Arterial stiffness and white matter integrity in the elderly: A diffusion tensor and magnetization transfer imaging study

BACKGROUND AND PURPOSE

The stiffness of large arteries and increased pulsatility can have an impact on the brain white matter (WM) microstructure, however those mechanisms are still poorly understood. The aim of this study was to investigate the association between central artery stiffness, axonal and myelin integrity in 54 cognitively unimpaired elderly subjects (65-75 years old).

METHODS

The neuronal fiber integrity of brain WM was assessed using diffusion tensor metrics and magnetization transfer imaging as measures of axonal organization (Fractional anisotropy, Radial diffusivity) and state of myelination (Myelin volume fraction). Central artery stiffness was measured by carotid-femoral pulse wave velocity (cfPWV). Statistical analyses included 4 regions (the corpus callosum, the internal capsule, the corona radiata and the superior longitudinal fasciculus) which have been previously denoted as vulnerable to increased central artery stiffness.

RESULTS

cfPWV was significantly associated with fractional anisotropy and radial diffusivity (p < 0.05, corrected for multiple comparisons) but not with myelin volume fraction. Findings from this study also show that improved executive function performance correlates with Fractional anisotropy positively (p < 0.05 corrected) as well as with myelin volume fraction and radial diffusivity negatively (p < 0.05 corrected).

CONCLUSIONS

These findings suggest that arterial stiffness is associated with axon degeneration rather than demyelination. Controlling arterial stiffness may play a role in maintaining the health of WM axons in the aging brain.

A Comparative Study on Selective PPAR Modulators through Quantitative Structure-activity Relationship, Pharmacophore and Docking Analyses

BACKGROUND

Metabolic syndrome is a matrix of different metabolic disorders which are the leading cause of death in human beings. Peroxysome proliferated activated receptor (PPAR) is a nuclear receptor involved in metabolism of fats and glucose.

OBJECTIVE

In order to explore structural requirements for selective PPAR modulators to control lipid and carbohydrate metabolism, the multi-cheminformatics studies have been performed.

METHODS

In silico modeling studies have been performed on a diverse set of PPAR modulators through quantitative structure-activity relationship (QSAR), pharmacophore mapping and docking studies.

RESULTS

It is observed that the presence of an amide fragment (-CONHRPh) has a detrimental effect while an aliphatic ether linkage has a beneficial effect on PPARα modulation. On the other hand, the presence of an amide fragment has a positive effect on PPARδ modulation, but the aliphatic ether linkage and substituted aromatic ring in the molecular scaffold are very much essential for imparting potent and selective PPARγ modulation. Negative ionizable features (i.e. polar fragments) must be present in PPARδ and α modulators, but a hydrophobic feature is the prime requirement for PPARγ modulation.

CONCLUSION

Here, the essential structural features have been explored for selective modulation of each subtype of PPAR in order to design new modulators with improved activity/selectivity.

Quantitative Structure- Property Relationship (QSPR) Investigation of Camptothecin Drugs Derivatives

AIM AND OBJECTIVE

Quantitative Structure- Property Relationship (QSPR) has been widely developed to derive a correlation between chemical structures of molecules to their known properties. In this study, QSPR models have been developed for modeling and predicting thermodynamic properties of 76 camptothecin derivatives using molecular descriptors.

MATERIALS AND METHODS

Thermodynamic properties of camptothecin such as the thermal energy, entropy and heat capacity were calculated at Hartree-Fock level of theory and 3-21G basis sets by Gaussian 09.

RESULTS

The appropriate descriptors for the studied properties are computed and optimized by the genetic algorithms (GA) and multiple linear regressions (MLR) method among the descriptors derived from the Dragon software. Leave-One-Out Cross-Validation (LOOCV) is used to evaluate predictive models by partitioning the total sample into training and test sets.

CONCLUSION

The predictive ability of the models was found to be satisfactory and could be used for predicting thermodynamic properties of camptothecin derivatives.

Modelling methods and cross-validation variants in QSAR: a multi-level analysis$

Prediction performance often depends on the cross- and test validation protocols applied. Several combinations of different cross-validation variants and model-building techniques were used to reveal their complexity. Two case studies (acute toxicity data) were examined, applying five-fold cross-validation (with random, contiguous and Venetian blind forms) and leave-one-out cross-validation (CV). External test sets showed the effects and differences between the validation protocols. The models were generated with multiple linear regression (MLR), principal component regression (PCR), partial least squares (PLS) regression, artificial neural networks (ANN) and support vector machines (SVM). The comparisons were made by the sum of ranking differences (SRD) and factorial analysis of variance (ANOVA). The largest bias and variance could be assigned to the MLR method and contiguous block cross-validation. SRD can provide a unique and unambiguous ranking of methods and CV variants. Venetian blind cross-validation is a promising tool. The generated models were also compared based on their basic performance parameters (r² and Q²). MLR produced the largest gap, while PCR gave the smallest. Although PCR is the best validated and balanced technique, SVM always outperformed the other methods, when experimental values were the benchmark. Variable selection was advantageous, and the modelling had a larger influence than CV variants.

Application of QSAR Analysis and Different Quantum Chemical Calculation Methods in Activity Evaluation of Selected Fluoroquinolones

BACKGROUND

A set of antibiotic fluoroquinolones with confirmed antimicrobial activity were analyzed with the use of two types of quantum chemical calculation methods and quantitative structure-activity relationships (QSAR).

OBJECTIVE

The purpose of this study was to demonstrate the common and differentiating characteristics of the above-mentioned chemical compounds alike physicochemically as well as pharmacologically based on the quantum chemical calculations and microbiological activity data.

METHODS

During the study PCA and MLR analysis were performed, as the types of proposed chemometric approach. The semi-empirical level of in silico molecular modeling was performed for calculations of molecular descriptors.

RESULTS

QSAR models were proposed based on chosen descriptors. The relationship between the structure and microbiological activity and physicochemical parameters data was able to class and describe them with the use of statistically significant molecular descriptors.

CONCLUSION

The applied chemometric approaches revealed the influential features of tested structures responsible for the antimicrobial activity of analyzed compounds.

Miniaturized High-Throughput Multiparameter Flow Cytometry Assays Measuring In Vitro Human Dendritic Cell Maturation and T-Cell Activation in Mixed Lymphocyte Reactions

Enhancing antitumor activities of the human immune system is a clinically proven approach with the advent of monoclonal antibodies recognizing programmed cell death protein-1 (PD1) receptors on immune cell surfaces. Historically, using flow cytometry as a means to assess next-generation agent activities was underused, largely due to limits on cell number and assay sensitivity. Here, we leveraged an IntelliCyt high-throughput flow cytometry platform to monitor human dendritic cell maturation and lymphocyte proliferation in mixed lymphocyte reactions. Specifically, we established flow cytometry-based immunophenotyping and screening methodologies capable of measuring T-cell activation as a result of cell-associated antigens presented on dendritic cell surfaces, as indicated by cell proliferation, cytokine secretion, and surface marker expression. Together, the overall novelty of this 384-well platform is its capability to measure multiple functional readouts in one well and consistently evaluate large numbers of compounds in a single study, as well as its ability to show increased assay sensitivity requiring considerably fewer primary cells and less reagents compared to more traditional 96-well flow cytometry methods.

QSAR study and rustic ligand-based virtual screening in a search for aminooxadiazole derivatives as PIM1 inhibitors

Background

Quantitative structure–activity relationship (QSAR) was carried out to study a series of aminooxadiazoles as PIM1 inhibitors having pk_i ranging from 5.59 to 9.62 (k_i in nM). The present study was performed using Genetic Algorithm method of variable selection (GFA), multiple linear regression analysis (MLR) and non-linear multiple regression analysis (MNLR) to build unambiguous QSAR models of 34 substituted aminooxadiazoles toward PIM1 inhibitory activity based on topological descriptors.

