958 resultados para Recurrence theorem
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The purpose of this study is to illustrate the development of piano variations as a genre during the Romantic era. In order to facilitate this examination of piano variations techniques, a brief look at the types of variation procedures used by composers of previous eras will assist in understanding developments that later occurred in the Romantic period. Throughout the Baroque era, composers preferred the fured-bass, fixed-melody, and harmonic forms of variation. The crowning achievement of Baroque keyboard music, Bach's Goldberg Variations (1725), contains examples of the "constantharmonic" method in its collection of 30 variations, each of which maintains both the bass and harmonic structure of the themes. While most composers of the classical period favored the "melodic-outline" form of variation, Haydn developed hybrid variation procedure that exhibits recurrence of material rather than repetition, alternating variation (ABABA), rondo variation (ABACA), and ternary variation (ABA). Haydn, Mozart and early Beethoven variations also exhibit simpler textures than do their Baroque predecessors. The nineteenth century produced numerous compositions that display variation techniques, some based on such older, classical models as melodic-outline variation and hybrid variation, others in the style of the character variation or fiee variation. At the beginning of the nineteenth century, Beethoven and Schubert used such classical variation techmques as melodic-outline variations and hybrid variations. Beethoven's late sonatas displayed such new means of expression as variation, fugue, and dramatic recitatives. The third movement of the Sonata in E major, Op. 109 (1820) has a theme and six variations of the melodic-outline type. Johannes Brahms was particularly fond of composing variations for piano. Among the best known examples of formal-outline variations are those found in the Variations and Fugue on a Theme of Handel, Op. 24 (1861). Character variations, in which styles are characterized by the retention and variability of particular elements, also flourished during the Romantic period. Cesar Franck's Variations Symphoniques (1885) are, perhaps, among the most important examples of free variations. This composition is a one-movement work consisting of three sections, Introduction, Variations, and Finale (all movements played "attaca"). This work combines two independent classical formal structures, the concerto and the variation.
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Prostate growth is dependent on circulating androgens, which can be influenced by hepatic function. Liver disease has been suggested to influence prostate cancer (CaP) incidence. However, the effect of hepatic function on CaP outcomes has not been investigated. A total of 1181 patients who underwent radical prostatectomy (RP) between 1988 and 2008 at four Veterans Affairs hospitals that comprise the Shared Equal Access Regional Cancer Hospital database and had available liver function test (LFT) data were included in the study. Independent associations of LFTs with unfavorable pathological features and biochemical recurrence were determined using logistic and Cox regression analyses. Serum glutamic oxaloacetic transaminase (SGOT) and serum glutamic pyruvic transaminase (SGPT) levels were elevated in 8.2 and 4.4% of patients, respectively. After controlling for CaP features, logistic regression revealed a significant association between SGOT levels and pathological Gleason sum > or =7(4+3) cancer (odds ratio=2.12; 95% confidence interval=1.11-4.05; P=0.02). Mild hepatic dysfunction was significantly associated with adverse CaP grade, but was not significantly associated with other adverse pathological features or biochemical recurrence in a cohort of men undergoing RP. The effect of moderate-to-severe liver disease on disease outcomes in CaP patients managed non-surgically remains to be investigated.
Association between DNA damage response and repair genes and risk of invasive serous ovarian cancer.
