Effects of dna-dependent protein kinase inhibition by NU7026 on dna repair and cell survival in irradiated gastric cancer cell line N87.

UNLABELLED: Repair of radiation-induced dna double-strand breaks is a key mechanism in cancer cell radio-resistance. The synthesized compound NU7026 specifically inhibits dna-dependent protein kinase (dna-pk) within the non-homologous end-joining repair mechanism. Earlier studies demonstrated increased radiosensitivity in dna-pk deficient cells compared with wild-type cells. In chronic leukemia cells, NU7026 appears to enhance the cytotoxic effect of chlorambucil. The radio-modifying effects of NU7026 on cell survival, cell cycle, apoptosis, and dna double-strand break repair have yet to be studied in gastric cancer cells. METHODS: The gastric cancer cell line N87 was treated with 0 Gy or 4 Gy in the presence of NU7026 at a dose range of 0-20 µmol/L. Clonogenic assays were used to assess cell survival after treatment. Cell-cycle distribution was analyzed using propidium iodide with fluorescence-activated cell sorting. Apoptosis was detected using annexin-V and propidium iodide with fluorescence-activated cell sorting. The γH2AX assay was used to measure dna double-strand breaks. RESULTS: Statistically significant increases in G2/M arrest were observed in N87 cells treated with radiation and NU7026 compared with those treated with radiation alone (p = 0.0004). Combined treatment also led to an increase in apoptosis (p = 0.01). At 24 hours, the γH2AX analysis revealed more dna double-strand breaks in N87 cells treated with radiation and NU7026 than in those treated with radiation alone (p = 0.04). Clonogenic assays demonstrated declining cell survival as both the radiation and the NU7026 dose increased. The dose enhancement factor at 0.1 survival fraction was 1.28 when N87 cells were treated with 4 Gy radiation and 5 µmol/L NU7026. CONCLUSIONS: In gastric cancer cells, NU7026 appears to enhance the cytotoxic effect of irradiation as assessed by clonogenic assays. This increased cytotoxicity might be the result of an increase in dna double-strand breaks resulting in G2/M cell arrest and possibly higher levels of apoptosis.

Potential of Müller Glial Cells in Regeneration of Retina; Clinical and Molecular Approach.

Retinal degenerative diseases are a group of heterogeneous eye diseases that affect a significant percentage of the world's population, i.e., age-related macular degeneration (AMD), diabetic retinopathy, retinitis pigmentosa (RP), and glaucoma. Regenerative medicines look for novel therapies for severe injuries or chronic diseases, e.g., retina degeneration. Müller glia is the only retinal glia type with a common embryonic origin, with retinal neurons derived from the neural crest. Also, the lack of neurons in the retina does not automatically regenerate. Therefore, Müller glial cells, which make up about 5% of retinal cells, are a potent source for retinal regeneration. Following the retinal damage, Müller glial cells dedifferentiate and re-enter the cell cycle, producing multipotent progenitor cells. This feature leads to applying Müller glial cells in the regeneration of the retina. This study reviews this feature's molecular and clinical approaches, focusing on the critical signaling pathways, generation and transplantation methods, and clinical and sub-clinical challenges.

QSAR study of heparanase inhibitors activity using artificial neural networks and Levenberg-Marquardt algorithm.

A linear and non-linear quantitative structure-activity relationship (QSAR) study is presented for modeling and predicting heparanase inhibitors' activity. A data set that consisted of 92 derivatives of 2,3-dihydro-1,3-dioxo-1H-isoindole-5-carboxylic acid, furanyl-1,3-thiazol-2-yl and benzoxazol-5-yl acetic acids is used in this study. Among a large number of descriptors, four parameters classified as physico-chemical, topological and electronic indices are chosen using stepwise multiple regression technique. The artificial neural networks (ANNs) model shows superiority over the multiple linear regressions (MLR) by accounting 87.9% of the variances of antiviral potency of the heparanase inhibitors. This paper focuses on investigating the role of weight update functions in developing ANNs. Levenberg-Marquardt (L-M) algorithm shows a better performance compared with basic back propagation (BBP) and conjugate gradient (CG) algorithms. The accuracy of 4-3-1 L-M ANN model was illustrated using leave-one-out (LOO), leave-multiple-out (LMO) cross-validations and Y-randomization. The mean effect of descriptors and sensitivity analysis show that log P is the most important parameter affecting the inhibitory behavior of the molecules.

