Conectar
Liver tumours are a leading cause of global morbidity and mortality. Current diagnostic tools, including computed tomography (CT), magnetic resonance imaging (MRI) and intraoperative ultrasound (IOUS), have limitations in detecting liver neoplasms. Indocyanine green (ICG) has emerged as a promising tool for improving liver tumour detection. This study aims to assess the impact of preoperative ICG on intraoperative tumour detection in minimally invasive surgery and develop a machine-learning algorithm to enhance tumour detection using ICG fluorescence.
This prospective, multicentre, phase IV clinical trial adheres to Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT) guidelines. Patients with liver tumours eligible for minimally invasive surgery and a preoperative imaging test will be included. ICG will be administered intravenously 24 hours before surgery. Intraoperative procedures will include IOUS, ICG mapping and photographic documentation. Patients will be followed for 90 days to assess tumour progression, morbidity and mortality. The photographic analysis will enable the development of an artificial intelligence algorithm using machine learning and neural networks to identify lesions based on ICG fluorescence. The estimated sample size is 173 patients and the trial is predicted to accrue in 3 years.
The trial will be conducted in accordance with the Declaration of Helsinki and the Spanish Agency of Medicines and Medical Devices (AEMPS) guidelines. Approved by the local institutional Ethics Committee and the AEMPS, the results will be shared with the scientific community through publications and conferences.
2023–5 08 316-27-00.
V.12, 18 March 2025
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 