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BACKGROUND: We analyzed the association between 53 genes related to DNA repair and p53-mediated damage response and serous ovarian cancer risk using case-control data from the North Carolina Ovarian Cancer Study (NCOCS), a population-based, case-control study. METHODS/PRINCIPAL FINDINGS: The analysis was restricted to 364 invasive serous ovarian cancer cases and 761 controls of white, non-Hispanic race. Statistical analysis was two staged: a screen using marginal Bayes factors (BFs) for 484 SNPs and a modeling stage in which we calculated multivariate adjusted posterior probabilities of association for 77 SNPs that passed the screen. These probabilities were conditional on subject age at diagnosis/interview, batch, a DNA quality metric and genotypes of other SNPs and allowed for uncertainty in the genetic parameterizations of the SNPs and number of associated SNPs. Six SNPs had Bayes factors greater than 10 in favor of an association with invasive serous ovarian cancer. These included rs5762746 (median OR(odds ratio)(per allele) = 0.66; 95% credible interval (CI) = 0.44-1.00) and rs6005835 (median OR(per allele) = 0.69; 95% CI = 0.53-0.91) in CHEK2, rs2078486 (median OR(per allele) = 1.65; 95% CI = 1.21-2.25) and rs12951053 (median OR(per allele) = 1.65; 95% CI = 1.20-2.26) in TP53, rs411697 (median OR (rare homozygote) = 0.53; 95% CI = 0.35 - 0.79) in BACH1 and rs10131 (median OR( rare homozygote) = not estimable) in LIG4. The six most highly associated SNPs are either predicted to be functionally significant or are in LD with such a variant. The variants in TP53 were confirmed to be associated in a large follow-up study. CONCLUSIONS/SIGNIFICANCE: Based on our findings, further follow-up of the DNA repair and response pathways in a larger dataset is warranted to confirm these results.
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Asymmetries in sagittal plane knee kinetics have been identified as a risk factor for anterior cruciate ligament (ACL) re-injury. Clinical tools are needed to identify the asymmetries. This study examined the relationships between knee kinetic asymmetries and ground reaction force (GRF) asymmetries during athletic tasks in adolescent patients following ACL reconstruction (ACL-R). Kinematic and GRF data were collected during a stop-jump task and a side-cutting task for 23 patients. Asymmetry indices between the surgical and non-surgical limbs were calculated for GRF and knee kinetic variables. For the stop-jump task, knee kinetics asymmetry indices were correlated with all GRF asymmetry indices (P < 0.05), except for loading rate. Vertical GRF impulse asymmetry index predicted peak knee moment, average knee moment, and knee work (R(2) ≥ 0.78, P < 0.01) asymmetry indices. For the side-cutting tasks, knee kinetic asymmetry indices were correlated with the peak propulsion vertical GRF and vertical GRF impulse asymmetry indices (P < 0.05). Vertical GRF impulse asymmetry index predicted peak knee moment, average knee moment, and knee work (R(2) ≥ 0.55, P < 0.01) asymmetry indices. The vertical GRF asymmetries may be a viable surrogate for knee kinetic asymmetries and therefore may assist in optimizing rehabilitation outcomes and minimizing re-injury rates.
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BACKGROUND: Nonparametric Bayesian techniques have been developed recently to extend the sophistication of factor models, allowing one to infer the number of appropriate factors from the observed data. We consider such techniques for sparse factor analysis, with application to gene-expression data from three virus challenge studies. Particular attention is placed on employing the Beta Process (BP), the Indian Buffet Process (IBP), and related sparseness-promoting techniques to infer a proper number of factors. The posterior density function on the model parameters is computed using Gibbs sampling and variational Bayesian (VB) analysis. RESULTS: Time-evolving gene-expression data are considered for respiratory syncytial virus (RSV), Rhino virus, and influenza, using blood samples from healthy human subjects. These data were acquired in three challenge studies, each executed after receiving institutional review board (IRB) approval from Duke University. Comparisons are made between several alternative means of per-forming nonparametric factor analysis on these data, with comparisons as well to sparse-PCA and Penalized Matrix Decomposition (PMD), closely related non-Bayesian approaches. CONCLUSIONS: Applying the Beta Process to the factor scores, or to the singular values of a pseudo-SVD construction, the proposed algorithms infer the number of factors in gene-expression data. For real data the "true" number of factors is unknown; in our simulations we consider a range of noise variances, and the proposed Bayesian models inferred the number of factors accurately relative to other methods in the literature, such as sparse-PCA and PMD. We have also identified a "pan-viral" factor of importance for each of the three viruses considered in this study. We have identified a set of genes associated with this pan-viral factor, of interest for early detection of such viruses based upon the host response, as quantified via gene-expression data.