Comparison between two different hemichromes of hemoglobins (HbA and HbS) induced by n-dodecyl trimethylammonium bromide: chemometric study.

The interaction of n-dodecyl trimethylammonium bromide (DTAB) with oxyhemoglobin A and oxyhemoglobin S is investigated using UV-visible absorption spectra and chemometric resolution techniques. Oxyhemoglobins (A and S) induced to partial oxidized form (ferrihemoglobin) by DTAB and finally transform to fully oxidized hemichrome. Hemichrome mole fractions of HbS are more than HbA because of more hydrophobic interaction of DTAB-HbS in second set of binding site relative to DTAB-HbA. The visible spectra between 500 and 650 nm are used for identifying the present components in solution because each species of hemoglobin has a specific spectrum in this region. The number of components and mole fraction of mentioned species were determined by employing chemometric resolution techniques. Subspace comparison was used for determination of the number of components in each concentration of hemoglobin and DTAB. After the determination of components, multivariate curve resolution-alternating least square (MCR-ALS) by initial estimates of spectral profiles and proper constraints, was used to resolve the data matrix into pure concentration and spectral profiles. The results show that both number and mole fraction of components which were formed during hemoglobin (HbA and HbS) oxidation by DTAB were initial hemoglobin concentrations independent. Furthermore, in average the mole fraction of hemichrome of HbS is 14.4% more than HbA. On the other hand, the mole fraction of HbA ferrihemoglobin is 15.6% higher than HbS averagely.

Model-based analysis for kinetic complexation study of Pizda and Cu(II).

In the present work, the multivariate kinetic complexation of a new synthesized ligand, 1-(2''-hydroxyl cyclohexyl)-3'-[aminopropyl]-4-[3'-aminopropyl]piperazine (Pizda) and Cu(2+) in 50% ethanol-water solution is investigated using the UV-vis stopped-flow technique and state-of-the-art multi-wavelength numerical analysis. Model-based least squares fitting analysis or hard modeling is a specific part of chemometrics which is based on mathematical relationships for describing the measurements. Some recent developments include the incorporation of the effects of non-ideal experimental conditions into the fitting algorithm so it can substantially simplify experimental procedures. In this study no buffers are required because pH changes are taken into computations. Some 21 multi-wavelength kinetic measurements, taken at various initial concentrations of [H(+)] were analyzed globally, i.e. simultaneously applying an all inclusive reaction mechanism and a common set of species spectra. Using numerical analysis, the pH of the experimental solutions was allowed to vary as a consequence of the proceeding reactions. This enabled the complete kinetic analysis of the formation and dissociation of Cu(Pizda)(n+). Here protonation equilibria have been directly incorporated into the rate law, so thus variable pH values have been allowed during each measurement. Using the independently estimated stability constants (from spectrophotometric and potentiometric measurements) for the Cu(Pizda)(n+) complexes, a total of six rate constants and one protonation constant could be elucidated. The results of the analysis include the concentration distribution and spectra of all chemical species involved in the reaction. A low standard deviation and residual profiles obtained validate the results.

Determination of essential oil components of Artemisia haussknechtii Boiss. using simultaneous hydrodistillation-static headspace liquid phase microextraction-gas chromatography mass spectrometry.