Results

Results showed that the MLR and MNLR predict activity in a satisfactory manner. We concluded that both models provide a high agreement between the predicted and observed values of PIM1 inhibitory activity. Also, they exhibit good stability towards data variations for the validation methods. Furthermore, based on the similarity principle we performed a database screening to identify putative PIM1 candidates inhibitors, and predict their inhibitory activities using the proposed MLR model.

Conclusions

This approach can be easily handled by chemists, to distinguish, which ones among the future designed aminooxadiazoles structures could be lead-like and those that couldn’t be, thus, they can be eliminated in the early stages of drug discovery process.

Shifts in Leukocyte Counts Drive the Differential Expression of Transcriptional Stroke Biomarkers in Whole Blood

Our group recently identified a panel of ten genes whose RNA expression levels in whole blood have utility for detection of stroke. The purpose of this study was to determine the mechanisms by which these genes become differentially expressed during stroke pathology. First, we assessed the transcriptional distribution of the ten genes across the peripheral immune system by measuring their expression levels on isolated neutrophils, monocytes, B-lymphocytes, CD-4+ T-lymphocytes, CD-8+ T-lymphocytes, and NK-cells generated from the blood of healthy donors (n = 3). Then, we examined the relationship between the whole-blood expression levels of the ten genes and white blood cell counts in a cohort of acute ischemic stroke patients (n = 36) and acute stroke mimics (n = 15) recruited at emergency department admission. All ten genes displayed strong patterns of lineage-specific expression in our analysis of isolated leukocytes, and their whole-blood expression levels were correlated with white blood cell differential across the total patient population, suggesting that many of them are likely differentially expressed in whole blood during stroke as an artifact of stroke-induced shifts in leukocyte counts. Specifically, factor analysis inferred that over 50% of the collective variance in their whole-blood expression levels across the patient population was driven by underlying variance in white blood cell counts alone. However, the cumulative expression levels of the ten genes displayed a superior ability to discriminate between stroke patients and stroke mimics relative to white blood cell differential, suggesting that additional less prominent factors influence their expression levels which add to their diagnostic utility. These findings not only provide insight regarding this particular panel of ten genes, but also into the results of prior stroke transcriptomics studies performed in whole blood.

Ezqsar: An R Package for Developing QSAR Models Directly From Structures

Background:

Quantitative Structure Activity Relationship (QSAR) is a difficult computational chemistry approach for beginner scientists and a time consuming one for even more experienced researchers.

Method and Materials:

Ezqsar which is introduced here addresses both the issues. It considers important steps to have a reliable QSAR model. Besides calculation of descriptors using CDK library, highly correlated descriptors are removed, a provided data set is divided to train and test sets, descriptors are selected by a statistical method, statistical parameter for the model are presented and applicability domain is investigated.

Results:

Finally, the model can be applied to predict the activities for an extra set of molecules for a purpose of either lead optimization or virtual screening. The performance is demonstrated by an example.

Conclusion:

The R package, ezqsar, is freely available viahttps://github.com/shamsaraj/ezqsar, and it runs on Linux and MS-Windows.

Extreme learning machines: a new approach for modeling dissolved oxygen (DO) concentration with and without water quality variables as predictors

In this paper, several extreme learning machine (ELM) models, including standard extreme learning machine with sigmoid activation function (S-ELM), extreme learning machine with radial basis activation function (R-ELM), online sequential extreme learning machine (OS-ELM), and optimally pruned extreme learning machine (OP-ELM), are newly applied for predicting dissolved oxygen concentration with and without water quality variables as predictors. Firstly, using data from eight United States Geological Survey (USGS) stations located in different rivers basins, USA, the S-ELM, R-ELM, OS-ELM, and OP-ELM were compared against the measured dissolved oxygen (DO) using four water quality variables, water temperature, specific conductance, turbidity, and pH, as predictors. For each station, we used data measured at an hourly time step for a period of 4 years. The dataset was divided into a training set (70%) and a validation set (30%). We selected several combinations of the water quality variables as inputs for each ELM model and six different scenarios were compared. Secondly, an attempt was made to predict DO concentration without water quality variables. To achieve this goal, we used the year numbers, 2008, 2009, etc., month numbers from (1) to (12), day numbers from (1) to (31) and hour numbers from (00:00) to (24:00) as predictors. Thirdly, the best ELM models were trained using validation dataset and tested with the training dataset. The performances of the four ELM models were evaluated using four statistical indices: the coefficient of correlation (R), the Nash-Sutcliffe efficiency (NSE), the root mean squared error (RMSE), and the mean absolute error (MAE). Results obtained from the eight stations indicated that: (i) the best results were obtained by the S-ELM, R-ELM, OS-ELM, and OP-ELM models having four water quality variables as predictors; (ii) out of eight stations, the OP-ELM performed better than the other three ELM models at seven stations while the R-ELM performed the best at one station. The OS-ELM models performed the worst and provided the lowest accuracy; (iii) for predicting DO without water quality variables, the R-ELM performed the best at seven stations followed by the S-ELM in the second place and the OP-ELM performed the worst with low accuracy; (iv) for the final application where training ELM models with validation dataset and testing with training dataset, the OP-ELM provided the best accuracy using water quality variables and the R-ELM performed the best at all eight stations without water quality variables. Fourthly, and finally, we compared the results obtained from different ELM models with those obtained using multiple linear regression (MLR) and multilayer perceptron neural network (MLPNN). Results obtained using MLPNN and MLR models reveal that: (i) using water quality variables as predictors, the MLR performed the worst and provided the lowest accuracy in all stations; (ii) MLPNN was ranked in the second place at two stations, in the third place at four stations, and finally, in the fourth place at two stations, (iii) for predicting DO without water quality variables, MLPNN is ranked in the second place at five stations, and ranked in the third, fourth, and fifth places in the remaining three stations, while MLR was ranked in the last place with very low accuracy at all stations. Overall, the results suggest that the ELM is more effective than the MLPNN and MLR for modelling DO concentration in river ecosystems.

Prediction of pork quality parameters by applying fractals and data mining on MRI

This work firstly investigates the use of MRI, fractal algorithms and data mining techniques to determine pork quality parameters non-destructively. The main objective was to evaluate the capability of fractal algorithms (Classical Fractal algorithm, CFA; Fractal Texture Algorithm, FTA and One Point Fractal Texture Algorithm, OPFTA) to analyse MRI in order to predict quality parameters of loin. In addition, the effect of the sequence acquisition of MRI (Gradient echo, GE; Spin echo, SE and Turbo 3D, T3D) and the predictive technique of data mining (Isotonic regression, IR and Multiple linear regression, MLR) were analysed. Both fractal algorithm, FTA and OPFTA are appropriate to analyse MRI of loins. The sequence acquisition, the fractal algorithm and the data mining technique seems to influence on the prediction results. For most physico-chemical parameters, prediction equations with moderate to excellent correlation coefficients were achieved by using the following combinations of acquisition sequences of MRI, fractal algorithms and data mining techniques: SE-FTA-MLR, SE-OPFTA-IR, GE-OPFTA-MLR, SE-OPFTA-MLR, with the last one offering the best prediction results. Thus, SE-OPFTA-MLR could be proposed as an alternative technique to determine physico-chemical traits of fresh and dry-cured loins in a non-destructive way with high accuracy.

