61 resultados para Sickle cell anemia in children.

em Deakin Research Online - Australia


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The article focuses on the study concerning the diagnosis of acute renal allograft in patient with sickle cell disease. The 33 year old underwent several surgeries with immediate graft function. The percutaneous renal biopsy of the patient shows focal cortical necrosis and acute tubular damage coherent with ischemia. It concludes that the allograft sickling is the primary cause of the acute renal dysfunction in patients with sickle cell disease.

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Poor nutritional status in patients with cystic fibrosis (CF) is associated with severe lung disease, and possible causative factors include inadequate intake, malabsorption, and increased energy requirements. Body cell mass (which can be quantified by measurement of total body potassium) provides an ideal standard for measurements of energy expenditure. The aim of this study was to compare resting energy expenditure (REE) in patients with CF with both predicted values and age-matched healthy children and to    determine whether REE was related to either nutritional status or pulmonary function. REE was measured by indirect calorimetry and body cell mass by scanning with total body potassium in 30 patients with CF (12 male, mean AGE = 13.07 ± 0.55 y) and 18 healthy children (six male, mean AGE = 12.56 ± 1.25 y). Nutritional status was expressed as a percentage of predicted total body potassium. Lung function was measured in the CF group by spirometry and expressed as the percentage of predicted forced expiratory volume in 1 s. Mean REE was significantly increased in the patients with CF compared with healthy children (119.3 ± 3.1% predicted versus 103.6 ± 5% predicted, P < 0.001) and, using multiple regression techniques, REE for total body potassium was significantly increased in patients with CF (P = 0.0001). There was no relation between REE and nutritional status or pulmonary disease status in the CF group. In conclusion, REE is increased in children and adolescents with CF but is not directly related to nutritional status or pulmonary disease.

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OBJECTIVE: Presleep activities have been implicated in the declining sleep duration of young people. A use-of-time approach may be used to describe the presleep period. The study aims were to describe the activities undertaken 90 minutes before sleep onset and to examine the association between activities and time of sleep onset in New Zealand young people. METHODS: Participants (N = 2017; 5-18 years) self-reported their time use as part of a national survey. All activities reported in the 90 minutes before sleep were extracted. The top 20 activities were grouped into 3 behavioral sets: screen sedentary time, nonscreen sedentary time, and self-care. An adjusted regression model was used to estimate presleep time spent in each behavioral set for 4 distinct categories of sleep onset (very early, early, late, or very late), and the differences between sleep onset categories were tested. RESULTS: In the entire sample, television watching was the most commonly reported activity, and screen sedentary time accounted for ∼30 minutes of the 90-minute presleep period. Participants with a later sleep onset had significantly greater engagement in screen time than those with an earlier sleep onset. Conversely, those with an earlier sleep onset spent significantly greater time in nonscreen sedentary activities and self-care. CONCLUSIONS: Screen sedentary time dominated the presleep period in this sample and was associated with a later sleep onset. The development of interventions to reduce screen-based behaviors in the presleep period may promote earlier sleep onset and ultimately improved sleep duration in young people.

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It is well established that arsenic toxicity is postulated to be primarily due to the binding of As(III) to sulfhydryl-containing enzymes. However, the mechanism of carcinogenesis induced by arsenic is still unclear. The interaction of arsenic with GSH and related enzymes seems a very important issue regarding mechanism of arsenical induced toxicity or carcinogenesis. The purpose of this work is to investigate the effect of chronic exposure to low dose of As(III) on GSH level, gene expression and cell transformation in NIH3T3 cells. The results showed that long-term, low dose arsenic treatment makes 3T3 cell more resistant to acute arsenic treatment. There were morphology changes after long-term arsenic treatment. First, partially immortalized 3T3 cell became immortalized. In addition, the cells were doubling more quickly than the control cells and attained higher density than the control cells at confluence. Second, cells treated with 0.1 µ.M As(III) exhibited anchorage-independent growth. Arsenic could enhance GSH level at 0.5 -10 µM dose of arsenic in 24 h treatment and decrease it at 25 µM and above. In long-term treatment with low dose of arsenic, GSH levels were decreased. As(I1I) can increase both glutathione S-transferase (GST) and glutathione reductase (GR) activities at low dose (0.5-10 M), but decreased GST and GR activities at 25 M and higher dose of arsenic, while in long-term As(III) treatment, GST and GR activities are increased. Both long-term and short-term treatments with As(III) can induce GR gene expression. GPx mRNA levels were decreased both in acute and chronic arsenic treated cells. Chronic treatment with As(III) also decreased the p53 mRNA level. Taken together, our results suggest that As(III) can alter GST, GR enzyme activities as well as GSH level and related gene expression both in long-term and short-term treatment but in a different manner in different doses. Alteration of cellular GSH level by As(III) might play all important role in gene expression and arsenic induced cell transformation.

