Conectar
The first therapeutic assessment of locally advanced (LA) head and neck squamous cell carcinomas (HNSCCs) is often performed 10–12 weeks after the end of chemoradiotherapy as a result of the delayed action of radiotherapy. Diagnostic uncertainty between persistent disease and treatment-related changes (oedema, necrosis) can delay confirmation of residual cancer. According to data in the literature, a correlation exists between the detection of circulating tumour DNA (ctDNA) at the end of chemoradiotherapy treatment and residual disease. However, additional data are required before this molecular tool can be used in routine clinical practice. The aim of this clinical trial (NeckTAR-IN) is to assess the usefulness of ctDNA to detect residual disease 3 months after the end of chemoradiotherapy among patients with LA HNSCC.
At M3, objective response (clinical and radiological) will be set against the detection or not of ctDNA in the blood. This is an interventional, multicentre, prospective trial, ancillary to the NeckTAR study. All the patients included in the NeckTAR study are eligible for the NeckTAR-IN study. We expect to enrol 59 patients in this ancillary trial. A blood sample will be taken 1 month and 3 months after the end of chemoradiotherapy. Approval from the ethics committee was granted on 29 August 2025.
The study protocol obtained approval from the French Ethics Committee (N°25.02461.000435). The results will be published in scientific journals and presented at conferences.
Multiple sclerosis (MS) frequently leads to mobility impairment, fatigue and a significant decline in health-related quality of life (QoL). Home-based assistive technology, such as robotic exoskeletons, offers a promising solution to enhance independent mobility and increase the intensity of motor training. Long-term functional and quality of life benefits of light lower-limb exoskeleton home use have yet to be determined.
The primary objective of this study is to determine the efficacy of an 8-week period, home-based use of a robotic exoskeleton in improving QoL in individuals with MS, compared with a no-device control period.
This is a multicentre, randomised, controlled and single-blinded cross-over trial. A total of 28 patients with confirmed MS (Expanded Disability Status Scale (EDSS) score 5.0–7.0) will be recruited across three rehabilitation centres. Participants will be randomly assigned to two 8-week phases: intervention (daily home-based exoskeleton use) or control (physical activity advice), separated by an 8-week wash-out period. The primary outcome is the change in the MS Quality of Life assessed by the Functional Assessment of Multiple Sclerosis (FAMS) physical composite score from baseline to the end of each phase. Secondary outcomes include changes in walking performance (2-minute Walk, 10 M Walk, Timed Up-and-Go, Four Square Step Test and Sit-to-Stand Test), fatigue severity (Fatigue Severity Scale and Fatigue Impact Measurement Scale (EMIF)-MS), and strength capacity (Manual Muscle Testing, Tardieu scale), self-confidence (Rosenberg), anxiety and depression (Hospital Anxiety and Depression Scale), satisfaction to use (Technical Aid Satisfaction Scale) and intention to use (Unified Theory of Acceptance and Use of Technology).
This study was registered on ClinicalTrials.gov on 1 February 2024 (Trial registration number: NCT05835622 https://clinicaltrials.gov/ct2/show/NCT05835622). Patient recruitment is currently underway and is anticipated to be completed by January 2026. Primary endpoint data collection is expected to be completed in June 2026.
This study protocol describes a rigorous trial designed to provide high-level evidence on the impact of a home-based robotic exoskeleton on QoL in individuals with MS. By determining intervention effectiveness, the results will provide clinical guidelines, potentially facilitating the widespread adoption of home-based assistive robotics to substantially improve the independence and overall QoL for patients with MS.