QSAR model for prediction of the therapeutic potency of N-benzylpiperidine derivatives as AChE inhibitors

A new family of AChE inhibitors, N-benzylpiperidines, showed exceptional efficacy in vitro and in vivo, minimal side effects and high selectivity for acetylcholinesterase (AChE). Three regression methods were chosen in this work to develop robust predictive models, namely multiple linear regression (MLR), genetic function approximation (GFA) and multilayer perceptron network (MLP). Ten descriptors were selected for a dataset of 99 molecules, using a genetic algorithm. The best results were obtained for MLP with a 10-6-1 artificial neural network model trained with the Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm. Statistical prediction for MLR and GFA were r² = 0.882 and r² = 0.875, respectively. Because internal and external validation strategies play an important role, we adopted all available validation strategies to check the robustness of the models. All criteria used to validate these models revealed the superiority of the GFA model. Therefore, the models developed in this study provide an excellent prediction of the inhibitory concentration of a new family of AChE inhibitors.

Heritability and GWAS Studies for Monocyte-Lymphocyte Ratio

The monocyte-lymphocyte ratio (MLR) is a useful biomarker for disease development, but little is known about the extent to which genetic and environmental factors influence MLR variation. Here, we study the genetic architecture of MLR and determine the influence of demographic and lifestyle factors on MLR in data from a Dutch non-patient twin-family population. Data were obtained in 9,501 individuals from the Netherlands Twin Register. We used regression analyses to determine the effects of age, sex, smoking, and body mass index (BMI) on MLR and its subcomponents. Data on twins, siblings and parents (N = 7,513) were analyzed by genetic structural equation modeling to establish heritability and genome wide single nucleotide polymorphism (SNP) data from a genotyped subsample (N = 5,892) and used to estimate heritability explained by SNPs. SNP and phenotype data were also analyzed in a genome-wide association study to identify the genes involved in MLR. Linkage disequilibrium (LD) score regression and expression quantitative trait loci (eQTL) analyses were performed to further explore the genetic findings. Results showed that age, sex, and age × sex interaction effects were present for MLR and its subcomponents. Variation in MLR was not related to BMI, but smoking was positively associated with MLR. Heritability was estimated at 40% for MLR, 58% for monocyte, and 58% for lymphocyte count. The Genome-wide association study (GWAS) identified a locus on ITGA4 that was associated with MLR and only marginally significantly associated with monocyte count. For monocyte count, additional genetic variants were identified on ITPR3, LPAP1, and IRF8. For lymphocyte count, GWAS provided no significant findings. Taking all measured SNPs together, their effects accounted for 13% of the heritability of MLR, while all known and identified genetic loci explained 1.3% of variation in MLR. eQTL analyses showed that these genetic variants were unlikely to be eQTLs. In conclusion, variation in MLR level in the general population is heritable and influenced by age, sex, and smoking. We identified gene variants in the ITGA4 gene associated with variation in MLR. The significant SNP-heritability indicates that more genetic variants are likely to be involved.

Exploring MIA-QSPR's for the modeling of biomagnification factors of aromatic organochlorine pollutants

Biomagnification of organic pollutants in food webs has been usually associated to hydrophobicity and other molecular descriptors. However, direct information on atoms and substituent positions in a molecular scaffold that most affect this biological property is not straightforward using traditional QSPR techniques. This work reports the QSPR modeling of biomagnification factors (logBMF) of a series of aromatic organochlorine compounds using three MIA-QSPR (multivariate image analysis applied to QSPR) approaches. The MIA-QSPR model based on augmented molecular images (described with atoms represented as circles with sizes proportional to the respective van der Waals radii and having colors numerically proportional to the Pauling's electronegativity) encoded better the logBMF data. The average results for the main statistical parameters used to attest the model's predictability were r²=0.85, q²=0.72 and r²_test=0.85. In addition, chemical insights on substituents and respective positions at the biphenyl rings A and B, and dibenzo-p-dioxin and dibenzofuran motifs are given to aid the design of more ecofriendly derivatives.

In Silico Screening of IL-1β Production Inhibitors Using Chemometric Tools

The IL-1β plays a major role in inflammatory disorders and IL-1β production inhibitors can be used in the treatment of inflammatory and related diseases. In this study, quantitative relationships between the structures of 46 pyridazine derivatives (inhibitors of IL-1β production) and their activities were investigated by Multiple Linear Regression (MLR) technique Stepwise Regression Method (ES-SWR). The genetic algorithm (GA) has been proposed for improvement of the performance of the MLR modeling by choosing the most relevant descriptors. The results show that eight descriptors are able to describe about 83.70% of the variance in the experimental activity of the molecules in the training set. The physical meaning of the selected descriptors is discussed in detail. Power predictions of the QSAR models developed were evaluated using cross-validation, and validation through an external prediction set. The results showed satisfactory goodness-of-fit, robustness and perfect external predictive performance. The applicability domain was used to define the area of reliable predictions. Furthermore, the in silico screening technique was applied in order to predict the structure and potency of new compounds of this type using the proposed QSAR model.

Audiological and Electrophysiological Changes in Patients with Vitiligo

The study was conducted to evaluate the audiological and electrophysiological findings in patients with Vitiligo and to compare the findings with otologically and audiologically normal controls. Study group included 50 subjects (25 Males, 25 Females) with Vitiligo (Mean age-27.4 years) and control group contained 40 age-matched normal hearing subjects. Pure tone audiometry (PTA) with extended high frequency audiometry, Otoacoustic emissions (OAEs), Tympanometry, Auditory brainstem responses (ABR) and Middle latency responses (MLR) were conducted in all subjects. Comparison of the study group with the control group showed statistically significant differences (p < 0.05) on PTA, in Transient otoacoustic emissions (TOAEs) at 1, 2, 3, 4 kHz and in distortion product otoacoustic emissions (DPOAEs) at 357, 499, 704, 1003 Hz. On ABR, statistically significant differences were observed between the groups in wave I (p < 0.01) in both ears, wave V (p < 0.05) in left ear and on interpeak latency of I-III (p < 0.01, p < 0.05), III-V (p < 0.01 in left ear) and I-V (p < 0.01, p < 0.05) in left and right ears respectively. When patients with localized vitiligo were compared with generalized vitiligo, the SNR of TOAEs was highly significant in both ears at 2 KHz (p < 0.05), 3 kHz (p < 0.01) and 4 kHz (p < 0.05). PTA average of 2 KHz, 4 and 8 kHz (PTA2) showed a significant difference (p < 0.01) when localized vitiligo was compared to generalized vitiligo. Results support possible auditory and electrophysiological changes in Vitiligo patients along with decreased cochlear function.

QSAR models of antiproliferative activity of imidazo[2,1-b][1,3,4]thiadiazoles in various cancer cell lines

Imidazo[2,1-b][1,3,4]thiadiazoles have been recognized to possess antiproliferative potency towards a wide spectrum of cancer cell lines. QSAR investigations on a set of 42 di(tri)substituted imidazo[2,1-b][1,3,4]thiadiazoles were carried out to find the descriptors determining their biological potency. Three-variable equations were obtained by combinatorial protocols in multiple linear regression (CP MLR) for all three studied cancer cell lines. They showed that lipophilicity, electronic, and steric factors are decisive for the antiproliferative potency of compounds and indicate the important role of nitrogen atoms of imidazothiadiazole ring in the interactions with the molecular target. The best models gave high r squared values in the range from 0.887 to 0.924. They also have good predictive accuracy confirmed by the high value LOO cross-validation coefficient [Formula: see text] (from 0.842 to 0.904) and by the external validation quantities.

