The effect of chronic lithium treatment on hippocampal progenitor cells: Transcriptomic analysis and systems pharmacology.

OBJECTIVE: To identify the genomics underpinning the increased volume of the hippocampus after long-term administration of lithium (Li) in bipolar disorder patients, hypothesizing the possible contribution of cell growth and differentiation pathways to this complication. METHODS: RNA-seq profiles of four samples of hippocampal progenitor cells chronically treated with a high dose of Li and three samples chronically treated with the therapeutic dose were retrieved from NCBI-GEO. The raw data underwent filtration, quality control, expression fold change, adjusted significance, functional enrichment, and pharmacogenomic analyses. RESULTS: CCND1, LOXL2, and PRNP were identified as the genes involved in the drug response and the chronic effects of Li in the hippocampal cells. GSK-3ß was also a hub in the pharmacogenomic network of Li. In addition, ZMPSTE24 and DHX35 were identified as the important genes in lithium therapy. CONCLUSIONS: As shown by gene ontology results, these findings conclude that lithium may increase the size of the hippocampus in bipolar patients by stimulating the generation of new neurons and promoting their differentiation into neuroblasts, neurons, or microglia.

Individuals' Intention to Engage in Outpatient Cardiac Rehabilitation Programs: Prediction Based on an Enhanced Model.

Motivation is an important factor in encouraging individuals to attend rehabilitation and underpins many approaches to engagement. The aims of this study were to develop an accurate model able to predict individual intention to engage in outpatient cardiac rehabilitation (CR) programs based on the first stage of the Model of Therapeutic Engagement integrated into a socio-environmental context. The cross-sectional study in the cardiology ward of an Australian hospital included a total of 217 individuals referred to outpatient CR. Through an ordinal logistic regression, the effect of random forest (RF)-selected profile features on individual intention to engage in outpatient CR was explored. The RF based on the conditional inference trees predicted the intention to engage in outpatient CR with high accuracy. The findings highlighted the significant roles of individuals' 'willingness to consider the treatment', 'perceived self-efficacy' and 'perceived need for rehabilitation' in their intention, while the involvement of 'barriers to engagement' and 'demographic and medical factors' was not evident.

Prediction of road traffic death rate using neural networks optimised by genetic algorithm.

Road traffic injuries (RTIs) are realised as a main cause of public health problems at global, regional and national levels. Therefore, prediction of road traffic death rate will be helpful in its management. Based on this fact, we used an artificial neural network model optimised through Genetic algorithm to predict mortality. In this study, a five-fold cross-validation procedure on a data set containing total of 178 countries was used to verify the performance of models. The best-fit model was selected according to the root mean square errors (RMSE). Genetic algorithm, as a powerful model which has not been introduced in prediction of mortality to this extent in previous studies, showed high performance. The lowest RMSE obtained was 0.0808. Such satisfactory results could be attributed to the use of Genetic algorithm as a powerful optimiser which selects the best input feature set to be fed into the neural networks. Seven factors have been known as the most effective factors on the road traffic mortality rate by high accuracy. The gained results displayed that our model is very promising and may play a useful role in developing a better method for assessing the influence of road traffic mortality risk factors.

The use of artificial neural networks and multiple linear regression to predict rate of medical waste generation.

Prediction of the amount of hospital waste production will be helpful in the storage, transportation and disposal of hospital waste management. Based on this fact, two predictor models including artificial neural networks (ANNs) and multiple linear regression (MLR) were applied to predict the rate of medical waste generation totally and in different types of sharp, infectious and general. In this study, a 5-fold cross-validation procedure on a database containing total of 50 hospitals of Fars province (Iran) were used to verify the performance of the models. Three performance measures including MAR, RMSE and R(2) were used to evaluate performance of models. The MLR as a conventional model obtained poor prediction performance measure values. However, MLR distinguished hospital capacity and bed occupancy as more significant parameters. On the other hand, ANNs as a more powerful model, which has not been introduced in predicting rate of medical waste generation, showed high performance measure values, especially 0.99 value of R(2) confirming the good fit of the data. Such satisfactory results could be attributed to the non-linear nature of ANNs in problem solving which provides the opportunity for relating independent variables to dependent ones non-linearly. In conclusion, the obtained results showed that our ANN-based model approach is very promising and may play a useful role in developing a better cost-effective strategy for waste management in future.

