Persistence of back pain symptoms after pregnancy and bone mineral density changes as measured by quantitative ultrasound - a two year longitudinal follow up study

<p>Abstract</p> <p>Background</p> <p>Previous research has shown a loss of bone mineral density (BMD) during pregnancy. This loss has been correlated to the occurrence of back pain symptoms during pregnancy. The objective of this study was to evaluate whether persistence of back pain symptoms 2 years after pregnancy could be associated with BMD changes as measured by quantitative USG of the os calcis.</p> <p>Methods</p> <p>A cohort of patients who reported significant back pain symptoms during pregnancy were surveyed for persistent back pain symptoms 24 to 28 months after the index pregnancy. Os calcis BMD was measured by quantitative ultrasound and compared with the BMD values during pregnancy.</p> <p>Results</p> <p>A cohort of 60 women who had reported significant back pain symptoms in their index pregnancy completed a 24-28 months follow-up survey and BMD reassessment. Persistence of significant back pain symptoms was seen in 24 (40%) of this cohort. These women had higher BMD loss during pregnancy compared to those without further pain (0.047 Vs 0.030 g/cm²; p = 0.03). Those that remained pain free after pregnancy appeared to have completely recovered their BMD loss in pregnancy, while those with persistent pain had lower BMD values (ΔBMD - 0.007 Vs - 0.025 g/cm²; p = 0.023) compared to their early pregnancy values.</p> <p>Conclusion</p> <p>Persistence of back pain symptoms after pregnancy could be related to an inability to recover fully from BMD loss during the index pregnancy.</p

Cancer Biomarker Discovery: The Entropic Hallmark

Background: It is a commonly accepted belief that cancer cells modify their transcriptional state during the progression of the disease. We propose that the progression of cancer cells towards malignant phenotypes can be efficiently tracked using high-throughput technologies that follow the gradual changes observed in the gene expression profiles by employing Shannon's mathematical theory of communication. Methods based on Information Theory can then quantify the divergence of cancer cells' transcriptional profiles from those of normally appearing cells of the originating tissues. The relevance of the proposed methods can be evaluated using microarray datasets available in the public domain but the method is in principle applicable to other high-throughput methods. Methodology/Principal Findings: Using melanoma and prostate cancer datasets we illustrate how it is possible to employ Shannon Entropy and the Jensen-Shannon divergence to trace the transcriptional changes progression of the disease. We establish how the variations of these two measures correlate with established biomarkers of cancer progression. The Information Theory measures allow us to identify novel biomarkers for both progressive and relatively more sudden transcriptional changes leading to malignant phenotypes. At the same time, the methodology was able to validate a large number of genes and processes that seem to be implicated in the progression of melanoma and prostate cancer. Conclusions/Significance: We thus present a quantitative guiding rule, a new unifying hallmark of cancer: the cancer cell's transcriptome changes lead to measurable observed transitions of Normalized Shannon Entropy values (as measured by high-throughput technologies). At the same time, tumor cells increment their divergence from the normal tissue profile increasing their disorder via creation of states that we might not directly measure. This unifying hallmark allows, via the the Jensen-Shannon divergence, to identify the arrow of time of the processes from the gene expression profiles, and helps to map the phenotypical and molecular hallmarks of specific cancer subtypes. The deep mathematical basis of the approach allows us to suggest that this principle is, hopefully, of general applicability for other diseases

Genetic studies of body mass index yield new insights for obesity biology

Note: A full list of authors and affiliations appears at the end of the article. Obesity is heritable and predisposes to many diseases. To understand the genetic basis of obesity better, here we conduct a genome-wide association study and Metabochip meta-analysis of body mass index (BMI), a measure commonly used to define obesity and assess adiposity, in up to 339,224 individuals. This analysis identifies 97 BMI-associated loci (P 20% of BMI variation. Pathway analyses provide strong support for a role of the central nervous system in obesity susceptibility and implicate new genes and pathways, including those related to synaptic function, glutamate signalling, insulin secretion/action, energy metabolism, lipid biology and adipogenesis.</p