Signal-to-noise of linear and volume measures of left ventricular and left atrial size.

BACKGROUND: Serial echocardiographic assessments are common in clinical cardiology, e.g., for timing of intervention in mitral and aortic regurgitation. When following patients with serial echocardiograms, each new measurement is a combination of true change and confounding noise. The current investigation compares linear chamber dimensions with volume estimates of chamber size. The aim is to assess which measure is best for serial echocardiograms, when the ideal parameter will be sensitive to change in chamber size and have minimal spurious variation (noise). We present a method that disentangles true change from noise. Linear regression of chamber size against elapsed time gives a slope, being the ability of the method to detect change. Noise is the scatter of individual points away from the trendline, measured as the standard error of the slope. The higher the signal-to-noise ratio (SNR), the more reliably a parameter will distinguish true change from noise. METHODS: LV and LA parasternal dimensions and apical biplane volumes were obtained from serial clinical echocardiogram reports. Change over time was assessed as the slope of the linear regression line, and noise was assessed as the standard error of the regression slope. Signal-to-noise ratio is the slope divided by its standard error. RESULTS: The median number of LV studies was 5 (4-11) for LV over a mean duration of 5.9 ± 3.0 years in 561 patients (diastole) and 386 (systole). The median number of LA studies was 5 (4-11) over a mean duration of 5.3 ± 2.0 years in 137 patients. Linear estimates of LV size had better signal-to-noise than volume estimates (p < 0.001 for diastolic and p = 0.035 for systolic). For the left atrium, the difference was not significant (p = 0.214). This may be due to sample size; the effect size was similar to that for LV systolic size. All three parameters had a numerical value of signal-to-noise that favoured linear dimensions over volumes. CONCLUSION: Linear measures of LV size have better signal-to-noise than volume measures. There was no difference in signal-to-noise between linear and volume measures of LA size, although this may be a Type II error. The use of regression lines may be better than relying on single measurements. Linear dimensions may clarify whether changes in volumes are real or spurious.

Design and Screening of New Lead Compounds for Autism Based on QSAR Model and Molecular Docking Studies.

The purpose of the present study aims to develop a satisfactory model for predicting pro-social and pro-cognitive effects on azinesulfonamides of cyclic amine derivatives as potential antipsychotics. The three dimensional-quantitative structure affinity relationship (3D-QSAR) study was performed on a series of azinesulfonamides of cyclic amine derivative using comparative molecular similarity indices analysis (CoMSIA). The best statistical model of CoMSIA q2, r2, SEE and F values are 0.664, 0.973, 0.087, and 82.344, respectively. Based on the model contour maps and the highest activity structure of the 43rd compound, serial new structures were designed and the 43k1 compound was selected as the best structure. The dock results showed a good binding of 43k1 with the protein (PDB ID: 6A93). The QSAR model analysis of the contour maps can help us to provide guidelines for finding novel potential antipsychotics.

Network Pharmacology Integrated Molecular Docking to Reveal the Autism and Mechanism of Baohewan Heshiwei Wen Dan Tang.

BACKGROUND: In recent years, the prevalence and mortality of autism spectrum disorder (ASD) have been increasing. The clinical features are different with different cases, so the treatment ways are different for each one. OBJECTIVE: Baohewan Heshiwei Wen Dan Tang (BHWDT) has been recommended for treating autistic spectrum disorder. To investigate the mechanism of action and how the compounds interact with ASD targets, network pharmacology and molecular docking methods were used in this study. METHODS: Traditional Chinese Medicine Systems Pharmacology (TCMSP) was used to screen the active components according to index of oral bio-activity and drug-likeness. Then, TCMSP and Swiss Target Prediction databases were used to screen potential target genes of active components. The related target genes of ASD were obtained from the Gene Cards database. Matescape database was utilized to get gene ontology (GO) function enrichment and Kyoto Encyclopedia of Genes and Genomes pathway annotation of gene targets. Composition- target-pathway (C-T-P) and a protein-protein interaction (PPI) networks were built with Cytoscape 3.8.2 software. RESULTS: The interaction of the main active components of BHWDT was verified by molecular docking. The key targets of MAPK1, IL6, CXCL8 and TP53 of BHWDT were obtained. The key active components Quercetin, Kaempferol and Iuteolin of BHWDT could bind with MAPK1, IL6, CXCL8 and TP53 of BHWDT, respectively. CONCLUSION: BHWDT can be highly effective for treating ASD and this study can help us to understand multiple targets and multiple pathways mechanism.

