Integration of Wearable Technologies into Patients’ Electronic Medical Records

Health Wearable Technologies are becoming more popular all around the world. Smart watches and fitness trackers are currently being used by many and the utilization is expected to continue to grow. The innovative technology will certainly play a key role to the optimal operating of future society, especially with applications in healthcare. This article will introduce the concept of wearable technology with advantages and challenges of its application in the healthcare industry

Cell Type-Specific TGF-β Mediated EMT in 3D and 2D Models and Its Reversal by TGF-β Receptor Kinase Inhibitor in Ovarian Cancer Cell Lines

Transcriptome profiling of 3D models compared to 2D models in various cancer cell lines shows differential expression of TGF-β-mediated and cell adhesion pathways. Presence of TGF-β in these cell lines shows an increased invasion potential which is specific to cell type. In the present study, we identified exogenous addition of TGF-β can induce Epithelial to Mesenchymal Transition (EMT) in a few cancer cell lines. RNA sequencing and real time PCR were carried out in different ovarian cancer cell lines to identify molecular profiling and metabolic profiling. Since EMT induction by TGF-β is cell-type specific, we decided to select two promising ovarian cancer cell lines as model systems to study EMT. TGF-β modulation in EMT and cancer invasion were successfully depicted in both 2D and 3D models of SKOV3 and CAOV3 cell lines. Functional evaluation in 3D and 2D models demonstrates that the addition of the exogenous TGF-β can induce EMT and invasion in cancer cells by turning them into aggressive phenotypes. TGF-β receptor kinase I inhibitor (LY364947) can revert the TGF-β effect in these cells. In a nutshell, TGF-β can induce EMT and migration, increase aggressiveness, increase cell survival, alter cell characteristics, remodel the Extracellular Matrix (ECM) and increase cell metabolism favorable for tumor invasion and metastasis. We concluded that transcriptomic and phenotypic effect of TGF-β and its inhibitor is cell-type specific and not cancer specific

Type 2 Diabetes Risk Allele Loci in the Qatari Population.

The prevalence of type 2 diabetes (T2D) is increasing in the Middle East. However, the genetic risk factors for T2D in the Middle Eastern populations are not known, as the majority of studies of genetic risk for T2D are in Europeans and Asians.All subjects were ≥3 generation Qataris. Cases with T2D (n = 1,124) and controls (n = 590) were randomly recruited and assigned to the 3 known Qatari genetic subpopulations [Bedouin (Q1), Persian/South Asian (Q2) and African (Q3)]. Subjects underwent genotyping for 37 single nucleotide polymorphisms (SNPs) in 29 genes known to be associated with T2D in Europeans and/or Asian populations, and an additional 27 tag SNPs related to these susceptibility loci. Pre-study power analysis suggested that with the known incidence of T2D in adult Qataris (22%), the study population size would be sufficient to detect significant differences if the SNPs were risk factors among Qataris, assuming that the odds ratio (OR) for T2D SNPs in Qatari's is greater than or equal to the SNP with highest known OR in other populations.Haplotype analysis demonstrated that Qatari haplotypes in the region of known T2D risk alleles in Q1 and Q2 genetic subpopulations were similar to European haplotypes. After Benjamini-Hochberg adjustment for multiple testing, only two SNPs (rs7903146 and rs4506565), both associated with transcription factor 7-like 2 (TCF7L2), achieved statistical significance in the whole study population. When T2D subjects and control subjects were assigned to the known 3 Qatari subpopulations, and analyzed individually and with the Q1 and Q2 genetic subpopulations combined, one of these SNPs (rs4506565) was also significant in the admixed group. No other SNPs associated with T2D in all Qataris or individual genetic subpopulations.With the caveats of the power analysis, the European/Asian T2D SNPs do not contribute significantly to the high prevalence of T2D in the Qatari population, suggesting that the genetic risks for T2D are likely different in Qataris compared to Europeans and Asians

Correction: Type 2 Diabetes Risk Allele Loci in the Qatari Population.

[This corrects the article DOI: 10.1371/journal.pone.0156834.]

Comparison of Qatari haplotypes in the regions flanking known single nucleotide polymorphisms (SNPs) associated with type 2 diabetes (T2D) or nearby tag SNPs to the relevant haplotypes of European, Asian, African, or Admixed 1000 Genomes populations.

<p>Admixture deconvolution was used to determine if Qatari haplotypes flanking 64 ‘T2D SNPs’ matched the populations where these SNPs were discovered. Haplotypes were inferred for 100 deeply sequenced Qatari genomes (n = 60 Bedouin Q1, n = 20 Persian Q2, n = 20 African Q3), and divided into 2000 SNP (0.5 cM) intervals. For each interval, SupportMix [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0156834#pone.0156834.ref012" target="_blank">12</a>] was used to infer the population with the most similar haplotype, using 1000 Genomes Project Phase 1 as a reference (Admixed = ASW, PUR, CLM, MXL; Asian = CHB, CHS, JPT; African = LWK, YRI; European = TSI, IBS, CEU, FIN, GBR). For each interval containing a T2D risk SNP (n = 64, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0156834#pone.0156834.s003" target="_blank">S1 Table</a>), the percentage of Qatari haplotypes assigned to European, Asian, African, or Admixed was determined. Shown is a heatmap of the results, where each column represents a SNP, and each row represents a 1000 Genomes group. Scaled from blue (0%) to tan (100%), colors represent the % of haplotypes for the SNP that are most similar to each Qatari population (Q1, Q2, or Q3).</p

Population risk allele frequencies.

<p>Population risk allele frequencies (RAF) in <b>A.</b> all Qataris, and <b>B.</b> Q1 and Q2 genetic subpopulations combined, compared to European RAF (obtained from Hapmap [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0156834#pone.0156834.ref023" target="_blank">23</a>]) for 37 SNPs previously associated with T2D, with the straight line indicating the regression line of best fit of the data.</p