Publications by authors named "F R de Aquino Neto"
The Satisfaction With Life Scale (SWLS) is a widely used self-report measure of subjective well-being, but studies of its measurement invariance across a large number of nations remain limited. Here, we utilised the Body Image in Nature (BINS) dataset-with data collected between 2020 and 2022 -to assess measurement invariance of the SWLS across 65 nations, 40 languages, gender identities, and age groups (N = 56,968). All participants completed the SWLS under largely uniform conditions.
View Article and Find Full Text PDFDiffuse noxious inhibitory control (DNIC), also known as conditioned pain modulation (CPM) in humans, is a paradigm wherein the heterotopic application of a noxious stimulus results in the attenuation of another spatially distant noxious input. The pre-clinical and clinical studies show the involvement of several neurochemical systems in DNIC/CPM and point to a major contribution of the noradrenergic, serotonergic, and opioidergic systems. Here, we thoroughly review the latest data on the monoaminergic and opioidergic studies, focusing particularly on pre-clinical models of chronic pain.
View Article and Find Full Text PDFIbrutinib (IBR) is a tyrosine kinase inhibitor under investigation in preclinical and clinical settings as an alternative treatment for melanoma. Nevertheless, the limited oral bioavailability of IBR and the need for high doses of the drug to kill melanoma cells are major drawbacks for this purpose. Considering that melanoma is restricted to the skin at early stages, the topical application of IBR might constitute an effective and safer administration route.
View Article and Find Full Text PDFBackground: Available empirical evidence on participant-level factors associated with dropout from psychotherapies for post-traumatic stress disorder (PTSD) is both limited and inconclusive. More comprehensive understanding of the various factors that contribute to study dropout from cognitive-behavioural therapy with a trauma focus (CBT-TF) is crucial for enhancing treatment outcomes.
Objective: Using an individual participant data meta-analysis (IPD-MA) design, we examined participant-level predictors of study dropout from CBT-TF interventions for PTSD.
Article Synopsis
- This systematic review evaluates the use of machine learning (ML) algorithms in diagnosing and managing sickle cell disease (SCD), focusing on aspects like organ failure detection, drug dosage, and pain classification.
- It analyzes various ML algorithms, including Multilayer Perceptron, Support Vector Machine, and Convolutional Neural Networks, showing encouraging outcomes for SCD diagnosis and monitoring.
- Despite the progress, the review points out challenges such as small datasets and interpretability issues, suggesting that future research should aim for better data and advanced ML techniques to fully realize ML's potential in SCD care.