by Freddy Oulia, Philippe Charton, Muhammad Kabir, Fabrice Gardebien, Cédric Damour, Frederic Cadet
Glioblastoma multiforme (GBM) is the most aggressive primary brain tumor in adults, with a median survival of 14.6 months under standard radiotherapy and temozolomide (TMZ) chemotherapy. The methylation status of the O⁶-methylguanine-DNA methyltransferase (MGMT) promoter is a critical biomarker predicting TMZ response; however, its determination currently requires invasive tissue sampling. Non-invasive prediction of MGMT promoter methylation from multiparametric MRI (mpMRI) through deep learning represents a compelling alternative, yet its clinical feasibility remains unresolved. Using the BraTS 2021 dataset (582 patients, four MRI sequences: FLAIR, T1w, T1wCE, T2w), we conducted a systematic comparative study of unimodal and multimodal deep learning approaches based on VGG-16, exploring 1,380 experimental configurations (unimodal: 192; multimodal: 1,188) across three imaging planes, eight slice counts, and three multimodal fusion strategies (early, intermediate, and late fusion). In the unimodal setting, the best model trained on T2w coronal images (32 slices, no transfer learning) achieved an accuracy of 0.6458 and an AUC of 0.6422 on the validation set, but dropped to 0.5586 and 0.5533 on the independent test set, revealing substantial overfitting attributable to limited dataset size. Strikingly, multimodal fusion consistently failed to outperform the best unimodal model, with all three fusion strategies plateauing at ~0.64 accuracy and ~0.64 AUC on validation data. Transfer learning improved generalization across train/test distributions at the cost of peak performance. These findings suggest, for the tested framework in this study, that MGMT methylation status prediction from mpMRI remains fundamentally constrained by dataset heterogeneity and size, irrespective of modality combination strategy, and that T2w coronal acquisitions could be more interesting in future data collection efforts.The aim of this study was to evaluate the feasibility, acceptability and preliminary effectiveness of I-PASS-structured (Identification—Patient—Action—Situation—Synthesis) bedside nursing handovers on the handover global quality and the patients trust in nurses.
Oral end-of-shift nursing handovers can become moments of patient vulnerability. Moving handovers from nurses' offices to patients' bedsides is a means of improving them; however, implementing this remains a challenge.
This was a Type-1 effectiveness–implementation hybrid study.
We measured the effectiveness using a simple interrupted time series with three measurement points before and after the introduction of I-PASS-structured bedside nursing handovers between August and November 2022. Implementation was explored using multi-method measurements of quantitative and qualitative data. As an implementation strategy, we developed a specific training session, including simulations.
Bedside nursing handovers were introduced into one surgery and one medicine ward, with the 831 handovers evaluated showing significant improvements in handover quality compared to before implementation, although handover duration increased. Patient outcomes validated this change in nursing practice. However, examining nurses' perspectives of the implementation process revealed several obstacles to using bedside nursing handovers that training alone was not strong enough to overcome.
Given the findings of the present project, the use of bedside nursing handovers should be extended to other units by developing strategies that will make the practice sustainable.
Bedside nursing handovers improved handover quality and created a true partnership with the patient: nurses feel more confident about seeing the patient quickly. Patients felt more taken into consideration and safer.
For feasibility reasons, patients and the public were not involved in the design, conduct, reporting or dissemination plans of this research. The trial was prospectively registered before the first participant was recruited under the ISRCTN # 81701569.
by Claude Emmanuel Koutouan, Marie Louisa Ramaroson, Angelina El Ghaziri, Laurent Ogé, Abdelhamid Kebieche, Raymonde Baltenweck, Patricia Claudel, Philippe Hugueney, Anita Suel, Sébastien Huet, Linda Voisine, Mathilde Briard, Jean Jacques Helesbeux, Latifa Hamama, Valérie Le Clerc, Emmanuel Geoffriau
Resistance of carrot to Alternaria leaf blight (ALB) caused by Alternaria dauci is a complex and quantitative trait. Numerous QTL for resistance (rQTLs) to ALB have been identified but the underlying mechanisms remain largely unknown. Some rQTLs have been recently proposed to be linked to the flavonoid content of carrot leaves. In this study, we performed a metabolic QTL analysis and shed light on the potential mechanisms underlying the most significant rQTL, located on carrot chromosome 6 and accounting for a large proportion of the resistance variation. The flavonoids apigenin 7-O-rutinoside, chrysoeriol 7-O-rutinoside and luteolin 7-O-rutinoside were identified as strongly correlated with resistance. The combination of genetic, metabolomic and transcriptomic approaches led to the identification of a gene encoding a bHLH162-like transcription factor, which may be responsible for the accumulation of these rutinosylated flavonoids. Transgenic expression of this bHLH transcription factor led to an over-accumulation of flavonoids in carrot calli, together with significant increase in the antifungal properties of the corresponding calli extracts. Altogether, the bHLH162-like transcription factor identified in this work is a strong candidate for explaining the flavonoid-based resistance to ALB in carrot.
FreshRSS 