The association between dietary vitamin B1 intake and constipation: a population-based study.

BACKGROUND: Numerous researches have indicated a correlation between the intake of dietary micronutrients and the occurrence of constipation. Nevertheless, the correlation between constipation and vitamin B1 remains uninvestigated. The main aim of this research was to examine the association between chronic constipation and the consumption of vitamin B1 in the diet among adult participants of the National Health and Nutrition Examination Survey (NHANES). METHODS: This study used data from the NHANES, a survey on health and nutrition conducted between 2005 and 2010. The respondents' dietary information was gathered by utilizing the 24-hour dietary records. Various statistical analyses, such as multiple logistic regression, subgroup analysis, and curve-fitting analysis, were employed to investigate the correlation between dietary intake of vitamin B1 and chronic constipation. RESULTS: In the trial, there were 10,371 participants, out of which 1,123 individuals (10.8%) were identified as having chronic constipation. Fully adjusted multiple logistic regression analyses showed that increasing dietary intake of vitamin B1 (OR = 0.87, 95% CI: 0.77-0.99) was significantly associated with a reduced risk of constipation. Following adjustment for multiple variables in Model 3, the odds ratio (OR) and 95% confidence interval (CI) for the third tertile, in comparison to the first tertile (reference group), was 0.80 (0.65, 0.99). In addition, subgroup analyses and interaction tests showed a significant inverse association between vitamin B1 intake and the prevalence of constipation, especially among men, non-hypertensive, and non-diabetic individuals (all P-values less than 0.05). CONCLUSION: This research uncovered an inverse correlation between the consumption of vitamin B1 in the diet and the occurrence of chronic constipation. One potential explanation for this phenomenon is that the consumption of vitamin B1 in one's diet is linked to the softening of stools and an augmented occurrence of colonic peristalsis. Additional extensive prospective research is required to thoroughly examine the significance of thiamine in long-term constipation.

Association between dietary magnesium intake and gallstones: the mediating role of atherogenic index of plasma.

BACKGROUND: Dyslipidemia and abnormalities in cholesterol metabolism are commonly observed in individuals with gallstone disease. Previous research has demonstrated that dietary magnesium can influence lipid metabolism. The atherogenic index of plasma (AIP) has emerged as a novel lipid marker. This study aimed to examine the possible correlation between dietary magnesium intake and gallstones and the potential mediating role of AIP in US adults. METHODS: A total of 4,841 adults were included in this study from the National Health and Nutrition Examination Survey (NHANES) conducted from 2017 to 2020. A variety of statistical techniques such as logistic regression, subgroup analysis, smoothed curve fitting, and causal mediation analysis were utilized to analyze the information collected from the participants. RESULTS: In the fully adjusted model, a statistically noteworthy inverse relationship was observed between dietary magnesium intake and the presence of gallstones, as indicated by an odds ratio (OR) of 0.58 and a 95% confidence interval (CI) of (0.42, 0.81). Causal intermediary analysis revealed that the association between magnesium intake and gallstones was partially mediated by AIP, with a mediation ratio of 3.2%. CONCLUSION: According to this study, dietary magnesium intake had a significant linear negative association with the prevalence of gallstones, in which AIP played a mediating role. This discovery offers novel perspectives on the prevention and management of gallstones.

Diabetes Mellitus Mediates the Relationship Between Atherogenic Index of Plasma and Gallstones: A Population-Based Cross-Sectional Study.

Purpose: Previous studies have shown a correlation between diabetes mellitus and gallstone formation. The atherogenic index of plasma (AIP) is associated with many metabolic diseases. However, insufficient evidence still exists to elucidate the association between AIP and gallstones. The primary objective of this study was to investigate the correlation between AIP and gallstones in US adults, and the secondary objective was to analyze whether diabetes plays a mediating role in the association. Patients and Methods: Using data from the National Health and Nutrition Survey (NHANES) conducted between 2017 and March 2020, this study investigated the association between AIP and gallstone incidence in US adults. A variety of statistical methods were used to analyze the data in this study, including multivariate logistic regression, subgroup analyses, restricted cubic spline curves (RCS), and mediation effects analysis. In addition, two-stage linear regression was used to detect possible threshold and saturation effects. Results: A total of 6952 subjects were enrolled in the trial, of which 748 patients were diagnosed with gallstones. A significant positive association between AIP and gallstones was observed by fully adjusted multivariate logistic regression analysis, with an odds ratio (OR) of 1.45 and a 95% confidence interval (CI) of (1.09, 1.93). In addition, a non-linear positive association and saturation effect between AIP and gallstones were found, with an inflection point of 0.2246. Mediation analysis showed that diabetes had a mediating effect of 16.9% in the association between AIP and gallstones. Conclusion: This study suggests that elevated levels of AIP are linked to an augmented vulnerability to gallstone development, with diabetes serving as a mediating factor. These findings present a novel perspective on clinical approaches to prevent and manage gallstones.

