Introduction: Diabetes is progressive condition which requires various ways of treatment. Adequate therapy prescribed in the right time helps patient to postpone development of complications. Adherence to complicated therapy is challenge for both patients and HCPs and is subject of research in many disciplines. Improvement in communication between HCP and patients is very important in patient’s adherence to therapy. Aim: Aim of this research was to explore validity and reliability of modified SERVQUAL instrument in attempt to explore ways of motivating diabetic patient to accept prescribed insulin therapy. Material and Methods: We used modified SERVQUAL questionnaire as instrument in the research. It was necessary to check validity and reliability of the new modified instrument. Results: Results show that modified Servqual instrument has excellent reliability (α=0.908), so we could say that it measures precisely Expectations, Perceptions and Motivation at patients. Factor analysis (EFA method) with Varimax rotation extracted 4 factors which together explain 52.902% variance of the results on this subscale. Bifactorial solution could be seen on Scree-plot diagram (break at second factor). Conclusion: Results in this research show that modified Servqual instrument which is created in order to measure expectations and perceptions of the patients is valid and reliable. Reliability and validity are proven indeed in additional dimension which was created originally for this research - motivation to accept insulin therapy.

The aim was to evaluate volume, diffusion, and perfusion metrics for better presurgical differentiation between high-grade gliomas (HGG), low-grade gliomas (LGG), and metastases (MET). For this retrospective study, 43 patients with histologically verified intracranial HGG (n = 18), LGG (n = 10), and MET (n = 15) were chosen. Preoperative magnetic resonance data included pre- and post-gadolinium contrast-enhanced T1-weighted fluid-attenuated inversion recover, cerebral blood flow (CBF), cerebral blood volume (CBV), fractional anisotropy, and apparent diffusion coefficient maps used for quantification of magnetic resonance biometrics by manual delineation of regions of interest. A binary logistic regression model was applied for multiparametric analysis and receiver operating characteristic (ROC) analysis. Statistically significant differences were found for normalized-ADC-tumor (nADC-T), normalized-CBF-tumor (nCBF-T), normalized-CBV-tumor (nCBV-T), and normalized-CBF-edema (nCBF-E) between LGG and HGG, and when these metrics were combined, HGG could be distinguished from LGG with a sensitivity and specificity of 100%. The only metric to distinguish HGG from MET was the normalized-ADC-E with a sensitivity of 68.8% and a specificity of 80%. LGG can be distinguished from MET by combining edema volume (Vol-E), Vol-E/tumor volume (Vol-T), nADC-T, nCBF-T, nCBV-T, and nADC-E with a sensitivity of 93.3% and a specificity of 100%. The present study confirms the usability of a multibiometric approach including volume, perfusion, and diffusion metrics in differentially diagnosing brain tumors in preoperative patients and adds to the growing body of evidence in the clinical field in need of validation and standardization.

A vertex-varying spectral content on graphs challenges the assumption of vertex invariance and requires vertex-frequency representations for an adequate analysis. In this letter, we introduce a class of vertex-frequency energy distributions inspired by traditional time-frequency energy distributions. These newly introduced distributions do not use localization windows. Their efficiency in energy concentration is illustrated through examples.