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Evolving well being fairness through addressing interpersonal determining factors involving wellbeing: Utilizing health files to improve academic outcomes.
INTRODUCTION Significant number of women present with advanced-stage breast cancer in Ghana. These women usually depend on family caregivers for their multi-dimensional needs. Yet, there are gaps in research about what motivates family caregivers to assume the caring role and their experiences with caregiving within the Ghanaian context. AIM To explore and describe the caregiving motivations and experiences among family caregivers of patients living with advanced breast cancer. METHODS In-depth, semi-structured qualitative interviews were conducted with 15 family caregivers who were providing unpaid care for women living with advanced breast cancer. Colaizzi's thematic analysis was used to analyze the data. RESULTS Family relationship normally prescribed the caregiving role among family caregivers. Due to the lack of home-based palliative services in Ghana, findings suggest that family caregivers are the main managers of advanced breast cancer-related symptoms in the home. These findings are discussed under three major themes (i) motivation for assuming the caregiving role; (ii) meeting self-care and psychosocial needs of the patient; and (iii) symptom management and monitoring. CONCLUSION Socio-cultural values influence the role of family caregivers in Ghana. This presents opportunities for health professionals and relevant stakeholders to develop a culturally-appropriate intervention to support informal caregivers in their home-based care for women living with advanced breast cancer in Ghana.This trial was registered with ClinicalTrials.gov, NCT02603471.Seafood mislabeling occurs in a wide range of seafood products worldwide, resulting in public distrust, economic fraud, and health risks for consumers. We quantified the extent of shrimp mislabeling in coastal and inland North Carolina. We used standard DNA barcoding procedures to determine the species identity of 106 shrimp sold as "local" by 60 vendors across North Carolina. Thirty-four percent of the purchased shrimp was mislabeled, and surprisingly the percentage did not differ significantly between coastal and inland counties. One third of product incorrectly marketed as "local" was in fact whiteleg shrimp an imported and globally farmed species native to the eastern Pacific, not found in North Carolina waters. In addition to the negative ecosystem consequences of shrimp farming (e.g., the loss of mangrove forests and the coastal buffering they provide), North Carolina fishers-as with local fishers elsewhere-are negatively impacted when vendors label farmed, frozen, and imported shrimp as local, fresh, and wild-caught.Knowledge, attitude and willingness of ethnic minorities in China towards cadaver donation programs were assessed. Questionnaire and interviews were conducted to investigate Yi, Bai, Hani, Dai and Han ethnicities. Educational level and per capita income of ethnic minorities were lesser than those of Han ethnicity (p less then 0.01). Agriculture was the primary occupation and proportions of technical personnel and public officials was lesser among ethnic minorities (p less then 0.01). Surveyed ethnic minorities universally practice religious traditions, Bai and Dai ethnicities practice Buddhist beliefs also (p less then 0.01). Knowledge of Yi, Bai, Hani and Dai ethnic respondents was lesser than those of Han ethnicity (p less then 0.01). Over 83.8% of Yi, Bai, Hani and Dai ethnicity residents were unwilling to register for body donation programs with receiving a driver's license (p less then 0.01). Less than 46.9% of ethnic minorities supported use of honorary certificates (p less then 0.01). check details Ethnic minorities were supportive of financial compensation for body donations and denied that financial compensation led to the commercialization of cadaver donation (p less then 0.01, p less then 0.01). Willingness of ethnic minorities to participate in cadaver donation programs was primarily related to religious beliefs (p less then 0.01), economic status (p less then 0.01). Knowledge, attitude and willingness of ethnic minorities to participate in cadaver donation programs were markedly different from those of Han ethnicity, and the religious belief and economic status played a decisive role. To increase participation, programs based on respecting religious belief should be developed to support improvements in economy, education, medical care and social security system.We report the synthesis and preliminary characterization of IODVA1, a potent small molecule that is active in xenograft mouse models of Ras-driven lung and breast cancers. In an effort to inhibit oncogenic Ras signaling, we combined in silico screening with inhibition of proliferation and colony formation of Ras-driven cells. NSC124205 fulfilled all criteria. HPLC analysis revealed that NSC124205 was a mixture of at least three compounds, from which IODVA1 was determined to be the active component. IODVA1 decreased 2D and 3D cell proliferation, cell spreading and ruffle and lamellipodia formation through downregulation of Rac activity. IODVA1 significantly impaired xenograft tumor growth of Ras-driven cancer cells with no observable toxicity. Immuno-histochemistry analysis of tumor sections suggests that cell death occurs by increased apoptosis. Our data suggest that IODVA1 targets Rac signaling to induce death of Ras-transformed cells. Therefore, IODVA1 holds promise as an anti-tumor therapeutic agent.In medicine, a misdiagnosis or the absence of specialists can affect the patient's health, leading to unnecessary tests and increasing the costs of healthcare. In particular, the lack of specialists in otolaryngology in third world countries forces patients to seek medical attention from general practitioners, whom might not have enough training and experience for making correct diagnosis in this field. To tackle this problem, we propose and test a computer-aided system based on machine learning models and image processing techniques for otoscopic examination, as a support for a more accurate diagnosis of ear conditions at primary care before specialist referral; in particular, for myringosclerosis, earwax plug, and chronic otitis media. To characterize the tympanic membrane and ear canal for each condition, we implemented three different feature extraction methods color coherence vector, discrete cosine transform, and filter bank. We also considered three machine learning algorithms support vector machine (SVM), k-nearest neighbor (k-NN) and decision trees to develop the ear condition predictor model.
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