The complete mitochondrial genome of the cocoa tussock moth Orgyia postica Walker, 1855 (Lepidoptera: Lymantriidae).

The high-throughput sequencing method was used for the first time to determined complete mitochondrial genome of Orgyia postica. The complete mitochondrial genome was a circular molecule with 15,258 bp in full-length, including 13 protein-coding genes (PCGs), 22 transfer RNA genes (tRNAs) and 2 ribosomal RNA genes (rRNAs). The nucleotide composition of O. postica mitogenome was A: 37.6%, C: 7.9%, G: 14.8%, T: 39.7%. In addition, the results of the phylogenetic analysis indicate that O. postica had the closest relationship with Laelia suffusa. Our study will contribute to further research on the evolution of Lymantriidae and the identification of larva species.

Identifying the potential regulators of neutrophils recruitment in hepatocellular carcinoma using bioinformatics method.

BACKGROUND: Neutrophils play a crucial role in the development and progression of hepatocellular carcinoma (HCC); however, the mechanism underlying neutrophil recruitment is not fully understood. Therefore, we aimed to explore the potential genes or pathways related to neutrophil recruitment in the cancer microenvironment. METHODS: We downloaded TCGA HCC gene expression profiles, the abundance of 22 different immune cells in HCC patients, and patient survival information. We used Kaplan-Meier survival analysis to determine if neutrophils were related to survival. Next, we screened different expression genes (DEGs) between patients with high and low level of neutrophils. We then identified the transcription factor and its targets in the fence of DEGs. Then, we carried out enrichment analysis and gene set variation analysis (GSVA) for targets. Finally, we explored the potential mechanism of targets via calculating correlation scores. RESULTS: Our survival analysis results showed that neutrophils were significantly associated with patient survival. A total of 736 DEGs were screened. Next, we identified transcription factor larger E26 transformation-specific (ETS) homologous factor (EHF) and 702 targets of EHF from 736 DEGs. Among these targets, the level of FGD6 expression had the highest correlation with the level of EHF expression. Enrichment and GSVA analysis for FGD6 showed that the level of GO:0043547 had a positive regulatory effect on GTPase activity and the GO:0007010 cytoskeleton organization was significantly difference between the high and low neutrophils counts. By calculating the correlation between FGD6 and genes in GO:0043547 and GO:0007010, we identified RIC8B and SIPA1L3. CONCLUSIONS: These findings demonstrated that transcription factor EHF can influence recruitment of neutrophils by mediating the transcription of FGD6. Further investigations are needed to shed new light on EHF and its target FGD6.

Multi-omics integrative analysis and survival risk model construction of non-small cell lung cancer based on The Cancer Genome Atlas datasets.

Lung cancer is a major cause of cancer-associated mortality worldwide. However, the association between multi-omics data and survival in lung cancer is not fully understood. The present study investigated the performance of the methylation survival risk model in multi-platform integrative molecular subtypes and aimed to identify copy number (CN) variations and mutations that are associated with survival risk. The present study analyzed 439 lung adenocarcinoma cases based on DNA methylation, RNA, microRNA (miRNA), DNA copy number and mutations from The Cancer Genome Atlas datasets. First, six cancer subtypes were identified using integrating DNA methylation, RNA, miRNA and DNA copy number data. The least absolute shrinkage and selection operator (LASSO) regression algorithm was used to extract methylation sites of survival model and calculate the methylation-based survival risk indices for all patients. Survival for patients in the high-risk group was significantly lower compared with that for patients in the low-risk group (P<0.05). The present study also assessed methylation-based survival risks of the six subtypes and analyzed the association between survival risk and non-silent mutation rate, number of segments, fraction of segments altered, aneuploidy score, number of segments with loss of heterozygosity (LOH), fraction of segments with LOH and homologous repair deficiency. Finally, the specific copy number regions and mutant genes associated with the different subtypes were identified (P<0.01). Chromosome regions 17q24.3 and 11p15.5 were identified as those with the most survival risk-associated copy number variation regions, while a total of 29 mutant genes were significantly associated with survival (P<0.01).