Identification of high-risk factors for recurrence of colon cancer following complete mesocolic excision: An 8-year retrospective study.

BACKGROUND: Colon cancer recurrence is a common adverse outcome for patients after complete mesocolic excision (CME) and greatly affects the near-term and long-term prognosis of patients. This study aimed to develop a machine learning model that can identify high-risk factors before, during, and after surgery, and predict the occurrence of postoperative colon cancer recurrence. METHODS: The study included 1187 patients with colon cancer, including 110 patients who had recurrent colon cancer. The researchers collected 44 characteristic variables, including patient demographic characteristics, basic medical history, preoperative examination information, type of surgery, and intraoperative information. Four machine learning algorithms, namely extreme gradient boosting (XGBoost), random forest (RF), support vector machine (SVM), and k-nearest neighbor algorithm (KNN), were used to construct the model. The researchers evaluated the model using the k-fold cross-validation method, ROC curve, calibration curve, decision curve analysis (DCA), and external validation. RESULTS: Among the four prediction models, the XGBoost algorithm performed the best. The ROC curve results showed that the AUC value of XGBoost was 0.962 in the training set and 0.952 in the validation set, indicating high prediction accuracy. The XGBoost model was stable during internal validation using the k-fold cross-validation method. The calibration curve demonstrated high predictive ability of the XGBoost model. The DCA curve showed that patients who received interventional treatment had a higher benefit rate under the XGBoost model. The external validation set's AUC value was 0.91, indicating good extrapolation of the XGBoost prediction model. CONCLUSION: The XGBoost machine learning algorithm-based prediction model for colon cancer recurrence has high prediction accuracy and clinical utility.

Identification of high-risk factors associated with mortality at 1-, 3-, and 5-year intervals in gastric cancer patients undergoing radical surgery and immunotherapy: an 8-year multicenter retrospective analysis.

Background: Combining immunotherapy with surgical intervention is a prevailing and radical therapeutic strategy for individuals afflicted with gastric carcinoma; nonetheless, certain patients exhibit unfavorable prognoses even subsequent to this treatment regimen. This research endeavors to devise a machine learning algorithm to recognize risk factors with a high probability of inducing mortality among patients diagnosed with gastric cancer, both prior to and during their course of treatment. Methods: Within the purview of this investigation, a cohort of 1015 individuals with gastric cancer were incorporated, and 39 variables encompassing diverse features were recorded. To construct the models, we employed three distinct machine learning algorithms, specifically extreme gradient boosting (XGBoost), random forest (RF), and k-nearest neighbor algorithm (KNN). The models were subjected to internal validation through employment of the k-fold cross-validation technique, and subsequently, an external dataset was utilized to externally validate the models. Results: In comparison to other machine learning algorithms employed, the XGBoost algorithm demonstrated superior predictive capacity regarding the risk factors that affect mortality after combination therapy in gastric cancer patients for a duration of one year, three years, and five years posttreatment. The common risk factors that significantly impacted patient survival during the aforementioned time intervals were identified as advanced age, tumor invasion, tumor lymph node metastasis, tumor peripheral nerve invasion (PNI), multiple tumors, tumor size, carcinoembryonic antigen (CEA) level, carbohydrate antigen 125 (CA125) level, carbohydrate antigen 72-4 (CA72-4) level, and H. pylori infection. Conclusion: The XGBoost algorithm can assist clinicians in identifying pivotal prognostic factors that are of clinical significance and can contribute toward individualized patient monitoring and management.

Applying interpretable machine learning algorithms to predict risk factors for permanent stoma in patients after TME.