A novel method for extraction and analysis of volatile compounds of Artemisia haussknechtii Boiss., using simultaneous hydro-distillation and static headspace liquid microextraction followed by gas chromatography-mass spectrometry (SHD-SHLPME-GCMS) is developed. SHLPME parameters including nature of extracting solvent, headspace volume and design, extraction time, sample weight and microdrop volume were optimized. Comparison of hydro-distillation gas chromatography-mass spectrometry and HD-SHLPME-GCMS showed that the latter method is fast, simple, inexpensive and effective for the analysis of volatile compounds of aromatic plants. By using this method, 56 compounds were extracted and identified for Artemisia haussknechtii Boiss. The main constituents of its essential oil that were extracted by HD-SHLPME method, include camphor (41.01%), 1,8-cineole (32.35%), cis-davanone (3.68%), 4-terpineol (2.99%), linalool (2.84%), beta-fenchyl alcohol (2.72%), and borneol (2.58%).

A novel resource model for underprivileged health support: Community Medical Outreach.

Community Medical Outreach is a student-run organization that provides healthcare access to medically underprivileged farm workers. The program exploits the substantial energy, enthusiasm and organizational capacity of pre-medical students as they prepare to apply for medical school. All of the partners benefit from the interchange. The students gain from a unique first-hand medical experience that demonstrates their leadership, management skills, commitment to a healthcare team, and focus on care at the community level. Those in most need gain healthcare access. Volunteer staff and physicians are inspired by the students and are energized by caring for those most in need of health care. The companies, agencies, and organizations donating supplies, drugs, expertise, and sponsorship benefit from enhanced public relations. The article describes the initiation, the lessons learned, the critical importance of linkages, and the essential components such as individual and institutional liability. Community Medical Outreach is an important vehicle for shaping students in the process of becoming physicians, shaping those involved with the process of admitting students, shaping training experiences, and shaping new models of health care.

Cooperative alpha-helix formation of beta-lactoglobulin induced by sodium n-alkyl sulfates.

It is generally assumed that folding intermediates contain partially formed native-like secondary structures. However, if we consider the fact that the conformational stability of the intermediate state is simpler than that of the native state, it would be expected that the secondary structures in a folding intermediate would not necessarily be similar to those of the native state. beta-Lactoglobulin is a predominantly beta-sheet protein, although it has a markedly high intrinsic preference for alpha-helical structure. The formation of non-native alpha-helical intermediate of beta-lactoglobulin was induced by n-alkyl sulfates including sodium octyl sulfate, SOS; sodium decyl sulfate, SDeS; sodium dodecyl sulfate, SDS; and sodium tetradecyl sulfate, STS at special condition. The effect of n-alkyl sulfates on the structure of native beta-lactoglobulin at pH 2 was utilized to investigate the contribution of hydrophobic interactions to the stability of non-native alpha-helical intermediate. The addition of various concentrations of n-alkyl sulfates to the native state of beta-lactoglobulin (pH 2) appears to support the stabilized form of non-native alpha-helical intermediate at pH 2. The m values of the intermediate state of beta-lactoglobulin by SOS, SDeS, SDS and STS showed substantial variation. The enhancement of m values as the stability criterion of non-native alpha-helical intermediate state corresponded with increasing chain length of the cited n-alkyl sulfates. The present results suggest that the folding reaction of beta-lactoglobulin follows a non-hierarchical mechanism and hydrophobic interactions play important roles in stabilizing the non-native alpha-helical intermediate state.

1H, 13C, NH, HH, CH COSY, HH NOESY NMR and UV-vis studies of Solophenyl red 3BL dye azo-hydrazone tautomerism in various solvents.

Azo-hydrazone tautomeric behavior of polyazo Solophenyl red 3BL (C.I. Direct 80) dye in different solvents (water, methanol and DMSO) was investigated using 1H, 13C, NH, HH, CH COSY, HH NOESY NMR techniques and UV-vis spectroscopy. Two-dimensional NMR experiments were used to assign 1H, 13C and 15N NMR lines unambiguously. Results showed that the hydrazone-form proton NMR signal appeared in the weakest field with respect to tetramethylsilane, in comparison with the amide and phenolic proton NMR signals. UV-vis absorption spectroscopic evidences showed that azo-hydrazone mixture exists in water and DMSO solvents, but in methanol, only azo tautomer was dominant, which was in a good agreement with NMR spectroscopic results.

