Intimate partner violence and the meaning of love.

Despite physical, emotional, verbal, and sexual abuse from their partner, many women remain in an abusive relationship, often proclaiming to love the one who is hurting them. Nineteen females who had experienced intimate partner violence were interviewed and asked to share their experiences and describe their meaning of love. An analysis of the transcripts was done using qualitative content analysis. With this approach, the contents of the verbal data were summarized and arranged in three major categories: (1) What love is not; (2) Attributes of a loving relationship; and (3) Attachment to the relationship. The findings demonstrate a woman's clear recognition of being in an abusive relationship, yearning to be truly loved, but often finding herself unable to detach from the relationship.

Electroconvulsive therapy: the struggles in the decision-making process and the aftermath of treatment.

The perceptions of patients and their family members about electroconvulsive therapy (ECT) are crucial to understanding the meaning attached to having ECT and the impact it has on quality of life. Thus, in this qualitative study, patients and their family members described their perceptions of having electroconvulsive therapy (ECT). The experience occurred in two distinct periods in the patient's life: making the decision to have ECT and the physical and emotional aftermath of treatment. One of the most important themes in the study was a need for patients and families to be better informed about the risks of ECT.

Linking dynamical and population genetic models of persistent viral infection.

This article develops a theoretical framework to link dynamical and population genetic models of persistent viral infection. This linkage is useful because, while the dynamical and population genetic theories have developed independently, the biological processes they describe are completely interrelated. Parameters of the dynamical models are important determinants of evolutionary processes such as natural selection and genetic drift. We develop analytical methods, based on coupled differential equations and Markov chain theory, to predict the accumulation of genetic diversity within the viral population as a function of dynamical parameters. These methods are first applied to the standard model of viral dynamics and then generalized to consider the infection of multiple host cell types by the viral population. Each cell type is characterized by specific parameter values. Inclusion of multiple cell types increases the likelihood of persistent infection and can increase the amount of genetic diversity within the viral population. However, the overall rate of gene sequence evolution may actually be reduced.

Molecular modeling calculations of HIV-1 reverse transcriptase nonnucleoside inhibitors: correlation of binding energy with biological activity for novel 2-aryl-substituted benzimidazole analogues.

The energies and physical descriptors for the binding of 20 novel 1-(2,6-difluorobenzyl)-2-(2,6-difluorophenyl)benzimidazole analogues (BPBIs) to HIV-1 reverse transcriptase (RT) have been determined using Monte Carlo (MC) simulations. The crystallographic structure of the lead compound, 1-(2,6-difluorobenzyl)-2-(2,6-difluorophenyl)-4-methylbenzimidazole, was used as a starting point to model the inhibitors in both the bound and the unbound states. The energy terms and physical descriptors obtained from the calculations were correlated with their respective experimental EC(50) values, resulting in an r(2) value of 0.70 and a root-mean-square deviation (rms) of 0.53 kcal/mol. The terms in the correlation include the change in total Coulombic energy and solvent-accessible surface area. Structural analysis of the data files from the BPBI calculations reveals that all of the analogues with good biological activity show the formation of a hydrogen bond between the ligand and the backbone nitrogen atom of lysine 103. By use of the structural results, two novel BPBI inhibitors have been designed and calculations have been carried out. The results show the formation of the desired hydrogen bonds, and the DeltaG(binding) values predict the compounds to be excellent RT inhibitors. Subsequent synthesis and biological activity testing of these analogues have shown the validity of the predictive calculations. If the BPBIs are modeled in a site constructed from the crystal coordinates of a member of another class of nonnucleoside inhibitors (the 4,5,6,7-tetrahydroimidazo[4,5,1-jk][1,4]benzodiazepine-2(1H)-thione and -one (TIBO) compounds), the correlation with the same terms drops slightly, giving an r(2) value of 0.61 with an associated root-mean-square value of 0.53 kcal/mol. Conversely, if the TIBO compounds are modeled in a site constructed from the BPBI complex crystal coordinates, a correlation can be obtained using the drug-protein interaction energy and change in the total number of hydrogen bonds, giving an r(2) value of 0.63. These are the same descriptors that were used for the TIBO compounds modeled in their own sites, where the r(2) value was 0.72. These data suggest that it may be possible, in some cases, to design novel inhibitors utilizing structural data from related, but not identical, inhibitors.

HIV-1 reverse transcriptase variants: molecular modeling of Y181C, V106A, L100I, and K103N mutations with nonnucleoside inhibitors using Monte Carlo simulations in combination with a linear response method.

The energies and physical descriptors for the binding of 21 novel 1-(2,6-difluorobenzyl)-2-(2,6-difluorophenyl)-benzimidazole (BPBI) analogs to HIV-1 reverse transcriptase (RT) variants Y181C, L100I, V106A, and K103N have been determined using Monte Carlo (MC) simulations. The crystallographic structure of the lead compound, 4-methyl BPBI, was used as a starting point to model the inhibitors in both the mutant bound and the unbound states. The energy terms and physical descriptors obtained from the calculations were reasonably correlated with the respective experimental EC50 values for the inhibitors against the various mutant RTs. Using the linear response correlations from the calculations, 2 novel BPBI inhibitors have been designed and simulations have been carried out. The results show the computed deltaG(binding) values match the experimental data for the analogs. Given the ongoing problem with drug resistance, the ability to predict the activity of novel analogs against variants prior to synthesis is highly advantageous.

Prediction of activity for nonnucleoside inhibitors with HIV-1 reverse transcriptase based on Monte Carlo simulations.

Results of Monte Carlo (MC) simulations for more than 200 nonnucleoside inhibitors of HIV-1 reverse transcriptase (NNRTIs) representing eight diverse chemotypes have been correlated with their anti-HIV activities in an effort to establish simulation protocols and methods that can be used in the development of more effective drugs. Each inhibitor was modeled in a complex with the protein and by itself in water, and potentially useful descriptors of binding affinity were collected during the MC simulations. A viable regression equation was obtained for each data set using an extended linear response approach, which yielded r(2) values between 0.54 and 0.85 and an average unsigned error of only 0.50 kcal/mol. The most common descriptors confirm that a good geometrical match between the inhibitor and the protein is important and that the net loss of hydrogen bonds with the inhibitor upon binding is unfavorable. Other physically reasonable descriptors of binding are needed on a chemotype case-by-case basis. By including descriptors in common from the individual fits, combination regressions that include multiple data sets were also developed. This procedure led to a refined "master" regression for 210 NNRTIs with an r(2) of 0.60 and a cross-validated q(2) of 0.55. The computed activities show an rms error of 0.86 kcal/mol in comparison with experiment and an average unsigned error of 0.69 kcal/mol. Encouraging results were obtained for the predictions of 27 NNRTIs, representing a new chemotype not included in the development of the regression model. Predictions for this test set using the master regression yielded a q(2) value of 0.51 and an average unsigned error of 0.67 kcal/mol. Finally, additional regression analysis reveals that use of ligand-only descriptors leads to models with much diminished predictive ability.