Placenta Ingestion Enhances Analgesia\ud Produced by Vaginal/Cervical\ud Stimulation in Rats

Ingestion of placenta has previously been shown to enhance opiate-mediated analgesia (measured as tail-flick latency) induced either by morphine injection or by footshock. The present study was designed to test whether placenta ingestion would enhance the partly opiate-mediated analgesia produced by vaginal/cervical stimulation. Nulliparous Sprague-Dawley rats were tested for analgesia, using tail-flick latency, during and after vaginal/cervical stimulation; the tests for vaginal/cervical stimulation-induced analgesia were administered both before and after the rats ate placenta or ground beef. Placenta ingestion, but not beef ingestion. significantly heightened vaginal/cervical stimulation-induced analgesia. A subsequent morphine injection provided evidence that, as in a previous report, placenta ingestion, but not beef ingestion, enhanced morphine-induced analgesia

Sensory innervation of the external and internal genitalia of the female rat

Using a whole-nerve recording method, the genitalia of the female rat were found to receive afferent innervation as follows. Pelvic nerve: vagina, cervix, and perineal skin; hypogastric nerve: cervix and proximal three fifths of the uterus; pudendal nerve: skin of perineum, inner thigh, and clitoral sheath. It is probable that the pudendal and pelvic nerves are activated during copulation, and that all 3 nerves are activated during parturition

Lack of analgesic efficacy in female rats of\ud the commonly recommended oral dose of\ud buprenorphine

Previous work in our laboratory showed that the recommended oral dose of buprenorphine (0.5 mg/kg) was not as effective\ud as the standard therapeutic subcutaneous dose for postoperative analgesia in male Long-Evans (hooded) and Sprague-Dawley (albino) rats. The aim of the current study was to extend this analysis to female rats. We measured the pain threshold in adult female rats in diestrus or proestrus before and 30 and 60 min after oral buprenorphine (0.5 mg/kg,), the standard subcutaneous dose of buprenorphine (0.05 mg/kg), or vehicle only (1 ml/kg each orally and subcutaneously). Female rats showed an increased pain threshold (analgesia) after subcutaneous buprenorphine but no change in pain threshold after either oral buprenorphine or vehicle only. Estrous cycle stage (proestrus versus diestrus) did not affect the analgesic effects of buprenorphine, but rats in proestrus showed significantly lower pain thresholds (less tolerance to pain) than did those in diestrus. These results show that the oral dose of buprenorphine recommended for postoperative analgesic care does not induce significant analgesia in female rats and therefore is not as effective as the standard subcutaneous dose

Development of Biomarkers Based on Diet-Dependent Metabolic Serotypes: Concerns and Approaches for Cohort and Gender Issues in Serum Metabolome Studies

This is the publisher's version, also available electronically from: http://online.liebertpub.com/doi/pdfplus/10.1089/omi.2004.8.209Mathematical models that reflect the effects of dietary restriction (DR) on the sera metabolome may have utility in understanding the mechanisms of DR and in applying this knowledge to human epidemiological studies. Previous studies demonstrated both the feasibility of identifying biomarkers through metabolome analysis and the validity of our approach in independent cohorts of 6-month-oId male and female ad libitum fed or DR rats. Cross-cohort studies showed that cohort-specific effects distorted the dataset The present study extends these observations across the entire sample set, thereby validating our markers independently of specific cohorts. Metabolites originally identified in males were examined in females and vice-versa. DR's effect on the metabolom e is partially gender-specific and is modulated by environmental factors. DR reduces inter-gender differences in the metabolome. Univariate statistical methods showed that 56/93 metabolites in the female samples and 39/93 metabolites in the male samples were significantly altered (using our previous cut-off criteria of p ^ 0.2) by DR. The metabolites modulated by DR present a wide spectrum of concentration, redox reactivity and hydrophilicity, suggesting that our serotype is broadly representative of the metabolome and that DR has broad effects on the metabolome. These studies, coupled with those in the preceding and following reports, also highlight the utility for consideration of the metabolome as a network of metabolites using appropriate data analysis approaches. The inter-cohort and inter-gender differences addressed herein suggest potential cautions, and potential approaches, for identification of multivariate biomarker profiles that reflect changes in physiological status, such as a metabolism that predisposes to increased risk of neoplasia

Effect of population trends in body mass index on prostate cancer incidence and mortality in the United States.