Prediction of oxidation parameters of purified Kilka fish oil including gallic acid and methyl gallate by adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network

BACKGROUND

As a result of concerns regarding possible health hazards of synthetic antioxidants, gallic acid and methyl gallate may be introduced as natural antioxidants to improve oxidative stability of marine oil. Since conventional modelling could not predict the oxidative parameters precisely, artificial neural network (ANN) and neuro-fuzzy inference system (ANFIS) modelling with three inputs, including type of antioxidant (gallic acid and methyl gallate), temperature (35, 45 and 55 °C) and concentration (0, 200, 400, 800 and 1600 mg L(-1) ) and four outputs containing induction period (IP), slope of initial stage of oxidation curve (k1 ) and slope of propagation stage of oxidation curve (k2 ) and peroxide value at the IP (PVIP ) were performed to predict the oxidation parameters of Kilka oil triacylglycerols and were compared to multiple linear regression (MLR).

RESULTS

The results showed ANFIS was the best model with high coefficient of determination (R(2)  = 0.99, 0.99, 0.92 and 0.77 for IP, k1 , k2 and PVIP , respectively). So, the RMSE and MAE values for IP were 7.49 and 4.92 in ANFIS model. However, they were to be 15.95 and 10.88 and 34.14 and 3.60 for the best MLP structure and MLR, respectively. So, MLR showed the minimum accuracy among the constructed models.

CONCLUSION

Sensitivity analysis based on the ANFIS model suggested a high sensitivity of oxidation parameters, particularly the induction period on concentrations of gallic acid and methyl gallate due to their high antioxidant activity to retard oil oxidation and enhanced Kilka oil shelf life. © 2016 Society of Chemical Industry.

Multilayer perceptron neural network-based approach for modeling phycocyanin pigment concentrations: case study from lower Charles River buoy, USA

This paper proposes multilayer perceptron neural network (MLPNN) to predict phycocyanin (PC) pigment using water quality variables as predictor. In the proposed model, four water quality variables that are water temperature, dissolved oxygen, pH, and specific conductance were selected as the inputs for the MLPNN model, and the PC as the output. To demonstrate the capability and the usefulness of the MLPNN model, a total of 15,849 data measured at 15-min (15 min) intervals of time are used for the development of the model. The data are collected at the lower Charles River buoy, and available from the US Environmental Protection Agency (USEPA). For comparison purposes, a multiple linear regression (MLR) model that was frequently used for predicting water quality variables in previous studies is also built. The performances of the models are evaluated using a set of widely used statistical indices. The performance of the MLPNN and MLR models is compared with the measured data. The obtained results show that (i) the all proposed MLPNN models are more accurate than the MLR models and (ii) the results obtained are very promising and encouraging for the development of phycocyanin-predictive models.

The contribution of inferior colliculus activity to the auditory brainstem response (ABR) in mice

In mice, the auditory brainstem response (ABR) is frequently used to assess hearing status in transgenic hearing models. The diagnostic value of the ABR depends on knowledge about the anatomical sources of its characteristic waves. Here, we studied the contribution of the inferior colliculus (IC) to the click-evoked scalp ABR in mice. We demonstrate a non-invasive correlate of the IC response that can be measured in the scalp ABR as a slow positive wave P₀ with peak latency 7-8 ms when recorded with adequate band-pass filtering. Wave P₀ showed close correspondence in latency, magnitude and shape with the sustained part of evoked spiking activity and local field potentials (LFP) in the central nucleus of the IC. In addition, the onset peaks of the IC response were related temporally to ABR wave V and to some extent to wave IV. This relation was further supported by depth-dependent modulation of the shape of ABR wave IV and V within the IC suggesting generation within or in close vicinity to the IC. In conclusion, the slow ABR wave P₀ in the scalp ABR may represent a complementary non-invasive marker for IC activity in the mouse. Further, the latency of synchronized click-evoked activity in the IC supports the view that IC contributes to ABR wave V, and possibly also to ABR wave IV.

Monitoring alloimmune response in kidney transplantation

Currently, immunosuppressive therapy in kidney transplant recipients is generally performed by protocols and adjusted according to functional or histological evaluation of the allograft and/or signs of drug toxicity or infection. As a result, a large fraction of patients are likely to receive too much or too little immunosuppression, exposing them to higher rates of infection, malignancy and drug toxicity, or increased risk of acute and chronic graft injury from rejection, respectively. Developing reliable biomarkers is crucial for individualizing therapy aimed at extending allograft survival. Emerging data indicate that many assays, likely used in panels rather than single assays, have potential to be diagnostic and predictive of short and also long-term outcome. While numerous cross-sectional studies have found associations between the results of these assays and the presence of clinically relevant post-transplantation outcomes, data from prospective studies are still scanty, thereby preventing widespread implementation in the clinic. Of note, some prospective, randomized, multicenter biomarker-driven studies are currently on-going aiming at confirming such preliminary data. These works as well as other future studies are highly warranted to test the hypothesis that tailoring immunosuppression on the basis of results offered by these biomarkers leads to better outcomes than current standard clinical practice.

Application of Electronic Nose for Measuring Total Volatile Basic Nitrogen and Total Viable Counts in Packaged Pork During Refrigerated Storage

The objective of this study was to predict the total viable counts (TVC) and total volatile basic nitrogen (TVB-N) in pork using an electronic nose (E-nose), and to assess the freshness of chilled pork during storage using different packaging methods, including pallet packaging (PP), vacuum packaging (VP), and modified atmosphere packaging (MAP, 40% O2 /40% CO2 /20% N2 ). Principal component analysis (PCA) was used to analyze the E-nose signals, and the results showed that the relationships between the freshness of chilled pork and E-nose signals could be distinguished in the loadings plots, and the freshness of chilled pork could be distributed along 2 first principal components. Multiple linear regression (MLR) was used to correlate TVC and TVB-N to E-nose signals. High F and R2 values were obtained in the MLR output of TVB-N (F = 32.1, 21.6, and 24.2 for PP [R2 = 0.93], VP [R2 = 0.94], and MAP [R2 = 0.95], respectively) and TVC (F = 34.2, 46.4, and 7.8 for PP [R2 = 0.98], VP [R2 = 0.89], and MAP [R2 = 0.85], respectively). The results of this study suggest that it is possible to use the E-nose technology to predict TVB-N and TVC for assessing the freshness of chilled pork during storage.

Isoindoline Derivatives of α-Amino Acids as Cyclooxygenase 1 and 2 Inhibitors

IC50 values were obtained for two series of isoindolines derived from α-amino acids over cyclooxygenase 1 and 2 (COX-1 and COX-2). In order to explain the biological activity observed, a structure-activity relationship (SAR) model was achieved for the tested compounds and 19 reference compounds with known selective inhibitory activity, through the correlation of the binding energies calculated from rigid docking of the best conformations into the catalytic sites of COX-1 and COX-2, as well as their molecular descriptors: Log P, molecular weight (MW), volume (V), and solvation energy (Esol) versus their experimental IC50 values by MLR and LS-SVM methods. The model probed whether the COX-1 and COX-2 inhibitory activities of the isoindolines correlate with steric, hydrophobic, and thermodynamic parameters. The correlation values with MLR for COX-1 and COX-2 (r(2)  = 0.4193 and r(2)  = 0.5929) were optimized with LS-SVM until r(2)  = 0.6818 for COX-1 and r(2)  = 0.8985 for COX-2, resulting in a good predictive ability for COX-1 and -2 inhibition with this model. In conclusion, the data suggests that the physicochemical descriptors evaluated have an impact on the inhibitory activity and selectivity of isoindolines over COX-1 and COX-2.