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BACKGROUND: Little is known about the constraints of optimizing health care for prostate cancer survivors in Alaska primary care. OBJECTIVE: To describe the experiences and attitudes of primary care providers within the Alaska Tribal Health System (ATHS) regarding the care of prostate cancer survivors. DESIGN: In late October 2011, we emailed a 22-item electronic survey to 268 ATHS primary care providers regarding the frequency of Prostate Specific Antigen (PSA) monitoring for a hypothetical prostate cancer survivor; who should be responsible for the patient's life-long prostate cancer surveillance; who should support the patient's emotional and medical needs as a survivor; and providers' level of comfort addressing recurrence monitoring, erectile dysfunction, urinary incontinence, androgen deprivation therapy, and emotional needs. We used simple logistic regression to examine the association between provider characteristics and their responses to the survivorship survey items. RESULTS: Of 221 individuals who were successfully contacted, a total of 114 responded (52% response rate). Most ATHS providers indicated they would order a PSA test every 12 months (69%) and believed that, ideally, the hypothetical patient's primary care provider should be responsible for his life-long prostate cancer surveillance (60%). Most providers reported feeling either "moderately" or "very" comfortable addressing topics such as prostate cancer recurrence (59%), erectile dysfunction (64%), urinary incontinence (63%), and emotional needs (61%) with prostate cancer survivors. These results varied somewhat by provider characteristics including female sex, years in practice, and the number of prostate cancer survivors seen in their practice. CONCLUSIONS: These data suggest that most primary care providers in Alaska are poised to assume the care of prostate cancer survivors locally. However, we also found that large minorities of providers do not feel confident in their ability to manage common issues in prostate cancer survivorship, implying that continued access to specialists with more expert knowledge would be beneficial.
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Given a probability distribution on an open book (a metric space obtained by gluing a disjoint union of copies of a half-space along their boundary hyperplanes), we define a precise concept of when the Fréchet mean (barycenter) is sticky. This nonclassical phenomenon is quantified by a law of large numbers (LLN) stating that the empirical mean eventually almost surely lies on the (codimension 1 and hence measure 0) spine that is the glued hyperplane, and a central limit theorem (CLT) stating that the limiting distribution is Gaussian and supported on the spine.We also state versions of the LLN and CLT for the cases where the mean is nonsticky (i.e., not lying on the spine) and partly sticky (i.e., is, on the spine but not sticky). © Institute of Mathematical Statistics, 2013.
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The time reversal of stochastic diffusion processes is revisited with emphasis on the physical meaning of the time-reversed drift and the noise prescription in the case of multiplicative noise. The local kinematics and mechanics of free diffusion are linked to the hydrodynamic description. These properties also provide an interpretation of the Pope-Ching formula for the steady-state probability density function along with a geometric interpretation of the fluctuation-dissipation relation. Finally, the statistics of the local entropy production rate of diffusion are discussed in the light of local diffusion properties, and a stochastic differential equation for entropy production is obtained using the Girsanov theorem for reversed diffusion. The results are illustrated for the Ornstein-Uhlenbeck process.
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Focal segmental glomerulosclerosis (FSGS) is a histological lesion with many causes, including inherited genetic defects, with significant proteinuria being the predominant clinical finding at presentation. Mutations in COL4A3 and COL4A4 are known to cause Alport syndrome (AS), thin basement membrane nephropathy, and to result in pathognomonic glomerular basement membrane (GBM) findings. Secondary FSGS is known to develop in classic AS at later stages of the disease. Here, we present seven families with rare or novel variants in COL4A3 or COL4A4 (six with single and one with two heterozygous variants) from a cohort of 70 families with a diagnosis of hereditary FSGS. The predominant clinical finding at diagnosis was proteinuria associated with hematuria. In all seven families, there were individuals with nephrotic-range proteinuria with histologic features of FSGS by light microscopy. In one family, electron microscopy showed thin GBM, but four other families had variable findings inconsistent with classical Alport nephritis. There was no recurrence of disease after kidney transplantation. Families with COL4A3 and COL4A4 variants that segregated with disease represent 10% of our cohort. Thus, COL4A3 and COL4A4 variants should be considered in the interpretation of next-generation sequencing data from such patients. Furthermore, this study illustrates the power of molecular genetic diagnostics in the clarification of renal phenotypes.
