Search Results (271)

Myeloid/lymphoid neoplasms with tyrosine kinase rearrangements (MLN-TKs) are rare clonal eosinophilias driven by PDGFRA, PDGFRB and other kinase fusions, highly sensitive to tyrosine kinase inhibitors. Their detection remains challenging, particularly for cryptic PDGFRA rearrangements. We performed a large multicenter real-world validation of the generic quantitative RT-PCR assay (gPDGFR), which detects 3′ PDGFRA/PDGFRB overexpression independently of fusion partner. A total of 231 consecutive patients with hypereosinophilia from 12 French centers were analyzed, and assay robustness was further assessed in an independent heterogeneous cohort of 102 tyrosine kinase inhibitor (TKI)-treated patients. Twenty-two PDGFR-rearranged cases (14 PDGFRA-r, 8 PDGFRB-r) were identified. The assay demonstrated 100% sensitivity and 100% negative predictive value. For PDGFRA, positive predictive value and specificity reached 100%. In contrast, PDGFRB overexpression showed lower specificity due to borderline false-positive cases, underscoring the need for confirmatory testing. In selected patients, longitudinal gPDGFR kinetics paralleled fusion-specific RT-qPCR, supporting its use for molecular follow-up when dedicated assays are unavailable, although it does not provide quantitative measurable residual disease assessment. Overall, gPDGFR represents a robust, partner-independent first-line screening strategy that can be readily integrated into routine diagnostic workflows to enable timely identification of patients eligible for targeted therapy. Full article

Background/Objectives: To identify existing evidence on strategies for standardising nursing handovers in paediatric hospital settings, given their impact on communication, safety, and quality of care. International bodies such as the WHO and The Joint Commission recommend standardisation as a key measure to reduce patient safety incidents. Methods: A scoping review was conducted in December 2022 using Medline, Cochrane Library, Scopus, and CINAHL databases. The search strategy included documents published between 2012 and 2022, in Spanish, English, Catalan, French, and/or Portuguese. We screened according to inclusion criteria (professional nurses and hospitalisation) and exclusion criteria (intensive care and medical professionals) and tabulated the results according to concurrent themes. The PRISMA-ScR guidelines were followed. Results: A total of 308 records were identified. After screening, 25 full-text articles were assessed for eligibility. Following quality appraisal, six were excluded for not meeting predefined criteria, resulting in 19 studies included in the final synthesis. The evidence mapped shows that most structured communication tools have been developed or validated in adult or medical contexts, with limited evaluation in paediatric nurse-to-nurse inpatient settings. Standardised structured communication tools used in hospital settings include SBAR, I-PASS, and Flex 11, while assessment instruments such as the Handoff CEX Scale and Handover Evaluation Scale have been applied to evaluate handover quality. Conclusions: Structured communication tools may contribute to improving information transfer and perceived quality of handover; however, paediatric nurse-specific evidence remains limited and frequently derives from non-nursing or adult contexts. Further adaptation and validation in paediatric inpatient nursing settings are required. Full article

Background/Objectives: We conducted a scoping review to map peer-reviewed evidence on occupational stress, burnout, and quality of life among radiographers and radiologic technologists and to identify measurement tools and reported consequences. Methods: Searches were conducted in Web of Science, Scopus, and PubMed. Eligible studies enrolled radiographers/radiologic technologists who were healthy adults; assessed at least one target construct (occupational stress, burnout, or quality of life) using validated instruments; and used cross-sectional, experimental, quasi-experimental, longitudinal, or mixed-methods designs. Articles published from 1995 onward in English, French, Spanish, or Portuguese were considered. Two reviewers independently screened, extracted data, and appraised methodological quality using Quality Assessment with Diverse Studies (QuADS). The synthesis was narrative only. Results: Of 2701 records, 10 studies from nine countries met inclusion. Most were cross-sectional, and two used mixed methods. Sample sizes ranged from 38 to 864. Frequently used instruments included MBI-HSS, OSI-R, HSE Indicator Tool, and SOC-13. Across studies, radiographers reported high stress and burnout—particularly emotional exhaustion and depersonalization—alongside reduced quality of life in multiple domains. Recurrent stressors involved workload and staffing pressures, role demands, anxiety about radiation exposure, and limited recognition. These factors were associated with intention to leave and a lower sense of coherence. Conclusions: The evidence base is largely cross-sectional, uses heterogeneous measures, and often relies on modest samples, with overall methodological quality mostly moderate. Findings indicate a persistent psychosocial risk profile in radiography and underscore the need for organizational and managerial actions—such as workplace physical activity programs—to reduce stress and burnout and protect the quality of life in this workforce. Full article