Candidemia in patients with prolonged fever in Kashan, Iran.

BACKGROUND AND PURPOSE: Candida species are considered a common cause of fungal blood stream infections, which are associated with considerable mortality and morbidity rates, especially in the admitted and immunocompromised patients. Despite the increase in new and available antifungal agents, the emergence of resistant strains is growing. Regarding this, the aim of the present study was to assess the fungal epide-miology of candidemia and the antifungal susceptibility patterns against five current antifungal agents among the patients with prolonged fever, who were admitted to Beheshti Educational Hospital, Kashan, Iran. MATERIALS AND METHODS: This cross-sectional study was conducted on 253 hospitalized patients with prolonged fever despite receiving broad-spectrum antibiotic therapy. Blood samples were collected aseptically, and then cultured using an automated blood culture system and conventional broth culture bottle. Candida isolates were identified at species level using morphological and physiological properties and produced color on the CHROMagar Candida. Furthermore, the antifungal susceptibility testing was performed using (CLSI M27-A3 and CLSI M27-S4) broth microdilution methods. RESULTS: The most positive cultures were detected by the automated blood culture system. C.albicans (%50) was the most prevalent species, followed by C. glabrata (%40), and C. parapsilosis, (%10) respectively .The mortality rate was high (%60) and most patients with candidemia were admitted to the Intensive Care Unit and Neonatal Intensive Care Unit. All isolates were susceptible to amphotericin B, while the highest resistance belonged to caspofungin. CONCLUSION: In this study, high resistance was reported, especially for caspofungin, which can be regarded as the emergence of caspofungin-resistant strains. Regarding this, the establishment of a surveillance and prevention program for the reduction of the emergence of resistant species is necessary.

Antimicrobial susceptibility testing of Brucella melitensis isolated from patients with acute brucellosis in a centre of Iran.

UNLABELLED: Structured to Purpose: Human brucellosis is one of the most common zoonotic infections worldwide, which remains one of the major problems for public health. Despite the World Health Organization's recommendation for human brucellosis treatment, sporadic cases of relapse have been reported. The aim of this study was to assess the susceptibility of Brucella isolates to common antibiotics that are prescribed by the physician for the treatment of brucellosis and also to determine the minimum inhibitory concentration 50% (MIC 50 ) and MIC 90 for these antibiotics. MATERIALS AND METHODS: Forty-eight Brucella strains were collected from patients with acute brucellosis. Species identification was made based on the conventional methods. MIC of rifampin, doxycycline, ciprofloxacin, trimethoprim- sulfamethoxazole, streptomycin, azithromycin and ceftriaxone was determined by E-test. RESULTS: All the 48 Brucella isolates (47 blood samples and one synovial fluid) were identified as Brucella melitensis. No antimicrobial-resistant strains were recognised. Trimethoprim-sulfamethoxazole had the lowest MIC 50 (0.016 µg/ml) and MIC 90 (0.064 µg/ml), whereas MIC 50 and MIC 90 of streptomycin and azithromycin had the highest level at 0.625, 1.5 µg/ml and 0.25, 1 µg/ml, respectively. All the isolates were susceptible to rifampin, and only one of the isolates had a reduced sensitivity to rifampin (1 µg/ml). CONCLUSIONS: Although all the Brucella isolates were susceptible, antimicrobial susceptibility test should be recommended in patients with recurrent brucellosis or life-threatening organ involvement.

Simulation of 13C nuclear magnetic resonance spectra of lignin compounds using principal component analysis and artificial neural networks.

Theoretical models relating atom-based structural descriptors to 13C NMR chemical shifts were used to accurately simulate 13C NMR spectra of lignin model compounds (poly-substituted phenols). The structure-activity relationship (SAR) studies for 15 lignins using pattern recognition methods of principal component analysis (PCA) and artificial neural networks (ANNs) were performed in this work. The most important parameters affecting the 13C chemical shifts of different carbons were descriptors consisting of the charge density of the atoms at different distances from the center carbon. Among the large number of parameters, these descriptors were selected using PCA and were used as ANN input. The least square regression analyses of the results indicate correlation coefficient (R) values in excess of 0.983 for the total data set.