3D-QSAR and Molecular Docking Studies on Design Anti-Prostate Cancer Curcumin Analogues.

BACKGROUND: Prostate cancer is one of the most common tumors in the world and the fifth leading cause of male cancer death. Although the treatment of localized androgen-dependent prostate cancer has been successful, the efficacy of androgen-independent metastatic disease is limited. Curcumin, a natural product, has been found to inhibit the proliferation of prostate cancer cells. OBJECTIVE: To design curcumin analogs with higher biological activity and lower toxicity and side effects for the treatment of prostate cancer. METHODS: In this study, the three dimensional-quantitative structure activity relationship (3DQSAR) and molecular docking studies were performed on 34 curcumin analogs as anti-prostate cancer compounds. We introduced OSIRIS Property Explorer to predict drug-related properties of newly designed compounds. RESULTS: The optimum CoMSIA model exhibited statistically significant results: the cross-validated correlation coefficient q2 is 0.540 and non-cross-validated R2 value is 0.984. The external predictive correlation coefficient Rext 2 is 0.792. The information of structure-activity relationship can be obtained from the CoMSIA contour maps. In addition, the molecular docking study of the compounds for 3ZK6 as the protein target revealed important interactions between active compounds and amino acids. CONCLUSION: Compound 28i may be a new type of anti-prostate cancer drug with higher biological activity and more promising development.

Quantitative structure-activity relationship and molecular docking studies on designing inhibitors of the perforin.

Quantitative structure-activity relationship (QSAR) studies were performed on a series of 5-arylidene-2thioxoimidazolidin-4-ones derivatives as the inhibitors of perforin and to gain insights about the structural determinants for designing new drug molecules. The heuristic method could explore the descriptors responsible for bioactivity and gain a best linear model with R2 .82. Gene expression programming method generated a novel nonlinear function model with R2 .92 for training set and R2 .85 for test set. The predicted IC50 by QSAR, molecular docking analysis, and property explorer applet show that 42a acts as a well-pleasing potent inhibitor for perforin. This study may lay a reliable theoretical foundation for the development of designing perforin inhibitor structures.

QSAR Study for Carcinogenic Potency of Aromatic Amines Based on GEP and MLPs.

A new analysis strategy was used to classify the carcinogenicity of aromatic amines. The physical-chemical parameters are closely related to the carcinogenicity of compounds. Quantitative structure activity relationship (QSAR) is a method of predicting the carcinogenicity of aromatic amine, which can reveal the relationship between carcinogenicity and physical-chemical parameters. This study accessed gene expression programming by APS software, the multilayer perceptrons by Weka software to predict the carcinogenicity of aromatic amines, respectively. All these methods relied on molecular descriptors calculated by CODESSA software and eight molecular descriptors were selected to build function equations. As a remarkable result, the accuracy of gene expression programming in training and test sets are 0.92 and 0.82, the accuracy of multilayer perceptrons in training and test sets are 0.84 and 0.74 respectively. The precision of the gene expression programming is obviously superior to multilayer perceptrons both in training set and test set. The QSAR application in the identification of carcinogenic compounds is a high efficiency method.

3D-QSAR modeling and molecular docking study on Mer kinase inhibitors of pyridine-substituted pyrimidines.