Background: Inadvertent arterial cannulation during central venous catheter placement is a recognized complication with potentially serious consequences, particularly when involving large-caliber catheters. While management strategies have evolved from mandatory surgical repair to various percutaneous approaches, limited data exist regarding collagen-based vascular closure devices for large-bore carotid arteriotomies. Case Presentation: We report the case of a 59-year-old male patient with acute Stanford Type A aortic dissection who underwent emergency surgical repair of the ascending aorta. During central venous cannulation, a five-lumen Certofix Quinto catheter (12-French outer diameter) was inadvertently inserted into the left common carotid artery. Given the complexity of concurrent cardiac surgery and the large-bore nature of the arteriotomy, percutaneous closure with an 18-French MANTA vascular closure device was successfully performed following completion of the aortic repair. The procedure achieved immediate hemostasis without complications. Outcomes: The patient remained neurologically intact throughout a 12-month follow-up period. Serial duplex ultrasonography and computed tomography angiography confirmed carotid artery patency without evidence of stenosis, dissection, pseudoaneurysm formation, or thromboembolic complications. Conclusions: This case demonstrates the technical feasibility of using a collagen-based vascular closure device for percutaneous management of a large-bore carotid arteriotomy in the acute surgical setting. While the outcome was favorable in this patient, this approach represents an off-label application that requires further validation and should be reserved for carefully selected cases in experienced centers where the benefits of percutaneous closure are judged to outweigh the uncertainties of supra-aortic device deployment. Full article

The grapevine (Vitis vinifera L.) is one of the most important horticultural crops, with thousands of varieties cultivated worldwide. In this study, we analyzed chloroplast SNV markers using a whole-genome shotgun sequencing approach to investigate the genetic diversity and phylogeny of 409 cultivated V. vinifera accessions originating from nine countries across Southeast and Central Europe, as well as a heterogeneous set of additional accessions maintained by INRAE. Shotgun sequencing allowed high coverage, enabling the detection of 93 SNVs across 24 chloroplast genes, including 11 non-synonymous variants. The ycf1 gene showed the highest variability, consistent with its role in species differentiation. Haplotype analysis revealed 102 distinct haplotypes, with clear geographic structuring: ATT predominated in the eastern Mediterranean, ATA in western Europe, and GTA mainly in a heterogeneous group of varieties from a French collection. To validate the shotgun approach, seven SNV markers were analyzed using target capture sequencing, confirming the accuracy of detected variants with only minimal discrepancies, which is mostly attributable to homopolymeric regions and low-frequency alleles. Phylogenetic analyses using both trees and networks delineated three major haplotype clusters, reflecting human-mediated dispersal of grapevine cultivars through historical viticultural practices. This study represents the largest chloroplast genome analysis of cultivated V. vinifera to date, providing a large cpDNA resource for assessing chloroplast diversity and maternal haplotype structure in cultivated grapevine. The results highlight the power of combining high-throughput sequencing and chloroplast genomics for population-level studies in perennial crops. Full article

The socio-ecological transition requires not only technological innovation but also new ways of recognizing the social, environmental, and territorial value generated by collective action. Many of these positive externalities remain invisible in conventional assessment frameworks, limiting the legitimacy, financing, and scaling of local sustainability initiatives. This article presents a strategic framework designed to identify and structure positive externalities in collective self-consumption and other transformative projects. The method combines four components: (i) normative identification through the Sustainable Development Goals; (ii) balanced multi-stakeholder participation to surface diverse perspectives; (iii) perceptive mapping using an adapted Kano model; and (iv) strategic articulation. The framework was applied in two contrasting contexts: an energy community centered on shared renewable production, and a women’s empowerment program focused on capability-building and social innovation. These applications do not aim at empirical replication or the validation of results, but at examining how the framework supports collective recognition and strategic structuring in different organizational settings. Across these distinct settings, it led to the formulation of coherent and actionable strategic roadmaps, illustrating how positive externalities can inform governance choices, strengthen institutional legitimacy, and support long-term project consolidation. These results suggest that collective recognition enables externalities to structure strategic action beyond their original sector, demonstrating the potential transferability of the approach. Developed within a research program supported by the French Agency for Ecological Transition (ADEME) and the national urban-transition initiative (PUCA), the framework provides a practical decision architecture for structuring shared value within coordinated strategies. Full article

