Cultural models of healing and health: An ethnography of professional nurses and healers

Cultural models of the domains healing and health are important in how people understand health and their behavior regarding it. The biomedicine model has been predominant in Western society. Recent popularity of holistic health and alternative healing modalities contrasts with the biomedical model and the assumptions upon which that model has been practiced. The holistic health movement characterizes an effort by health care providers and others such as nurses to expand the biomedical model and has often incorporated alternative modalities. This research described and compared the cultural models of healing of professional nurses and alternative healers. A group of nursing faculty who promote a holistic model were compared to a group of healers using healing touch. Ethnographic methods of participant observation, free listing and pile sort were used. Theoretical sampling in the free listings reached saturation at 18 in the group of nurses and 21 in the group of healers. Categories consistent for both groups emerged from the data. These were: physical, mental, attitude, relationships, spiritual, self management, and health seeking including biomedical and alternative resources. The healers had little differentiation between the concepts health and healing. The nurses, however, had more elements in self management for health and in health seeking for healing. This reflects the nurse's role in facilitating the shift in locus of responsibility between health and healing. The healers provided more specific information regarding alternative resources. The healer's conceptualization of health was embedded in a spiritual belief system and contrasted dramatically with that of biomedicine. The healer's models also contrasted with holistic health in the areas of holism, locus of responsibility, and dealing with uncertainty. The similarity between the groups and their dissimilarity to biomedicine suggest a larger cultural shift in beliefs regarding health care. ^

Variables affecting the stability of multiple cloning sites and hairpin structures in retroviral vectors

Retroviruses are RNA viruses that replicate through a double-stranded DNA intermediate. The viral enzyme reverse transcriptase copies the retroviral genomic RNA into this DNA intermediate through the process of reverse transcription. Many variables can affect the fidelity of reverse transcriptase during reverse transcription, including specific sequences within the retroviral genome. ^ Previous studies have observed that multiple cloning sites (MCS) and sequences predicted to form stable hairpin structures are hotspots for deletion during retroviral replication. The studies described in this dissertation were performed to elucidate the variables that affect the stability of MCS and hairpin structures in retroviral vectors. Two series of retroviral vectors were constructed and characterized in these studies. ^ Spleen necrosis virus-based vectors were constructed containing separate MCS insertions of varying length, orientation, and symmetry. The only MCS that was a hotspot for deletion formed a stable hairpin structure. Upon more detailed study, the MCS previously reported as a hotspot for deletion was found to contain a tandem linker insertion that formed a hairpin structure. Murine leukemia virus-based vectors were constructed containing separate sequence insertions of either inverted repeat symmetry (122IR) that could form a hairpin structure, or little symmetry (122c) that would form a less stable structure. These insertions were made into either the neomycin resistance marker ( neo) or the hygromycin resistance marker (hyg) of the vector. 122c was stable in both neo and hyg, while 122IR was preferentially deleted in neo and was remarkably unstable in hyg. ^ These results suggest that MCS are hotspots for deletion in retroviral vectors if they can form hairpin structures, and that hairpin structures can be highly unstable at certain locations in retroviral vectors. This information may contribute to improved design of retroviral vectors for such uses as human gene therapy, and will contribute to a greater understanding of the basic science of retroviral reverse transcription. ^

Molecular cross talk between misfolded proteins in animal models of Alzheimer's and prion diseases.

The central event in protein misfolding disorders (PMDs) is the accumulation of a misfolded form of a naturally expressed protein. Despite the diversity of clinical symptoms associated with different PMDs, many similarities in their mechanism suggest that distinct pathologies may cross talk at the molecular level. The main goal of this study was to analyze the interaction of the protein misfolding processes implicated in Alzheimer's and prion diseases. For this purpose, we inoculated prions in an Alzheimer's transgenic mouse model that develop typical amyloid plaques and followed the progression of pathological changes over time. Our findings show a dramatic acceleration and exacerbation of both pathologies. The onset of prion disease symptoms in transgenic mice appeared significantly faster with a concomitant increase on the level of misfolded prion protein in the brain. A striking increase in amyloid plaque deposition was observed in prion-infected mice compared with their noninoculated counterparts. Histological and biochemical studies showed the association of the two misfolded proteins in the brain and in vitro experiments showed that protein misfolding can be enhanced by a cross-seeding mechanism. These results suggest a profound interaction between Alzheimer's and prion pathologies, indicating that one protein misfolding process may be an important risk factor for the development of a second one. Our findings may have important implications to understand the origin and progression of PMDs.

Genetic and hormonal factors modulate spreading depression and transient hemiparesis in mouse models of familial hemiplegic migraine type 1.