2D and 3D QSAR models for identifying diphenylpyridylethanamine based inhibitors against cholesteryl ester transfer protein

Cholesteryl ester transfer protein (CETP) inhibitors hold promise as new agents against coronary heart disease. Molecular modeling techniques such as 2D-QSAR and 3D-QSAR analysis were applied to establish models to distinguish potent and weak CETP inhibitors. 2D and 3D QSAR models-based a series of diphenylpyridylethanamine (DPPE) derivatives (newly identified as CETP inhibitors) were then performed to elucidate structural and physicochemical requirements for higher CETP inhibitory activity. The linear and spline 2D-QSAR models were developed through multiple linear regression (MLR) and support vector machine (SVM) methods. The best 2D-QSAR model obtained by SVM gave a high predictive ability (R(2)train=0.929, R(2)test=0.826, Q(2)LOO=0.780). Also, the 2D-QSAR models uncovered that SlogP_VSA0, E_sol and Vsurf_DW23 were important features in defining activity. In addition, the best 3D-QSAR model presented higher predictive ability (R(2)train=0.958, R(2)test=0.852, Q(2)LOO=0.734) based on comparative molecular field analysis (CoMFA). Meanwhile, the derived contour maps from 3D-QSAR model revealed the significant structural features (steric and electronic effects) required for improving CETP inhibitory activity. Consequently, twelve newly designed DPPE derivatives were proposed to be robust and potent CETP inhibitors. Overall, these derived models may help to design novel DPPE derivatives with better CETP inhibitory activity.

Isotopic correlation for 242Pu composition prediction: Multivariate regresssion approach

Multivariate regression calibration using multiple linear regression (MLR), principle component regression (PCR) and partial least squares regression (PLSR) algorithm was performed on ²³⁸Pu, ²³⁹Pu, ²⁴⁰Pu and ²⁴¹Pu atom% abundances to predict ²⁴²Pu isotopic abundance. The MLR algorithm was found to be the best among these three algorithms. The effect of ²³⁸Pu composition on the ²⁴²Pu abundance prediction was found to be small but significant especially for achieving high accuracy of <0.5%. PCR and PLSR generated nearly identical results and were inferior to the MLR results. A comparison of MLR results with those obtained by employing seven previously reported empirical methods revealed far superior prediction capability of MLR model. Among the seven empirical models, the best prediction capability was found for Bignan correlation containing ²³⁸Pu isotopic data. The study clearly demonstrates that the production of ²³⁸Pu and ²⁴²Pu has some small correlation and the use of ²³⁸Pu in isotopic correlation for ²⁴²Pu prediction is important to get accurate results.

Exploring QSARs of some benzenesulfonamides incorporating cyanoacrylamide moieties as a carbonic anhydrase inhibitors (specifically against tumor-associated isoforms IX and XII)

Benzenesulfonamides incorporating cyanoacrylamide moieties with activity against tumour-associated human (h) isoforms hCA IX and XII (which are validated antitumor targets) were investigated for their quantitative structural activity relationships (QSAR). Multiple linear regression analysis was used to develop model relationships between molecular descriptors and inhibition constants (Ki). The molecular geometry optimization were performed on all molecules at DFT-B3LYP/6-311++G(d,p) level. Over 1250 molecular descriptors were calculated using Gaussian 09, Hyperchem and EDRAGON programs. Multiple linear regression equations have been developed and validated using leave-one-out cross-validated technique. The derived QSAR models are found to be statistically significant and show good predictive ability.

A Quantum Chemical and Statistical Study of Phenolic Schiff Bases with Antioxidant Activity against DPPH Free Radical

Phenolic Schiff bases are known as powerful antioxidants. To select the electronic, 2D and 3D descriptors responsible for the free radical scavenging ability of a series of 30 phenolic Schiff bases, a set of molecular descriptors were calculated by using B3P86 (Becke's three parameter hybrid functional with Perdew 86 correlation functional) combined with 6-31 + G(d,p) basis set (i.e., at the B3P86/6-31 + G(d,p) level of theory). The chemometric methods, simple and multiple linear regressions (SLR and MLR), principal component analysis (PCA) and hierarchical cluster analysis (HCA) were employed to reduce the dimensionality and to investigate the relationship between the calculated descriptors and the antioxidant activity. The results showed that the antioxidant activity mainly depends on the first and second bond dissociation enthalpies of phenolic hydroxyl groups, the dipole moment and the hydrophobicity descriptors. The antioxidant activity is inversely proportional to the main descriptors. The selected descriptors discriminate the Schiff bases into active and inactive antioxidants.

Pharmacological preconditioning with adenosine A(1) receptor agonist suppresses cellular immune response by an A(2A) receptor dependent mechanism

Under stressful conditions such as ischemia, sepsis, and severe trauma, adenosine levels are elevated and protect the tissue by interaction with G coupled receptors. In a model of peritonitis, we previously found that pharmacological preconditioning (PPC) of mice with a selective adenosine A1 receptor (A1R) agonist, 2-chloro-N(6)-cyclopentyladenosine (CCPA), induced the A2AR which reduces cytokine secretion and leukocyte recruitment. In our present study we determined whether mice PPC will moderate cellular immune response by the same mechanism. Similar to the effect on inflammation, PPC reduced the response to lymphocyte mitogens and allogeneic MLR response. The inhibitory effect of PPC on the immune response was A1R and A2AR dependent as illustrated by experiments with antagonists of these receptors and mice with knock down (KO) receptors. In MLR with PPC splenocytes we found reduced levels of pro-inflammatory cytokines (IFN-γ, IL-15, TNF-α) and elevation of IL-10, as well as elevation of regulatory T-cell. Our data indicate that PPC is able to remarkably suppress cellular immune response due to the sensitization A2AR. This effect of PPC sheds light on the protective role of adenosine in ischemic preconditioning and makes this treatment candidate for the prevention of graft rejection.

Gating of steering signals through phasic modulation of reticulospinal neurons during locomotion

The neural control of movements in vertebrates is based on a set of modules, like the central pattern generator networks (CPGs) in the spinal cord coordinating locomotion. Sensory feedback is not required for the CPGs to generate the appropriate motor pattern and neither a detailed control from higher brain centers. Reticulospinal neurons in the brainstem activate the locomotor network, and the same neurons also convey signals from higher brain regions, such as turning/steering commands from the optic tectum (superior colliculus). A tonic increase in the background excitatory drive of the reticulospinal neurons would be sufficient to produce coordinated locomotor activity. However, in both vertebrates and invertebrates, descending systems are in addition phasically modulated because of feedback from the ongoing CPG activity. We use the lamprey as a model for investigating the role of this phasic modulation of the reticulospinal activity, because the brainstem-spinal cord networks are known down to the cellular level in this phylogenetically oldest extant vertebrate. We describe how the phasic modulation of reticulospinal activity from the spinal CPG ensures reliable steering/turning commands without the need for a very precise timing of on- or offset, by using a biophysically detailed large-scale (19,600 model neurons and 646,800 synapses) computational model of the lamprey brainstem-spinal cord network. To verify that the simulated neural network can control body movements, including turning, the spinal activity is fed to a mechanical model of lamprey swimming. The simulations also predict that, in contrast to reticulospinal neurons, tectal steering/turning command neurons should have minimal frequency adaptive properties, which has been confirmed experimentally.