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BACKGROUND: Arrhythmia recurrence after cardiac radiofrequency ablation (RFA) for atrial fibrillation has been linked to conduction through discontinuous lesion lines. Intraprocedural visualization and corrective ablation of lesion line discontinuities could decrease postprocedure atrial fibrillation recurrence. Intracardiac acoustic radiation force impulse (ARFI) imaging is a new imaging technique that visualizes RFA lesions by mapping the relative elasticity contrast between compliant-unablated and stiff RFA-treated myocardium. OBJECTIVE: To determine whether intraprocedure ARFI images can identify RFA-treated myocardium in vivo. METHODS: In 8 canines, an electroanatomical mapping-guided intracardiac echo catheter was used to acquire 2-dimensional ARFI images along right atrial ablation lines before and after RFA. ARFI images were acquired during diastole with the myocardium positioned at the ARFI focus (1.5 cm) and parallel to the intracardiac echo transducer for maximal and uniform energy delivery to the tissue. Three reviewers categorized each ARFI image as depicting no lesion, noncontiguous lesion, or contiguous lesion. For comparison, 3 separate reviewers confirmed RFA lesion presence and contiguity on the basis of functional conduction block at the imaging plane location on electroanatomical activation maps. RESULTS: Ten percent of ARFI images were discarded because of motion artifacts. Reviewers of the ARFI images detected RFA-treated sites with high sensitivity (95.7%) and specificity (91.5%). Reviewer identification of contiguous lesions had 75.3% specificity and 47.1% sensitivity. CONCLUSIONS: Intracardiac ARFI imaging was successful in identifying endocardial RFA treatment when specific imaging conditions were maintained. Further advances in ARFI imaging technology would facilitate a wider range of imaging opportunities for clinical lesion evaluation.
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Histopathology is the clinical standard for tissue diagnosis. However, histopathology has several limitations including that it requires tissue processing, which can take 30 minutes or more, and requires a highly trained pathologist to diagnose the tissue. Additionally, the diagnosis is qualitative, and the lack of quantitation leads to possible observer-specific diagnosis. Taken together, it is difficult to diagnose tissue at the point of care using histopathology.
Several clinical situations could benefit from more rapid and automated histological processing, which could reduce the time and the number of steps required between obtaining a fresh tissue specimen and rendering a diagnosis. For example, there is need for rapid detection of residual cancer on the surface of tumor resection specimens during excisional surgeries, which is known as intraoperative tumor margin assessment. Additionally, rapid assessment of biopsy specimens at the point-of-care could enable clinicians to confirm that a suspicious lesion is successfully sampled, thus preventing an unnecessary repeat biopsy procedure. Rapid and low cost histological processing could also be potentially useful in settings lacking the human resources and equipment necessary to perform standard histologic assessment. Lastly, automated interpretation of tissue samples could potentially reduce inter-observer error, particularly in the diagnosis of borderline lesions.
To address these needs, high quality microscopic images of the tissue must be obtained in rapid timeframes, in order for a pathologic assessment to be useful for guiding the intervention. Optical microscopy is a powerful technique to obtain high-resolution images of tissue morphology in real-time at the point of care, without the need for tissue processing. In particular, a number of groups have combined fluorescence microscopy with vital fluorescent stains to visualize micro-anatomical features of thick (i.e. unsectioned or unprocessed) tissue. However, robust methods for segmentation and quantitative analysis of heterogeneous images are essential to enable automated diagnosis. Thus, the goal of this work was to obtain high resolution imaging of tissue morphology through employing fluorescence microscopy and vital fluorescent stains and to develop a quantitative strategy to segment and quantify tissue features in heterogeneous images, such as nuclei and the surrounding stroma, which will enable automated diagnosis of thick tissues.
