Diagnosis Code Assignment Using Sparsity-Based Disease Correlation Embedding

© 1989-2012 IEEE. With the latest developments in database technologies, it becomes easier to store the medical records of hospital patients from their first day of admission than was previously possible. In Intensive Care Units (ICU), modern medical information systems can record patient events in relational databases every second. Knowledge mining from these huge volumes of medical data is beneficial to both caregivers and patients. Given a set of electronic patient records, a system that effectively assigns the disease labels can facilitate medical database management and also benefit other researchers, e.g., pathologists. In this paper, we have proposed a framework to achieve that goal. Medical chart and note data of a patient are used to extract distinctive features. To encode patient features, we apply a Bag-of-Words encoding method for both chart and note data. We also propose a model that takes into account both global information and local correlations between diseases. Correlated diseases are characterized by a graph structure that is embedded in our sparsity-based framework. Our algorithm captures the disease relevance when labeling disease codes rather than making individual decision with respect to a specific disease. At the same time, the global optimal values are guaranteed by our proposed convex objective function. Extensive experiments have been conducted on a real-world large-scale ICU database. The evaluation results demonstrate that our method improves multi-label classification results by successfully incorporating disease correlations

Long-Term Results and Prognostic Factors of Gastric Cancer Patients with Microscopic Peritoneal Carcinomatosis

BACKGROUND: Clinical significance of microscopic peritoneal carcinomatosis remained unclear. The aim of this study was to evaluate the prognostic value of microscopic peritoneal carcinomatosis in gastric cancer. METHODS: From 1996 to 2007, 4426 patients underwent gastrectomy for gastric cancer at Fudan University Shanghai Cancer Center. The clinical and pathological data were reviewed to identify patients with microscopic peritoneal carcinomatosis (group 1). The clinicopathological features and prognosis were examined. Additionally, 242 stage-matched gastric cancer patients without microscopic peritoneal carcinomatosis (group 2) and 118 with macroscopic peritoneal carcinomatosis (group 3) were selected as control groups. RESULTS: Microscopic peritoneal carcinomatosis was found in 121 patients. There were 85 males and 36 females (2.36:1). There was a higher incidence rate of large size tumor (≥5 cm) (P = 0.045), Borrmann IV (P = 0.000), and serosal invasion (P = 0.000) in gastric cancer with microscopic peritoneal carcinomatosis compared with the control group. The 5-year survival rate of gastric cancer with microscopic peritoneal carcinomatosis was 24%, significantly poorer than that of the stage-matched control group but better than that of patients with macroscopic peritoneal carcinomatosis. The independent prognostic factors identified included pathological stage and operative curability. CONCLUSIONS: The presence of microscopic peritoneal carcinomatosis was associated with worse prognosis for gastric cancer, but curative surgery showed potential to improve prognosis

Observation of CR Anisotropy with ARGO-YBJ

The measurement of the anisotropies of cosmic ray arrival direction provides important informations on the propagation mechanisms and on the identification of their sources. In this paper we report the observation of anisotropy regions at different angular scales. In particular, the observation of a possible anisotropy on scales between $\sim$ 10 $^{\circ}$ and $\sim$ 30 $^{\circ}$ suggests the presence of unknown features of the magnetic fields the charged cosmic rays propagate through, as well as potential contributions of nearby sources to the total flux of cosmic rays. Evidence of new weaker few-degree excesses throughout the sky region $195^{\circ}\leq$ R.A. $\leq 315^{\circ}$ is reported for the first time.Comment: Talk given at 12th TAUP Conference 2011, 5-9 September 2011, Munich, German

Feeder Cells Support the Culture of Induced Pluripotent Stem Cells Even after Chemical Fixation

Chemically fixed mouse embryonic fibroblasts (MEFs), instead of live feeder cells, were applied to the maintenance of mouse induced pluripotent stem (miPS) cells. Formaldehyde and glutaraldehyde were used for chemical fixation. The chemically fixed MEF feeders maintained the pluripotency of miPS cells, as well as their undifferentiated state. Furthermore, the chemically fixed MEF feeders were reused several times without affecting their functions. These results indicate that chemical fixation can be applied to modify biological feeders chemically, without losing their original functions. Chemically fixed MEF feeders will be applicable to other stem cell cultures as a reusable extracellular matrix candidate that can be preserved on a long-term basis

