Risk Prediction for Epithelial Ovarian Cancer in 11 United States-Based Case-Control Studies: Incorporation of Epidemiologic Risk Factors and 17 Confirmed Genetic Loci.

Previously developed models for predicting absolute risk of invasive epithelial ovarian cancer have included a limited number of risk factors and have had low discriminatory power (area under the receiver operating characteristic curve (AUC) < 0.60). Because of this, we developed and internally validated a relative risk prediction model that incorporates 17 established epidemiologic risk factors and 17 genome-wide significant single nucleotide polymorphisms (SNPs) using data from 11 case-control studies in the United States (5,793 cases; 9,512 controls) from the Ovarian Cancer Association Consortium (data accrued from 1992 to 2010). We developed a hierarchical logistic regression model for predicting case-control status that included imputation of missing data. We randomly divided the data into an 80% training sample and used the remaining 20% for model evaluation. The AUC for the full model was 0.664. A reduced model without SNPs performed similarly (AUC = 0.649). Both models performed better than a baseline model that included age and study site only (AUC = 0.563). The best predictive power was obtained in the full model among women younger than 50 years of age (AUC = 0.714); however, the addition of SNPs increased the AUC the most for women older than 50 years of age (AUC = 0.638 vs. 0.616). Adapting this improved model to estimate absolute risk and evaluating it in prospective data sets is warranted.

Real-Time Decision Making and Aggressive Behavior in Youth: A Heuristic Model of Response Evaluation and Decision (RED).

Considerable scientific and intervention attention has been paid to judgment and decision-making systems associated with aggressive behavior in youth. However, most empirical studies have investigated social-cognitive correlates of stable child and adolescent aggressiveness, and less is known about real-time decision making to engage in aggressive behavior. A model of real-time decision making must incorporate both impulsive actions and rational thought. The present paper advances a process model (response evaluation and decision; RED) of real-time behavioral judgments and decision making in aggressive youths with mathematic representations that may be used to quantify response strength. These components are a heuristic to describe decision making, though it is doubtful that individuals always mentally complete these steps. RED represents an organization of social-cognitive operations believed to be active during the response decision step of social information processing. The model posits that RED processes can be circumvented through impulsive responding. This article provides a description and integration of thoughtful, rational decision making and nonrational impulsivity in aggressive behavioral interactions.

An evaluation of remifentanil-sevoflurane response surface models in patients emerging from anesthesia: model improvement using effect-site sevoflurane concentrations.

INTRODUCTION: We previously reported models that characterized the synergistic interaction between remifentanil and sevoflurane in blunting responses to verbal and painful stimuli. This preliminary study evaluated the ability of these models to predict a return of responsiveness during emergence from anesthesia and a response to tibial pressure when patients required analgesics in the recovery room. We hypothesized that model predictions would be consistent with observed responses. We also hypothesized that under non-steady-state conditions, accounting for the lag time between sevoflurane effect-site concentration (Ce) and end-tidal (ET) concentration would improve predictions. METHODS: Twenty patients received a sevoflurane, remifentanil, and fentanyl anesthetic. Two model predictions of responsiveness were recorded at emergence: an ET-based and a Ce-based prediction. Similarly, 2 predictions of a response to noxious stimuli were recorded when patients first required analgesics in the recovery room. Model predictions were compared with observations with graphical and temporal analyses. RESULTS: While patients were anesthetized, model predictions indicated a high likelihood that patients would be unresponsive (> or = 99%). However, after termination of the anesthetic, models exhibited a wide range of predictions at emergence (1%-97%). Although wide, the Ce-based predictions of responsiveness were better distributed over a percentage ranking of observations than the ET-based predictions. For the ET-based model, 45% of the patients awoke within 2 min of the 50% model predicted probability of unresponsiveness and 65% awoke within 4 min. For the Ce-based model, 45% of the patients awoke within 1 min of the 50% model predicted probability of unresponsiveness and 85% awoke within 3.2 min. Predictions of a response to a painful stimulus in the recovery room were similar for the Ce- and ET-based models. DISCUSSION: Results confirmed, in part, our study hypothesis; accounting for the lag time between Ce and ET sevoflurane concentrations improved model predictions of responsiveness but had no effect on predicting a response to a noxious stimulus in the recovery room. These models may be useful in predicting events of clinical interest but large-scale evaluations with numerous patients are needed to better characterize model performance.

Correcting the errors: Volatility forecast evaluation using high-frequency data and realized volatilities

We develop general model-free adjustment procedures for the calculation of unbiased volatility loss functions based on practically feasible realized volatility benchmarks. The procedures, which exploit recent nonparametric asymptotic distributional results, are both easy-to-implement and highly accurate in empirically realistic situations. We also illustrate that properly accounting for the measurement errors in the volatility forecast evaluations reported in the existing literature can result in markedly higher estimates for the true degree of return volatility predictability.

