Nonresectable Hepatocellular Carcinoma: Long-term Toxicity in Patients Treated with Transarterial Chemoembolization - Single-Center Experience

Purpose: To determine the toxicity profile of transarterial chemoembolization (TACE) at 6 months and 1 year after treatment in patients with hepatocellular carcinoma (HCC) in a standardized oncology protocol so that TACE could be compared with systemic chemotherapeutic regimens for liver cancer. Materials and Methods: The study was authorized by the institutional review board. Between January 2002 and January 2007, 190 patients (155 men, 35 women; median age, 65 years; age range, 18 – 84 years) with HCC who underwent TACE treatment were identified from a prospectively collected database. Clinical records of complete blood cell counts and chemical profiles at baseline and at 6 and 12 months after treatment were studied retrospectively. Toxicity was graded according to the common terminology criteria for adverse events (CTCAE). A transition (survival) analysis perspective was used to estimate the distribution of toxicity grades. Patient survival from the first TACE session was calculated with Kaplan-Meier analysis. Results: Grade 3 or 4 toxicity 6 and 12 months, respectively, after treatment included leukocytopenia (7% and 19%); anemia (9% and 19%); thromobocytopenia (13% and 23%); prolonged activated partial thromboplastin time (8% and 18%); elevated aspartate aminotransferase (15% and 18%), alanine aminotransferase (10% and 18%), and alkaline phosphatase (8% and 18%) levels; hypoalbuminemia (10% and 19%); hyperbilirubinemia (10% and 22%); and alopecia (18%). The cumulative survival rate was 58% at 1 year, 39% at 2 years, and 29% at 3 years. These toxicity rates were considerably lower than those reported after treatment with currently used systemic chemotherapeutic agents. Conclusion: Study results show that TACE has a favorable long-term toxicity profile in patients with HCC. Data clearly support the role of TACE in the treatment of patients with nonresectable HHC

3-D Ultrastructure of O. tauri: Electron Cryotomography of an Entire Eukaryotic Cell

The hallmark of eukaryotic cells is their segregation of key biological functions into discrete, membrane-bound organelles. Creating accurate models of their ultrastructural complexity has been difficult in part because of the limited resolution of light microscopy and the artifact-prone nature of conventional electron microscopy. Here we explored the potential of the emerging technology electron cryotomography to produce three-dimensional images of an entire eukaryotic cell in a near-native state. Ostreococcus tauri was chosen as the specimen because as a unicellular picoplankton with just one copy of each organelle, it is the smallest known eukaryote and was therefore likely to yield the highest resolution images. Whole cells were imaged at various stages of the cell cycle, yielding 3-D reconstructions of complete chloroplasts, mitochondria, endoplasmic reticula, Golgi bodies, peroxisomes, microtubules, and putative ribosome distributions in-situ. Surprisingly, the nucleus was seen to open long before mitosis, and while one microtubule (or two in some predivisional cells) was consistently present, no mitotic spindle was ever observed, prompting speculation that a single microtubule might be sufficient to segregate multiple chromosomes

Effects of growth rate, size, and light availability on tree survival across life stages: a demographic analysis accounting for missing values and small sample sizes.

The data set supporting the results of this article is available in the Dryad repository, http://dx.doi.org/10.5061/dryad.6f4qs. Moustakas, A. and Evans, M. R. (2015) Effects of growth rate, size, and light availability on tree survival across life stages: a demographic analysis accounting for missing values.Plant survival is a key factor in forest dynamics and survival probabilities often vary across life stages. Studies specifically aimed at assessing tree survival are unusual and so data initially designed for other purposes often need to be used; such data are more likely to contain errors than data collected for this specific purpose

Crude incidence in two-phase designs in the presence of competing risks.

BackgroundIn many studies, some information might not be available for the whole cohort, some covariates, or even the outcome, might be ascertained in selected subsamples. These studies are part of a broad category termed two-phase studies. Common examples include the nested case-control and the case-cohort designs. For two-phase studies, appropriate weighted survival estimates have been derived; however, no estimator of cumulative incidence accounting for competing events has been proposed. This is relevant in the presence of multiple types of events, where estimation of event type specific quantities are needed for evaluating outcome.MethodsWe develop a non parametric estimator of the cumulative incidence function of events accounting for possible competing events. It handles a general sampling design by weights derived from the sampling probabilities. The variance is derived from the influence function of the subdistribution hazard.ResultsThe proposed method shows good performance in simulations. It is applied to estimate the crude incidence of relapse in childhood acute lymphoblastic leukemia in groups defined by a genotype not available for everyone in a cohort of nearly 2000 patients, where death due to toxicity acted as a competing event. In a second example the aim was to estimate engagement in care of a cohort of HIV patients in resource limited setting, where for some patients the outcome itself was missing due to lost to follow-up. A sampling based approach was used to identify outcome in a subsample of lost patients and to obtain a valid estimate of connection to care.ConclusionsA valid estimator for cumulative incidence of events accounting for competing risks under a general sampling design from an infinite target population is derived

