of RNA secondary structures is an important problem in computational biology and bioinformatics, since RNA secondary structures are fundamental for functional analysis of RNA molecules. However, small RNA secondary structures are scarce and few algorithms have been specifically designed for predicting the secondary structures of small RNAs. Here we propose an algorithm named "PSRna" for predicting small-RNA secondary structures using reverse complementary folding and characteristic hairpin loops of small RNAs. Unlike traditional algorithms that usually generate multi-branch loops and 5[Formula: see text] end self-folding, PSRna first estimated the maximum number of base pairs of RNA secondary structures based on the dynamic programming algorithm and a path matrix is constructed at the same time. Second, the backtracking paths are extracted from the path matrix based on backtracking algorithm, and each backtracking path represents a secondary structure. To improve accuracy, the predicted RNA secondary structures are filtered based on their free energy, where only the secondary structure with the minimum free energy was identified as the candidate secondary structure. Our experiments on real data show that the proposed algorithm is superior to two popular methods, RNAfold and RNAstructure, in terms of sensitivity, specificity and Matthews correlation coefficient (MCC).

MatPred: Computational Identification of Mature MicroRNAs within Novel Pre-MicroRNAs.

BACKGROUND: MicroRNAs (miRNAs) are short noncoding RNAs integral for regulating gene expression at the posttranscriptional level. However, experimental methods often fall short in finding miRNAs expressed at low levels or in specific tissues. While several computational methods have been developed for predicting the localization of mature miRNAs within the precursor transcript, the prediction accuracy requires significant improvement. METHODOLOGY/PRINCIPAL FINDINGS: Here, we present MatPred, which predicts mature miRNA candidates within novel pre-miRNA transcripts. In addition to the relative locus of the mature miRNA within the pre-miRNA hairpin loop and minimum free energy, we innovatively integrated features that describe the nucleotide-specific RNA secondary structure characteristics. In total, 94 features were extracted from the mature miRNA loci and flanking regions. The model was trained based on a radial basis function kernel/support vector machine (RBF/SVM). Our method can predict precise locations of mature miRNAs, as affirmed by experimentally verified human pre-miRNAs or pre-miRNAs candidates, thus achieving a significant advantage over existing methods. CONCLUSIONS: MatPred is a highly effective method for identifying mature miRNAs within novel pre-miRNA transcripts. Our model significantly outperformed three other widely used existing methods. Such processing prediction methods may provide important insight into miRNA biogenesis.

5-(4-Meth-ylphenyl)-1,3,4-thia-diazol-2-amine.

The title compound, C(9)H(9)N(3)S, was synthesized by the reaction of 4-methyl-benzoic acid and thio-semicarbazide. The thia-diazol ring adopts a planar conformation and makes a dihedral angle of 31.19 (18)° with the phenyl ring. In the crystal, mol-ecules are linked by N-H⋯N hydrogen bonds.