Objective: The purpose of this study was to develop a machine learning model to identify preoperative and intraoperative high-risk factors and to predict the occurrence of permanent stoma in patients after total mesorectal excision (TME). Methods: A total of 1,163 patients with rectal cancer were included in the study, including 142 patients with permanent stoma. We collected 24 characteristic variables, including patient demographic characteristics, basic medical history, preoperative examination characteristics, type of surgery, and intraoperative information. Four machine learning algorithms including extreme gradient boosting (XGBoost), random forest (RF), support vector machine (SVM) and k-nearest neighbor algorithm (KNN) were applied to construct the model and evaluate the model using k-fold cross validation method, ROC curve, calibration curve, decision curve analysis (DCA) and external validation. Results: The XGBoost algorithm showed the best performance among the four prediction models. The ROC curve results showed that XGBoost had a high predictive accuracy with an AUC value of 0.987 in the training set and 0.963 in the validation set. The k-fold cross-validation method was used for internal validation, and the XGBoost model was stable. The calibration curves showed high predictive power of the XGBoost model. DCA curves showed higher benefit rates for patients who received interventional treatment under the XGBoost model. The AUC value for the external validation set was 0.89, indicating that the XGBoost prediction model has good extrapolation. Conclusion: The prediction model for permanent stoma in patients with rectal cancer derived from the XGBoost machine learning algorithm in this study has high prediction accuracy and clinical utility.

Uncertain Dynamic Characteristic Analysis for Structures with Spatially Dependent Random System Parameters.

This work presents a robust non-deterministic free vibration analysis for engineering structures with random field parameters in the frame of stochastic finite element method. For this, considering the randomness and spatial correlation of structural physical parameters, a parameter setting model based on random field theory is proposed to represent the random uncertainty of parameters, and the stochastic dynamic characteristics of different structural systems are then analyzed by incorporating the presented parameter setting model with finite element method. First, Gauss random field theory is used to describe the uncertainty of structural material parameters, the random parameters are then characterized as the standard deviation and correlation length of the random field, and the random field parameters are then discretized with the Karhunen-Loeve expansion method. Moreover, based on the discretized random parameters and finite element method, structural dynamic characteristics analysis is addressed, and the probability distribution density function of the random natural frequency is estimated based on multi-dimensional kernel density estimation method. Finally, the random field parameters of the structures are quantified by using the maximum likelihood estimation method to verify the effectiveness of the proposed method and the applicability of the constructed model. The results indicate that (1) for the perspective of maximum likelihood estimation, the parameter setting at the maximum value point is highly similar to the input parameters; (2) the random field considering more parameters reflects a more realistic structure.

Myosin head as the main off-odors binding region: Key binding residues and conformational changes in the binding process.

This study aims to elucidate the dynamic binding characteristics between off-odors (hexanal, 1-octen-3-ol, nonanal) and myosin at cold storage (277 K) and oral temperatures (310 K) through spatial-temporal molecular dynamics (MD) simulation. Conformational analysis indicated that binding with off-odors could not significantly change the myosin secondary structure, and the myosin/(off-odors) structure became a stable and compact state in the later binding stages. Myosin head was the primary binding region with hydrophobic interactions as the dominant force rather than hydrogen bonds (average bond number 202.62 vs 55.20). Furthermore, the myosin/(off-odors) had larger binding energy at 310 than 277 K, and the myosin-nonanal showed the highest binding strength of 4050.93 kJ/mol at 310 K. The binding sites were observed to be concentrated in the 180-249, 350-410, 800-950 amino acid regions of myosin head, especially, Lys185, Tyr347, Leu901. These results provide accurate linkages between off-odors and myosin, laying a theoretical foundation for deodorization of fish products.

Operation decision of competitive mining supply chain based on social responsibility.

The development of the green economy has significantly impact the traditional mining industry. Mining enterprises must invest in green technology to reduce the environmental pollution caused by flying dust and soil erosion and are subject to increased scrutiny to be socially responsible when conducting their business. To address this issue, we consider a competitive mining supply chain system consisting of two excavators and two exclusive retailers. Among them, the excavators have a certain sense of corporate social responsibility (CSR), that is, in addition to pursuing economic profits, they also consciously pay attention to the interests of consumers. We establish three different game models that two excavators exhibit no CSR behaviour (NN), two excavators exhibit CSR behaviour (SS) and one excavator exhibits CSR behaviour (SN). We examine the optimal decision-making strategies and analyse the impact of social responsibility. Analytical results show that the optimal strategies of mining supply chain are different under different supply chain structures. The optimal decisions of the mining supply chain members are the same in each case under the NN and SS models. In the SN model, the optimal decision strategy value of mining supply chain members is always greater than non-socially responsible supply chain members. In SS model, when the intensity of social responsibility competition is low, two excavators reduce the wholesale price, and retailers reduce the sales price; when the intensity of social responsibility competition is strong, two excavators will increase the wholesale price, and retailers will increase the sales price. These help to promote product sales and increase the profits of the supply chain system. In SN model, with the increase of social responsibility competition intensity, the wholesale price of two excavators and the sales price of retailers first increased and then decreased. Finally, numerical examples illustrated to justify the proposed model.