Clostridium difficile sortase recognizes a (S/P)PXTG sequence motif and can accommodate diaminopimelic acid as a substrate for transpeptidation

AbstractCovalent attachment of surface proteins to the cell wall of Gram-positive bacteria requires a sortase-mediated transpeptidation reaction. In almost all Gram-positive bacteria, the housekeeping sortase, sortase A, recognizes the canonical recognition sequence LPXTG (X=any amino acid). The human pathogen Clostridium difficile carries a single putative sortase gene (cd2718) but neither transpeptidation activity nor specificity of CD2718 has been investigated. We produced recombinant CD2718 and examined its transpeptidation activity in vitro using synthetic peptides and MALDI-ToF(-ToF) MS analysis. We demonstrate that CD2718 has sortase activity with specificity for a (S/P)PXTG motif and can accommodate diaminopimelic acid as a substrate for transpeptidation

Simple estimators of the intensity of seasonal occurrence

<p>Abstract</p> <p>Background</p> <p>Edwards's method is a widely used approach for fitting a sine curve to a time-series of monthly frequencies. From this fitted curve, estimates of the seasonal intensity of occurrence (i.e., peak-to-low ratio of the fitted curve) can be generated.</p> <p>Methods</p> <p>We discuss various approaches to the estimation of seasonal intensity assuming Edwards's periodic model, including maximum likelihood estimation (MLE), least squares, weighted least squares, and a new closed-form estimator based on a second-order moment statistic and non-transformed data. Through an extensive Monte Carlo simulation study, we compare the finite sample performance characteristics of the estimators discussed in this paper. Finally, all estimators and confidence interval procedures discussed are compared in a re-analysis of data on the seasonality of monocytic leukemia.</p> <p>Results</p> <p>We find that Edwards's estimator is substantially biased, particularly for small numbers of events and very large or small amounts of seasonality. For the common setting of rare events and moderate seasonality, the new estimator proposed in this paper yields less finite sample bias and better mean squared error than either the MLE or weighted least squares. For large studies and strong seasonality, MLE or weighted least squares appears to be the optimal analytic method among those considered.</p> <p>Conclusion</p> <p>Edwards's estimator of the seasonal relative risk can exhibit substantial finite sample bias. The alternative estimators considered in this paper should be preferred.</p

A perfect correlate does not a surrogate make

BACKGROUND: There is common belief among some medical researchers that if a potential surrogate endpoint is highly correlated with a true endpoint, then a positive (or negative) difference in potential surrogate endpoints between randomization groups would imply a positive (or negative) difference in unobserved true endpoints between randomization groups. We investigate this belief when the potential surrogate and unobserved true endpoints are perfectly correlated within each randomization group. METHODS: We use a graphical approach. The vertical axis is the unobserved true endpoint and the horizontal axis is the potential surrogate endpoint. Perfect correlation within each randomization group implies that, for each randomization group, potential surrogate and true endpoints are related by a straight line. In this scenario the investigator does not know the slopes or intercepts. We consider a plausible example where the slope of the line is higher for the experimental group than for the control group. RESULTS: In our example with unknown lines, a decrease in mean potential surrogate endpoints from control to experimental groups corresponds to an increase in mean true endpoint from control to experimental groups. Thus the potential surrogate endpoints give the wrong inference. Similar results hold for binary potential surrogate and true outcomes (although the notion of correlation does not apply). The potential surrogate endpointwould give the correct inference if either (i) the unknown lines for the two group coincided, which means that the distribution of true endpoint conditional on potential surrogate endpoint does not depend on treatment group, which is called the Prentice Criterion or (ii) if one could accurately predict the lines based on data from prior studies. CONCLUSION: Perfect correlation between potential surrogate and unobserved true outcomes within randomized groups does not guarantee correct inference based on a potential surrogate endpoint. Even in early phase trials, investigators should not base conclusions on potential surrogate endpoints in which the only validation is high correlation with the true endpoint within a group