Asunto(s)
Neurofibromatosis 2/diagnóstico , Cráneo/patología , Biopsia , Niño , Terapia Combinada , Pruebas Genéticas , Humanos , Imagen por Resonancia Magnética , Masculino , Neurofibromatosis 2/tratamiento farmacológico , Neurofibromatosis 2/genética , Neurofibromatosis 2/cirugía , Fenotipo , Evaluación de Síntomas , Tomografía Computarizada por Rayos X , Resultado del TratamientoRESUMEN
Primary gastric Burkitt's lymphoma (BL) is rare in the pediatric population. Furthermore, the association of Burkitt's lymphoma with Helicobacter pylori is not well defined. We report a case of primary gastric Burkitt's lymphoma associated with Helicobacter pylori diagnosed in a pediatric patient. This diagnosis was made with the aid of endoscopic ultrasound (EUS)-guided fine-needle biopsy (FNB). This is one of the first pediatric cases of EUS-guided FNB for the diagnosis of H. pylori-associated gastric BL.
Asunto(s)
Linfoma de Burkitt , Infecciones por Helicobacter , Helicobacter pylori , Neoplasias Gástricas , Adolescente , Linfoma de Burkitt/diagnóstico por imagen , Linfoma de Burkitt/microbiología , Femenino , Infecciones por Helicobacter/complicaciones , Humanos , Neoplasias Gástricas/diagnóstico por imagen , Neoplasias Gástricas/microbiología , Ultrasonografía IntervencionalRESUMEN
Pain management is a crucial part in Sickle Cell Disease treatment. Accurate pain assessment is the first stage in pain management. However, pain is a subjective response and hard to assess via objective approaches. In this paper, we proposed a system to map objective physiological measures to subjective self-reported pain scores using machine learning techniques. Using Multinomial Logistic Regression and data from 40 patients, we were able to predict patients' pain scores on an 11-point rating scale with an average accuracy of 0.578 at the intra-individual level, and an accuracy of 0.429 at the inter-individual level. With a condensed 4-point rating scale, the accuracy at the inter-individual level was further improved to 0.681. Overall, we presented a preliminary machine learning model that can predict pain scores in SCD patients with promising results. To our knowledge, such a system has not been proposed earlier within the SCD or pain domains by exploiting machine learning concepts within the clinical framework.