Improvement of methanol tolerance and catalytic activity of Rhizomucor miehei lipase for one-step synthesis of biodiesel by semi-rational design.

Exploiting highly active and methanol-resistant lipase is of great significance for biodiesel production. A semi-rational directed evolution method combined with N-glycosylation is reported, and all mutants exhibiting higher catalytic activity and methanol tolerance than the wild type (WT). Mutant N267 retained 64% activity after incubation in 50% methanol for 8 h, which was 48% greater than that of WT. The catalytic activity of mutants N267 and N167 was 30- and 71- fold higher than that of WT. Molecular dynamics simulations of N267 showed that the formation of new strong hydrogen bonds between glycan and the protein stabilized the structure of lipase and improved its methanol tolerance. N267 achieved biodiesel yields of 99.33% (colza oil) and 81.70% (waste soybean oil) for 24 h, which was much higher than WT (51.6% for rapeseed oil and 44.73% for wasted soybean oil). The engineered ProRML mutant has high potential for commercial biodiesel production.

A New Approach for Fatigue Reliability Analysis of Thin-Walled Structures with DC-ILSSVR.

Fatigue analysis is of great significance for thin-walled structures in the spacecraft industry to ensure their service reliability during operation. Due to the complex loadings of thin-walled structures under thermal-structural-acoustic coupling conditions, the calculation cost of finite element (FE) simulations is relatively expensive. To improve the computational efficiency of dynamic reliability analysis on thin-walled structures to within acceptable accuracy, a novel probabilistic approach named DC-ILSSVR was developed, in which the rotation matrix optimization (RMO) method was used to initially search for the model parameters of least squares support vector regression (LS-SVR). The distributed collaborative (DC) strategy was then introduced to enhance the efficiency of a component suffering from multiple failure modes. Moreover, a numerical example with respect to thin-walled structures was used to validate the proposed method. The results showed that RMO performed on LS-SVR model parameters provided competitive prediction accuracy, and hence the reliability analysis efficiency of thin-walled pipe was significantly improved.

Arginine methylation of METTL14 promotes RNA N6-methyladenosine modification and endoderm differentiation of mouse embryonic stem cells.

RNA N6-methyladenosine (m6A), the most abundant internal modification of mRNAs, plays key roles in human development and health. Post-translational methylation of proteins is often critical for the dynamic regulation of enzymatic activity. However, the role of methylation of the core methyltransferase METTL3/METTL14 in m6A regulation remains elusive. We find by mass spectrometry that METTL14 arginine 255 (R255) is methylated (R255me). Global mRNA m6A levels are greatly decreased in METTL14 R255K mutant mouse embryonic stem cells (mESCs). We further find that R255me greatly enhances the interaction of METTL3/METTL14 with WTAP and promotes the binding of the complex to substrate RNA. We show that protein arginine N-methyltransferases 1 (PRMT1) interacts with and methylates METTL14 at R255, and consistent with this, loss of PRMT1 reduces mRNA m6A modification globally. Lastly, we find that loss of R255me preferentially affects endoderm differentiation in mESCs. Collectively, our findings show that arginine methylation of METTL14 stabilizes the binding of the m6A methyltransferase complex to its substrate RNA, thereby promoting global m6A modification and mESC endoderm differentiation. This work highlights the crosstalk between protein methylation and RNA methylation in gene expression.

Structural insights into acyl-ACP selective recognition by the Aeromonas hydrophila AHL synthase AhyI.