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This study examined whether age, gender, intelligence, communication ability and shyness predict intellectually disabled children&rsquo;s susceptibility to an interviewer’s misleading suggestions. Further, the study examined whether the relative influence of these factors differs between intellectually disabled and mainstream samples. Participants included 75 children with mild and borderline intellectual disabilities (aged 77–158 months) and 83 mainstream children (aged 68–152 months). All children were individually administered the Yield and Shift subscales of the Gudjonsson Suggestibility Scale (Form 2) as well as standardised measures of IQ, shyness and communication ability. For the intellectually disabled children, multiple regression analyses revealed that age, IQ and communication inversely predicted Yield suggestibility, however, none of the factors predicted Shift suggestibility. For the mainstream children, age made a significant independent contribution to both Yield and Shift suggestibility, while IQ was a significant predictor of Shift suggestibility. When comparing the relative impact of these factors across the samples, age had a significantly greater impact on mainstream (compared with intellectually disabled) children&rsquo;s Shift suggestibility, while IQ had a significantly greater influence on intellectually disabled (compared with mainstream) children&rsquo;s Yield scores. These findings highlight the limited generalisability of previous findings involving mainstream children&rsquo;s suggestibility to intellectually disabled samples.


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Two major predictors of subjective quality oflife (SQOL) in adults are known to beself-esteem and a sense of primary control.Moreover, secondary control is known to be animportant defence strategy when primary controlfails. This study aimed to determine whetherthese relationships also apply to children. Asample of 66 children aged from 5 to 12 yearswere compared on their use of primary andsecondary control and on their ratings of SQOLand self-esteem. SQOL was measured using theComprehensive Quality of Life Scale,self-esteem by using the CoopersmithSelf-Esteem Inventory, and primary andsecondary control were measured by codingchildren's responses to three short video clipsof children in stressful situations. It wasfound that younger children use more primarycontrol and less secondary control than olderchildren. However, five year olds were foundcapable of producing secondary controlstrategies. Contrary to expectation, primaryand secondary control did not predict eitherself-esteem or SQOL. However, self-esteempredicted SQOL as expected and no sexdifferences were found. These findingsemphasise important differences from the adultliterature and the reasons for this arediscussed.

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Objective:
To examine trends in active transport to and from school, in school sport and physical education (PE), and in weight status among children from high and low socio-economic status (SES) areas in Melbourne, Victoria, between 1985 and 2001.

Methods:
Cross-sectional survey data and measured height and weight from 1985 (n=557) and 2001 (n=926) were compared for children aged between 9–13 years within high and low SES areas.

Results:

From 1985 to 2001, the frequency of walking to or from school declined (4.38±4.3 vs. 3.61 ± 3.8 trips/wk, p<0.001), cycling to or from school also declined (1.22±2.9 vs. 0.36±1.5 trips/wk, p<0.001), and the frequency of PE lessons declined (1.64±1.1 vs. 1.18±0.9 lessons/wk, p<0.001). However, the frequency of school sport increased (0.9±1.22 vs. 1.24±0.8 sessions/wk, p<0.001). In 1985, 11.7% of children were overweight or obese compared with 28.7% in 2001 (p<0.001). Apart from walking to school and school sport, there were greater relative declines in cycling to school and PE, and increases in overweight and obesity among children attending schools in low SES areas compared with those attending schools in high SES areas.

Conclusions:

Declines in active school transport and PE have occurred at the same time as increases in overweight and obesity among Australian children.

Implications:
Promoting active school transport and maintaining school sport and PE should be important public health priorities in Australia. Current inequities in school sport and PE and in prevalence of overweight and obesity by area-level SES also need to be addressed.

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A longitudinal study was used to examine age differences in the role of body mass index (BMI) and sociocultural pressures in predicting changes in body image and strategies to both lose weight and increase muscles among 443 children aged between 8 and 12 years (207 boys, 236 girls) over a 16-month period. The strongest predictors of body image and these strategies were BMI, the media and mothers, and to a lesser extent fathers and best friends. Girls were focused on losing weight, whereas boys were focused on both increasing muscle and losing weight. Surprisingly, there was a reduction in strategies both to lose weight and increase muscles as children approached adolescence. The implications of these findings for preventative educational programs for boys and girls are discussed.