Ambient polychlorinated biphenyl levels and their evaluation in a metropolitan city

In this study, summer and autumn ambient PCB concentrations were investigated in metropolitan city of Istanbul. 84 congeners were targeted from di-CBs to nona-CBs on both particle and gaseous phases. Gaseous ambient concentrations were determined to be 372 ± 134 pg·m(-3), while on the particle phase this value was 49 ± 17 pg·m(-3), corresponding to an average of 420 pg·m(-3). About one-tenth of all PCBs lay in ambient aerosols, while 90% of all comprise 2-, 3-, 4-, and 5-CBs. Measured ambient concentrations of each congener group were tested against meteorological data. The di-CB concentrations were independent of ambient temperature while northerly winds lead to an increase in their concentrations, which was an indicator of considerable contribution to di-CB concentrations from the medical waste incineration plant in Istanbul. In contrast, other congeners' concentrations were found to be correlated with southerly winds. Being an inland sea and having been contaminated, for years, by industrial discharges along the coastline, volatilization from Marmara Sea was considered as the most probable source of other congeners. PSCF analysis was run with 12-hour trajectories to locate possible local sources and check these results. Gas/particle partitioning was applied using three different models. mr and br values for log PL(0) model were determined as -0.23 ± 0.09 and -3.25 ± 0.38, respectively. For absorption based log Koa model, m and b values were calculated as 0.23 ± 0.08 and -4.73 ± 0.83, respectively.

CD141⁺ myeloid dendritic cells are enriched in healthy human liver

BACKGROUND & AIMS

Extensive populations of liver immune cells detect and respond to homeostatic perturbation caused by damage, infection or malignancy. Dendritic cells (DCs) are central to these activities, governing the balance between tolerance and immunity. Most of our knowledge about human liver DCs is derived from studies on peritumoral tissue. Little is known about the phenotype and function of DCs, in particular the recently described CD141(+) subset, in healthy human liver and how this profile is altered in liver disease.

METHODS

During liver transplantation, healthy donor and diseased explant livers were perfused and hepatic mononuclear cells isolated. Dendritic cell subset frequency and phenotype were characterised in liver perfusates by flow cytometry and the function of CD141(+) DCs was evaluated by mixed lymphocyte reactions (MLRs) and measuring cytokine secretion.

RESULTS

Almost one third of liver CD11c(+) myeloid DCs (mDCs) expressed CD141 compared to <5% of circulating mDCs. Hepatic CD141(+) DCs demonstrated pro-inflammatory function in allogeneic MLRs, inducing T cell production of interferon gamma (IFN-γ) and interleukin (IL)-17. While CD123(+) plasmacytoid DCs (pDCs) and CD1c(+) mDCs were expanded in diseased liver perfusates, CD141(+) DCs were significantly depleted. Despite their depletion, CD141(+) DCs from explant livers produced markedly increased poly(I:C)-induced IFN lambda (IFN-λ) compared with donor DCs.

CONCLUSIONS

Accumulation of CD141(+) DCs in healthy liver, which are significantly depleted in liver disease, suggests differential involvement of mDC subsets in liver immunity.

Human umbilical cord blood mesenchymal stem cells reduce colitis in mice by activating NOD2 signaling to COX2

BACKGROUND & AIMS

Decreased levels or function of nucleotide-binding oligomerization domain 2 (NOD2) are associated with Crohn's disease. NOD2 regulates intestinal inflammation, and also is expressed by human umbilical cord blood-derived mesenchymal stem cells (hUCB-MSCs), to regulate their differentiation. We investigated whether NOD2 is required for the anti-inflammatory activities of MSCs in mice with colitis.

METHODS

Colitis was induced in mice by administration of dextran sulfate sodium or trinitrobenzene sulfonic acid. Mice then were given intraperitoneal injections of NOD2-activated hUCB-MSCs; colon tissues and mesenteric lymph nodes were collected for histologic analyses. A bromodeoxyuridine assay was used to determine the ability of hUCB-MSCs to inhibit proliferation of human mononuclear cells in culture.

RESULTS

Administration of hUCB-MSCs reduced the severity of colitis in mice. The anti-inflammatory effects of hUCB-MSCs were greatly increased by activation of NOD2 by its ligand, muramyl dipeptide (MDP). Administration of NOD2-activated hUCB-MSCs increased anti-inflammatory responses in colons of mice, such as production of interleukin (IL)-10 and infiltration by T regulatory cells, and reduced production of inflammatory cytokines. Proliferation of mononuclear cells was inhibited significantly by co-culture with hUCB-MSCs that had been stimulated with MDP. MDP induced prolonged production of prostaglandin (PG)E2 in hUCB-MSCs via the NOD2-RIP2 pathway, which suppressed proliferation of mononuclear cells derived from hUCB. PGE2 produced by hUCB-MSCs in response to MDP increased production of IL-10 and T regulatory cells. In mice, production of PGE2 by MSCs and subsequent production of IL-10 were required to reduce the severity of colitis.

CONCLUSIONS

Activation of NOD2 is required for the ability of hUCB-MSCs to reduce the severity of colitis in mice. NOD2 signaling increases the ability of these cells to suppress mononuclear cell proliferation by inducing production of PGE2.

Immunotherapy of cancer via mediation of cytotoxic T lymphocytes by methionine enkephalin (MENK)

The aim of this study was to investigate the immunological mechanisms by which synthetic methionine enkephalin (MENK) exerts therapeutic effects on tumor growth. Our findings in vivo or in vitro show that MENK treatment either in vivo or in vitro could up-regulate the percentages of CD8+T cells, induce markers of activated T cells, increased cytotoxic activity against mouse S180 tumor cells and increase secretion of IFNγ. In addition, the adoptively transferred CD8+T cells, after either in vitro or in vivo treatment with MENK, result in significantly increased survival of S180 tumor-bearing mice and significant shrinkage in tumor growth. Opioid receptors are detected on normal CD8+T cells and exposure to MENK leads to increased expression of opioid receptors. Interaction between MENK and the opioid receptors on CD8+T cells appears to be essential for the activation of CTL, since the addition of naltrexone (NTX), an opioid receptor antagonist, significantly inhibits all of the effects of MENK. The evidence obtained indicates that the MENK-induced T cell signaling is associated with a significant up-regulation of Ca2+ influx into the cytoplasm and the translocation of NFAT2 into nucleus, and these signaling effects are also inhibited by naltrexone.

Dynamics of serum metabolites in patients with chronic hepatitis C receiving pegylated interferon plus ribavirin: a metabolomics analysis

OBJECTIVES

Serum samples from patients with chronic hepatitis C were subjected to metabolomics analysis to clarify the pretreatment characteristics of their metabolites and also changes in specific metabolites resulting from antiviral therapy with pegylated interferon plus ribavirin (PegIFN/RBV).

MATERIALS/METHODS

The serum levels of low-molecular-weight metabolites in the twenty patients before and 24weeks after completion of PegIFN/RBV therapy were analyzed using capillary electrophoresis and liquid chromatography-mass spectrometry.

RESULTS

Ten patients showed a non-virological response (NVR) and 10 achieved a sustained virological response (SVR) with eradication of viremia. The pretreatment levels of tryptophan were significantly higher in the patients of SVR than in those of NVR (p=0.010). The area under the curve (AUC) value of tryptophan calculated from the receiver operating characteristic (ROC) curve for discriminating SVR from NVR was 0.84 (95% confidential interval, 0.66-1.02, p=0.010). The ROC curve of multiple logistic regression model incorporating the pretreatment levels of tryptophan and γ-glutamate-arginine showed that the AUC value was highly significant (AUC=0.92, 95% confidential interval, 0.79-1.05, p=0.002). Twenty four weeks after completion of treatment, the levels of γ-glutamyl dipeptides, glutamic acid, 5-oxoproline, glucosamine and methionine sulfoxide were decreased, whereas those of 5-methoxy-3-indoleacetate, glutamine, kynurenine and lysine were increased significantly (p<0.05) in both the NVR and SVR patients.

CONCLUSIONS

The pretreatment serum levels of certain metabolites including tryptophan are associated with the response to PegIFN/RBV therapy. PegIFN/RBV therapy can ameliorate the oxidative stress responsible for glutathione metabolism.