Gait and behavior in an IL1β-mediated model of rat knee arthritis and effects of an IL1 antagonist.

Interleukin-1 beta (IL1β) is a proinflammatory cytokine that mediates arthritic pathologies. Our objectives were to evaluate pain and limb dysfunction resulting from IL1β over-expression in the rat knee and to investigate the ability of local IL1 receptor antagonist (IL1Ra) delivery to reverse-associated pathology. IL1β over-expression was induced in the right knees of 30 Wistar rats via intra-articular injection of rat fibroblasts retrovirally infected with human IL1β cDNA. A subset of animals received a 30 µl intra-articular injection of saline or human IL1Ra on day 1 after cell delivery (0.65 µg/µl hIL1Ra, n = 7 per group). Joint swelling, gait, and sensitivity were investigated over 1 week. On day 8, animals were sacrificed and joints were collected for histological evaluation. Joint inflammation and elevated levels of endogenous IL1β were observed in knees receiving IL1β-infected fibroblasts. Asymmetric gaits favoring the affected limb and heightened mechanical sensitivity (allodynia) reflected a unilateral pathology. Histopathology revealed cartilage loss on the femoral groove and condyle of affected joints. Intra-articular IL1Ra injection failed to restore gait and sensitivity to preoperative levels and did not reduce cartilage degeneration observed in histopathology. Joint swelling and degeneration subsequent to IL1β over-expression is associated limb hypersensitivity and gait compensation. Intra-articular IL1Ra delivery did not result in marked improvement for this model; this may be driven by rapid clearance of administered IL1Ra from the joint space. These results motivate work to further investigate the behavioral consequences of monoarticular arthritis and sustained release drug delivery strategies for the joint space.

Evaluating intra-articular drug delivery for the treatment of osteoarthritis in a rat model.

Osteoarthritis (OA) is a degenerative joint disease that can result in joint pain, loss of joint function, and deleterious effects on activity levels and lifestyle habits. Current therapies for OA are largely aimed at symptomatic relief and may have limited effects on the underlying cascade of joint degradation. Local drug delivery strategies may provide for the development of more successful OA treatment outcomes that have potential to reduce local joint inflammation, reduce joint destruction, offer pain relief, and restore patient activity levels and joint function. As increasing interest turns toward intra-articular drug delivery routes, parallel interest has emerged in evaluating drug biodistribution, safety, and efficacy in preclinical models. Rodent models provide major advantages for the development of drug delivery strategies, chiefly because of lower cost, successful replication of human OA-like characteristics, rapid disease development, and small joint volumes that enable use of lower total drug amounts during protocol development. These models, however, also offer the potential to investigate the therapeutic effects of local drug therapy on animal behavior, including pain sensitivity thresholds and locomotion characteristics. Herein, we describe a translational paradigm for the evaluation of an intra-articular drug delivery strategy in a rat OA model. This model, a rat interleukin-1beta overexpression model, offers the ability to evaluate anti-interleukin-1 therapeutics for drug biodistribution, activity, and safety as well as the therapeutic relief of disease symptoms. Once the action against interleukin-1 is confirmed in vivo, the newly developed anti-inflammatory drug can be evaluated for evidence of disease-modifying effects in more complex preclinical models.

Evaluation of Altered Kras Codon Bias and NOS Inhibition During Lung Tumorigenesis

The small GTPases HRAS, NRAS and KRAS are mutated in approximately one-third of all human cancers, rendering the proteins constitutively active and oncogenic. Lung cancer is the leading cause of cancer deaths worldwide, and more than 20% of human lung cancers harbor mutations in RAS, with 98% of those occurring in the KRAS isoform. While there have been many advances in the understanding of KRAS–driven lung tumorigenesis, it remains a therapeutic challenge. To further this understanding and assess novel approaches for treatment, I have investigated two aspects of Kras–driven tumorigenesis in the lung:

(I) Despite nearly identical protein sequences, the three RAS proto-oncogenes exhibit divergent codon usage. Of the three isoforms, KRAS contains the most rare codons resulting in lower levels of KRAS protein expression relative to HRAS and NRAS. To determine the consequences of rare codon bias during de novo tumorigenesis, we created a knock-in Krasex3op mouse in which synonymous mutations in exon 3 converted codons from rare to common. These mice had reduced tumor burden and fewer oncogenic mutations in the Krasex3op allele following carcinogen exposure. The reduction in tumorigenesis appeared to be a product of rare codons affecting both the oncogenic and non–oncogenic alleles. Converting rare codons to common codons yielded a more potent oncogenic allele that promoted growth arrest and enhanced tumor suppression by the non-oncogenic allele. Thus, rare codons play an integral role in Kras tumorigenesis.

