The objectives of this study were to determine arginine and glutamate levels in the gingival crevicular fluid (GCF) of adult chronic periodontitis patients versus periodontally healthy controls, and to compare two kinds of microdialysis probes: normal and U-shaped probes. The analysis of GCF components was developed to improve the diagnosis of periodontal disease (PD). Proteolysis in the periodontal tissues increases the concentration of amino acids (aa) in the GCF and the levels of these aa may reveal PD features and stages. GCF samples were collected by microdialysis in situ from 5 periodontally affected sites (probing depth 5 mm, clinical attachment loss 3 mm) in 14 adult chronic periodontitis patients and from 14 adult periodontally healthy controls. Capillary zone electrophoresis coupled to laser induced fluorescence detection was used to measure concentration of arginine and glutamate in the GCF. Data were analyzed statistically by ANOVA and Tukey's post-hoc tests (á=0.05). Arginine concentration was increased (p<0.001) and glutamate concentration was decreased (p<0.001) in chronic periodontitis patients as compared to controls. There were no significant differences (p=0.069) between the normal and U-shaped probes. In conclusion, the increase of arginine and decrease of glutamate concentration in GCF were associated to the presence of periodontitis, and might be used as markers to recognize periodontally susceptible subjects as well as to evaluate the treatment course.

Background Sjögren’s Syndrome compromises the exocrine function, producing xerostomia and xerophthalmia. It can appear as an isolated condition or associated with other autoimmune diseases (polyautoimmunity). The Unstimulated Salivary Flow rate (UWSF) is used to quantify saliva production. There is no objective evidence to differentiate the values in patients with Sjögren’s versus healthy people or patients with non-Sjögren’s sicca. The objective of the present review was to evaluate the UWSF in patients with Sjögren’s syndrome in comparison to controls (healthy and non-Sjögren’s sicca patients). Methods A systematic literature review was carried out (PRISMA guidelines). Analytical observational studies of cases and controls, cross-sectional studies, cohort studies and randomized clinical trials (including healthy controls) were considered. The Medline/OVID, Lilacs, Embase, and Cochrane/OVID databases were consulted. MeSH, DeCS, keywords, and Boolean operators were used. The meta-analysis (RevMan 5.2) was done through the random-effects model [mean difference (MD)]. Level and quality of evidence were evaluated by the Oxford Center Levels of Evidence and Joanna Brigs list respectively. Results Thirty-two articles were included (20 were case-control studies, 6 were cross-sectional, 2 prospective cohort, 2 retrospective cohort, and 2 studies were abstracts) and 28 were meta-analyzed. The unstimulated whole salivary flow rate in the Sjögren’s group was lower than in controls (healthy and patients with non-Sjögren Sicca syndrome) (MD-0.18 ml/min; 95% CI, − 0.24 to − 0.13; chi2-P-value < 0.00001). Heterogeneity was 97% and there was publication bias (funnel plot). The level of evidence was mostly 3 or 4. The quality of evidence was met (97% of items valued). Conclusion For the first time, the unstimulated whole salivary flow rate is found to be lower in patients with Sjögren’s syndrome compared to controls (healthy and non-SS sicca) through a meta-analysis. Trial registration PROSPERO CRD42020211325.

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Objective: The aim of this study was to characterize the capability of detection of the resting state networks (RSNs) with functional magnetic resonance imaging (fMRI) in healthy subjects using a 1.5T scanner in a middle-income country. Materials and methods: Ten subjects underwent a complete blood-oxygen-level dependent imaging (BOLD) acquisition on a 1.5T scanner. For the imaging analysis, we used the spatial independent component analysis (sICA). We designed a computer tool for 1.5 T (or above) scanners for imaging processing. We used it to separate and delineate the different components of the RSNs of the BOLD signal. The sICA was also used to differentiate the RSNs from noise artifact generated by breathing and cardiac cycles. Results: For each subject, 20 independent components (IC) were computed from the sICA (a total of 200 ICs). From these ICs, a spatial pattern consistent with RSNs was identified in 161 (80.5%). From the 161, 131 (65.5%) were fit for study. The networks that were found in all subjects were: the default mode network, the right executive control network, the medial visual network, and the cerebellar network. In 90% of the subjects, the left executive control network and the sensory/motor network were observed. The occipital visual network was present in 80% of the subjects. In 39 (19.5%) of the images, no any neural network was identified. Conclusions: Reproduction and differentiation of the most representative RSNs was achieved using a 1.5T scanner acquisitions and sICA processing of BOLD imaging in healthy subjects.

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