To achieve these goals, three specific aims were proposed. The first aim was to develop an image processing method that can differentiate nuclei from background tissue heterogeneity and enable automated diagnosis of thick tissue at the point of care. A computational technique called sparse component analysis (SCA) was adapted to isolate features of interest, such as nuclei, from the background. SCA has been used previously in the image processing community for image compression, enhancement, and restoration, but has never been applied to separate distinct tissue types in a heterogeneous image. In combination with a high resolution fluorescence microendoscope (HRME) and a contrast agent acriflavine, the utility of this technique was demonstrated through imaging preclinical sarcoma tumor margins. Acriflavine localizes to the nuclei of cells where it reversibly associates with RNA and DNA. Additionally, acriflavine shows some affinity for collagen and muscle. SCA was adapted to isolate acriflavine positive features or APFs (which correspond to RNA and DNA) from background tissue heterogeneity. The circle transform (CT) was applied to the SCA output to quantify the size and density of overlapping APFs. The sensitivity of the SCA+CT approach to variations in APF size, density and background heterogeneity was demonstrated through simulations. Specifically, SCA+CT achieved the lowest errors for higher contrast ratios and larger APF sizes. When applied to tissue images of excised sarcoma margins, SCA+CT correctly isolated APFs and showed consistently increased density in tumor and tumor + muscle images compared to images containing muscle. Next, variables were quantified from images of resected primary sarcomas and used to optimize a multivariate model. The sensitivity and specificity for differentiating positive from negative ex vivo resected tumor margins was 82% and 75%. The utility of this approach was further tested by imaging the in vivo tumor cavities from 34 mice after resection of a sarcoma with local recurrence as a bench mark. When applied prospectively to images from the tumor cavity, the sensitivity and specificity for differentiating local recurrence was 78% and 82%. The results indicate that SCA+CT can accurately delineate APFs in heterogeneous tissue, which is essential to enable automated and rapid surveillance of tissue pathology.
Two primary challenges were identified in the work in aim 1. First, while SCA can be used to isolate features, such as APFs, from heterogeneous images, its performance is limited by the contrast between APFs and the background. Second, while it is feasible to create mosaics by scanning a sarcoma tumor bed in a mouse, which is on the order of 3-7 mm in any one dimension, it is not feasible to evaluate an entire human surgical margin. Thus, improvements to the microscopic imaging system were made to (1) improve image contrast through rejecting out-of-focus background fluorescence and to (2) increase the field of view (FOV) while maintaining the sub-cellular resolution needed for delineation of nuclei. To address these challenges, a technique called structured illumination microscopy (SIM) was employed in which the entire FOV is illuminated with a defined spatial pattern rather than scanning a focal spot, such as in confocal microscopy.
Thus, the second aim was to improve image contrast and increase the FOV through employing wide-field, non-contact structured illumination microscopy and optimize the segmentation algorithm for new imaging modality. Both image contrast and FOV were increased through the development of a wide-field fluorescence SIM system. Clear improvement in image contrast was seen in structured illumination images compared to uniform illumination images. Additionally, the FOV is over 13X larger than the fluorescence microendoscope used in aim 1. Initial segmentation results of SIM images revealed that SCA is unable to segment large numbers of APFs in the tumor images. Because the FOV of the SIM system is over 13X larger than the FOV of the fluorescence microendoscope, dense collections of APFs commonly seen in tumor images could no longer be sparsely represented, and the fundamental sparsity assumption associated with SCA was no longer met. Thus, an algorithm called maximally stable extremal regions (MSER) was investigated as an alternative approach for APF segmentation in SIM images. MSER was able to accurately segment large numbers of APFs in SIM images of tumor tissue. In addition to optimizing MSER for SIM image segmentation, an optimal frequency of the illumination pattern used in SIM was carefully selected because the image signal to noise ratio (SNR) is dependent on the grid frequency. A grid frequency of 31.7 mm-1 led to the highest SNR and lowest percent error associated with MSER segmentation.