Concurrent with increasing prostate cancer incidence and declining prostate cancer mortality in the United States, the prevalence of obesity has been increasing steadily. Several studies have reported that obesity is associated with increased risk of high-grade prostate cancer and prostate cancer mortality, and it is thus likely that the increase in obesity has increased the burden of prostate cancer. In this study, we assess the potential effect of increasing obesity on prostate cancer incidence and mortality. We first estimate obesity-associated relative risks of low- and high-grade prostate cancer using data from the Prostate Cancer Prevention Trial. Then, using obesity prevalence data from the National Health and Nutrition Examination Survey and prostate cancer incidence data from the Surveillance, Epidemiology, and End Results program, we convert annual grade-specific prostate cancer incidence rates into incidence rates conditional on weight category. Next, we combine the conditional incidence rates with the 1980 prevalence rates for each weight category to project annual grade-specific incidence under 1980 obesity levels. We use a simulation model based on observed survival and mortality data to translate the effects of obesity trends on prostate cancer incidence into effects on disease-specific mortality. The predicted increase in obesity prevalence since 1980 increased high-grade prostate cancer incidence by 15.5% and prostate cancer mortality by between 7.0% (under identical survival for obese and nonobese cases) and 23.0% (under different survival for obese and nonobese cases) in 2002. We conclude that increasing obesity prevalence since 1980 has partially obscured declines in prostate cancer mortality

Allowing for never and episodic consumers when correcting for error in food record measurements of dietary intake

Food records, including 24-hour recalls and diet diaries, are considered to provide generally superior measures of long-term dietary intake relative to questionnaire-based methods. Despite the expense of processing food records, they are increasingly used as the main dietary measurement in nutritional epidemiology, in particular in sub-studies nested within prospective cohorts. Food records are, however, subject to excess reports of zero intake. Measurement error is a serious problem in nutritional epidemiology because of the lack of gold standard measurements and results in biased estimated diet–disease associations. In this paper, a 3-part measurement error model, which we call the never and episodic consumers (NEC) model, is outlined for food records. It allows for both real zeros, due to never consumers, and excess zeros, due to episodic consumers (EC). Repeated measurements are required for some study participants to fit the model. Simulation studies are used to compare the results from using the proposed model to correct for measurement error with the results from 3 alternative approaches: a crude approach using the mean of repeated food record measurements as the exposure, a linear regression calibration (RC) approach, and an EC model which does not allow real zeros. The crude approach results in badly attenuated odds ratio estimates, except in the unlikely situation in which a large number of repeat measurements is available for all participants. Where repeat measurements are available for all participants, the 3 correction methods perform equally well. However, when only a subset of the study population has repeat measurements, the NEC model appears to provide the best method for correcting for measurement error, with the 2 alternative correction methods, in particular the linear RC approach, resulting in greater bias and loss of coverage. The NEC model is extended to include adjustment for measurements from food frequency questionnaires, enabling better estimation of the proportion of never consumers when the number of repeat measurements is small. The methods are applied to 7-day diary measurements of alcohol intake in the EPIC-Norfolk study

WHITE PAPER: AN OVERVIEW OF CONCEPTUAL FRAMEWORKS, ANALYTICAL APPROACHES AND RESEARCH QUESTIONS IN THE FOOD-ENERGY-WATER NEXUS