Gamma-turn types prediction in proteins using the two-stage hybrid neural discriminant model.

Due to the slightly success of protein secondary structure prediction using the various algorithmic and non-algorithmic techniques, similar techniques have been developed for predicting gamma-turns in proteins by Kaur and Raghava [2003. A neural-network based method for prediction of gamma-turns in proteins from multiple sequence alignment. Protein Sci. 12, 923-929]. However, the major limitation of previous methods was inability in predicting gamma-turn types. In a recent investigation we introduced a sequence based predictor model for predicting gamma-turn types in proteins [Jahandideh, S., Sabet Sarvestani, A., Abdolmaleki, P., Jahandideh, M., Barfeie, M, 2007a. gamma-turn types prediction in proteins using the support vector machines. J. Theor. Biol. 249, 785-790]. In the present work, in order to analyze the effect of sequence and structure in the formation of gamma-turn types and predicting gamma-turn types in proteins, we applied novel hybrid neural discriminant modeling procedure. As the result, this study clarified the efficiency of using the statistical model preprocessors in determining the effective parameters. Moreover, the optimal structure of neural network can be simplified by a preprocessor in the first stage of hybrid approach, thereby reducing the needed time for neural network training procedure in the second stage and the probability of overfitting occurrence decreased and a high precision and reliability obtained in this way.

Analysis of factors that induce cysteine bonding state.

Regarding the fact that the protein structure is principally encoded in its sequence, investigating the bonding state of cysteine has gained a great deal of attention due to its significance in the formation of protein structure. Due to lack of evident influence of free cysteines on the protein structure, it may be expected that only half-cystines convey encoded information. The results obtained from the analysis of amino acid distribution in proximity of both states of cysteines explicitly indicated that perquisite information for inducing cysteine bonding state is present even in the flanking amino acid sequences of free cysteines.

Elucidating the protein cold-adaptation: Investigation of the parameters enhancing protein psychrophilicity.

To investigate the role of the critical parameters in adaptation of proteins to low temperatures, a comparative systematic analysis was performed. Several parameters were proposed to have contribution to cold adaptation of proteins. Among proposed parameters, total values of residual structure states, secondary structure states and oligomeric states were alike in both psychrophilic and mesophilic proteins. In addition, our results provided new quantitative information about the trends in the substitution preference of Ile, Phe, Tyr, Lys, Arg, His, Glu and Leu with most of amino acids and substitution avoidance of Gly, Thr and Ala with most of amino acids. These findings would help future efforts propose a strategy for designing psychrophilic proteins.

gamma-Turn types prediction in proteins using the support vector machines.

Recently, two different models have been developed for predicting gamma-turns in proteins by Kaur and Raghava [2002. An evaluation of beta-turn prediction methods. Bioinformatics 18, 1508-1514; 2003. A neural-network based method for prediction of gamma-turns in proteins from multiple sequence alignment. Protein Sci. 12, 923-929]. However, the major limitation of previous methods is inability in predicting gamma-turns types. Thus, there is a need to predict gamma-turn types using an approach which will be useful in overall tertiary structure prediction. In this work, support vector machines (SVMs), a powerful model is proposed for predicting gamma-turn types in proteins. The high rates of prediction accuracy showed that the formation of gamma-turn types is evidently correlated with the sequence of tripeptides, and hence can be approximately predicted based on the sequence information of the tripeptides alone.

