RESUMO
SUMMARY OBJECTIVE: Gliomas are immune system suppressive tumors, and the role of vitamin D is pivotal in the immune system. This study aimed to observe if there is any significant association between the serum levels of 25-hydroxyvitamin D with hematological indices and anthropometric measurements. METHODS: A total of 75 glioma patients were included, and the information was collected on gender, age group, area, socioeconomic status, intake of vitamin D and calcium in food and supplements, skin color, sunlight exposure, body mass index, and muscle strength. A nonparametric Kendall's tau-b correlation test was performed to find a correlation between 25-hydroxyvitamin D levels and blood counts, body mass index, and muscle strength. RESULTS: The majority of patients (72%) were having low lymphocytes followed by high granulocytes and high white blood cells. The majority were having low levels of both 25-hydroxyvitamin D (84%) and calcium (73%). Patients were mainly from urban areas, and the majority belonged to middle-class families having sedentary lifestyles. The majority of patients were not taking vitamin D supplements. An insufficient amount of sunlight exposure was found in most of them. The majority of the patients were although had normal weight but weak muscle strength (74.6%). An insignificant correlation was found between 25-hydroxyvitamin D levels with the hematological indices or anthropometric measurements in brain tumor patients. CONCLUSION: Vitamin D is a powerful immune modulator, and there is a great need for sufficient amounts of sunlight exposure and vitamin D-enriched diets to prevent cancer.
RESUMO
SUMMARY OBJECTIVES: This study aimed to develop artificial intelligence and machine learning-based models to predict alterations in liver enzymes from the exposure of low annual average effective doses in radiology and nuclear medicine personnel of Institute of Nuclear Medicine and Oncology Hospital. METHODS: Ninety workers from the Radiology and Nuclear Medicine departments were included. A high-capacity thermoluminescent was used for annual average effective radiation dose measurements. The liver function tests were conducted for all subjects and controls. Three supervised learning models (multilayer precentron; logistic regression; and random forest) were applied and cross-validated to predict any alteration in liver enzymes. The t-test was applied to see if subjects and controls were significantly different in liver function tests. RESULTS: The annual average effective doses were in the range of 0.07-1.15 mSv. Alanine transaminase was 50% high and aspartate transaminase was 20% high in radiation workers. There existed a significant difference (p=0.0008) in Alanine-aminotransferase between radiation-exposed and radiation-unexposed workers. Random forest model achieved 90-96.6% accuracies in Alanine-aminotransferase and Aspartate-aminotransferase predictions. The second best classifier model was the Multilayer perceptron (65.5-80% accuracies). CONCLUSION: As there is a need of regular monitoring of hepatic function in radiation-exposed people, our artificial intelligence-based predicting model random forest is proved accurate in prediagnosing alterations in liver enzymes.