Medicina
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Prolactinomas are the commonest form of pituitary neuroendocrine tumor (PitNET), representing approximately half of such tumors. Dopamine agonists (DAs) have traditionally been the primary treatment for the majority of prolactinomas, with surgery considered the second line. The aim of this review is to examine the historical and modern management of prolactinomas, including medical therapy with DAs, transsphenoidal surgery, and multimodality therapy for the treatment of aggressive prolactinomas and metastatic PitNETs, with an emphasis on the efficacy, safety, and future directions of current therapeutic modalities. ⋯ Aggressive prolactinomas and metastatic PitNETS should receive multimodality therapy including high dose cabergoline, surgery, radiation therapy (preferably using stereotactic radiosurgery where suitable), and temozolomide. DAs remain a reliable mode of therapy for most prolactinomas but results from transsphenoidal surgery in expert hands have improved considerably over the last one to two decades. Surgery should be strongly considered as primary therapy, particularly in the setting of microprolactinomas, non-invasive macroprolactinomas, or prior to attempting pregnancy, and has an important role in the management of DA resistant and aggressive prolactinomas.
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Background and Objectives: Clinical diagnosis has become very significant in today's health system. The most serious disease and the leading cause of mortality globally is brain cancer which is a key research topic in the field of medical imaging. The examination and prognosis of brain tumors can be improved by an early and precise diagnosis based on magnetic resonance imaging. ⋯ Finally, we adopt the eXplainable Artificial Intelligence (XAI) method to explain the result. Conclusions: Our proposed approach for brain tumor detection and classification has outperformed prior methods. These findings demonstrate that the proposed approach obtained higher performance in terms of both visually and enhanced quantitative evaluation with improved accuracy.
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Background and Objectives: Interest in artificial intelligence (AI) for outcome prediction has grown substantially in recent years. However, the prognostic role of AI using advanced cardiac magnetic resonance imaging (CMR) remains unclear. This systematic review assesses the existing literature on AI in CMR to predict outcomes in patients with cardiovascular disease. ⋯ Results: A total of 5 studies were included, with a total of 3679 patients, with 225 deaths and 265 major adverse cardiovascular events. Three methods demonstrated high prognostic accuracy: (1) three-dimensional motion assessment model in pulmonary hypertension (hazard ratio (HR) 2.74, 95%CI 1.73−4.34, p < 0.001), (2) automated perfusion quantification in patients with coronary artery disease (HR 2.14, 95%CI 1.58−2.90, p < 0.001), and (3) automated volumetric, functional, and area assessment in patients with myocardial infarction (HR 0.94, 95%CI 0.92−0.96, p < 0.001). Conclusion: There is emerging evidence of the prognostic role of AI in predicting outcomes for three-dimensional motion assessment in pulmonary hypertension, ischaemia assessment by automated perfusion quantification, and automated functional assessment in myocardial infarction.
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The care of individuals with diabetes needs a holistic perspective, taking into account both the physical disease and the mental health problems that may be associated. Different studies show a higher prevalence of depression or anxiety issues in diabetes patients than in the general population, which is why diabetes can be considered one of the chronic diseases in which psychological care is crucial to maintain quality of life. The objective of this review is to examine the published articles that relate the bidirectional associations between objective and subjective measures of anxiety, depressive symptomatology, stress, sleep quality, and salivary biomarkers in patients with diabetes. ⋯ Low melatonin concentrations showed a negative correlation with sleep quality. As it is an easy-to-apply and non-invasive method, the measurement of salivary biomarkers can be very useful for predicting psychological alterations in patients with diabetes. Further scientific studies are required to determine the sensitivity of these biological substances acting as biomarkers for detecting sleep disorders and psychological alterations.
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Background and Objectives: The aim of this study was to investigate the impact of oral administration of the combination of astaxanthin (AXT), lutein, folic acid, vitamin D3, and bromelain with antioxidants on choroidal blood flow in patients with age-related intermediate macular degeneration (AMD). Materials and Methods: Patients affected by intermediate AMD and treated with daily oral nutritional supplement with AXT, bromelain, vitamin D3, folic acid, lutein, and antioxidants for a period of at least 6 months were included in this retrospective study. A control group homogenous for age and sex was also included in the analysis. ⋯ Results: CCVD values showed statistically significant difference between cases and controls at baseline (p < 0.001) and in the cases during follow-up (p < 0.001). The CHT measurements showed statistically significant difference between cases and controls (p = 0.002) and in the cases during follow-up (p < 0.001). Conclusions: The combined use of structural OCT and OCTA allows for a detailed analysis in vivo of perfusion parameters of the choriocapillaris and choroid and evaluation of changes of choroidal blood flow after oral nutritional supplements that affect blood flow velocity.