Publications by authors named "C Clua Provost"

Pediatric brain tumor survivors (PBTS) are at risk for late effects related to their diagnosis and treatment. Long-term medical follow-ups are deemed essential, implying a transition from pediatric to adult healthcare settings. This pilot study aims to assess the feasibility, acceptability, and preliminary effects of a targeted transition readiness intervention for PBTS.

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Objective: In a recent sham-controlled 13-session prolonged continuous theta burst stimulation intervention protocol, recovery from upper limb fracture at both 1 and 3 months was better than anticipated in patients assigned to the sham intervention group. To determine whether potential placebo effect and close patient monitoring affected recovery, the current study aimed to compare clinical outcomes between sham-treated participants who also received standard care with similarly injured patients who only received standard care.

Methods: Twenty participants with isolated upper limb fractures from the sham group were seen 13 times post-fracture (1 baseline session, 10 treatments, and 2 follow-ups [1 and 3 months]) over 3 months.

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The tomato brown rugose fruit virus (ToBRFV) poses a considerable threat to tomato production worldwide. Substantial experimental evidence supports the role of infected seeds as a contamination route, but the epidemiologic portrait of the virus has received less attention. This study reports the first survey of ToBRFV prevalence in commercial greenhouses.

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Aims: FGFR-fused central nervous system (CNS) tumours are rare and are usually within the glioneuronal and neuronal tumours or the paediatric-type diffuse low-grade glioma spectrum. Among this spectrum, FGFR2 fusion has been documented in tumours classified by DNA-methylation profiling as polymorphous low-grade neuroepithelial tumours of the young (PLNTY), a recently described tumour type. However, FGFR2 fusions have also been reported in glioneuronal tumours, highlighting the overlapping diagnostic criteria and challenges.

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Article Synopsis
  • - The study examines how a deep learning reconstruction (DLR) algorithm can improve MRI quality for brain tumor assessment while reducing long scan times.
  • - In a trial with 22 brain tumor patients, the DLR technique maintained important quantitative MRI parameters like Fractional Anisotropy and T1/T2 relaxation times despite faster scans.
  • - The results suggest that using DLR can create better quality imaging maps, potentially enabling more frequent use of these imaging biomarkers in clinical practice.
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