Matching Items (29)

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Avastin: Roche Pharmaceutical's Cancer Treatment & The United States Healthcare System

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This paper seeks to put a spotlight on much that is wrong in the United States with cancer drug development, pricing, marketing and outcomes. Roche Pharmaceutical's cancer drug, Avastin will be used as an example to highlight these issues. Drug

This paper seeks to put a spotlight on much that is wrong in the United States with cancer drug development, pricing, marketing and outcomes. Roche Pharmaceutical's cancer drug, Avastin will be used as an example to highlight these issues. Drug patents, Medicare policies, weak metrics of efficacy and ceaseless demand—allow drug manufacturers to price their oncology treatments as they choose, regardless of results, and with virtually no competition, avenue or institution that serves to lower prices in the United States. Avastin will be established as an oncology drug that is overpriced and poorly evaluated based on its effectiveness. Facts, opinions and study analytics will be offered (from industry experts, insiders, doctors and scientists) that in almost all cases show that patients treated with Avastin receive marginal benefit. Allowing Medicare to negotiate drug prices with manufacturers, reducing conflicts of interest for doctors, setting research & development investment requirements and creating more relevant clinical metrics for use in FDA approvals would help reduce the financial burden on cancer patients and taxpayers.

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2017-05

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The Reconceptualization of Palliative Care in Oncology Nursing: A Meta-Synthesis

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Often equated with hospice or end-of-life care, palliative care is the expansion of traditional disease-model medical treatments to include the goals of enhancing quality of life, facilitating patient autonomy, and addressing physical or emotional suffering. This interdisciplinary model is essential

Often equated with hospice or end-of-life care, palliative care is the expansion of traditional disease-model medical treatments to include the goals of enhancing quality of life, facilitating patient autonomy, and addressing physical or emotional suffering. This interdisciplinary model is essential throughout the cancer continuum and offers the best patient outcomes when initiated at the time of diagnosis. While extensive research exists on the purpose and benefits of palliative care, substantial barriers to early and effective implementation remain. This study aims to examine and integrate current research literature on oncology nurses' perceptions of palliative care, including comparison to evidence-based preferred practice. Synthesis of qualitative findings offers transformative reconceptualization aimed to inform nursing education and improve patient care.

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2016-05

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AFM study of gene silencing by DNA methylation and its interactions involving chromatin and methyl CpG binding proteins

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CpG methylation is an essential requirement for the normal development of mammals, but aberrant changes in the methylation can lead to tumor progression and cancer. An in-depth understanding of this phenomenon can provide insights into the mechanism of gene repression.

CpG methylation is an essential requirement for the normal development of mammals, but aberrant changes in the methylation can lead to tumor progression and cancer. An in-depth understanding of this phenomenon can provide insights into the mechanism of gene repression. We present a study comparing methylated DNA and normal DNA wrt its persistence length and contour length. Although, previous experiments and studies show no difference between the physical properties of the two, the data collected and interpreted here gives a different picture to the methylation phenomena and its effect on gene silencing. The study was extended to the artificially reconstituted chromatin and its interactions with the methyl CpG binding proteins were also probed.

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Date Created
2012

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Evaluating and controlling glioblastoma infiltration

Description

Glioblastoma (GBM) is the most common primary brain tumor with an incidence of approximately 11,000 Americans. Despite decades of research, average survival for GBM patients is a modest 15 months. Increasing the extent of GBM resection increases patient survival. However,

