Social pressure was associated with a change in intention suggesting that the intervention accomplished exactly what it was supposed to do: preparing children for secondary school.

One question is whether the transition to a different school instead of the intervention is responsible for the difference between the intervention and control students. Other findings indicated, among others, that students are more likely susceptible to smoking if they have two or more close friends who smoke, attend a school with a relatively high smoking rate among the older students or a school with less (endorsed) smoking restrictions (Leatherdale et al., Lenvatinib 2006 and Wakefield et al., 2000). If a larger part of the control students went to schools with a higher smoking rate, this change in school instead of the intervention might have caused the difference in smoking. Although we could not verify this school transition effect properly, we do not think that the effect of the transition HER2 inhibitor to secondary school differs for intervention or control

students. First, in each participating region, we have randomized schools to the intervention or control group, meaning that an important part of the students in both conditions went to the same regional secondary schools. Secondly, there were no important differences in perceived non-smoking policies between the intervention and control group. The largest effect of the intervention is found in girls. Other studies already have shown that there are gender differences in smoking uptake in adolescence and that smoking is more prevalent Florfenicol in girls than in boys (Rodham et al., 2005 and de Vries et al., 2003). Moreover, Mercken et al. (2010) found that particularly girls are influenced to smoke by their peers concluding that an intervention preparing girls to resist peer pressure might be more effective in girls than in boys. This might explain the larger effect of the present intervention among girls. The schools were randomly assigned to the intervention and control group

in order to reduce the chance of selection bias. In spite of the randomization procedure, differences between the groups at baseline were found. Chance confounding, due to randomization at school level, may explain these differences, so we adjusted for this in our analysis. Loss to follow-up was somewhat selective but seemed to have a limited effect on the results, while there were no significant differences in smoking behavior between the non-response of intervention and control condition. Moreover, intention-to-treat analyses by carrying the last observation of smoking behavior forward did not have different effects on smoking behavior. The response rate also did not differ between groups. Therefore, it is highly unlikely that selective response has affected the impact of the intervention. All measurements were self-reports, meaning that information bias could have occurred, especially in the intervention group.

For Ratio spectrum of GBP, MCB and ALP, spectrum of the mixture was divided by standard spectrum of MCB (0.5 μg/ml) and ALP (100 μg/ml); GBP (100 μg/ml) and ALP (100 μg/ml); and MCB (0.5 μg/ml)

and GBP (100 μg/ml) respectively. Obtained ratio spectra were smoothed (Δλ = 10) and converted to first order derivative spectrum (Δλ = 10, SF = 10 for GBP and MCB; Δλ = 10, SF = 1 for ALP). Amplitude (dA/dλ) of GBP, MCB and ALP were measured at 731.10 nm, 768.53 nm and 242.21 nm Dinaciclib research buy respectively. Concentrations of GBP, MCB and ALP were computed by putting value of their amplitudes in respective standard regression equation obtained from calibration curve. The analysis procedure was repeated six times with tablet formulation. Excellent linearity was obtained for all the three drugs in the range of 100–500 μg/ml for GBP and ALP; and 0.5–2.5 μg/ml MCB. Linearity of GBP, MCB and ALP were shown in Fig. 2, Fig. 3 and Fig. 4 respectively. The correlation coefficients (r2) were found to be greater than 0.998 (n = 6) in all instances. LOD and LOQ were found to be 3.09 μg/ml and 9.37 μg/ml for GBP; 0.03 μg/ml and 0.10 μg/ml for MCB; and 4.79 μg/ml and 14.52 μg/ml for ALP ( Table 1). The proposed method afforded high recoveries for GBP,

MCB and ALP tablets. Results obtained from recovery studies shown in Table 2 indicate that selleck this assay procedure can be used for routine quality control analysis of this ternary mixture in tablets. Precision of the analytical method was found to be reliable based on % RSD (<2%) corresponding to the peak areas. The % RSD values were less than 2, for intra-day and inter-day precision. Hence, the method was found to be precise for all the three

drugs. In all deliberately varied conditions for robustness study, the % RSD of GBP, MCB and ALP were found to be well within the acceptable limit (<1.5%) for robustness study ( Table 3). The validated method was used in the analysis of marketed conventional tablet trigabantin 100 with a label claim: 100 mg GBP, 500 μg MCB and 100 mg ALP per tablet. The results for the drugs assay shown in Table 4 indicate a good agreement with the label claims. The spectrum of blank does not show any interference at the detection Tolmetin of GBP, MCB and ALP as it can be seen from the respective spectra ( Fig. 5). The results of stability study of drugs shown in Table 5. The developed Ratio spectra derivative spectroscopic method is simple, accurate and precise for the simultaneous determination of GBP, MCB and ALP from tablets. It was successfully validated in terms of linearity, range, accuracy, precision, LOD, LOQ and robustness in accordance with ICH Guidelines. Thus, the described method is suitable for routine analysis and quality control of pharmaceutical preparations containing these drugs in combination. All authors have none to declare.