BACKGROUND: Aeromonas hydrophila is a gram-negative bacterium and the major causative agent of the fish disease motile aeromonad septicemia (MAS). It uses N-acyl-homoserine lactone (AHL) quorum sensing signals to coordinate biofilm formation, motility, and virulence gene expression. The AHL signaling pathway is therefore considered to be a therapeutic target against pathogenic A. hydrophila infection. In A. hydrophila, AHL autoinducers biosynthesis are specifically catalyzed by an ACP-dependent AHL synthase AhyI using the precursors SAM and acyl-ACP. Our previously reported AhyI was heterologously expressed in E. coli, which showed the production characteristics of medium-long chain AHLs. This contradicted the prevailing understanding that AhyI was only a short-chain C4/C6-HSL synthase. RESULTS: In this study, six linear acyl-ACP proteins with C-terminal his-tags were synthesized in Vibrio harveyi AasS using fatty acids and E. coli produced active holo-ACP proteins, and in vitro biosynthetic assays of six AHL molecules and kinetic studies of recombinant AhyI with a panel of four linear acyl-ACPs were performed. UPLC-MS/MS analyses indicated that AhyI can synthesize short-, medium- and long-chain AHLs from SAM and corresponding linear acyl-ACP substrates. Kinetic parameters measured using a DCPIP colorimetric assay, showed that there was a notable decrease in catalytic efficiency with acyl-chain lengths above C6, and hyperbolic or sigmoidal responses in rate curves were observed for varying acyl-donor substrates. Primary sequence alignment of the six representative AHL synthases offers insights into the structural basis for their specific acyl substrate preference. To further understand the acyl chain length preference of AhyI for linear acyl-ACP, we performed a structural comparison of three ACP-dependent LuxI homologs (TofI, BmaI1 and AhyI) and identified three key hydrophobic residues (I67, F125 and L157) which confer AhyI to selectively recognize native C4/C6-ACP substrates. These predictions were further supported by a computational Ala mutation assay. CONCLUSIONS: In this study, we have redefined AhyI as a multiple short- to long-chain AHL synthase which uses C4/C6-ACP as native acyl substrates and longer acyl-ACPs (C8 ~ C14) as non-native ones. We also theorized that the key residues in AhyI would likely drive acyl-ACP selective recognition.

Green Supply Chain Decisions Under Different Power Structures: Wholesale Price vs. Revenue Sharing Contract.

In the market, once consumers have a low-carbon preference, they will choose green low-carbon products. The market demand for green products is not only related to product price, but also consumers' low-carbon preference. In this way, enterprise has to consider the cost of carbon emissions in the process of production and operation. In this paper, we consider a two-level supply chain system composed of a manufacturer and a retailer. The supply chain system can determine the price of products and the level of carbon emission reduction through different supply chain contracts: wholesale price contract and revenue sharing contract. However, the power control structure of a manufacturer and a retailer is different, which will further affect the decision-making strategy of the supply chain system. We set up four models (Wholesale Price-NM and NR, and Revenue-Sharing-SR and SM) of the supply chain with carbon emission reduction, and calculated and analyzed. The results show that firstly, regardless of whether the manufacturer's power control structure or the retailer power structure is dominant, the manufacturer wholesale price with a contract on revenue-sharing is always higher than on wholesale price, and it is inversely proportional to the revenue-sharing proportion. Secondly, under the two power control structures, the carbon emission level of the manufacturer with a contract on revenue-sharing is always lower than on wholesale price, and it gradually decreases with the increase of the revenue-sharing proportion of the manufacturers. Thirdly, when the retailer dominates the supply chain, the retailer selling price with a contract on revenue-sharing is always higher than on wholesale price. Under the manufacturer's power control structure, when the revenue-sharing ratio is small, the retailer selling price with a contract on revenue-sharing is higher than on wholesale price; when the revenue-sharing ratio is large, the retailer selling price with a contract on revenue-sharing is lower than on wholesale price. Finally, the validity of the model is verified by an example, and the sensitivity of the parameters is analyzed.

Identification of a Novel N-Acyl Homoserine Lactone Synthase, AhyI, in Aeromonas hydrophila and Structural Basis for Its Substrate Specificity.

