Platelet Counts, Acute Kidney Injury, and Mortality after Coronary Artery Bypass Grafting Surgery.

BACKGROUND: Cardiac surgery requiring cardiopulmonary bypass is associated with platelet activation. Because platelets are increasingly recognized as important effectors of ischemia and end-organ inflammatory injury, the authors explored whether postoperative nadir platelet counts are associated with acute kidney injury (AKI) and mortality after coronary artery bypass grafting (CABG) surgery. METHODS: The authors evaluated 4,217 adult patients who underwent CABG surgery. Postoperative nadir platelet counts were defined as the lowest in-hospital values and were used as a continuous predictor of postoperative AKI and mortality. Nadir values in the lowest 10th percentile were also used as a categorical predictor. Multivariable logistic regression and Cox proportional hazard models examined the association between postoperative platelet counts, postoperative AKI, and mortality. RESULTS: The median postoperative nadir platelet count was 121 × 10/l. The incidence of postoperative AKI was 54%, including 9.5% (215 patients) and 3.4% (76 patients) who experienced stages II and III AKI, respectively. For every 30 × 10/l decrease in platelet counts, the risk for postoperative AKI increased by 14% (adjusted odds ratio, 1.14; 95% CI, 1.09 to 1.20; P < 0.0001). Patients with platelet counts in the lowest 10th percentile were three times more likely to progress to a higher severity of postoperative AKI (adjusted proportional odds ratio, 3.04; 95% CI, 2.26 to 4.07; P < 0.0001) and had associated increased risk for mortality immediately after surgery (adjusted hazard ratio, 5.46; 95% CI, 3.79 to 7.89; P < 0.0001). CONCLUSION: The authors found a significant association between postoperative nadir platelet counts and AKI and short-term mortality after CABG surgery.

Making marine life count: a new baseline for policy.

The Census of Marine Life aids practical work of the Convention on Biological Diversity, discovers and tracks ocean biodiversity, and supports marine environmental planning.

cDNA for the human beta 2-adrenergic receptor: a protein with multiple membrane-spanning domains and encoded by a gene whose chromosomal location is shared with that of the receptor for platelet-derived growth factor.

We have isolated and sequenced a cDNA encoding the human beta 2-adrenergic receptor. The deduced amino acid sequence (413 residues) is that of a protein containing seven clusters of hydrophobic amino acids suggestive of membrane-spanning domains. While the protein is 87% identical overall with the previously cloned hamster beta 2-adrenergic receptor, the most highly conserved regions are the putative transmembrane helices (95% identical) and cytoplasmic loops (93% identical), suggesting that these regions of the molecule harbor important functional domains. Several of the transmembrane helices also share lesser degrees of identity with comparable regions of select members of the opsin family of visual pigments. We have localized the gene for the beta 2-adrenergic receptor to q31-q32 on chromosome 5. This is the same position recently determined for the gene encoding the receptor for platelet-derived growth factor and is adjacent to that for the FMS protooncogene, which encodes the receptor for the macrophage colony-stimulating factor.

Advances in Dynamic Modeling and Computation for Count Data

A class of multi-process models is developed for collections of time indexed count data. Autocorrelation in counts is achieved with dynamic models for the natural parameter of the binomial distribution. In addition to modeling binomial time series, the framework includes dynamic models for multinomial and Poisson time series. Markov chain Monte Carlo (MCMC) and Po ́lya-Gamma data augmentation (Polson et al., 2013) are critical for fitting multi-process models of counts. To facilitate computation when the counts are high, a Gaussian approximation to the P ́olya- Gamma random variable is developed.

Three applied analyses are presented to explore the utility and versatility of the framework. The first analysis develops a model for complex dynamic behavior of themes in collections of text documents. Documents are modeled as a “bag of words”, and the multinomial distribution is used to characterize uncertainty in the vocabulary terms appearing in each document. State-space models for the natural parameters of the multinomial distribution induce autocorrelation in themes and their proportional representation in the corpus over time.

The second analysis develops a dynamic mixed membership model for Poisson counts. The model is applied to a collection of time series which record neuron level firing patterns in rhesus monkeys. The monkey is exposed to two sounds simultaneously, and Gaussian processes are used to smoothly model the time-varying rate at which the neuron’s firing pattern fluctuates between features associated with each sound in isolation.

The third analysis presents a switching dynamic generalized linear model for the time-varying home run totals of professional baseball players. The model endows each player with an age specific latent natural ability class and a performance enhancing drug (PED) use indicator. As players age, they randomly transition through a sequence of ability classes in a manner consistent with traditional aging patterns. When the performance of the player significantly deviates from the expected aging pattern, he is identified as a player whose performance is consistent with PED use.

All three models provide a mechanism for sharing information across related series locally in time. The models are fit with variations on the P ́olya-Gamma Gibbs sampler, MCMC convergence diagnostics are developed, and reproducible inference is emphasized throughout the dissertation.