Mer kinase is a novel therapeutic target for many cancers, and overexpression of Mer receptor tyrosine kinase has been observed in several kinds of tumors. To deeply understand the structure-activity correlation of a series of pyridine/pyrimidine analogs as potent Mer inhibitors, a combined molecular docking and three-dimensional quantitative structure-activity relationship modeling was carried out. A comparative molecular similarity indices analysis model was developed based on the maximum common substructure alignment. The optimum model exhibited statistically significant results: the cross-validated correlation coefficient q2 was 0.599, and non-cross-validated r2 value was 0.984. Furthermore, the results of internal validation such as bootstrapping, Y-randomization as well as external validation (the external predictive correlation coefficient r2 ext = 0.728) confirmed the rationality and good predictive ability of the model. Using the crystal structure of Mer kinase, the selected pyridine/pyrimidine compounds were docked into the enzyme active site. Some key amino acid residues were determined, and hydrogen bonding and hydrophobic interactions between Mer kinase and inhibitors were identified. The satisfactory results from this study may aid in the research and development of novel potent Mer kinase inhibitors.

Removal of nitric oxide by the highly reactive anatase TiO2 (001) surface: a density functional theory study.

In this paper, density functional theory (DFT) calculation was employed to study the adsorption of nitric oxide (NO) on the highly reactive anatase TiO2 (001) surface. For comparison, the adsorption of NO on the (101) surface was also considered. Different from the physical adsorption on the (101) surface, NO molecules are found to chemisorb on the TiO2 (001) surface. The twofold coordinate oxygen atoms (O2c) on the anatase (001) surface are the active sites. Where NO is oxidized into a nitrite species (NO2(-)) trapping efficiently on the surface, with one of the surface Ti5c-O2c bonds adjacent to the adsorption site broken. Our results, therefore, supply a theoretical guidance to remove NO pollutants using highly reactive anatase TiO2 (001) facets.

Density functional study of organocatalytic cross-aldol reactions between two aliphatic aldehydes: insight into their functional differentiation and origins of chemo- and stereoselectivities.

The chemo-, diastereo-, and enantioselectivities in proline and axially chiral amino sulfonamide-catalyzed direct aldol reactions between two enolizable aldehydes with different electronic nature have been studied with the aid of density functional theory (DFT) method. The potential energy profiles for the enamine formation between each aliphatic aldehyde and the catalyst confirm that two subject catalysts can successfully differentiate between 3-methylbutanal as an enamine component and α-chloroaldehydes as a carbonyl component. Transition states associated with the stereochemistry-determining C-C bond-forming step with the enamine intermediate addition to the aldehyde acceptor for proline and chiral amino sulfonamide-promoted aldol reactions are reported. DFT calculations not only provide a good explanation for the formation of the sole cross-aldol product between two aliphatic aldehydes both bearing α-methylene protons but also well reproduce the opposite syn vs anti diastereoselectivities in the chiral amino sulfonamide and proline-catalyzed aldol reactions.

Study of human dopamine sulfotransferases based on gene expression programming.

A quantitative model is developed to predict the Km of 47 human dopamine sulfotransferases by gene expression programming. Each kind of compound is represented by several calculated structural descriptors of moment of inertia A, average electrophilic reactivity index for a C atom, relative number of triple bonds, RNCG relative negative charge, HA-dependent HDSA-1, and HBCA H-bonding charged surface area. Eight fitness functions of the gene expression programming method are used to find the best nonlinear model. The best quantitative model with squared standard error and square of correlation coefficient are 0.096 and 0.91 for training data set, and 0.102 and 0.88 for test set, respectively. It is shown that the gene expression programming-predicted results with fitness function are in good agreement with experimental ones.

Location of Si vacancies and [Ti(OSi)4] and [Ti(OSi)3OH] sites in the MFI framework: a large cluster and full ab initio study.