Computational identification of condition-specific miRNA targets based on gene expression profiles and sequence information

<p>Abstract</p> <p>Background</p> <p>MicroRNAs (miRNAs) are small and noncoding RNAs that play important roles in various biological processes. They regulate target mRNAs post-transcriptionally through complementary base pairing. Since the changes of miRNAs affect the expression of target genes, the expression levels of target genes in specific biological processes could be different from those of non-target genes. Here we demonstrate that gene expression profiles contain useful information in separating miRNA targets from non-targets.</p> <p>Results</p> <p>The gene expression profiles related to various developmental processes and stresses, as well as the sequences of miRNAs and mRNAs in <it>Arabidopsis</it>, were used to determine whether a given gene is a miRNA target. It is based on the model combining the support vector machine (SVM) classifier and the scoring method based on complementary base pairing between miRNAs and mRNAs. The proposed model yielded low false positive rate and retrieved condition-specific candidate targets through a genome-wide screening.</p> <p>Conclusion</p> <p>Our approach provides a novel framework into screening target genes by considering the gene regulation of miRNAs. It can be broadly applied to identify condition-specific targets computationally by embedding information of gene expression profiles.</p

Measurement of the Bottom-Strange Meson Mixing Phase in the Full CDF Data Set

We report a measurement of the bottom-strange meson mixing phase \beta_s using the time evolution of B0_s -> J/\psi (->\mu+\mu-) \phi (-> K+ K-) decays in which the quark-flavor content of the bottom-strange meson is identified at production. This measurement uses the full data set of proton-antiproton collisions at sqrt(s)= 1.96 TeV collected by the Collider Detector experiment at the Fermilab Tevatron, corresponding to 9.6 fb-1 of integrated luminosity. We report confidence regions in the two-dimensional space of \beta_s and the B0_s decay-width difference \Delta\Gamma_s, and measure \beta_s in [-\pi/2, -1.51] U [-0.06, 0.30] U [1.26, \pi/2] at the 68% confidence level, in agreement with the standard model expectation. Assuming the standard model value of \beta_s, we also determine \Delta\Gamma_s = 0.068 +- 0.026 (stat) +- 0.009 (syst) ps-1 and the mean B0_s lifetime, \tau_s = 1.528 +- 0.019 (stat) +- 0.009 (syst) ps, which are consistent and competitive with determinations by other experiments.Comment: 8 pages, 2 figures, Phys. Rev. Lett 109, 171802 (2012

Bisphenol A and 17β-Estradiol Promote Arrhythmia in the Female Heart via Alteration of Calcium Handling

There is wide-spread human exposure to bisphenol A (BPA), a ubiquitous estrogenic endocrine disruptor that has been implicated as having potentially harmful effects on human heart health. Higher urine BPA concentrations have been shown to be associated with cardiovascular diseases in humans. However, neither the nature nor the mechanism(s) of BPA action on the heart are understood. leak suppressed estrogen-induced triggered activities. The rapid response of female myocytes to estrogens was abolished in an estrogen receptor (ER) β knockout mouse model. leak. Our study provides the first experimental evidence suggesting that exposure to estrogenic endocrine disrupting chemicals and the unique sensitivity of female hearts to estrogens may play a role in arrhythmogenesis in the female heart

HOXB13 is downregulated in colorectal cancer to confer TCF4-mediated transactivation

Mutations in the Wnt signalling cascade are believed to cause aberrant proliferation of colorectal cells through T-cell factor-4 (TCF4) and its downstream growth-modulating factors. HOXB13 is exclusively expressed in prostate and colorectum. In prostate cancers, HOXB13 negatively regulates β-catenin/TCF4-mediated transactivation and subsequently inhibits cell growth. To study the role of HOXB13 in colorectal tumorigenesis, we evaluated the expression of HOXB13 in 53 colorectal tumours originated from the distal left colon to rectum with their matching normal tissues using quantitative RT–PCR analysis. Expression of HOXB13 is either lost or diminished in 26 out of 42 valid tumours (62%), while expression of TCF4 RNA is not correlated with HOXB13 expression. TCF4 promoter analysis showed that HOXB13 does not regulate TCF4 at the transcriptional level. However, HOXB13 downregulated the expression of TCF4 and its target gene, c-myc, at the protein level and consequently inhibited β-catenin/TCF-mediated signalling. Functionally, forced expression of HOXB13 drove colorectal cancer (CRC) cells into growth suppression. This is the first description of the downregulation of HOXB13 in CRC and its mechanism of action is mediated through the regulation of TCF4 protein stability. Our results suggest that loss of HOXB13 may be an important event for colorectal cell transformation, considering that over 90% of colorectal tumours retain mutations in the APC/β-catenin pathway

