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'It Feels Very Weird and Normal at the Same Time': Sibling Perceptions of Their Relationships With an Autistic Brother or Sister With Complex Care Needs.
J Appl Res Intellect Disabil
January 2025
The Autism Community Research Network @Southampton [ACoRNS] and the Centre for Research in Mental Health, School of Psychology, University of Southampton, Southampton, UK.
Background: The impact of having a disabled brother or sister on siblings' psychological well-being and sibling relationships has been the subject of several research studies. However, research which focuses on the relationship between siblings and their autistic brother or sister with an intellectual disability and complex care needs is rare. We explored siblings' views and experiences of their sibling relationship with their autistic brother or sister with complex care needs.
View Article and Find Full Text PDFPerceptions and experiences of directly observed treatment in tuberculosis: insights from a mixed-methods cross-sectional study.
J Bras Pneumol
January 2025
. EPIUnit, Instituto de Saúde Pública da Universidade do Porto, Porto, Portugal.
Objective: The demanding nature and psychosocial burdens of directly observed treatment (DOT) have opened a path to alternative strategies such as video-observed therapy (VOT), which offers comparable treatment outcomes and patient satisfaction while potentially saving time and reducing costs. The objective of this study was to evaluate the perceptions and experiences of patients and health care professionals regarding DOT and other treatment strategies implemented in Portugal.
Methods: Patients with a confirmed diagnosis of tuberculosis, treated at the Vila Nova de Gaia Outpatient Tuberculosis Centre in the last two years, were asked to complete a brief questionnaire, as were health care professionals working in the northern region of Portugal.
Pharmacokinetics and safety of bictegravir in pregnant and postpartum persons with HIV and their infants.
J Acquir Immune Defic Syndr
November 2024
Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA.
Background: Limited data exist on bictegravir pharmacokinetics in pregnancy among persons with HIV (PWH) and infant washout.
Setting: Nonrandomized, open-label, multi-center phase-IV prospective study of bictegravir pharmacokinetics and safety in pregnant PWH and their infants.
Methods: Steady-state 24-hour pharmacokinetic sampling of oral bictegravir 50 mg once daily (a component of fixed-dose combination bictegravir/emtricitabine/tenofovir alafenamide) during the 2nd and 3rd trimesters and postpartum was performed.
Dynamics and triggers of misinformation on vaccines.
PLoS One
January 2025
Centro Ricerche Enrico Fermi, Rome, Italy.
The Covid-19 pandemic has sparked renewed attention to the risks of online misinformation, emphasizing its impact on individuals' quality of life through the spread of health-related myths and misconceptions. In this study, we analyze 6 years (2016-2021) of Italian vaccine debate across diverse social media platforms (Facebook, Instagram, Twitter, YouTube), encompassing all major news sources-both questionable and reliable. We first use the symbolic transfer entropy analysis of news production time-series to dynamically determine which category of sources, questionable or reliable, causally drives the agenda on vaccines.
View Article and Find Full Text PDFMVCL-DTI: Predicting Drug-Target Interactions Using a Multiview Contrastive Learning Model on a Heterogeneous Graph.
J Chem Inf Model
January 2025
School of Computer Science and Technology, Soochow University, Jiangsu 215006, China.
Accurate prediction of drug-target interactions (DTIs) is pivotal for accelerating the processes of drug discovery and drug repurposing. MVCL-DTI, a novel model leveraging heterogeneous graphs for predicting DTIs, tackles the challenge of synthesizing information from varied biological subnetworks. It integrates neighbor view, meta-path view, and diffusion view to capture semantic features and employs an attention-based contrastive learning approach, along with a multiview attention-weighted fusion module, to effectively integrate and adaptively weight the information from the different views.
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