Titanium silicalite-1 (TS-1) is an important catalyst for selective oxidation reactions. However, the nature and structure of the active sites and the mechanistic details of the catalytic reactions over TS-1 have not been well-understood, leaving a continuous debate on the genesis of active sites on the TS-1 surface in the literature. In this work, the location of Si vacancies and [Ti(OSi)(4)] and [Ti(OSi)(3)OH] sites in the MFI (Framework Type Code of ZSM-5 (Zeolite Socony Mobile-Five)) framework has been studied using a full ab initio method with 40T clusters with a Si:Ti molar ratio of 39:1. It was shown that the former four energetically favorable sites for Si vacancies are T6, T12, T4, and T8 and for Ti centers of [Ti(OSi)(4)] are T10, T4, T8 and T11, being partially the same sites. Whether by replacing Si vacancies or substituting the fully coordinated Si sites, the most preferential site for Ti is T10, which indicates that the insertion mechanism does not affect the favorable sites of Ti in the MFI lattice. For the defective [Ti(OSi)(3)OH] sites, it was found that the Si vacancy at T6 with a Ti at its neighboring T9 site (T6-def-T9-Ti pair) is the most energetically favorable one, followed by a T6-def-T5-Ti pair with a small energy gap. These findings are significant to elucidate the nature of the active sites and the mechanism of reactions catalyzed by TS-1 and to design the TS-1 catalyst.

On the origin of the visible-light activity of titanium dioxide doped with carbonate species.

Plane-wave-based pseudopotential density functional theory (DFT) calculations are used to elucidate the origin of the high photocatalytic efficiency of carbonate-doped TiO(2). Two geometrically possible doping positions are considered, including interstitial and substitutional carbon atoms on Ti sites. From the optical absorption properties calculations, we believe that the formation of carbonates after doping with interstitial carbon atoms is crucial, whereas the contribution from the cationic doping on Ti sites is negligible. The carbonate species doped TiO(2) exhibits excellent absorption in the visible-light region of 400-800 nm, in good agreement with experimental observations. Electronic structure analysis shows that the carbonate species introduce an impurity state from Ti 3d below the conduction band. Excitations from the impurity state to the conduction band may be responsible for the high visible-light activity of the carbon doped TiO(2) materials.

Predicting the activity of drugs for a group of imidazopyridine anticoccidial compounds.

Gene expression programming (GEP) is a novel machine learning technique. The GEP is used to build nonlinear quantitative structure-activity relationship model for the prediction of the IC(50) for the imidazopyridine anticoccidial compounds. This model is based on descriptors which are calculated from the molecular structure. Four descriptors are selected from the descriptors' pool by heuristic method (HM) to build multivariable linear model. The GEP method produced a nonlinear quantitative model with a correlation coefficient and a mean error of 0.96 and 0.24 for the training set, 0.91 and 0.52 for the test set, respectively. It is shown that the GEP predicted results are in good agreement with experimental ones.

Origins of opposite syn-anti diastereoselectivities in primary and secondary amino acid-catalyzed intermolecular aldol reactions involving unmodified alpha-hydroxyketones.

The effects of different amino acid catalysts on the stereoselectivity of the direct intermolecular aldol reactions between alpha-hydroxyketones and isobutyraldehyde or 4-nitrobenzaldehyde have been studied with the aid of density functional theory methods. The transition states of the crucial C-C bond-forming step with the enamine intermediate addition to the aldehyde for the proline and threonine-catalyzed asymmetric aldol reactions are reported. B3LYP/6-31+G** calculations provide a good explanation for the opposite syn vs anti diastereoselectivity of these two kinds of amino acid catalysts (anti-selectivity for the secondary cyclic amino acids proline, syn-selectivity for the acyclic primary amino acids like threonine). Calculated and observed diastereomeric ratio and enantiomeric excess values are in good agreement.

Quantitative structure activity relationship model for predicting the depletion percentage of skin allergic chemical substances of glutathione.

A quantitative model was developed to predict the depletion percentage of glutathione (DPG) compounds by gene expression programming (GEP). Each kind of compound was represented by several calculated structural descriptors involving constitutional, topological, geometrical, electrostatic and quantum-chemical features of compounds. The GEP method produced a nonlinear and five-descriptor quantitative model with a mean error and a correlation coefficient of 10.52 and 0.94 for the training set, 22.80 and 0.85 for the test set, respectively. It is shown that the GEP predicted results are in good agreement with experimental ones, better than those of the heuristic method.

The fracture risk index and bone mineral density as predictors of vertebral structural failure.