Systematic identification of yeast cell cycle transcription factors using multiple data sources

<p>Abstract</p> <p>Background</p> <p>Eukaryotic cell cycle is a complex process and is precisely regulated at many levels. Many genes specific to the cell cycle are regulated transcriptionally and are expressed just before they are needed. To understand the cell cycle process, it is important to identify the cell cycle transcription factors (TFs) that regulate the expression of cell cycle-regulated genes.</p> <p>Results</p> <p>We developed a method to identify cell cycle TFs in yeast by integrating current ChIP-chip, mutant, transcription factor binding site (TFBS), and cell cycle gene expression data. We identified 17 cell cycle TFs, 12 of which are known cell cycle TFs, while the remaining five (Ash1, Rlm1, Ste12, Stp1, Tec1) are putative novel cell cycle TFs. For each cell cycle TF, we assigned specific cell cycle phases in which the TF functions and identified the time lag for the TF to exert regulatory effects on its target genes. We also identified 178 novel cell cycle-regulated genes, among which 59 have unknown functions, but they may now be annotated as cell cycle-regulated genes. Most of our predictions are supported by previous experimental or computational studies. Furthermore, a high confidence TF-gene regulatory matrix is derived as a byproduct of our method. Each TF-gene regulatory relationship in this matrix is supported by at least three data sources: gene expression, TFBS, and ChIP-chip or/and mutant data. We show that our method performs better than four existing methods for identifying yeast cell cycle TFs. Finally, an application of our method to different cell cycle gene expression datasets suggests that our method is robust.</p> <p>Conclusion</p> <p>Our method is effective for identifying yeast cell cycle TFs and cell cycle-regulated genes. Many of our predictions are validated by the literature. Our study shows that integrating multiple data sources is a powerful approach to studying complex biological systems.</p

Identification of a gene signature in cell cycle pathway for breast cancer prognosis using gene expression profiling data

<p>Abstract</p> <p>Background</p> <p>Numerous studies have used microarrays to identify gene signatures for predicting cancer patient clinical outcome and responses to chemotherapy. However, the potential impact of gene expression profiling in cancer diagnosis, prognosis and development of personalized treatment may not be fully exploited due to the lack of consensus gene signatures and poor understanding of the underlying molecular mechanisms.</p> <p>Methods</p> <p>We developed a novel approach to derive gene signatures for breast cancer prognosis in the context of known biological pathways. Using unsupervised methods, cancer patients were separated into distinct groups based on gene expression patterns in one of the following pathways: apoptosis, cell cycle, angiogenesis, metastasis, p53, DNA repair, and several receptor-mediated signaling pathways including chemokines, EGF, FGF, HIF, MAP kinase, JAK and NF-κB. The survival probabilities were then compared between the patient groups to determine if differential gene expression in a specific pathway is correlated with differential survival.</p> <p>Results</p> <p>Our results revealed expression of cell cycle genes is strongly predictive of breast cancer outcomes. We further confirmed this observation by building a cell cycle gene signature model using supervised methods. Validated in multiple independent datasets, the cell cycle gene signature is a more accurate predictor for breast cancer clinical outcome than the previously identified Amsterdam 70-gene signature that has been developed into a FDA approved clinical test MammaPrint^®.</p> <p>Conclusion</p> <p>Taken together, the gene expression signature model we developed from well defined pathways is not only a consistently powerful prognosticator but also mechanistically linked to cancer biology. Our approach provides an alternative to the current methodology of identifying gene expression markers for cancer prognosis and drug responses using the whole genome gene expression data.</p