This paper develops a novel hybrid framework that integrates clustering-enhanced Particle Swarm Optimization (PSO) with stretching techniques to solve Markowitz’s quadratic portfolio optimization problem. The proposed approach avoids local optima traps that plague traditional optimization methods, while the stretching function modifications enhance the algorithm’s global search capabilities. The framework comprises four distinct algorithmic variants: a baseline SWARM PSO with stretching algorithm, and three clustering-enhanced extensions incorporating Hierarchical, K-means, and DBSCAN techniques. These clustering enhancements strategically group assets based on risk–return characteristics to improve portfolio diversification and risk management. Implementation in R enables comprehensive analysis of portfolio weight allocation patterns and diversification metrics across varying market structures. Empirical validation using daily price data from six major international stock market indices spanning January 2020 to December 2025 demonstrates the framework’s generalization capability in constructing buy-and-hold investment portfolios. The results reveal significant market-specific algorithmic effectiveness, with K-means variants achieving competitive efficacy in Eurostoxx and Belgian markets, DBSCAN demonstrating strong effectiveness in Chinese equity markets, Hierarchical clustering showing robust results in Indian market conditions, and the baseline SWARM algorithm exhibiting relative efficiency in French and Danish indices. Performance evaluation encompasses comprehensive risk-adjusted metrics, including Portfolio Return, Volatility, Sharpe Ratio, Calmar Ratio, and Value at Risk, providing portfolio managers with an adaptive, market-responsive optimization toolkit. Full article

Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) represents a major advancement in remote sensing for terrestrial observation, substantially improving the capability to map vegetation structural parameters. However, spatial heterogeneity poses significant challenges to data accuracy. To evaluate the performance of ICESat-2 and improve its inversion accuracy, this study used airborne LiDAR data to validate ICESat-2 terrain and canopy height measurements in boreal forests of Alberta, Canada, and in three tropical rainforest regions—Costa Rica, French Guiana, and Gabon. Machine-learning approaches were further applied to calibrate ICESat-2 canopy height estimates. Our results show that the uncalibrated ICESat-2 data exhibit strong consistency in boreal forests, with higher accuracy under snow-covered nighttime conditions (terrain error < 1 m, canopy height error of 3.19 m). In contrast, the uncertainties in tropical rainforests are considerably larger, with terrain errors of 3–7 m and canopy height errors of 5–7 m. After calibration, XGBoost reduced canopy height error by 0.84 m in boreal forests, whereas Random Forest calibration improved canopy height accuracy by 1.09 m in tropical regions. Overall, our findings provide additional scientific evidence supporting the reliability of ICESat-2 measurements and substantially enhance the accuracy of satellite-based canopy height estimation. Full article

Accurate classification of precipitation phase (liquid, mixed, or solid) is essential in high mountain environments, where rapid changes in elevation can lead to abrupt phase transitions over short distances, significantly affecting hydro-meteorological, ecological, and socio-economic activities. However, most existing classification schemes have not been evaluated over long periods using real observational data, but mainly through simulations. This study addresses this gap by introducing a new methodology based on X-band polarimetric radar and by validating it against real precipitation events over an extended time period. The machine learning model is trained and tested using a four-year dataset including X-band radar, Micro Rain Radar, disdrometer, and temperature profile data from the Grenoble region (French Alps). To improve the classification accuracy, three temperature profile sources were tested: lapse rates obtained from automatic weather stations, interpolation of the temperature profile from the freezing level detected by the Micro Rain Radar, and temperature profiles from the operational AROME model forecast. Three different phase classification schemes were tested: two existing schemes based on fuzzy-logic, and the new method based on random forest. Results show that the random forest method, trained with radar polarimetric variables, AROME temperature profiles, and target labels derived from Micro Rain Radar observations, achieves the highest accuracy. Despite the overall good classification results, limitations persist in identifying mixed-phase precipitation due to its transitional nature and vertical variability. Feature importance analysis indicates that temperature is the most influential variable in the classification scheme, followed by reflectivity factor measured in the horizontal plane ($Z_e)$ and differential reflectivity ($Z_{dr})$. This methodology demonstrates the potential of combining machine learning techniques with multi-instrument observations to improve hydrometeor classification in complex terrain. The approach offers valuable insights for operational forecasting, water resource management, and climate impact assessments in mountainous regions. Full article