Comparison of Multiple Linear Regressions and Neural Networks based QSAR models for the design of new antitubercular compounds

The performance of two QSAR methodologies, namely Multiple Linear Regressions (MLR) and Neural Networks (NN), towards the modeling and prediction of antitubercular activity was evaluated and compared. A data set of 173 potentially active compounds belonging to the hydrazide family and represented by 96 descriptors was analyzed. Models were built with Multiple Linear Regressions (MLR), single Feed-Forward Neural Networks (FFNNs), ensembles of FFNNs and Associative Neural Networks (AsNNs) using four different data sets and different types of descriptors. The predictive ability of the different techniques used were assessed and discussed on the basis of different validation criteria and results show in general a better performance of AsNNs in terms of learning ability and prediction of antitubercular behaviors when compared with all other methods. MLR have, however, the advantage of pinpointing the most relevant molecular characteristics responsible for the behavior of these compounds against Mycobacterium tuberculosis. The best results for the larger data set (94 compounds in training set and 18 in test set) were obtained with AsNNs using seven descriptors (R(2) of 0.874 and RMSE of 0.437 against R(2) of 0.845 and RMSE of 0.472 in MLRs, for test set). Counter-Propagation Neural Networks (CPNNs) were trained with the same data sets and descriptors. From the scrutiny of the weight levels in each CPNN and the information retrieved from MLRs, a rational design of potentially active compounds was attempted. Two new compounds were synthesized and tested against M. tuberculosis showing an activity close to that predicted by the majority of the models.

Functional aspects of fish lymphocytes

After almost 40 years of studies in comparative immunology, some light has been shed on the evolutive immunobiology of vertebrates, and experimental evidences have shown that acquired immunity, defined by somatic recombination of antigen-binding molecules and memory, is an achievement as ancient as jawless vertebrates. However, the molecular processes generating antigen receptors evolved independently between jawless and jawed fishes, and produced lymphocytic cells with similar functions but employing different sets of genes. In recent years, data have been provided describing some in vitro and in vivo functional responses of fish lymphocytes. After a long gap, the number of specific markers for fish lymphocytes is increasing, thus allowing a first characterisation of lymphocyte subsets. Overall, in the near future it will be possible to open a new chapter in fish immunology and investigate functional immunity of lymphocyte responses by combining the extensive knowledge on immune gene products with markers for molecules and cells. The present review summarizes current knowledge on functional features of fish lymphocytes.

On-line monitoring of food fermentation processes using electronic noses and electronic tongues: a review

Fermentation processes are often sensitive to even slight changes of conditions that may result in unacceptable end-product quality. Thus, close follow-up of this type of processes is critical for detecting unfavorable deviations as early as possible in order to save downtime, materials and resources. Nevertheless the use of traditional analytical techniques is often hindered by the need for expensive instrumentation and experienced operators and complex sample preparation. In this sense, one of the most promising ways of developing rapid and relatively inexpensive methods for quality control in fermentation processes is the use of chemical multisensor systems. In this work we present an overview of the most important contributions dealing with the monitoring of fermentation processes using electronic noses and electronic tongues. After a brief description of the fundamentals of both types of devices, the different approaches are critically commented, their strengths and weaknesses being highlighted. Finally, future trends in this field are also mentioned in the last section of the article.

Probiotic modulation of dendritic cell function is influenced by ageing

Dendritic cells (DCs) are critical for the generation of T-cell responses. DC function may be modulated by probiotics, which confer health benefits in immunocompromised individuals, such as the elderly. This study investigated the effects of four probiotics, Bifidobacterium longum bv. infantis CCUG 52486, B. longum SP 07/3, Lactobacillus rhamnosus GG (L.GG) and L. casei Shirota (LcS), on DC function in an allogeneic mixed leucocyte reaction (MLR) model, using DCs and T-cells from young and older donors in different combinations. All four probiotics enhanced expression of CD40, CD80 and CCR7 on both young and older DCs, but enhanced cytokine production (TGF-β, TNF-α) by old DCs only. LcS induced IL-12 and IFNγ production by DC to a greater degree than other strains, while B. longum bv. infantis CCUG 52486 favoured IL-10 production. Stimulation of young T cells in an allogeneic MLR with DC was enhanced by probiotic pretreatment of old DCs, which demonstrated greater activation (CD25) than untreated controls. However, pretreatment of young or old DCs with LPS or probiotics failed to enhance the proliferation of T-cells derived from older donors. In conclusion, this study demonstrates that ageing increases the responsiveness of DCs to probiotics, but this is not sufficient to overcome the impact of immunosenescence in the MLR.

CD11b(+) cells in donor-specific transfusion prolonged allogenic skin graft survival through indoleamine 2,3-dioxygenase

The aim of this study is to show the effect of donor-specific transfusion (DST) in inducing immunological tolerance mediated by regulatory T cells (Treg) and indoleamine 2,3-dioxygenase (IDO). Skin grafts from H2(d) Balb/c were transplanted into H2(k) C3H/He 7days after the infusion of donor splenocytes, isolated each immune cell populations. Graft survival prolonged in recipients who received splenocytes, MHC class II(+) CD90(-) cells and CD3(-)CD19(-) cells (p<0.001, p<0.05 and p<0.01, respectively). CD11b(+) cell infusion resulted in prolongation of graft survival when compared to CD11c(+) cell infusion (p<0.01). Foxp3(+)CD4(+)CD25(+) T cells were increased after the transplant in recipients infused with CD11b(+) cells (p<0.05). The mixed lymphocyte reaction showed donor-specificity (p<0.001). High IDO expression was observed in CD11b(+) cell infusion group. Graft survival with DST using IDO antagonist (1MT) were not prolonged. In conclusion, DST allows induction of donor-specific tolerance which involves Foxp3(+)CD4(+)CD25(+) T cells and IDO expression.

Quantitative Structure-Activity Relationship Studies of 4-Imidazolyl- 1,4-dihydropyridines as Calcium Channel Blockers

OBJECTIVE(S)

The structure- activity relationship of a series of 36 molecules, showing L-type calcium channel blocking was studied using a QSAR (quantitative structure-activity relationship) method.

MATERIALS AND METHODS

Structures were optimized by the semi-empirical AM1 quantum-chemical method which was also used to find structure-calcium channel blocking activity trends. Several types of descriptors, including electrotopological, structural and thermodynamics were used to derive a quantitative relationship between L-type calcium channel blocking activity and structural properties. The developed QSAR model contributed to a mechanistic understanding of the investigated biological effects.

RESULTS

Multiple linear regressions (MLR) was employed to model the relationships between molecular descriptors and biological activities of molecules using stepwise method and genetic algorithm as variable selection tools. The accuracy of the proposed MLR model was illustrated using cross-validation, and Y-randomisation -as the evaluation techniques.

CONCLUSION

The predictive ability of the model was found to be satisfactory and could be used for designing a similar group of 1,4- dihydropyridines , based on a pyridine structure core which can block calcium channels.

