Objectives Saliva metabolome is a promising diagnostic tool concerning oral and systemic diseases. We aimed at establishing a suitable protocol for saliva collection and gauging the relative impacts of gender, dentition stage, and caries on the saliva metabolome of a small children cohort. Subjects and methods A nuclear magnetic resonance‐based metabolomics cross‐sectional study of children saliva (n = 38) compared the effects of: (a) stimulation and unstimulation conditions, and (b) collection through passive drool and using an absorbing device. Multivariate and univariate statistical analyses were applied to evaluate such effects and those related to gender, dentition stage and caries. Results No significant differences were found between unstimulated and stimulated saliva, and the former was used for subsequent studies. Swab collection induced significant changes in sample composition, indicating passive drool as preferential. The impacts of gender and dentition stage were not significant compared to that of caries, which induced variations in the levels of 21 metabolites. These comprised amino acids and monosaccharides observed for the first time to our knowledge regarding children caries, suggesting protein hydrolysis and deglycosylation. Conclusions Unstimulated passive drool saliva metabolome may carry a caries signature.

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Objectives We aimed to evaluate children with type 1 diabetes (T1D) with early age at onset (EAO) for clinical, immune and metabolic features in order to identify age-related disease phenotypes. Methods Comparative study of two groups of T1D children: EAO (≤5 years) and later age at onset (LAO; >5 years), regarding the presence of other autoimmune (AI) diseases, diabetes ketoacidosis and immunologic profile at onset and metabolic data 1 year after diagnosis. Statistical analysis was performed with significance set for p < 0.05. Results The study included 137 children (EAO = 52, mean age 3.6 ± 1.5 [mean ± standard deviation (SD)] and LAO = 85, mean age 10.4 ± 2.9). EAO was more associated with concomitant AI diseases (p = 0.032). Despite no differences in disease onset, EAO presented with lower C-peptide levels (p = 0.01) and higher absolute lymphocyte number (p < 0.0001), with an inverse correlation between these two variables (p = 0.028). Additionally, the EAO group had a higher frequency of serum detection of three antibodies (Abs) (p = 0.0008), specifically insulin Abs (p = 0.0001). One year after diagnosis, EAO had higher total daily insulin (TDI) dose (p = 0.008), despite similar hemoglobin A1c (HbA1c). Conclusions Our data show an association of EAO T1D with more AI diseases, higher number of Abs, lower initial insulin reservoir and higher insulin requirements 1 year after diagnosis. In this group, immune imbalance seems more evident and disease progression faster, probably reflecting distinct “immune environment” with different ages at disease onset. Further studies in the field of immunogenetics and immune tolerance are required, to improve patient stratification and find novel targets for therapeutic intervention.

The main aim of this study is to contribute knowledge about the impact of the use of information and communication technologies (ICT) on the self-concept, mood, and quality of life of institutionalized older adults in retirement homes and day care centers (Portuguese institutions). It also studies the influence of independent variables such as age; education; referral to the institution (own initiative, indicated by friends, indicated by family, indicated by social security); attendance schedule; and visits from family and friends. The study comprised 41 participants organized into two groups: 22 older adults in the ICT Group and 19 older adults in the Passive Control Group. There were three evaluation periods: before the onset of intervention and 11 and 22 months after intervention began. The results demonstrate that involvement with information and communication technologies produced positive effects on older adults' social behavior and their self-perception of physical and environment facets of quality of life. Moreover, they suggest that there is a relationship between psychosocial variables and independent variables.