The food-energy-water (FEW) nexus is increasingly emphasized and prioritized as a framework for research, technology, and policy to deal with complex socio-environmental problems. Producing food in sufficient quantity and of sufficient quality, ensuring enough but not too much water, and generating energy, all to meet human needs and desires, requires an understanding of how those goals complement or counteract one another in specific places and through specific processes. FEW nexus research focuses on understanding the interconnections among each system, in order to provide a more complete picture about the causes and consequences of changes within and across aspects of those systems. This paper synthesizes the current state of thinking and research in FEW nexus field. We first overview the systems underpinnings of the FEW nexus as a conceptual framework, and identify the assumptions, similarities and contrasts among the most cited models from current literature. Several analytical approaches – coupled systems, ecosystem services, flows and risk analysis – are emerging as key tools for conducting interdisciplinary FEW nexus research, and we identify their conceptual connections to systems thinking broadly as well as the specific assumptions that each make about the relationships among systems. Finally, based on expert consultations and assessment of current data availability, we highlight several topical areas of contemporary relevance for FEW nexus research at various scales. Characterizing the conceptual, analytical and empirical similarities and distinctions among approaches to FEW nexus research with a starting point for identifying innovative research questions and approaches.This work was supported by the National Socio-Environmental Synthesis Center (SESYNC), which is funded by National Science Foundation Grant # DBI-1052875

Development of Biomarkers Based on Diet-Dependent Metabolic Serotypes: Practical Issues in Development of Expert System-Based Classification Models in Metabolomic Studies

This is the publisher's official version, also available electronically from: http://online.liebertpub.com/doi/pdfplus/10.1089/omi.2004.8.197Dietary restriction (DR)-induced changes in the serum metabolome may be biomarkers for physiological status (e.g., relative risk of developing age-related diseases such as cancer). Megavariate analysis (unsupervised hierarchical cluster analysis IHCAJ; principal components analysis [PCAJ) of serum metabolites reproducibly distinguish DR from ad libitum fed rats. Component-based approaches (i.e., PCA) consistently perform as well as or better than distance-based metrics (i.e., HCA). We therefore tested the following: (A) Do identified subsets of serum metabolites contain sufficient information to construct mathematical models of class membership (i.e., expert systems)? (B) Do component-based metrics out-perform distance-based metrics? Testing was conducted using KNN (k-nearest neighbors, supervised HCA) and SIMCA (soft independent modeling of class analogy, supervised PCA). Models were built with single cohorts, combined cohorts or mixed samples from previously studied cohorts as training sets. Both algorithms over-fit models based on single cohort training sets. KNN models had >85% accuracy within training/test sets, but were unstable (i.e., values of k could not be accurately set in advance). SIMCA models had 100% accuracy within all training sets, 89% accuracy in test sets, did not appear to over-fit mixed cohort training sets, and did not require post-hoc modeling adjustments. These data indicate that (i) previously defined metabolites are robust enough to construct classification models (expert systems) with SIMCA that can predict unknowns by dietary category; (ii) component-based analyses outperformed distance-based metrics; (iii) use of over-fitting controls is essential; and (iv) subtle inter-cohort variability may be a critical issue for high data density biomarker studies that lack state markers

Development of Biomarkers Based on Diet-Dependent Metabolic Serotypes: Characteristics of Component-Based Models of Metabolic Serotypes

This is the publisher's version, also available electronically from: http://online.liebertpub.com/doi/pdfplus/10.1089/omi.2004Our research seeks to identify a scrum profile, or serotype, that reflects the systemic physiologic modifications resultant from dietary restriction (DR), in part such that this knowledge can be applied for biomarker studies. Direct comparison suggests that component-based classification algorithms consistently out-perform distance-based metrics for studies of nutritional modulation of metabolic serotype, but are subject to over-fitting concerns. Intercohort differences in the sera metabolome could partially obscure the effects of DR. Further analysis now shows that implementation of component-based approaches (also called projection methods) optimized for class separation and controlled for over-fitting have >97% accuracy for distinguishing sera from control or DR rats. DR's effect on the metabolome is shown to be robust across cohorts, but differs in males and females (although some metabolites are affected in both). We demonstrate the utility of projection-based methods for both sample and variable diagnostics, including identification of critical metabolites and samples that are atypical with respect to both class and variable models. Inclusion of non-statistically different variables enhances classification models. Variables that contribute to these models are sharply dependent on mathematical processing techniques; some variables that do not contribute under one paradigm arc powerful under alternative mathematical paradigms. In practical terms, this information may find purpose in other endeavors, such as mechanistic studies of DR. Application of these approaches confirms the utility of megavariate data analysis techniques for optimal generation of biomarkers based on nutritional modulation of physiological processes