METHODS: PSAV was calculated using logistic regression to determine if PSA or PSAV predicted the result of prostate biopsy (PB) in men with elevated PSA values. Cox regression was used to determine whether PSA or PSAV predicted PSA elevation in men with low PSAs. Interaction terms were included in the models to determine whether BRCA status influenced the predictiveness of PSA or PSAV.
RESULTS: 1634 participants had ⩾3 PSA readings of whom 174 underwent PB and 45 PrCas diagnosed. In men with PSA >3.0 ng ml-l, PSAV was not significantly associated with presence of cancer or high-grade disease. PSAV did not add to PSA for predicting time to an elevated PSA. When comparing BRCA1/2 carriers to non-carriers, we found a significant interaction between BRCA status and last PSA before biopsy (P=0.031) and BRCA2 status and PSAV (P=0.024). However, PSAV was not predictive of biopsy outcome in BRCA2 carriers.
CONCLUSIONS: PSA is more strongly predictive of PrCa in BRCA carriers than non-carriers. We did not find evidence that PSAV aids decision-making for BRCA carriers over absolute PSA value alone.
METHODS: A clinically validated insulin/glucose model was used to calculate SI with the standard fasting assumption (SFA) G0 = GTARGET. Then GTARGET was treated as a variable in a second analysis (VGT). The outcomes were contrasted across twelve participants with established type 2 diabetes mellitus that were recruited to take part in a 24-week dietary intervention. Participants underwent three insulin-modified intravenous glucose tolerance tests (IM-IVGTT) at 0, 12, and 24 weeks.
RESULTS: SIVGT had a median value of 3.36×10-4 L·mU-1·min-1 (IQR: 2.30 - 4.95×10-4) and were significantly lower ( P < .05) than the median SISFA (6.38×10-4 L·mU-1·min-1, IQR: 4.87 - 9.39×10-4). The VGT approach generally yielded lower SI values in line with expected participant physiology and more effectively tracked changes in participant state over the 24-week trial. Calculated GTARGET values were significantly lower than G0 values (median GTARGET = 5.48 vs G0 = 7.16 mmol·L-1 P < .001) and were notably higher in individuals with longer term diabetes.
CONCLUSIONS: Typical modeling approaches can overestimate SI when GTARGET does not equal G0. Hence, calculating GTARGET may enable more precise SI measurements in individuals with type 2 diabetes, and could imply a dysfunction in diabetic metabolism.
METHODS: Ten focus group discussions were held with opinion leaders (chiefs, elders, assemblymen, leaders of women groups) and 16 in-depth interviews were conducted with healthcare workers (District Directors of Health, Medical Assistants in-charge of health centres, and district Public Health Nurses and Midwives). The interviews and discussions were audio recorded, transcribed into English and imported into NVivo 10 for content analysis.
RESULTS: As heads of the family, men control resources, consult soothsayers to determine the health seeking or treatment for pregnant women, and serve as the final authority on where and when pregnant women should seek medical care. Beyond that, they have no expectation of any further role during antenatal care and therefore find it unnecessary to attend clinics with their partners. There were conflicting views about whether men needed to provide any extra support to their pregnant partners within the home. Health workers generally agreed that men provided little or no support to their partners. Although health workers had facilitated the formation of father support groups, there was little evidence of any impact on antenatal support.
CONCLUSIONS: In patriarchal settings, the role of men can be complex and social and cultural traditions may conflict with public health recommendations. Initiatives to promote male involvement should focus on young men and use chiefs and opinion leaders as advocates to re-orient men towards more proactive involvement in ensuring the health of their partners.
METHODS: In this study, multi-locus sequence typing (MLST) was performed on clinical B. pseudomallei isolates collected from Kelantan state of Malaysia, patients' clinical data were reviewed and then genotype-risk correlations were investigated.
RESULTS: Genotyping of 83 B. pseudomallei isolates revealed 32 different STs, of which 13(40%) were novel. The frequencies of the STs among the 83 isolates ranged from 1 to 12 observations, and ST54, ST371 and ST289 were predominant. All non-novel STs reported in this study have also been identified in other Asian countries. Based on the MLST data analysis, the phylogenetic tree showed clustering of the STs with each other, as well as with the STs from Southeast Asia and China. No evidence for associations between any of B. pseudomallei STs and clinical melioidosis presentation was detected. In addition, the bacterial genotype clusters in relation with each clinical outcome were statistically insignificant, and no risk estimate was reported. This study has expanded the data for B. pseudomallei on MLST database map and provided insights into the molecular epidemiology of melioidosis in Peninsular Malaysia.
CONCLUSION: This study concurs with previous reports concluding that infecting strain type plays no role in determining disease presentation.