Interaction of membrane/lipid rafts with the cytoskeleton: impact on signaling and function: membrane/lipid rafts, mediators of cytoskeletal arrangement and cell signaling

The plasma membrane in eukaryotic cells contains microdomains that are enriched in certain glycosphingolipids, gangliosides, and sterols (such as cholesterol) to form membrane/lipid rafts (MLR). These regions exist as caveolae, morphologically observable flask-like invaginations, or as a less easily detectable planar form. MLR are scaffolds for many molecular entities, including signaling receptors and ion channels that communicate extracellular stimuli to the intracellular milieu. Much evidence indicates that this organization and/or the clustering of MLR into more active signaling platforms depends upon interactions with and dynamic rearrangement of the cytoskeleton. Several cytoskeletal components and binding partners, as well as enzymes that regulate the cytoskeleton, localize to MLR and help regulate lateral diffusion of membrane proteins and lipids in response to extracellular events (e.g., receptor activation, shear stress, electrical conductance, and nutrient demand). MLR regulate cellular polarity, adherence to the extracellular matrix, signaling events (including ones that affect growth and migration), and are sites of cellular entry of certain pathogens, toxins and nanoparticles. The dynamic interaction between MLR and the underlying cytoskeleton thus regulates many facets of the function of eukaryotic cells and their adaptation to changing environments. Here, we review general features of MLR and caveolae and their role in several aspects of cellular function, including polarity of endothelial and epithelial cells, cell migration, mechanotransduction, lymphocyte activation, neuronal growth and signaling, and a variety of disease settings. This article is part of a Special Issue entitled: Reciprocal influences between cell cytoskeleton and membrane channels, receptors and transporters. Guest Editor: Jean Claude Hervé.

Leukoreduction system chambers are an efficient, valid, and economic source of functional monocyte-derived dendritic cells and lymphocytes

The demand for human monocyte-derived dendritic cells (moDCs), as well as for primary human B and T lymphocytes for immunological research purposes has been increased in recent years. Classically, these monocytes are isolated from blood, leukapheresis products or buffy coats of healthy donors by plastic adherence of peripheral blood mononuclear cells (PBMCs), followed by stimulation with granulocyte macrophage colony-stimulating factor (GM-CSF) and interleukin (IL)-4, while lymphocytes are usually isolated from the non-adherent fraction (NAF) by magnetic cell sorting. However, donor-blood is a limited resource and not every blood bank offers leukapheresis products or buffy coats for laboratory use. Additionally, a leukapheresis is very expensive and also the generation/isolation of cells is time- and cost-intensive. To overcome some of these obstacles, we evaluated if low-cost leukoreduction system chambers (LRSCs), which arise after routine donor plateletpheresis procedures, and are usually discarded, would be an alternative and appropriate source of PBMCs to generate moDCs and to isolate lymphocytes. By analyzing the number and phenotype of immature and mature dendritic cells (DCs), as well as of B and T lymphocytes derived from LRSCs, we found all cells to be of high quantity and quality. Further investigations on DCs comprising transwell migration assays, allogeneic mixed lymphocyte reactions (MLR), cytokine secretion assays, and cytotoxic T cell induction assays revealed high migratory, as well as stimulatory capacity of these cells. In addition, DCs and T cells were efficiently electroporated with mRNA and showed characteristic cytokine production after co-culture, demonstrating LRSCs as an efficient, valid, and economic source for generation of moDCs and lymphocytes for research purposes.

Structural requirements of 3-carboxyl-4(1H)-quinolones as potential antimalarials from 2D and 3D QSAR analysis

Malaria is a fatal tropical and subtropical disease caused by the protozoal species Plasmodium. Many commonly available antimalarial drugs and therapies are becoming ineffective because of the emergence of multidrug resistant Plasmodium falciparum, which drives the need for the development of new antimalarial drugs. Recently, a series of 3-carboxyl-4(1H)-quinolone analogs, derived from the famous compound endochin, were reported as promising candidates for orally efficacious antimalarials. In this study, to analyze the structure-activity relationships (SAR) of these quinolones and investigate the structural requirements for antimalarial activity, the 2D multiple linear regressions (MLR) method and 3D comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) methods are employed to evolve different QSAR models. All these models give satisfactory results with highly accurate fitting and strong external predictive abilities for chemicals not used in model development. Furthermore, the contour maps from 3D models can provide an intuitive understanding of the key structure features responsible for the antimalarial activities. In conclusion, we summarize the detailed position-specific structural requirements of these derivatives accordingly. All these results are helpful for the rational design of new compounds with higher antimalarial bioactivities.

Advances of vibrational spectroscopic methods in phytomics and bioanalysis

During the last couple of years great advances in vibrational spectroscopy including near-infrared (NIR), mid-infrared (MIR), attenuated total reflection (ATR) and imaging and also mapping techniques could be achieved. On the other hand spectral treatment features have improved dramatically allowing filtering out relevant information from spectral data much more efficiently and providing new insights into the biochemical composition. These advances offer new possible quality control strategies in phytomics and enable to get deeper insights into biochemical background in terms of medicinal relevant questions. It is the aim of the present article pointing out the technical and methodological advancements in the NIR and MIR field and to demonstrate the individual methods efficiency by discussing distinct selected applications.

Effects of transmembrane region variability on cell surface expression and allorecognition of HLA-DP3

The functional relevance of polymorphisms outside the peptide binding groove of HLA molecules is poorly understood. Here we have addressed this issue by studying HLA-DP3, a common antigen relevant for functional matching algorithms of unrelated hematopoietic stem cell transplantation (HSCT) encoded by two transmembrane (TM) region variants, DPB1(*)03:01 and DPB1(*)104:01. The two HLA-DP3 variants were found at a overall allelic frequency of 10.4% in 201 volunteer stem cell donors, at a ratio of 4.2:1. No significant differences were observed in cell surface expression levels of the two variants on B lymphoblastoid cell lines (BLCL), primary B cells or monocytes. Three different alloreactive T cell lines or clones showed similar levels of activation marker CD107a and/or CD137 upregulation in response to HLA-DP3 encoded by DPB1(*)03:01 and DPB1(*)104:01, either endogenously on BLCL or after lentiveral-vector mediated transfer into the same cellular background. These data provide, for the first time, direct evidence for a limited functional role of a TM region polymorphism on expression and allorecognition of HLA-DP3 and are compatible with the notion that the two variants can be considered as a single functional entity for unrelated stem cell donor selection.

Caveolins: targeting pro-survival signaling in the heart and brain

The present review discusses intracellular signaling moieties specific to membrane lipid rafts (MLRs) and the scaffolding proteins caveolin and introduces current data promoting their potential role in the treatment of pathologies of the heart and brain. MLRs are discreet microdomains of the plasma membrane enriched in gylcosphingolipids and cholesterol that concentrate and localize signaling molecules. Caveolin proteins are necessary for the formation of MLRs, and are responsible for coordinating signaling events by scaffolding and enriching numerous signaling moieties in close proximity. Specifically in the heart and brain, caveolins are necessary for the cytoprotective phenomenon termed ischemic and anesthetic preconditioning. Targeted overexpression of caveolin in the heart and brain leads to induction of multiple pro-survival and pro-growth signaling pathways; thus, caveolins represent a potential novel therapeutic target for cardiac and neurological pathologies.

2D Quantitative structure-property relationship study of mycotoxins by multiple linear regression and support vector machine

In the present work, support vector machines (SVMs) and multiple linear regression (MLR) techniques were used for quantitative structure–property relationship (QSPR) studies of retention time (t_R) in standardized liquid chromatography–UV–mass spectrometry of 67 mycotoxins (aflatoxins, trichothecenes, roquefortines and ochratoxins) based on molecular descriptors calculated from the optimized 3D structures. By applying missing value, zero and multicollinearity tests with a cutoff value of 0.95, and genetic algorithm method of variable selection, the most relevant descriptors were selected to build QSPR models. MLR and SVMs methods were employed to build QSPR models. The robustness of the QSPR models was characterized by the statistical validation and applicability domain (AD). The prediction results from the MLR and SVM models are in good agreement with the experimental values. The correlation and predictability measure by r² and q² are 0.931 and 0.932, repectively, for SVM and 0.923 and 0.915, respectively, for MLR. The applicability domain of the model was investigated using William’s plot. The effects of different descriptors on the retention times are described.