Structural failure becomes increasingly likely as the load on bone approximates or exceeds the bone's ability to withstand it. The vertebral fracture risk index (FRI) expresses the risk for structural failure as a ratio of compressive stress (load per unit area) to estimated failure stress, and so should be a more sensitive and specific predictor of vertebral fracture than spine areal BMD (aBMD) or volumetric BMD (vBMD), surrogates of bone strength alone. To address this issue, we analyzed the results of a case-control study of 89 postmenopausal women with vertebral fractures and 306 controls in Melbourne, Australia, and a 10-year community-based prospective study in which 30 postmenopausal women who had incident vertebral fractures were compared with 150 controls in Lyon, France. The FRI and vBMD of the third lumbar vertebral body and spine aBMD were derived using dual X-ray absorptiometry. In the cross-sectional analysis, each SD increase in FRI was associated with 2.1-fold (95% confidence interval [CI], 1.55-2.73) increased vertebral fracture risk, while each SD decrease in aBMD or vBMD was associated with 4.0-fold (95% CI, 2.69-6.18 and 2.65-6.94, respectively) increase in risk. Using receiver operating characteristic (ROC) analysis, the FRI was less sensitive and specific than aBMD in discriminating cases and controls (area under ROC, 0.76 vs 0.84, p<0.01). The area under ROC curve did not differ between FRI and vBMD (0.76 vs 0.79, NS). In the prospective data set, the FRI was not predictive [hazard ratio, HR, 1.20 (95% CI, 0.9-1.7)] and was in contrast to aBMD [HR, 2.4 (95% CI, 1.5-3.8)] and vBMD [HR, 2.1 (95% CI, 1.39-3.17)]. There was also lower sensitivity using a cutoff value of FRI>or=1 compared with aBMD T-score of -2.5 SD in both studies. There was poor agreement (kappa=0.13-0.18) between FRI and aBMD T -scores in detecting fractures; each method only identified around 50% of fractured cases. Within the constraints of the sample size, we concluded that applying a biomechanical index such as FRI at the spine is no better in discriminating fracture cases and controls than conventional aBMD or vBMD. The FRI may not predict incident vertebral fractures.

Varying contributions of growth and ageing to racial and sex differences in femoral neck structure and strength in old age.

The structural basis of racial and sex differences in femoral neck (FN) fragility in old age was assessed in a cross-sectional study of 829 healthy Chinese and 1181 healthy Caucasian subjects aged 18 to 93 years in Melbourne, Australia. We measured FN bone mineral density (BMD), periosteal diameter, and estimated endocortical diameter, cortical thickness, volumetric BMD (vBMD), section modulus, and buckling ratio using dual X-ray absorptiometry. Racial and sex differences in structural and strength indices were adjusted for age, bone length and body weight and were expressed in standard deviation (SD) unit. In young adulthood, Chinese women had a 0.85 SD narrower FN, a 0.47 SD thinner cortex and a 0.79 SD shorter FN axis length (FNAL) than Caucasian women. Across age, Chinese and Caucasian women had similar increments in endocortical and periosteal diameters and similar decrements in cortical thickness and vBMD (both approximately 20%). In young adult males, FN periosteal diameter did not differ by race, but cortical thickness was 0.35 SD lower in Chinese than Caucasians. Across age, increments in periosteal and endocortical diameters were less in Chinese than Caucasian men so cortical thickness and vBMD diminished less in Chinese than in Caucasian men. In both races, young adult women had narrower FN than men. As Chinese women had a greater increment in periosteal diameter than Chinese men across age, the sex difference in FN periosteal diameter established in young adulthood diminished in old age. As Caucasian men had a greater increment in periosteal diameter than Caucasian women, the sex difference in FN periosteal diameter established in young adulthood increased with age. In old age, for both sexes, Chinese had a higher fracture risk in bending than Caucasians, but a lower fracture risk by buckling. For both races, women had a higher fracture risk in bending than men. Racial and sexual dimorphism in the absolute and relative behavior of the periosteal and endocortical surfaces throughout life produce race- and sex-specific differences in FN size, cortical thickness, and indices of bone strength in old age.