Familial hemiplegic migraine type 1 (FHM1) is an autosomal dominant subtype of migraine with aura that is associated with hemiparesis. As with other types of migraine, it affects women more frequently than men. FHM1 is caused by mutations in the CACNA1A gene, which encodes the alpha1A subunit of Cav2.1 channels; the R192Q mutation in CACNA1A causes a mild form of FHM1, whereas the S218L mutation causes a severe, often lethal phenotype. Spreading depression (SD), a slowly propagating neuronal and glial cell depolarization that leads to depression of neuronal activity, is the most likely cause of migraine aura. Here, we have shown that transgenic mice expressing R192Q or S218L FHM1 mutations have increased SD frequency and propagation speed; enhanced corticostriatal propagation; and, similar to the human FHM1 phenotype, more severe and prolonged post-SD neurological deficits. The susceptibility to SD and neurological deficits is affected by allele dosage and is higher in S218L than R192Q mutants. Further, female S218L and R192Q mutant mice were more susceptible to SD and neurological deficits than males. This sex difference was abrogated by ovariectomy and senescence and was partially restored by estrogen replacement, implicating ovarian hormones in the observed sex differences in humans with FHM1. These findings demonstrate that genetic and hormonal factors modulate susceptibility to SD and neurological deficits in FHM1 mutant mice, providing a potential mechanism for the phenotypic diversity of human migraine and aura.

A comparison of Markov and generalized linear models of hospital mortality

This paper reports a comparison of three modeling strategies for the analysis of hospital mortality in a sample of general medicine inpatients in a Department of Veterans Affairs medical center. Logistic regression, a Markov chain model, and longitudinal logistic regression were evaluated on predictive performance as measured by the c-index and on accuracy of expected numbers of deaths compared to observed. The logistic regression used patient information collected at admission; the Markov model was comprised of two absorbing states for discharge and death and three transient states reflecting increasing severity of illness as measured by laboratory data collected during the hospital stay; longitudinal regression employed Generalized Estimating Equations (GEE) to model covariance structure for the repeated binary outcome. Results showed that the logistic regression predicted hospital mortality as well as the alternative methods but was limited in scope of application. The Markov chain provides insights into how day to day changes of illness severity lead to discharge or death. The longitudinal logistic regression showed that increasing illness trajectory is associated with hospital mortality. The conclusion is reached that for standard applications in modeling hospital mortality, logistic regression is adequate, but for new challenges facing health services research today, alternative methods are equally predictive, practical, and can provide new insights. ^

A comparative analysis of Texas and Louisiana Oil-field NORM Waste Policy

NORM (Naturally Occurring Radioactive Material) Waste Policies for the nation's oil and gas producing states have been in existence since the 1980's, when Louisiana was the first state to develop a NORM regulatory program in 1989. Since that time, expectations for NORM Waste Policies have evolved, as Health, Safety, Environment, and Social responsibility (HSE & SR) grows increasingly important to the public. Therefore, the oil and gas industry's safety and environmental performance record will face challenges in the future, about its best practices for managing the co-production of NORM wastes. ^ Within the United States, NORM is not federally regulated. The U.S. EPA claims it regulates NORM under CERCLA (superfund) and the Clean Water Act. Though, there are no universally applicable regulations for radium-based NORM waste. Therefore, individual states have taken responsibility for developing NORM regulatory programs, because of the potential radiological risk it can pose to man (bone and lung cancer) and his environment. This has led to inconsistencies in NORM Waste Policies as well as a NORM management gap in both state and federal regulatory structures. ^ Fourteen different NORM regulations and guidelines were compared between Louisiana and Texas, the nation's top two petroleum producing states. Louisiana is the country's top crude oil producer when production from its Federal offshore waters are included, and fourth in crude oil production, behind Texas, Alaska, and California when Federal offshore areas are excluded. Louisiana produces more petroleum products than any state but Texas. For these reasons, a comparative analysis between Louisiana and Texas was undertaken to identify differences in their NORM regulations and guidelines for managing, handling and disposing NORM wastes. Moreover, this analysis was undertaken because Texas is the most explored and drilled worldwide and yet appears to lag behind its neighboring state in terms of its NORM Waste Policy and developing an industry standard for handling, managing and disposing NORM. As a result of this analysis, fourteen recommendations were identified.^

Using spatial linear models with SAR and CAR structure to examine Texas lung cancer incidence rates

Scholars have found that socioeconomic status was one of the key factors that influenced early-stage lung cancer incidence rates in a variety of regions. This thesis examined the association between median household income and lung cancer incidence rates in Texas counties. A total of 254 individual counties in Texas with corresponding lung cancer incidence rates from 2004 to 2008 and median household incomes in 2006 were collected from the National Cancer Institute Surveillance System. A simple linear model and spatial linear models with two structures, Simultaneous Autoregressive Structure (SAR) and Conditional Autoregressive Structure (CAR), were used to link median household income and lung cancer incidence rates in Texas. The residuals of the spatial linear models were analyzed with Moran's I and Geary's C statistics, and the statistical results were used to detect similar lung cancer incidence rate clusters and disease patterns in Texas.^