Background: Brain tumors are the most common solid tumors found in children. Current research is determining whether diagnosing brain tumors earlier will help improve prognosis and reduce long-term deficits; however, childhood brain tumors are often diagnosed late with a median time of 1-4 months from onset of symptoms. Prolonged symptom intervals before diagnosis have been associated with life-threatening risks, neuro-cognitive disabilities, and detrimental professional relationships between healthcare providers and families. Pediatric brain tumor clinical presentations are often non-specific and resemble less serious illnesses; therefore, healthcare providers are failing to include this in their differential diagnoses list. Purpose: To assess healthcare provider knowledge of signs and symptoms of pediatric brain tumors using The Brain Pathways Guideline. Methods: A one group pre-test and post-test e-mailed separately to nurse practitioners that have active membership in National Association of Pediatric Nurse Practitioners (NAPNAP) Arizona Chapter. Results: The Wilcoxon Signed Rank Test revealed that the matched test scores were not statistically significant (p=0.157) after viewing The Brain Pathways Guideline educational materials. Conclusion: The results of this study did not show a statistically significant difference in the test scores and therefore it cannot be concluded that presenting an evidence-based guideline to assist healthcare providers to assess and diagnose patients with brain tumors will be helpful to improve pre-diagnostic symptom intervals.

Parkinson’s disease (PD) has long been conceptualized as a motor disorder, but significant clinical features arise before motor symptoms are present. Although prospective, longitudinal research offers the most valid approach for determining pre-diagnostic indicators of PD, it is costly and requires a long time-course. Leveraging existing epidemiological datasets offers the opportunity to identify pre-diagnostic features that may predict later PD diagnosis. This project used the Framingham Heart Study (FHS) database of prospective follow-up on a community-based sample that spans over six decades. Regular surveillance identified 156 incident cases of PD. Promising biomarker and other clinical marker candidates were derived from cohort-based samples without prospective follow-up and included cognition, depression, and inflammation. The main hypothesis was that potential markers would discriminate between individuals who did/ did not go on to a later PD diagnosis. The FHS database provided clinical markers (cognition, depression) and fluid biomarkers (levels of plasma inflammation) for interrogation. Cognition was indexed by performance on the Mini-Mental State Examination and a comprehensive neuropsychological assessment, including measures of attention, memory, and executive functioning. Depression was derived from scores on the Center for Epidemiologic Studies Depression Scale (CES-D). Separate means comparison and logistic regression analyses to maximize sample sizes were conducted on available data for candidate (bio)markers at time-points 1-3 years or 1-5 years pre-diagnosis for PD cases (N=7-33) and control participants (N=28-224), in samples matched for age, sex, and education level. No significant differences were found between PD and control participants on any measure of cognitive functioning 1-3 years pre-diagnosis. No significant differences were found for total CES-D scores or levels of plasma inflammation 1-5 years pre-diagnosis. Higher levels of C-reactive protein and TNF-alpha were significantly correlated with increasing age in the total sample but not for PD specifically. These results indicate that cognition, self-reported depression, and plasma inflammation may not be useful as markers of PD risk, and efforts should likely focus on alternative candidate markers. Detecting PD in the earliest stages is an important goal, as it could lead to treatments that attenuate progression, improve clinical prognosis, and enhance the possibility of disease prevention. / 2021-02-20T00:00:00Z

Made available in DSpace on 2014-12-17T14:54:59Z (GMT). No. of bitstreams: 1 CeciliaRML_TESE.pdf: 2405004 bytes, checksum: b79db46caa488d92a5933e45e80ca647 (MD5) Previous issue date: 2011-06-10 / The area of the hospital automation has been the subject a lot of research, addressing relevant issues which can be automated, such as: management and control (electronic medical records, scheduling appointments, hospitalization, among others); communication (tracking patients, staff and materials), development of medical, hospital and laboratory equipment; monitoring (patients, staff and materials); and aid to medical diagnosis (according to each speciality). This thesis presents an architecture for a patient monitoring and alert systems. This architecture is based on intelligent systems techniques and is applied in hospital automation, specifically in the Intensive Care Unit (ICU) for the patient monitoring in hospital environment. The main goal of this architecture is to transform the multiparameter monitor data into useful information, through the knowledge of specialists and normal parameters of vital signs based on fuzzy logic that allows to extract information about the clinical condition of ICU patients and give a pre-diagnosis. Finally, alerts are dispatched to medical professionals in case any abnormality is found during monitoring. After the validation of the architecture, the fuzzy logic inferences were applied to the trainning and validation of an Artificial Neural Network for classification of the cases that were validated a priori with the fuzzy system / A ?rea da automa??o hospitalar tem sido alvo de muitas pesquisas, abordando problemas pertinentes que podem ser automatizados, como: gerenciamento e controle (prontu?rio eletr?nico, marca??o de consulta, internamento, entre outros); comunica??o (rastreamento de pacientes, materiais e funcion?rios); desenvolvimento de equipamentos m?dicos, hospitalares e laboratoriais; monitoramento (pacientes, materiais e funcion?rios); e aux?lio ao diagn?stico m?dico (de acordo com cada especialidade). Esta tese de doutorado apresenta uma Arquitetura de um Sistema Inteligente de Monitoramento e Envio de Alertas de Pacientes (SIMAp). A arquitetura est? baseada em t?cnicas de sistemas inteligentes e aplicada na automa??o hospitalar, mais especificamente em Unidade de Terapia Intensiva (UTI) para monitoramento de pacientes. O objetivo do SIMAp ? a transforma??o dos dados do monitor multiparam?trico em informa??es, por meio do conhecimento dos especialistas e dos par?metros de normalidade dos sinais vitais de pacientes, utilizando l?gica fuzzy na extra??o das informa??es a respeito do quadro cl?nico de pacientes internados em UTI. Por fim, alertas s?o gerados e podem ser enviados para a equipe m?dica, caso seja encontrada alguma anormalidade no monitoramento. Ap?s a valida??o da arquitetura, as infer?ncias oriundas do modelo fuzzy foram aplicadas no treinamento e valida??o de uma RNA para a classifica??o das situa??es previstas no modelo, resultando no pr?-diagn?sticos

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