Prediction of thermal conductivity detection response factors using an artificial neural network.

The main aim of the present work was the development of a quantitative structure-activity relationship method using an artificial neural network (ANN) for predicting the thermal conductivity detector response factor. As a first step a multiple linear regression (MLR) model was developed and the descriptors appearing in this model were considered as inputs for the ANN. The descriptors of molecular mass, number of vibrational modes of the molecule, molecular surface area and Balaban index appeared in the MLR model. In agreement with the molecular diameter approach, molecular mass and molecular surface area play a major role in estimating the thermal conductivity detector response factor (TCD-RF). A 4-7-1 neural network was generated for the prediction of the TCD-RFs of a collection of 110 organic compounds including hydrocarbons, benzene derivatives, esters, alcohols, aldehydes, ketones and heterocyclics. The mean absolute error between the ANN calculated and the experimental values of the response factors was 0.02 for the prediction set.

Development of comprehensive descriptors for multiple linear regression and artificial neural network modeling of retention behaviors of a variety of compounds on different stationary phases.

A new series of six comprehensive descriptors that represent different features of the gas-liquid partition coefficient, K(L), for commonly used stationary phases is developed. These descriptors can be considered as counterparts of the parameters in the Abraham solvatochromic model of solution. A separate multiple linear regression (MLR) model was developed by using the six descriptors for each stationary phase of poly(ethylene glycol adipate) (EGAD), N,N,N',N'-tetrakis(2-hydroxypropyl) ethylenediamine (THPED), poly(ethylene glycol) (Ucon 50 HB 660) (U50HB), di(2-ethylhexyl)phosphoric acid (DEHPA) and tetra-n-butylammonium N,N-(bis-2-hydroxylethyl)-2-aminoethanesulfonate (QBES). The results obtained using these models are in good agreement with the experiment and with the results of the empirical model based on the solvatochromic theory. A 6-6-5 neural network was developed using the descriptors appearing in the MLR models as inputs. Comparison of the mean square errors (MSEs) shows the superiority of the artificial neural network (ANN) over that of the MLR. This indicates that the retention behavior of the molecules on different columns show some nonlinear characteristics. The experimental solvatochromic parameters proposed by Abraham can be replaced by the calculated descriptors in this work.

Prediction of electrophoretic mobilities of sulfonamides in capillary zone electrophoresis using artificial neural networks.

Artificial neural networks (ANNs) were successfully developed for the modeling and prediction of electrophoretic mobility of a series of sulfonamides in capillary zone electrophoresis. The cross-validation method was used to evaluate the prediction ability of the generated networks. The mobility of sulfonamides as positively charged species at low pH and negatively charged species at high pH was investigated. The results obtained using neural networks were compared with the experimental values as well as with those obtained using the multiple linear regression (MLR) technique. Comparison of the results shows the superiority of the neural network models over the regression models.

Prediction of relative response factors for flame ionization and photoionization detection using self-training artificial neural networks.

The relative response factors (RRFs) of a flame ionization detection (FID) system and two pulsed discharge photoionization detection (PID) systems with different discharge gases are predicted for a set of organic compounds containing various functional groups. As a first step, numerical descriptors were calculated based on the molecular structures of compounds. Then, multiple linear regression (MLR) was employed to find informative subsets of descriptors that can predict the RRFs of these compounds. The selected MLR model for the FID system includes seven descriptors and two selected MLR models for the PID systems with argon- and krypton-doped helium as the discharge gases, respectively, include six and five descriptors. The descriptors appearing in the MLR models were considered as inputs for the self-training artificial neural networks (STANNs). A 7-7-1 STANN was generated for prediction of RRFs of the FID system, and two STANNs with the topologies of 6-7-1 and 5-6-1 were generated for the two PID systems. Comparison of the results indicates the superiority of neural networks over that of the MLR method. This is due to the nonlinear behaviors of relative response factors for all type of detectors studied in this work.