Glioblastoma (GBM) is the most common primary brain tumor with an incidence of approximately 11,000 Americans. Despite decades of research, average survival for GBM patients is a modest 15 months. Increasing the extent of GBM resection increases patient survival. However, extending neurosurgical margins also threatens the removal of eloquent brain. For this reason, the infiltrative nature of GBM is an obstacle to its complete resection. We hypothesize that targeting genes and proteins that regulate GBM motility, and developing techniques that safely enhance extent of surgical resection, will improve GBM patient survival by decreasing infiltration into eloquent brain regions and enhancing tumor cytoreduction during surgery. Chapter 2 of this dissertation describes a gene and protein we identified; aquaporin-1 (aqp1) that enhances infiltration of GBM. In chapter 3, we describe a method for enhancing the diagnostic yield of GBM patient biopsies which will assist in identifying future molecular targets for GBM therapies. In chapter 4 we develop an intraoperative optical imaging technique that will assist identifying GBM and its infiltrative margins during surgical resection. The topic of this dissertation aims to target glioblastoma infiltration from molecular and cellular biology and neurosurgical disciplines. In the introduction we; 1. Provide a background of GBM and current therapies. 2. Discuss a protein we found that decreases GBM survival. 3. Describe an imaging modality we utilized for improving the quality of accrued patient GBM samples. 4. We provide an overview of intraoperative contrast agents available for neurosurgical resection of GBM, and discuss a new agent we studied for intraoperative visualization of GBM.

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2014

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A Retrospective Investigation to Assess the Potential Application of Predictive Machine Learning Algorithms in Oncology Clinical Trials

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The purpose of this investigation is to apply a machine learning algorithm with de-identified, historic oncology clinical trial data to assess the theoretical understanding of predictive modeling to derive potential clinical practice recommendations. Within this study, electronic medical records from

The purpose of this investigation is to apply a machine learning algorithm with de-identified, historic oncology clinical trial data to assess the theoretical understanding of predictive modeling to derive potential clinical practice recommendations. Within this study, electronic medical records from the HonorHealth Virginia G. Piper Institute will undergo data visualization to identify potential correlations and trends critical for model creation as well as further identify potential expansions or limitations of scope regarding model purpose. Hypothesis pursued post data visualization was the development of a predictive model for 6-month survival. Current standard is estimated physician accuracy at 56.5% accuracy at 6 months out. This study created supervised learning models using decision trees, KNN, SVM and Ensemble methods using combinations of LASSO Logistic Regression and Know-GRFF Random Forest for feature selection. SVM trained on a combined set of LASSO and Know-GRRF featured produced the highest performing model at 75.5% with an AUC of 0.82. This study demonstrates the potential for applying predictive modeling on readily available EMR records to drive clinical practice recommendations. The models developed could potentially, with further development, be used as an ancillary tool for jumpstarting patient-physician conversations on survival and life expectancy.

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2019-05

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Current Trends in the Use of Complementary Medicine in Oncology

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This paper focuses on the current use of complementary medicine in Oncology. First, it reviews the general trends in the rise of complementary therapies in the United States and look at the organizations responsible for the advancement of research. Next

This paper focuses on the current use of complementary medicine in Oncology. First, it reviews the general trends in the rise of complementary therapies in the United States and look at the organizations responsible for the advancement of research. Next reviewed is the specific use of complementary medicine in cancer prevention, during treatment, and post-treatment therapy for increased quality of life. There are many modalities used in the management of this disease including yoga, tai chi chuan, botanicals, probiotics and meditation practices. Each of these therapies has their own unique benefits and are used at different stages of disease prevention and treatment.

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2013-05

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Addicted to autophagy: Ph+ B-ALL may acquire iImatinib-resistance and enhanced malignancy through a highly-active autophagy pathway

Description

The majority of chronic myeloid leukemia (CML) and some of acute lymphocytic leukemia (ALL) cases are associated with possessing the BCR-Abl fusion protein from an oncogenic translocation, resulting in a constantly active form of Abl and rapid proliferation. CML and

