Association of sociodemographic factors and emotional intelligence with academic performance in clinical and preclinical dental courses.

BACKGROUND: The dental graduation program is stressful and challenging as it is essential to master various skills. The emotional intelligence (EI) is reported to help in perceiving, moderating emotions and also play a significant role in academic excellence. AIM: The aim of this study was to assess the sociodemographic factors influencing EI and to determine the association of EI in academic performance of clinical and preclinical courses. MATERIALS AND METHODS: This cross-sectional study included the 113 dental clinical internship students from five different dental schools at Pakistan. The self-administered questionnaire data included sociodemographic factors, academic performance in preclinical, clinical courses, and San Diego City College MESA Program-based EI responses. The results were analyzed with multiple linear regression and ordinal regression to identify the independent predictor for EI and academic performance. RESULTS: The female participants had marginally higher mean EI score (109.67) in comparison to male counterparts (108.10). The independent predictors among sociodemographic factors for EI were having siblings (P = 0.016), loss of parents (P = 0.002), parents' education (P = 0.022), and relation with parents (P = 0.03). The students enjoy studying dentistry were also associated with higher EI scores (P = 0.002). The mean EI score was an independent predictor of academic performance predominantly in clinical courses [ß = -0.041 (95% confidence interval - 0.063 to - 0.020); P = 0.000]. CONCLUSION: The finding of the study indicates the influence of family and social factors in the development of EI. The dental students' EI is vital for higher academic performance in clinical courses.

Principal components analysis of Raman spectral data for screening of Hepatitis C infection.

In the current study, Raman spectroscopy is employed for the identification of the biochemical changes taking place during the development of Hepatitis C. The Raman spectral data acquired from the human blood plasma samples of infected and healthy individuals is analysed by Principal Components Analysis and the Raman spectral markers of the Hepatitis C Virus (HCV) infection are identified. Spectral changes include those associated with nucleic acidsat720 cm-1, 1077 cm-1 1678 (CO stretching mode of dGTP of RNA), 1778 cm-1 (RNA), with proteins at 1641 cm-1(amide-I), 1721 cm-1(CC stretching of proteins) and lipids at 1738 cm-1(CO of ester group in lipids). These differences in Raman spectral features of blood plasma samples of the patients and healthy volunteers can be associated with the development of the biochemical changes during HCV infection.

Raman spectroscopy of blood plasma samples from breast cancer patients at different stages.

Raman spectroscopy was employed for the characterization of blood plasma samples from patients at different stages of breast cancer. Blood plasma samples taken from clinically diagnosed breast cancer patients were compared with healthy controls using multivariate data analysis techniques (principal components analysis - PCA) to establish Raman spectral features which can be considered spectral markers of breast cancer development. All the stages of the disease can be differentiated from normal samples. It is also found that stage 2 and 3 are biochemically similar, but can be differentiated from each other by PCA. The Raman spectral data of the stage 4 is found to be biochemically distinct, but very variable between patients. Raman spectral features associated with DNA and proteins were identified, which are exclusive to patient plasma samples. Moreover, there are several other spectral features which are strikingly different in the blood plasma samples of different stages of breast cancer. In order to further explore the potential of Raman spectroscopy as the basis of a minimally invasive screening technique for breast cancer diagnosis and staging, PCA-Factorial Discriminant Analysis (FDA) was employed to classify the Raman spectral datasets of the blood plasma samples of the breast cancer patients, according to different stages of the disease, yielding promisingly high values of sensitivity and specificity for all stages.

Raman spectral analysis for rapid screening of dengue infection.

Infection with the dengue virus is currently clinically detected according to different biomarkers in human blood plasma, commonly measured by enzyme linked immunosorbent assays, including non-structural proteins (Ns1), immunoglobulin M (IgM) and immunoglobulin G (IgG). However, there is little or no mutual correlation between the biomarkers, as demonstrated in this study by a comparison of their levels in samples from 17 patients. As an alternative, the label free, rapid screening technique, Raman spectroscopy has been used for the characterisation/diagnosis of healthy and dengue infected human blood plasma samples. In dengue positive samples, changes in specific Raman spectral bands associated with lipidic and amino acid/protein content are observed and assigned based on literature and these features can be considered as markers associated with dengue development. Based on the spectroscopic analysis of the current, albeit limited, cohort of samples, Principal Components Analysis (PCA) coupled Factorial Discriminant Analysis, yielded values of 97.95% sensitivity and 95.40% specificity for identification of dengue infection. Furthermore, in a comparison of the normal samples to the patient samples which scored low for only one of the biomarker tests, but high or medium for either or both of the other two, PCA-FDA demonstrated a sensitivity of 97.38% and specificity of 86.18%, thus providing an unambiguous screening technology.