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Objectives: To describe parental concerns about their child’s weight, to determine the proportion of parents taking preventive action to avoid obesity in their children and the predictors of taking preventive action, and to describe the strategies adopted by parents.
Design: A cross-sectional survey was conducted. Children&rsquo;s heights and weights were measured, and parents completed a questionnaire that included measures of their own weight status, perceptions of their child’s weight, concerns about their child’s current weight and future weight as an adolescent and adult, and the strategies used to prevent obesity.
Setting: The study was conducted in Melbourne, Australia.
Subjects: A total of 291 families of children aged 5–6 years and 919 families of children aged 10–12 years participated.
Results: Eighty-nine per cent of parents of overweight 5–6-year-olds and 63% of parents of overweight 10–12-year-olds were unaware their child was overweight. Seventy-one per cent of parents of overweight 5–6-year-olds and 43% of parents of overweight 10–12-year-olds were not concerned about their child’s current weight. Although 31% of parents of 5–6-year-olds and 43% of parents of 10–12-year-olds were taking action to prevent unhealthy weight gain in their children, less-educated parents were less likely to do so.
Conclusions: Public health programmes are required to raise parental recognition of childhood overweight and of related risk behaviours, and to provide parents with practical strategies to prevent unhealthy weight gain in their children.

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Understanding potential determinants of change in television (TV) viewing among children may enhance the effectiveness of programs targeting this behaviour. This study aimed to investigate the contribution of individual, social and home environment factors among 10-year-old Australian children to change in TV viewing over a 21-month period. A total of 164 children (49% boys) completed a 19-lesson (9-month) intervention program to reduce TV viewing time. Children completed self-administered surveys four times over 21 months (pre- and post-intervention, 6- and 12-month follow-up). Baseline factors associated with change in TV viewing during the intervention and follow-up periods were: ‘asking parents ≥once/week to switch off the TV and play with them’ (21.6 min/day more than those reporting <once/week, p = 0.007); being able to ‘watch just 1 h of TV per day’ (26.1 min/day less than those who could not, p = 0.010); ‘watching TV no matter what was on’ (36.6 min/day more than those who did not, p < 0.001); and ‘continuing to watch TV after their program was over’ (33.0 min/day more than those who did not, p = 0.006). With every unit increase in baseline frequency of TV viewing with family and friends, children spent on average 4.0 min/day more watching TV over the 21-month period (p = 0.047). Baseline number and placement of TVs at home did not predict change in children's TV viewing over the 21 months. Greater understanding of the family dynamics and circumstances, as well as the individual and social determinants of TV viewing, will be required if we are to develop effective strategies for reducing TV viewing in children.

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BACKGROUND: Estimating changes in weight from changes in energy balance is important for predicting the effect of obesity prevention interventions. OBJECTIVE: The objective was to develop and validate an equation for predicting the mean weight of a population of children in response to a change in total energy intake (TEI) or total energy expenditure (TEE). DESIGN: In 963 children with a mean (+/-SD) age of 8.1 +/- 2.8 y (range: 4-18 y) and weight of 31.5 +/- 17.6 kg, TEE was measured by using doubly labeled water. Log weight (dependent variable) and log TEE (independent variable) were analyzed in a linear regression model with height, age, and sex as covariates. It was assumed that points of dynamic balance, called "settling points," occur for populations wherein energy is in balance (TEE = TEI), weight is stable (ignoring growth), and energy flux (EnFlux) equals TEE. RESULTS: TEE (or EnFlux) explained 74% of the variance in weight. The unstandardized regression coefficient was 0.45 (95% CI: 0.38, 0.51; R(2) = 0.86) after including covariates. Conversion into proportional changes (time(1) to time(2)) gave the equation (weight(2)/weight(1)) = (EnFlux(2)/EnFlux(1))(0.45). In 3 longitudinal studies (n = 212; mean follow-up of 3.4 y), the equation predicted the mean follow-up measured weight to within 0.5%. CONCLUSIONS: The relation of EnFlux with weight was positive, which implied that a high TEI (rather than low physical activity and low TEE) was the main determinant of high body weight. Two populations of children with a 10% difference in mean EnFlux would have a 4.5% difference in mean weight.

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The International Obesity Taskforce (IOTF) recommends using age- and gender-specific body mass index (BMI) cut-points for defining the prevalence of overweight and obesity in children. These are given in both 6- and 12-month age intervals. Since the BMI-for-age curves are nonlinear, a degree of bias will be introduced when age intervals are wide. We aimed to quantify this bias in prevalence estimates in 2178 Australian children aged 4-12 years using 12- versus 6-month age intervals. Using the 12-month interval, the prevalence of overweight and obesity was underestimated by 1.4% compared to the 6-month interval estimates; however, this was age-dependent. It overestimated prevalence for 4-year olds, but underestimated it for older ages by up to 2.6%. Overweight prevalence was generally affected more than obesity prevalence. The use of different age intervals for IOTF cut-points introduces a small but systematic bias in prevalence estimates of overweight and obesity.