Once MSER was optimized for SIM image segmentation and the optimal grid frequency was selected, a quantitative model was developed to diagnose mouse sarcoma tumor margins that were imaged ex vivo with SIM. Tumor margins were stained with acridine orange (AO) in aim 2 because AO was found to stain the sarcoma tissue more brightly than acriflavine. Both acriflavine and AO are intravital dyes, which have been shown to stain nuclei, skeletal muscle, and collagenous stroma. A tissue-type classification model was developed to differentiate localized regions (75x75 µm) of tumor from skeletal muscle and adipose tissue based on the MSER segmentation output. Specifically, a logistic regression model was used to classify each localized region. The logistic regression model yielded an output in terms of probability (0-100%) that tumor was located within each 75x75 µm region. The model performance was tested using a receiver operator characteristic (ROC) curve analysis that revealed 77% sensitivity and 81% specificity. For margin classification, the whole margin image was divided into localized regions and this tissue-type classification model was applied. In a subset of 6 margins (3 negative, 3 positive), it was shown that with a tumor probability threshold of 50%, 8% of all regions from negative margins exceeded this threshold, while over 17% of all regions exceeded the threshold in the positive margins. Thus, 8% of regions in negative margins were considered false positives. These false positive regions are likely due to the high density of APFs present in normal tissues, which clearly demonstrates a challenge in implementing this automatic algorithm based on AO staining alone.
Thus, the third aim was to improve the specificity of the diagnostic model through leveraging other sources of contrast. Modifications were made to the SIM system to enable fluorescence imaging at a variety of wavelengths. Specifically, the SIM system was modified to enabling imaging of red fluorescent protein (RFP) expressing sarcomas, which were used to delineate the location of tumor cells within each image. Initial analysis of AO stained panels confirmed that there was room for improvement in tumor detection, particularly in regards to false positive regions that were negative for RFP. One approach for improving the specificity of the diagnostic model was to investigate using a fluorophore that was more specific to staining tumor. Specifically, tetracycline was selected because it appeared to specifically stain freshly excised tumor tissue in a matter of minutes, and was non-toxic and stable in solution. Results indicated that tetracycline staining has promise for increasing the specificity of tumor detection in SIM images of a preclinical sarcoma model and further investigation is warranted.
In conclusion, this work presents the development of a combination of tools that is capable of automated segmentation and quantification of micro-anatomical images of thick tissue. When compared to the fluorescence microendoscope, wide-field multispectral fluorescence SIM imaging provided improved image contrast, a larger FOV with comparable resolution, and the ability to image a variety of fluorophores. MSER was an appropriate and rapid approach to segment dense collections of APFs from wide-field SIM images. Variables that reflect the morphology of the tissue, such as the density, size, and shape of nuclei and nucleoli, can be used to automatically diagnose SIM images. The clinical utility of SIM imaging and MSER segmentation to detect microscopic residual disease has been demonstrated by imaging excised preclinical sarcoma margins. Ultimately, this work demonstrates that fluorescence imaging of tissue micro-anatomy combined with a specialized algorithm for delineation and quantification of features is a means for rapid, non-destructive and automated detection of microscopic disease, which could improve cancer management in a variety of clinical scenarios.
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© 2014 UICC.Cytokines such as Interleukin (IL)212p70 ("IL-12") and IL-23 can influence tumor progression. We tested the hypothesis that blood levels of IL-12p40, the common subunit of both cytokines, are associated with melanoma progression. Blood from 2,048 white melanoma patients were collected at a single institution between March 1998 and March 2011. Plasma levels of IL-12p40 were determined for 573 patients (discovery), 249 patients (Validation 1) and 244 patients (Validation 2). Per 10-unit change of IL-12p40 level was used to investigate associations with melanoma patient outcome among all patients or among patients with early or advanced stage. Among stage I/II melanoma patients in the pooled data set, after adjustment for sex, age, stage and blood draw time from diagnosis, elevated IL-12p40 was associated with melanoma recurrence [hazard ratio (HR)51.04 per 10-unit increase in IL-12p40, 95% CI 1.02-1.06, p58.48 × 10-5]; Elevated IL-12p40 was also associated with a poorer melanoma specific survival (HR51.06, 95% CI 1.03-1.09, p53.35 × 10-5) and overall survival (HR51.05, 95% CI 1.03-1.08, p58.78 × 10-7) in multivariate analysis. Among stage III/IV melanoma patients in the pooled data set, no significant association was detected between elevated IL-12p40 and overall survival, or with melanoma specific survival, with or without adjustment for the above covariates. Early stage melanoma patients with elevated IL-12p40 levels are more likely to develop disease recurrence and have a poorer survival. Further investigation with a larger sample size will be needed to determine the role of IL-12p40 in advanced stage melanoma patients.
