Change, stability and prediction of gross motor co-ordination in portuguese children

The knowledge about intra- and inter-individual variation can stimulate attempts at description, interpretation and prediction of motor co-ordination (MC). Aim: To analyse change, stability and prediction of motor co-ordination (MC) in children. Subjects and methods: A total of 158 children, 83 boys and 75 girls, aged 6, 7 and 8 years, were evaluated in 2006 and re-evaluated in 2012 at 12, 13 and 14 years of age. MC was assessed through the Kiphard-Schilling’s body co-ordination test and growth, skeletal maturity, physical fitness, fundamental motor skills (FMS), physical activity and socioeconomic status (SES) were measured and/or estimated. Results: Repeated-measures MANOVA indicated that there was a significant effect of group, sex and time on a linear combination of the MC tests. Univariate tests revealed that group 3 (8–14 years) scored significantly better than group 1 (6–12 years) in all MC tests and boys performed better than girls in hopping for height and moving sideways. Scores in MC were also higher at follow-up than at baseline. Inter-age correlations for MC were between 0.15–0.74. Childhood predictors of MC were growth, physical fitness, FMS, physical activity and SES. Biological maturation did not contribute to prediction of MC. Conclusion: MC seemed moderately stable from childhood through adolescence and, additionally, inter-individual predictors at adolescence were growth, FMS, physical fitness, physical activity and SES.

Investigation of urinary volatile organic metabolites as potential cancer biomarkers by solid-phase microextraction in combination with gas chromatography-mass spectrometry

BACKGROUND: Non-invasive diagnostic strategies aimed at identifying biomarkers of cancer are of great interest for early cancer detection. Urine is potentially a rich source of volatile organic metabolites (VOMs) that can be used as potential cancer biomarkers. Our aim was to develop a generally reliable, rapid, sensitive, and robust analytical method for screening large numbers of urine samples, resulting in a broad spectrum of native VOMs, as a tool to evaluate the potential of these metabolites in the early diagnosis of cancer. METHODS: To investigate urinary volatile metabolites as potential cancer biomarkers, urine samples from 33 cancer patients (oncological group: 14 leukaemia, 12 colorectal and 7 lymphoma) and 21 healthy (control group, cancer-free) individuals were qualitatively and quantitatively analysed. Dynamic solid-phase microextraction in headspace mode (dHS-SPME) using a carboxenpolydimethylsiloxane (CAR/PDMS) sorbent in combination with GC-qMS-based metabolomics was applied to isolate and identify the volatile metabolites. This method provides a potential non-invasive method for early cancer diagnosis as a first approach. To fulfil this objective, three important dHS-SPME experimental parameters that influence extraction efficiency (fibre coating, extraction time and temperature of sampling) were optimised using a univariate optimisation design. The highest extraction efficiency was obtained when sampling was performed at 501C for 60min using samples with high ionic strengths (17% sodium chloride, wv 1) and under agitation. RESULTS: A total of 82 volatile metabolites belonging to distinct chemical classes were identified in the control and oncological groups. Benzene derivatives, terpenoids and phenols were the most common classes for the oncological group, whereas ketones and sulphur compounds were the main classes that were isolated from the urine headspace of healthy subjects. The results demonstrate that compound concentrations were dramatically different between cancer patients and healthy volunteers. The positive rates of 16 patients among the 82 identified were found to be statistically different (Po0.05). A significant increase in the peak area of 2-methyl3-phenyl-2-propenal, p-cymene, anisole, 4-methyl-phenol and 1,2-dihydro-1,1,6-trimethyl-naphthalene in cancer patients was observed. On average, statistically significant lower abundances of dimethyl disulphide were found in cancer patients. CONCLUSIONS: Gas chromatographic peak areas were submitted to multivariate analysis (principal component analysis and supervised linear discriminant analysis) to visualise clusters within cases and to detect the volatile metabolites that are able to differentiate cancer patients from healthy individuals. Very good discrimination within cancer groups and between cancer and control groups was achieved.