The majority of chronic myeloid leukemia (CML) and some of acute lymphocytic leukemia (ALL) cases are associated with possessing the BCR-Abl fusion protein from an oncogenic translocation, resulting in a constantly active form of Abl and rapid proliferation. CML and ALL cells that possess the BCR-Abl fusion protein are known as Philadelphia chromosome positive (Ph+). Currently, Imatinib (selective Abl inhibitor) is used as therapy against CML and ALL. However, some patients may have malignancies which show resistance to Imatinib. Previous work displays that the transformation of progenitor B cells with the v-Abl oncogene of Abelson murine leukemia virus results in cell cycle progression, rapid proliferation, and potentially malignant transformation while preventing any further differentiation. Progenitor B cells transformed with the temperature-sensitive form of the v-Abl oncogene have served as a model to study cellular response to Imatinib treatment. After some manipulation, very few cells were forced to progress to malignancy, forming tumor in vivo. These cells were no long sensitive to v-Abl inactivation, resembling the Imatinib resistant ALL. Autophagy is the process by which proteins and organelles are broken-down and recycled within the eukaryotic cell and has been hypothesized to play a part in cancer cell survival and drug-resistance. LC3 processing is a widely accepted marker of autophagy induction and progression. It has also been shown that Imatinib treatment of Ph+ leukemia can induce autophagy. In this study, we examined the autophagy induction in response to v-Abl inactivation in a Ph+-B-ALL cell model that shows resistance to Imatinib. In particular, we wonder whether the tumor cell line resistant to v-Abl inactivation may acquire a high level of autophagy to become resistant to apoptosis induced by v-Abl inactivation, and thus become addicted to autophagy. Indeed, this tumor cell line displays a high basal levels of LC3 I and II expression, regardless of v-Abl activity. We further demonstrated that inhibition of the autophagy pathway enhances the tumor line's sensitivity to Imatinib, resulting in cell cycle arrest and massive apoptosis. The combination of autophagy and Abl inhibitions may serve as an effective therapy for BCR-Abl positive CML.

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Date Created
2011

Ultra structurally based impedance model for oral cancer detection

Description

This research investigated using impedance as a minimally invasive oral cancer-screening tool by modeling healthy and diseased tissue. This research developed an ultra-structurally based tissue model for oral mucosa that is versatile enough to be easily modified to mimic the

This research investigated using impedance as a minimally invasive oral cancer-screening tool by modeling healthy and diseased tissue. This research developed an ultra-structurally based tissue model for oral mucosa that is versatile enough to be easily modified to mimic the passive electrical impedance responses of multiple benign and cancerous tissue types. This new model provides answers to biologically meaningful questions related to the impedance response of healthy and diseased tissues. This model breaks away from the old empirical top down "black box" Thèvinin equivalent model. The new tissue model developed here was created from a bottom up perspective resulting in a model that is analogous to having a "Transparent Box" where each network element relating to a specific structural component is known. This new model was developed starting with sub cellular ultra-structural components such as membranes, proteins and electrolytes. These components formed the basic network elements and topology of the organelles. The organelle networks combine to form the cell networks. The cell networks combine to make networks of cell layers and the cell layers were combined into tissue networks. This produced the complete "Transparent Box" model for normal tissue. This normal tissue model was modified for disease based on the ultra-structural pathology of each disease. The diseased tissues evaluated include cancers type one through type three; necrotic-inflammation, hyperkeratosis and the compound condition of hyperkeratosis over cancer type two. The impedance responses for each of the disease were compared side by side with the response of normal healthy tissue. Comparative evidence from the models showed the structural changes in cancer produce a unique identifiable impedance "finger print." The evaluation of the "Transparent Box" model for normal tissues and diseased tissues show clear support for using comparative impedance measurements as a clinical tool for oral cancer screening.