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Recent investigation has identified association of IL-12p40 blood levels with melanoma recurrence and patient survival. No studies have investigated associations of single-nucleotide polymorphisms (SNPs) with melanoma patient IL-12p40 blood levels or their potential contributions to melanoma susceptibility or patient outcome. In the current study, 818,237 SNPs were available for 1,804 melanoma cases and 1,026 controls. IL-12p40 blood levels were assessed among 573 cases (discovery), 249 cases (case validation), and 299 controls (control validation). SNPs were evaluated for association with log[IL-12p40] levels in the discovery data set and replicated in two validation data sets, and significant SNPs were assessed for association with melanoma susceptibility and patient outcomes. The most significant SNP associated with log[IL-12p40] was in the IL-12B gene region (rs6897260, combined P=9.26 × 10(-38)); this single variant explained 13.1% of variability in log[IL-12p40]. The most significant SNP in EBF1 was rs6895454 (combined P=2.24 × 10(-9)). A marker in IL12B was associated with melanoma susceptibility (rs3213119, multivariate P=0.0499; OR=1.50, 95% CI 1.00-2.24), whereas a marker in EBF1 was associated with melanoma-specific survival in advanced-stage patients (rs10515789, multivariate P=0.02; HR=1.93, 95% CI 1.11-3.35). Both EBF1 and IL12B strongly regulate IL-12p40 blood levels, and IL-12p40 polymorphisms may contribute to melanoma susceptibility and influence patient outcome.
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BACKGROUND: Given the potential importance of epithelial plasticity (EP) to cancer metastasis, we sought to investigate biomarkers related to EP in men with localized prostate cancer (PC) for the association with time to PSA recurrence and other clinical outcomes after surgery. METHODS: Men with localized PC treated with radical prostatectomy at the Durham VA Medical Center and whose prostatectomy tissues were included in a tissue microarray (TMA) linked to long-term outcomes. We performed immunohistochemical studies using validated antibodies against E-cadherin and Ki-67 and mesenchymal biomarkers including N-cadherin, vimentin, SNAIL, ZEB1 and TWIST. Association studies were conducted for each biomarker with baseline clinical/pathologic characteristics an risk of PSA recurrence over time. RESULTS: Two hundred and five men contributed TMA tissue and had long-term follow-up (median 11 years). Forty-three percent had PSA recurrence; three died of PC. The majority had high E-cadherin expression (86%); 14% had low/absent E-cadherin expression. N-cadherin was rarely expressed (<4%) and we were unable to identify an E-to-N-cadherin switch as independently prognostic. No associations with clinical risk group, PSA recurrence or Gleason sum were noted for SNAIL, ZEB1, vimentin or TWIST, despite heterogeneous expression between patients. We observed an association of higher Ki-67 expression with Gleason sum (P=0.043), National Comprehensive Cancer Network risk (P=0.013) and PSA recurrence (hazard ratio 1.07, P=0.016). CONCLUSIONS: The expression of EP biomarkers in this cohort of men with a low risk of PC-specific mortality was not associated with aggressive features or PSA relapse after surgery.
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We recently developed an approach for testing the accuracy of network inference algorithms by applying them to biologically realistic simulations with known network topology. Here, we seek to determine the degree to which the network topology and data sampling regime influence the ability of our Bayesian network inference algorithm, NETWORKINFERENCE, to recover gene regulatory networks. NETWORKINFERENCE performed well at recovering feedback loops and multiple targets of a regulator with small amounts of data, but required more data to recover multiple regulators of a gene. When collecting the same number of data samples at different intervals from the system, the best recovery was produced by sampling intervals long enough such that sampling covered propagation of regulation through the network but not so long such that intervals missed internal dynamics. These results further elucidate the possibilities and limitations of network inference based on biological data.