In the Gram-negative bacterium Aeromonas hydrophila, N-acyl homoserine lactone (AHL)-mediated quorum sensing (QS) influences pathogenicity, protein secretion, and motility. However, the catalytic mechanism of AHL biosynthesis and the structural basis and substrate specificity for AhyI members remain unclear. In this study, we cloned the ahyI gene from the isolate A. hydrophila HX-3, and the overexpressed AhyI protein was confirmed to produce six types of AHLs by ultraperformance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) analysis, contrasting with previous reports that AhyI only produces N-butanoyl-l-homoserine lactone (C4-HSL) and N-hexanoyl-l-homoserine lactone (C6-HSL). The results of an in vitro biosynthetic assay showed that purified AhyI can catalyze the formation of C4-HSL using S-adenosyl-l-methionine (SAM) and butyryl-acyl carrier protein (ACP) as substrates and indicated that the fatty acyl substrate used in AhyI-mediated AHL synthesis is derived from acyl-ACP rather than acyl-CoA. The kinetic data of AhyI using butyryl-ACP as an acyl substrate indicated that the catalytic efficiency of the A. hydrophila HX-3 AhyI enzyme is within an order of magnitude compared to other LuxI homologues. In this study, for the first time, the tertiary structural modeling results of AhyI and those of molecular docking and structural and functional analyses showed the importance of several crucial residues, as well as the secondary structure with respect to acylation. A Phe125-Phe152 clamp grasps the terminal methyl group to assist in stabilizing the long acyl chains in a putative binding pocket. The stacking interactions within a strong hydrophobic environment, a hydrogen-bonding network, and a ß bulge presumably stabilize the ACP acyl chain for the attack of the SAM α-amine toward the thioester carbon, offering a relatively reasonable explanation for how AhyI can synthesize AHLs with diverse acyl-chain lengths. Moreover, Trp34 participates in forming the binding pocket for C4-ACP and becomes ordered upon SAM binding, providing a good basis for catalysis. The novel finding that AhyI can produce both short- and long-chain AHLs enhances current knowledge regarding the variety of AHLs produced by this enzyme. These structural data are expected to serve as a molecular rationale for AHL synthesis by AhyI.

An effective HIV-1 integrase inhibitor screening platform: Rationality validation of drug screening, conformational mobility and molecular recognition analysis for PFV integrase complex with viral DNA.

As an important target for the development of novel anti-AIDS drugs, HIV-1 integrase (IN) has been widely concerned. However, the lack of a complete accurate crystal structure of HIV-1 IN greatly blocks the discovery of novel inhibitors. In this work, an effective HIV-1 IN inhibitor screening platform, namely PFV IN, was filtered from all species of INs. Next, the 40.8% similarity with HIV-1 IN, as well as the high efficiency of virtual screening and the good agreement between calculated binding free energies and experimental ones all proved PFV IN is a promising screening platform for HIV-1 IN inhibitors. Then, the molecular recognition mechanism of PFV IN by its substrate viral DNA and six naphthyridine derivatives (NRDs) inhibitors was investigated through molecular docking, molecular dynamics simulations and water-mediated interactions analyses. The functional partition of NRDs IN inhibitors could be divided into hydrophobic and hydrophilic ones, and the Mg2+ ions, water molecules and conserved DDE motif residues all interacted with the hydrophilic partition, while the bases in viral DNA and residues like Tyr212, Pro214 interacted with the hydrophobic one. Finally, the free energy landscape (FEL) and cluster analyses were performed to explore the molecular motion of PFV IN-DNA system. It is found that the association with NRDs inhibitors would obviously decrease the motion amplitude of PFV IN-DNA, which may be one of the most potential mechanisms of IN inhibitors. This work will provide a theoretical basis for the inhibitor design based on the structure of HIV-1 IN.

3D-QSAR, Molecular Docking and Molecular Dynamics Simulation of Pseudomonas aeruginosa LpxC Inhibitors.

As an important target for the development of novel antibiotics, UDP-3-O-(R-3-hydroxymyristoyl)-N-acetylglucosamine deacetylase (LpxC) has been widely studied. Pyridone methylsulfone hydroxamate (PMH) compounds can effectively inhibit the catalytic activity of LpxC. In this work, the three-dimensional quantitative structure-activity relationships of PMH inhibitors were explored and models with good predictive ability were established using comparative molecular field analysis and comparative molecular similarity index analysis methods. The effect of PMH inhibitors' electrostatic potential on the inhibitory ability of Pseudomonas aeruginosa LpxC (PaLpxC) is revealed at the molecular level via molecular electrostatic potential analyses. Then, two molecular dynamics simulations for the PaLpxC and PaLpxC-inhibitor systems were also performed respectively to investigate the key residues of PaLpxC hydrolase binding to water molecules. The results indicate that orderly alternative water molecules can form stable hydrogen bonds with M62, E77, T190, and H264 in the catalytic center, and tetracoordinate to zinc ion along with H78, H237, and D241. It was found that the conformational transition space of PaLpxC changes after association with PMH inhibitors through free energy landscape and cluster analyses. Finally, a possible inhibitory mechanism of PMH inhibitors was proposed, based on our molecular simulation. This paper will provide a theoretical basis for the molecular design of LpxC-targeting antibiotics.