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2012

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The ketogenic diet in the treatment of malignant glioma: mechanistic effects on hypoxia and angiogenesis

Description

Patients with malignant brain tumors have a median survival of approximately 15 months following diagnosis, regardless of currently available treatments which include surgery followed by radiation and chemotherapy. Improvement in the survival of brain cancer patients requires the design of

Patients with malignant brain tumors have a median survival of approximately 15 months following diagnosis, regardless of currently available treatments which include surgery followed by radiation and chemotherapy. Improvement in the survival of brain cancer patients requires the design of new therapeutic modalities that take advantage of common phenotypes. One such phenotype is the metabolic dysregulation that is a hallmark of cancer cells. It has therefore been postulated that one approach to treating brain tumors may be by metabolic alteration such as that which occurs through the use of the ketogenic diet (KD). The KD is high-fat, low-carbohydrate diet that induces ketosis and has been utilized for the non-pharmacologic treatment of refractory epilepsy. It has been shown that this metabolic therapy enhances survival and potentiates standard therapy in mouse models of malignant gliomas, yet the anti-tumor mechanisms are not fully understood.

The current study reports that KetoCal® (KC; 4:1 fat:protein/carbohydrates), fed ad libitum, alters hypoxia, angiogenic, and inflammatory pathways in a mouse model of glioma. Tumors from animals maintained on KC showed reduced expression of the hypoxia marker carbonic anhydrase 9 (CA IX), a reduction in hypoxia inducible factor 1-alpha (HIF-1α) and decreased activation of nuclear factor kappa B (NF-κB). Animals maintained on KC also showed a reduction in expression of vascular endothelial growth factor receptor 2 (VEGFR2) and decreased microvasculature in their tumors. Further, peritumoral edema was significantly reduced in animals fed the KC and protein analysis showed significantly altered expression of the tight junction protein zona occludens-1 (ZO-1) and the water channeling protein aquaporin-4 (AQP4), both of which have been implicated in malignant processes in glioma, including the formation of peritumoral edema in patients. Taken together the data suggests that KC alters multiple processes involved in malignant progression of gliomas. A greater understanding of the effects of the ketogenic diet as an adjuvant therapy will allow for a more rational approach to its clinical use.

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Date Created
2014

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Applications of the Droop cell quota model to data based cancer growth and treatment models

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The phycologist, M. R. Droop, studied vitamin B12 limitation in the flagellate Monochrysis lutheri and concluded that its specific growth rate depended on the concentration of the vitamin within the cell; i.e. the cell quota of the vitamin B12. The

The phycologist, M. R. Droop, studied vitamin B12 limitation in the flagellate Monochrysis lutheri and concluded that its specific growth rate depended on the concentration of the vitamin within the cell; i.e. the cell quota of the vitamin B12. The Droop model provides a mathematical expression to link growth rate to the intracellular concentration of a limiting nutrient. Although the Droop model has been an important modeling tool in ecology, it has only recently been applied to study cancer biology. Cancer cells live in an ecological setting, interacting and competing with normal and other cancerous cells for nutrients and space, and evolving and adapting to their environment. Here, the Droop equation is used to model three cancers.

First, prostate cancer is modeled, where androgen is considered the limiting nutrient since most tumors depend on androgen for proliferation and survival. The model's accuracy for predicting the biomarker for patients on intermittent androgen deprivation therapy is tested by comparing the simulation results to clinical data as well as to an existing simpler model. The results suggest that a simpler model may be more beneficial for a predictive use, although further research is needed in this field prior to implementing mathematical models as a predictive method in a clinical setting.

Next, two chronic myeloid leukemia models are compared that consider Imatinib treatment, a drug that inhibits the constitutively active tyrosine kinase BCR-ABL. Both models describe the competition of leukemic and normal cells, however the first model also describes intracellular dynamics by considering BCR-ABL as the limiting nutrient. Using clinical data, the differences in estimated parameters between the models and the capacity for each model to predict drug resistance are analyzed.

Last, a simple model is presented that considers ovarian tumor growth and tumor induced angiogenesis, subject to on and off anti-angiogenesis treatment. In this environment, the cell quota represents the intracellular concentration of necessary nutrients provided through blood supply. Mathematical analysis of the model is presented and model simulation results are compared to pre-clinical data. This simple model is able to fit both on- and off-treatment data using the same biologically relevant parameters.

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Date Created
2015