Browsing by Author "Medeiros, R."
Now showing 1 - 3 of 3
Results Per Page
Sort Options
- Influence of Il-18 genetic polymorphisms in antidepressant treatment phenotypesPublication . Santos, Marlene; Carvalho, S.; Lima, L.; Mota-Pereira, J.; Pimentel, P.; Maia, D.; Correia, D.; Gomes, S.; Cruz, Agostinho; Medeiros, R.Recent studies suggested that immune activation and cytokines might be involved in depression. The proinflammatory cytokine interleukin-18 (IL-18) is less reported in depression but is still relevant since it is expressed in the brain and serum levels of IL-18 have been found to be increased in patients with moderate to severe depression. Therefore, it seems reasonable that IL-18 promoter SNPs may have an effect in antidepressant response phenotypes.
- Role of genetic polymorphisms on neuroplasticity pathways in a cohort of Portuguese patients with Major Depressive DisorderPublication . Santos, M.; Carvalho, S.; Lima, L.; Mota-Pereira, J.; Pimentel, P.; Correia, D.; Maia, D.; Gomes, S.; Cruz, A.; Medeiros, R.Growing evidence suggests the implication of brain plasticity in antidepressant drug (AD) efficacy. Several authors have been pointing out the role of the BDNF-TrkB signaling pathway, including the downstream kinases Akt and ERK, and the mTOR pathway in neuroplasticity [1-3]. Furthermore, the prediction of AD response phenotypes of depressed patients treated with AD drugs remains a challenge for clinicians. Although previous studies have suggested that genetic variants may play a key role in the mechanism of Treatment Resistance Depression and Relapse, attempts to identify risk polymorphisms within genes with putative interest in AD response, had a limited success.
- Validity of central pain processing biomarkers for predicting the occurrence of oncological chronic pain: a study protocolPublication . Carrillo‑de‑la‑Peña, M. T.; Fernandes, C.; Castro, Catarina; Medeiros, R.Despite recent improvements in cancer detection and survival rates, managing cancer-related pain remains a significant challenge. Compared to neuropathic and inflammatory pain conditions, cancer pain mechanisms are poorly understood, despite pain being one of the most feared symptoms by cancer patients and significantly impairing their quality of life, daily activities, and social interactions. The objective of this work was to select a panel of biomarkers of central pain processing and modulation and assess their ability to predict chronic pain in patients with cancer using predictive artificial intelligence (AI) algorithms. We will perform a prospective longitudinal cohort, multicentric study involving 450 patients with a recent cancer diagnosis. These patients will undergo an in-person assessment at three different time points: pretreatment, 6 months, and 12 months after the first visit. All patients will be assessed through demographic and clinical questionnaires and self-report measures, quantitative sensory testing (QST), and electroencephalography (EEG) evaluations. We will select the variables that best predict the future occurrence of pain using a comprehensive approach that includes clinical, psychosocial, and neurophysiological variables. This study aimed to provide evidence regarding the links between poor pain modulation mechanisms at precancer treatment in patients who will later develop chronic pain and to clarify the role of treatment modality (modulated by age, sex and type of cancer) on pain. As a final output, we expect to develop a predictive tool based on AI that can contribute to the anticipation of the future occurrence of pain and help in therapeutic decision making.