Psychometric validation of the Spanish version of the Dundee Ready Education Environment Measure applied to dental students.

Aim: To carry out a psychometric evaluation of the Spanish-language version of the Dundee Ready Education Environment Measure (DREEM) applied to dental students. Methods: A total of 1,391 students from nine Spanish public schools of dentistry responded to the DREEM questionnaire. To analyse the reliability of the DREEM questionnaire, the internal consistency was assessed and a 'test-retest' carried out. Validity was evaluated through analysis of item response rate, floor and ceiling effects, corrected item-total and item-subscale correlations and factor structure. A confirmatory factor analysis was performed to analyse the structure of the original DREEM scale. Results: Cronbach's alpha coefficient for the 'Educational Climate'(EC) global scale was 0.92. In the subscales, the 'observed' Cronbach's alpha coefficients ranged between 0.57 and 0.79 and were higher than the 'expected' ones; except for the Social subscale. In the DREEM questionnaire, all of the corrected correlation coefficients between the items and the EC global scale, and the items and their corresponding subscales, were >0.2; except for items 50 and 17. All goodness-of-fit indices of confirmatory factor analysis showed acceptable values (close to one or zero, depending on the case), and there was consistency in the results. Conclusions: The Spanish-language version of the DREEM questionnaire is a reliable and valid instrument for analysing the EC for dental students and its factor structure is supported by the data. Although our findings indicate that the DREEM may be as culturally independent as was originally stated, more research should be directed at verifying the factor structure in various languages and cultural environments

Development of an epigenetic age predictor for costal cartilage with a simultaneous somatic tissue differentiation system

Age prediction from DNA has been a topic of interest in recent years due to the promising results obtained when using epigenetic markers. Since DNA methylation gradually changes across the individual's lifetime, prediction models have been developed accordingly for age estimation. The tissue-dependence for this biomarker usually necessitates the development of tissue-specific age prediction models, in this way, multiple models for age inference have been constructed for the most commonly encountered forensic tissues (blood, oral mucosa, semen). The analysis of skeletal remains has also been attempted and prediction models for bone have now been reported. Recently, the VISAGE Enhanced Tool was developed for the simultaneous DNA methylation analysis of 8 age-correlated loci using targeted high-throughput sequencing. It has been shown that this method is compatible with epigenetic age estimation models for blood, buccal cells, and bone. Since when dealing with decomposed cadavers or postmortem samples, cartilage samples are also an important biological source, an age prediction model for cartilage has been generated in the present study based on methylation data collected using the VISAGE Enhanced Tool. In this way, we have developed a forensic cartilage age prediction model using a training set composed of 109 samples (19–74 age range) based on DNA methylation levels from three CpGs in FHL2, TRIM59 and KLF14, using multivariate quantile regression which provides a mean absolute error (MAE) of ± 4.41 years. An independent testing set composed of 72 samples (19–75 age range) was also analyzed and provided an MAE of ± 4.26 years. In addition, we demonstrate that the 8 VISAGE markers, comprising EDARADD, TRIM59, ELOVL2, MIR29B2CHG, PDE4C, ASPA, FHL2 and KLF14, can be used as tissue prediction markers which provide reliable blood, buccal cells, bone, and cartilage differentiation using a developed multinomial logistic regression model. A training set composed of 392 samples (n = 87 blood, n = 86 buccal cells, n = 110 bone and n = 109 cartilage) was used for building the model (correct classifications: 98.72%, sensitivity: 0.988, specificity: 0.996) and validation was performed using a testing set composed of 192 samples (n = 38 blood, n = 36 buccal cells, n = 46 bone and n = 72 cartilage) showing similar predictive success to the training set (correct classifications: 97.4%, sensitivity: 0.968, specificity: 0.991). By developing both a new cartilage age model and a tissue differentiation model, our study significantly expands the use of the VISAGE Enhanced Tool while increasing the amount of DNA methylation-based information obtained from a single sample and a single forensic laboratory analysis. Both models have been placed in the open-access Snipper forensic classification website.</p