Is adjunctive treatment with medication of liver-soothing-oriented method beneficial for depression after cerebrovascular accident?: A PRISMA-compliant meta-analysis.

BACKGROUND: Adjunctive treatment with medication of liver-soothing-oriented method (MLSM) is one of the most commonly used approaches for subjects with depression after cerebrovascular accident (DCVA) in China. The purpose of this meta-analysis was to evaluate the outcome of MLSM treatment in subjects with DCVA using relevant published literature. METHODS: The PubMed, Cochrane Library, Embase, Chinese databases of China National Knowledge Infrastructure, WanFang, Sinomed, and VIP were used to collect all publications until March 2016. Randomized controlled trials comparing treatments with and without MLSM for subjects with DCVA were included. The quality of each publication was assessed based on the recent Handbook (5.1 version) for Cochrane Reviewers. Cochrane Collaboration's software RevMan 5.3 software was applied for data analysis. RESULTS: Thirty studies, including 2599 cases, were identified and collected. Adjunctive treatment with MLSM noticeably enhanced total effective rates (odds ratio 3.76; 95% confidence interval [CI] 2.92-4.85, I = 0%, P = 0.96) in comparison to non-MLSM conventional pharmacotherapy. Compared to non-MLSM treatment, the changes of Hamilton Depression Scale in adjunctive treatment with MLSM, respectively, decreased and showed beneficial effects after 3 weeks (weighted mean difference [WMD] -4.83; 95% CI -6.82 to -2.83; I = 86%, P < 0.001), 4 weeks (WMD -4.20; 95% CI -5.06 to -3.33; I = 78%, P < 0.001), 6 weeks (WMD -3.36; 95% CI -4.05 to -2.68; I = 54%, P = 0.02), 8 weeks (WMD -4.83; 95% CI -5.62 to -4.04; I = 73%, P < 0.001), and 12 weeks (WMD -2.88; 95% CI -4.09 to -1.67; I = 58%, P = 0.09). As for changes in inflammatory cytokine levels, adjunctive treatment with MLSM was associated with a significant decrease in tumor necrosis factor-α, IL-6, and interleukin-1ß levels in comparison to non-MLSM treatment. Moreover, there were positive effects on score changes for National Institute of Health Stroke Scale, activities of daily living, Hamilton Anxiety Scale, Modified Edinburgh Scandinavian Stroke Scale, and Self-Rating Anxiety Scale. No serious adverse events were reported. CONCLUSION: MLSM appears to improve symptoms of depressive disorders, enhance immediate responses, and the quality of life in subjects with DCVA. The positive action of MLSM might be potentially connected with its immunoregulating effects. More prospective trials with strict design and larger sample sizes are warranted to clarify its effectiveness and safety.

[Molecular recognition mechanism and motion of HCV NS3/4A protease with Faldaprevir analogue].

Faldaprevir analogue molecule (FAM) has been reported to effectively inhibit the catalytic activity of HCV NS3/4A protease, making it a potential lead compound against HCV. A series of HCV NS3/4A protease crystal structures were analyzed by bioinformatics methods, and the FAM-HCV NS3/4A protease crystal structure was chosen for this study. A 20.4 ns molecular dynamics simulation of the complex consists of HCV NS3/4A protease and FAM was conducted. The key amino acid residues for interaction and the binding driving force for the molecular recognition between the protease and FAM were identified from the hydrogen bonds and binding free energy analyses. With the driving force of hydrogen bonds and van der Waals, FAM specifically bind to the active pocket of HCV NS3/4A protease, including V130-S137, F152-D166, D77-D79 and V55, which agreed with the experimental data. The effect of R155K, D168E/V and V170T site-directed mutagenesis on FAM molecular recognition was analyzed for their effect on drug resistance, which provided the possible molecular explanation of FAM resistance. Finally, the system conformational change was explored by using free energy landscape and conformational cluster. The result showed four kinds of dominant conformation, which provides theoretical basis for subsequent design of Faldaprevir analogue inhibitors based on the structure of HCV NS3/4A protease.