OBJECTIVE To compare the effect of a Serenoa repens extract with placebo for symptoms of benign prostatic hyperplasia (BPH). PATIENTS AND METHODS In a double‐blind placebo‐controlled randomized trial between January 1999 and March 2000, 100 men with symptoms of BPH, aged < 80 years, with a maximum urinary flow rate of 5–15 mL/s for a voiding volume of 150 mL, were randomly and equally allocated to 320 mg S. repens extract or placebo (paraffin oil). The main outcome measures were the International Prostate Symptom Score (IPSS), peak urinary flow rate, and the Rosen International Index of Erectile Function (IIEF) questionnaire. RESULTS There was no significant difference between the treatments over the 12 weeks of the study in the IPSS, peak urinary flow rate or for the IIEF questionnaire. CONCLUSIONS During the trial all participants had some improvement in their symptoms of BPH but there was no significant beneficial effect of this S. repens extract over placebo in this 12‐week trial.

OBJECTIVE: Studies in school-age children have consistently shown a positive association between maternal paid work hours and child obesity. However, there is conflicting evidence about the impact of maternal work hours scheduled at nonstandard times (for example, evenings, nights or weekends), and no previous examination of paternal work schedules and child weight. We examined the associations between maternal, paternal and combined parental paid work schedules and overweight/obesity in children at age 9 years. METHODS: Data were analysed from the most recent follow-up of 9-year-old children (n ¼ 434) in an Australian birth cohort study. Children were measured and classified as overweight/obese using the International Obesity Taskforce body mass index cutoff points. Current working conditions of parents were obtained from a structured interview with the primary caregiver. Logistic regression analyses were used to investigate the effect of parental work schedules on child overweight/obesity with adjustment for a range of sociodemographic and household factors associated with parental employment and child weight. RESULTS: At 9 years of age, 99 children (22.8%) were overweight or obese. When parental work schedules were examined separately, child overweight/obesity was significantly associated with paternal nonstandard work schedules (adjusted odds ratio (OR) 1.97, 95% confidence interval (CI) 1.08 --3.61). There was no association with any type of maternal work schedule. We also found an association between child overweight/obesity and circumstances in which both parents worked nonstandard schedules; however, this was of borderline statistical significance in the adjusted models (adjusted OR 2.26, 95% CI 0.99 --5.16). CONCLUSION: Work hours scheduled at nonstandard times, when worked by the father or both parents, were associated with child overweight and obesity. These findings indicate the potential importance of fathers' paid work arrangements for child overweight/obesity, which until recently has largely been ignored.

Effective cardiovascular disease (CVD) prevention relies on timely identification and intervention for individuals at risk. Conventional formula-based techniques have been demonstrated to over- or under-predict the risk of CVD in the Australian population. This study assessed the ability of machine learning models to predict CVD mortality risk in the Australian population and compare performance with the well-established Framingham model. Data is drawn from three Australian cohort studies: the North West Adelaide Health Study (NWAHS), the Australian Diabetes, Obesity, and Lifestyle study, and the Melbourne Collaborative Cohort Study (MCCS). Four machine learning models for predicting 15-year CVD mortality risk were developed and compared to the 2008 Framingham model. Machine learning models performed significantly better compared to the Framingham model when applied to the three Australian cohorts. Machine learning based models improved prediction by 2.7% to 5.2% across three Australian cohorts. In an aggregated cohort, machine learning models improved prediction by up to 5.1% (area-under-curve (AUC) 0.852, 95% CI 0.837–0.867). Net reclassification improvement (NRI) was up to 26% with machine learning models. Machine learning based models also showed improved performance when stratified by sex and diabetes status. Results suggest a potential for improving CVD risk prediction in the Australian population using machine learning models.

This report describes the authors' early outcomes with implantable gastric stimulation (IGS) used to achieve weight loss in patients with a low body mass index (BMI). After prescreening of potential candidates with a selection algorithm, 24 patients (21 women and 3 men) with a low BMI (30-34.9) underwent IGS implantation at two centers. The patients had a mean age of 43 years (range, 32-60 years), a mean BMI of 33 (range, 30-36), and a mean weight of 92 kg (range, 80-117 kg). At this writing, 6 months postoperatively, there have been no serious adverse events related to the device. The mean percentage of excess weight loss (EWL) was 5.9%, with three patients explanted because of noncompliance. The mean waist circumference decreased 5.8%, which was significant (p = 0.009). A subset of nine patients (37.5%) had an EWL exceeding 10% (mean, 20.1%). A subset of low BMI patients lost a clinically significant amount of weight with IGS within 6 months. Further study is required for better identification of potential candidates for this novel approach.