Objectives. We examined association between incidence rate of low birth weight in liveborn infants and maternal sociodemographic status in Tuzla Canton during 1992–1995 war in Bosnia and Herzegovina. Methods. The present study covers a 22-year period (1988–2009), including the war period (1992–1995), and we retrospectively collected data on a total of 108 316 liveborn infants and their mothers from three different socioeconomic periods: before (1988–1991), during (1992–1995), and after the war (1996–2009). Association between incidence rate of low birth weight in liveborn infants and maternal sociodemographic status were determined for each study period. Results. There were 23 194 live births in the prewar, 18 302 during the war, and 66 820 in the postwar period. Among the liveborn infants born during the war, 1373 (7.5%) had birth weight of <2500 g, which is significantly more in comparison with 851 (3.6%) liveborn infants in this birth weight group born before and 1864 (2.8%) after the war. We found the number of examinations during pregnancy was 1.8 per pregnant woman in the war period, which was low in comparison with the number of examinations before (4.6 per pregnant woman) and after (7.1 per pregnant woman) the war (P<.001 for both). Prewar perinatal mortality LBW infants of 6.2 per 1000 live births increased to 10.8 per 1000 live births during the war (P<.001), but after the war, perinatal mortality LBW infants (5.2‰) and early neonatal mortality (2.4‰) decreased. Conclusions. We found statistically significant association between low-birth-weight and maternal sociodemographic status in Tuzla Canton during 1992–1995 war in Bosnia and Herzegovina.

This study aims to quantify the heterogeneity of tumour enhancement in dynamic contrast-enhanced MRI (DCE-MRI) using texture analysis methods. The suitability of the coherence and the fractal dimension to monitor tumour response was evaluated in 18 patients with limb sarcomas imaged by DCE-MRI pre- and post-treatment. According to the histopathology, tumours were classified into responders and non-responders. Pharmacokinetic (Ktrans) and heuristic model-based parametric maps (slope, max enhancement, AUC) were computed from the DCE-MRI data. A substantial correlation was found between the pharmacokinetic and heuristic model-based parametric maps: ρ = 0.56 for the slope, ρ = 0.44 for maximum enhancement, and ρ = 0.61 for AUC. From all four parametric maps, the enhancing fraction, and the heterogeneity features (i.e. coherence and fractal dimension) were determined. In terms of monitoring tumour response, using both pre- and post-treatment DCE-MRI, the enhancing fraction and the coherence showed significant differences between the response group and the non-response group (i.e. the highest sensitivity (91%) for Ktrans, and the highest specificity (83%) for max enhancement). In terms of treatment prediction, using solely the pre-treatment DCE-MRI, the enhancing fraction and coherence discriminated between responders and non-responders. For prediction, the highest sensitivity (91%) was shared by Ktrans, slope and max enhancement, and the highest specificity (71%) was achieved by Ktrans. On average, tumours that responded showed a high enhancing fraction and high coherence on the pre-treatment scan. These results suggest that specific heterogeneity features, computed from both pharmacokinetic and heuristic model-based parametric maps, show potential as a biomarker for monitoring tumour response.

In this paper the probability density function of the Switch and Stay Combiner (SSC) output signal at one time instant and the joint probability density function of the SSC combiner output signal at two time instants, in the presence of log-normal fading, are determined in the closed form expressions. The results are shown graphically for different variance values and decision threshold values. If the digital telecommunication systems work on the manner described in this paper, the error probability will be significantly reduced. Ill. 6, bibl. 24 (in English; abstracts in English and Lithuanian). http://dx.doi.org/10.5755/j01.eee.109.3.161