Artificial neural network modeling of Kováts retention indices for noncyclic and monocyclic terpenes.

A quantitative structure-property relationship study based on multiple linear regression (MLR) and artificial neural network (ANN) techniques was carried out to investigate the retention behavior of some terpenes on the polar stationary phase (Carbowax 20 M). A collection of 53 noncyclic and monocyclic terpenes was chosen as data set that was randomly divided into two groups, a training set and a prediction set consist of 41 and 12 molecules, respectively. A total of six descriptors appearing in the MLR model consist of one electronic, two geometric, two topological and one physicochemical descriptors. Except for the geometric parameters the remaining descriptors have a pronounced effect on the retention behavior of the terpenes. A 6-5-1 ANN was generated by using the six descriptors appearing in the MLR model as inputs. The mean of relative errors between the ANN calculated and the experimental values of the Kováts retention indexs for the prediction set was 1.88%. This is in aggreement with the relative error obtained by experiment.

Use of self-training artificial neural networks in modeling of gas chromatographic relative retention times of a variety of organic compounds.

A quantitative structure-activity relationship study based on multiple linear regression (MLR), artificial neural network (ANN), and self-training artificial neural network (STANN) techniques was carried out for the prediction of gas chromatographic relative retention times of 13 different classes of organic compounds. The five descriptors appearing in the selected MLR model are molecular density, Winer number, boiling point, polarizability and square of polarizability. A 5-6-1 ANN and a 5-4-1 STANN were generated using the five descriptors appearing in the MLR model as inputs. Comparison of the standard errors and correlation coefficients shows the superiority of ANN and STANN over the MLR model. This is due to the fact that the retention behaviors of molecules show non-linear characteristics. Inspection of the results of STANN and ANN shows there are few differences between these methods. However, optimization of STANN is much faster and the number of adjustable parameters for this technique is much less compared with those of the conventional ANN.

Prediction of electrophoretic mobilities of alkyl- and alkenylpyridines in capillary electrophoresis using artificial neural networks.

The electrophoretic mobilities of 31 isomeric alkyl- and alkenylpyridines in capillary electrophoresis were predicted using an artificial neural network (ANN). The multiple linear regression (MLR) technique was used to select the descriptors as inputs for the artificial neural network. The neural network is a fully connected back-propagation model with a 3-6-1 architecture. The results obtained using the neural network were compared with those obtained using the MLR technique. Standard error of training and standard error of prediction were 6.28 and 5.11%, respectively, for the MLR model and 1.03 and 1.20%, respectively, for the ANN model. Two geometric parameters and one electronic descriptor that were used as inputs in the ANN are able to distinguish between the isomers.

Prediction of relative response factors of electron-capture detection for some polychlorinated biphenyls using chemometrics.

The relative response factor (RRF) of an electron-capture detection (ECD) system is predicted for a set of 118 polychlorinated biphenyls (PCBs). Due to the wide range of relative retention times of PCB congeners, the RRFs of these compounds were calculated based on two different internal standards. Therefore, the compounds were divided into two molecular subsets. As a first step, multiple linear regression (MLR) was employed to find informative descriptors that can predict the RRFs of these compounds. Two descriptors of molecular ion ionization potential (MIIP) and ionization potential of the molecule (IP) that are related to affinity of the compounds for the electrons show the highest mean effects in subsets 1 and 2. respectively. The descriptors appearing in the MLR models were considered as inputs for developing the back-propagation artificial neural networks (BP-ANN). Two networks with the architectures of 5-5-1 and 7-6-1 were generated for the prediction of RRFs of molecules of subsets 1 and 2, respectively. Comparison of the results indicates the superiority of neural networks over that of the MLR method indicating the nonlinear behaviors of the ECD system. Inspection of the models reveals that the surface of the molecules play different roles in response factors of two subsets due to rotation of one phenyl group with respect to the other for the subset consisting of larger number of chlorine atoms.