(II) Lung cancer patients exhale higher levels of NO and iNOS-/- mice are resistant to chemically induced lung tumorigenesis. I hypothesize that NO promotes Kras–driven lung adenocarcinoma, and NOS inhibition may decrease Kras–driven lung tumorigenesis. To test this hypothesis, I assessed efficacy of the NOS inhibitor L–NAME in a genetically engineered mouse model of Kras-driven lung adenocarcinoma. Adenoviral Cre recombinase was delivered into the lungs intranasally, resulting in expression of oncogenic KrasG12D and dominant-negative Trp53R172H in lung epithelial cells. L–NAME treatment was provided in the water and continued until survival endpoints. In this model, L–NAME treatment decreased tumor growth and prolonged survival. These data establish a potential clinical role for NOS inhibition in lung cancer treatment.

Intravital microscopy evaluation of angiogenesis and its effects on glucose sensor performance.

An optical window model for the rodent dorsum was used to perform chronic and quantitative intravital microscopy and laser Doppler flowmetry of microvascular networks adjacent to functional and non-functional glucose sensors. The one-sided configuration afforded direct, real-time observation of the tissue response to bare (unmodified, smooth surface) sensors and sensors coated with porous poly-L-lactic acid (PLLA). Microvessel length density and red blood cell flux (blood perfusion) within 1 mm of the sensors were measured bi-weekly over 2 weeks. When non-functional sensors were fully implanted beneath the windows, the porous coated sensors had two-fold more vasculature and significantly higher blood perfusion than bare sensors on Day 14. When functional sensors were implanted percutaneously, as in clinical use, no differences in baseline current, neovascularization, or tissue perfusion were observed between bare and porous coated sensors. However, percutaneously implanted bare sensors had two-fold more vascularity than fully implanted bare sensors by Day 14, indicating the other factors, such as micromotion, might be stimulating angiogenesis. Despite increased angiogenesis adjacent to percutaneous sensors, modest sensor current attenuation occurred over 14 days, suggesting that factors other than angiogenesis may play a dominant role in determining sensor function.

Live Imaging of Host-Parasite Interactions in a Zebrafish Infection Model Reveals Cryptococcal Determinants of Virulence and Central Nervous System Invasion.

UNLABELLED: The human fungal pathogen Cryptococcus neoformans is capable of infecting a broad range of hosts, from invertebrates like amoebas and nematodes to standard vertebrate models such as mice and rabbits. Here we have taken advantage of a zebrafish model to investigate host-pathogen interactions of Cryptococcus with the zebrafish innate immune system, which shares a highly conserved framework with that of mammals. Through live-imaging observations and genetic knockdown, we establish that macrophages are the primary immune cells responsible for responding to and containing acute cryptococcal infections. By interrogating survival and cryptococcal burden following infection with a panel of Cryptococcus mutants, we find that virulence factors initially identified as important in causing disease in mice are also necessary for pathogenesis in zebrafish larvae. Live imaging of the cranial blood vessels of infected larvae reveals that C. neoformans is able to penetrate the zebrafish brain following intravenous infection. By studying a C. neoformans FNX1 gene mutant, we find that blood-brain barrier invasion is dependent on a known cryptococcal invasion-promoting pathway previously identified in a murine model of central nervous system invasion. The zebrafish-C. neoformans platform provides a visually and genetically accessible vertebrate model system for cryptococcal pathogenesis with many of the advantages of small invertebrates. This model is well suited for higher-throughput screening of mutants, mechanistic dissection of cryptococcal pathogenesis in live animals, and use in the evaluation of therapeutic agents. IMPORTANCE: Cryptococcus neoformans is an important opportunistic pathogen that is estimated to be responsible for more than 600,000 deaths worldwide annually. Existing mammalian models of cryptococcal pathogenesis are costly, and the analysis of important pathogenic processes such as meningitis is laborious and remains a challenge to visualize. Conversely, although invertebrate models of cryptococcal infection allow high-throughput assays, they fail to replicate the anatomical complexity found in vertebrates and, specifically, cryptococcal stages of disease. Here we have utilized larval zebrafish as a platform that overcomes many of these limitations. We demonstrate that the pathogenesis of C. neoformans infection in zebrafish involves factors identical to those in mammalian and invertebrate infections. We then utilize the live-imaging capacity of zebrafish larvae to follow the progression of cryptococcal infection in real time and establish a relevant model of the critical central nervous system infection phase of disease in a nonmammalian model.