Protein psychrophilicity: role of residual structural properties in adaptation of proteins to low temperatures.

In order to investigate the structural distribution responsible for protein psychrophilicity, a systematic comparative analysis of 13 pairs of psychrophilic and mesophilic proteins is reported. Three kinds of residue structural states such as exposed, intermediate and buried were considered for analyzing the structural patterns of single amino acids and amino acids in different groups. The statistical test revealed that higher frequency in exposed state of Ala, higher frequency in intermediate state of His, lower frequency in buried state of Lys, lower frequency in exposed state of Gln, higher frequency in exposed state and in intermediate state of Thr, higher frequency in exposed and intermediate state of tiny and small amino acids groups could be critical factors related with protein psychrophilicity. Such structure-based differences of residual properties would help to develop a strategy for designing psychrophilic proteins.

Novel two-stage hybrid neural discriminant model for predicting proteins structural classes.

In order to establish novel hybrid neural discriminant model, linear discriminant analysis (LDA) was used at the first stage to evaluate the contribution of sequence parameters in determining the protein structural class. An in-house program generated parameters including single amino acid and all dipeptide composition frequencies for 498 proteins came from Zhou [An intriguing controversy over protein structural class prediction, J. Protein Chem. 17(8) (1998) 729-738]. Then, 127 statistically effective parameters were selected by stepwise LDA and were used as inputs of the artificial neural networks (ANNs) to build a two-stage hybrid predictor. In this study, self-consistency and jackknife tests were used to verify the performance of this hybrid model, and were compared with some of prior works. The results showed that our two-stage hybrid neural discriminant model approach is very promising and may play a complementary role to the existing powerful approaches.

Sequence and structural parameters enhancing adaptation of proteins to low temperatures.

A systematic analysis compared sequence and structural parameters distributions between 13 pairs of psychrophilic and mesophilic proteins for elucidating the cold adaptation parameters. The results of statistical test (t-test) revealed that helical content, tight turn content, disulfide bonds and hydrogen bonds do not show significant difference between psychrophilic and mesophilic proteins. However, it was demonstrated in this study that a larger proportion of open beta-turn in psychrophilic proteins is an effective parameter in specific activity at low temperature. In addition, substitution of amino acids of charged and aliphatic groups with amino acids of tiny and small groups in protein chains, tight turns and alpha-helices in the direction from mesophilic to psychrophilic proteins is one of the mechanisms of low temperature adaptation. Such sequence and structural parameter differences would help to develop a strategy for designing cold-adapted proteins.

Novel hybrid method for the evaluation of parameters contributing in determination of protein structural classes.

Due to the increasing gap between structure-determined and sequenced proteins, prediction of protein structural classes has been an important problem. It is very important to use efficient sequential parameters for developing class predictors because of the close sequence-structure relationship. The multinomial logistic regression model was used for the first time to evaluate the contribution of sequence parameters in determining the protein structural class. An in-house program generated parameters including single amino acid and all dipeptide composition frequencies. Then, the most effective parameters were selected by a multinomial logistic regression. Selected variables in the multinomial logistic model were Valine among single amino acid composition frequencies and Ala-Gly, Cys-Arg, Asp-Cys, Glu-Tyr, Gly-Glu, His-Tyr, Lys-Lys, Leu-Asp, Leu-Arg, Pro-Cys, Gln-Met, Gln-Thr, Ser-Trp, Val-Asn and Trp-Asn among dipeptide composition frequencies. Also a neural network model was constructed and fed by the parameters selected by multinomial logistic regression to build a hybrid predictor. In this study, self-consistency and jackknife tests on a database constructed by Zhou [1998. An intriguing controversy over protein structural class prediction. J. Protein Chem. 17(8), 729-738] containing 498 proteins are used to verify the performance of this hybrid method, and are compared with some of prior works. The results showed that our two-stage hybrid model approach is very promising and may play a complementary role to the existing powerful approaches.