Objective: To validate a French version of the Empty Nose Syndrome 6-Item Questionnaire (Fr-ENS6Q). Methods: Thirty-three patients with a diagnosis of empty nose syndrome (ENS) and 50 healthy individuals completed the Fr-ENS6Q and the French versions of the Sinonasal Outcome Tool-22 (Fr-SNOT22) and Nasal Obstruction Symptom Evaluation (Fr-NOSE). The internal consistency (Cronbach-α), test–retest reliability (intraclass correlation coefficient (ICC)), and external validity (correlations between Fr-ENS6Q, Fr-SNOT-22, and Fr-NOSE) were evaluated. The threshold of Fr-ENS6Q for suspecting ENS diagnosis was calculated using the receiver operating characteristic (ROC) curve. Depression and anxiety were investigated with the General Anxiety Disorder-7 (GAD-7) and the Patient Health Questionnaire-9 (PHQ-9). Results: The Cronbach-α of Fr-ENS6Q was 0.81, indicating an adequate internal consistency. Patients reported higher ENS6Q scores than controls, indicating high internal validity. The Fr-ENS6Q was significantly correlated with the Fr-NOSE (r_s = 0.56; p = 0.001) and Fr-SNOT22 (r_s = 0.67; p = 0.001), which supports a high external validity. The test–retest reliability was high for ENS6Q scores (ICC = 0.895; 95%CI: 0.763–0.971). An ENS diagnosis can be suspected with an Fr-ENS6Q cutoff ≥ 12 for French-speaking ENS patients. Depression was detected in 97% of patients, with 84.9% requiring further assessment. Patients reported missing symptoms in the ENS6Q, such as sleep disturbance related to nasal airflow disorder, face/eye/dental pain, and fresh nasal sensations during airflow. Conclusions: The Fr-ENS6Q is a valid and reliable patient-reported outcome questionnaire for assessing the severity of ENS symptoms in the French-speaking population. Further improvements to the ENS6Q could consider the inclusion of symptoms that are not present in the current ENS6Q. Full article

Background: Ghosting, the sudden cessation of communication without explanation, is a growing phenomenon in digital interactions. Objective: This study translated and validated the Ghosting Questionnaire (GHOST) into French to address the lack of a culturally appropriate tool for French-speaking populations. Methods: Using a cross-sectional design, we followed rigorous translation protocols, including forward and back translation, expert review, and pilot testing. A diverse group of 274 French-speaking adults participated in a multisite study in Bahrain and Tunisia by completing the French language GHOST and the Romantic Ghosting Scale (RG-C). Results: Psychometric analyses revealed strong reliability (Cronbach’s α = 0.880, test–retest ICC = 0.920) and a unidimensional structure (CFI = 0.963, RMSEA = 0.058). Convergent validity was confirmed through significant correlations with RG-C subscales: Role of Aggressor (r = 0.899), Role of Victim (r = 0.829), and Emotional Impact (r = 0.632, all p < 0.001). Item response theory analysis demonstrated proper category functioning and meaningful item hierarchy across ghosting severity levels. Conclusions: The French GHOST offers a robust instrument for researchers and clinicians to explore ghosting’s psychological impacts in French-speaking communities. Full article

This study proposes a lightweight framework for transferring pretrained land cover classification architectures without additional training. The system utilizes French IGN imagery and Korean UAV and aerial imagery. It employs FLAIR U-Net models with ResNet34 and MiTB5 backbones, along with the AI-HUB U-Net. The implementation consists of four sequential stages. First, we perform class mapping between heterogeneous schemes and unify coordinate systems. Second, a quadratic polynomial regression equation is constructed. This formula uses multispectral band statistics as hyperparameters and class-wise IoU as the dependent variable. Third, optimal parameters are identified using the stationary point condition of Response Surface Methodology (RSM). Fourth, the final land cover map is generated by fusing class-wise optimal results at the pixel level. Experimental results show that optimization is typically completed within 60 inferences. This procedure achieves IoU improvements of up to 67.86 percentage points compared to the baseline. For automated application, these optimized values from a source domain are successfully transferred to target areas. This includes transfers between high-altitude mountainous and low-lying coastal territories via proportional mapping. This capability demonstrates cross-regional and cross-platform generalization between ResNet34 and MiTB5. Statistical validation confirmed that the performance surface followed a systematic quadratic response. Adjusted R² values ranged from 0.706 to 0.999, with all p-values below 0.001. Consequently, the performance function is universally applicable across diverse geographic zones, spectral distributions, spatial resolutions, sensors, neural networks, and land cover classes. This approach achieves more than a 4000-fold reduction in computational resources compared to full model training, using only 32 to 150 tiles. Furthermore, the proposed technique demonstrates 10–74× superior resource efficiency (resource consumption per unit error reduction) over prior transfer learning schemes. Finally, this study presents a practical solution for inference and performance optimization of land cover semantic segmentation on standard commodity CPUs, while maintaining equivalent or superior IoU. Full article

Background/Objectives: While previous studies have evaluated AI algorithms for cervical spine fracture (CSFx) detection on CT, many have lacked validation on diverse, multinational datasets or have focused primarily on overall case-level classification This study aimed to evaluate the performance of an AI application for acute CSFx detection in case-level classification, fracture localization, and spinal level labeling on multicenter, multinational, and multivendor CT data. Methods: Non-enhanced CTs were retrospectively collected from a U.S. teleradiology company, a French teleradiology company, and a U.S. university hospital. Four radiologists independently labeled the presence and location (including the spinal level) of acute CSFx to establish the reference standard. Per-case diagnostic performance, per-bounding box positive predictive value (PPV) for localization, and overall agreement of cervical vertebral level labeling of the AI were assessed. Results: A total of 155 patients (60.6 years ± 21.2 years, 104 men) with acute CSFx and 173 patients (51.9 years ± 22.7 years, 91 men) without acute CSFx were evaluated. Data were acquired using scanners from five manufacturers. For acute CSFx diagnosis, the AI achieved a per-case sensitivity of 90.3%, a specificity of 91.9%, an accuracy of 91.2%, an area under the receiver operating characteristic curve (AUC) of 0.91, and Matthews correlation coefficient of 0.82. Among 192 bounding boxes representing acute CSFx generated for 154 positive cases by the AI, 162 were true positives (per-bounding box PPV, 84.4%). Of the 186 bounding boxes for which the AI displayed cervical spinal level, 181 were labeled correctly (overall agreement, 97.3%). Conclusions: The AI application for detecting acute CSFx demonstrated high diagnostic performance on multicenter, multinational, and multivendor data, with high performance in fracture localization and spinal level labeling. Full article

Traditional assessment methods in dental education, such as written tests and multiple-choice questions, primarily measure theoretical knowledge but inadequately evaluate clinical and interpersonal competencies. The Objective Structured Clinical Examination (OSCE), recognized globally for its validity and reliability, addresses these limitations and is widely adopted in medical curricula; however, its implementation in dental education remains poorly undocumented. This study explored perceptions of OSCE compared to traditional formats within the Clinical and Therapeutic Synthesis Certificate (CTSC) at Toulouse Faculty of Health during its first OSCE-based session in January 2019. Eighty-four fifth-year students and eight faculty assessors completed a validated questionnaire assessing fairness, educational value, and stress levels. Results indicated that OSCE was perceived as covering diverse clinical skills (86%) and offering authentic scenarios (83%). Despite being stressful (76%), OSCE was considered the fairest (60% vs. MCQ 31%, WT 41%; p < 0.001) and most educational (77% vs. MCQ 17%, WT 31%). Eighty-three percent of students recommended its broader use, while assessors unanimously endorsed its fairness and utility. Both groups highlighted its formative potential. These findings support OSCE’s integration into French dental curricula to strengthen competency-based assessment and enhance clinical skill evaluation. Full article

The marine environment in the South Pacific Island Countries (SPICs) is sensitive and vulnerable to climate change. While large-scale changes in this region are well-documented, national-scale analyses that address management needs remain limited. This study evaluated the performance of satellite-derived datasets—including sea surface temperature (SST), sea surface salinity (SSS), Secchi disk depth (SDD), chlorophyll-a (Chl-a), net primary production (NPP), and sea level anomaly (SLA)—against in situ observations, and analyzed their spatial and temporal variability across 12 national Exclusive Economic Zones (EEZs) during 1998–2023. Validation results presented that current satellite datasets could provide applicable information for EEZ-scale analyses. In the past decades, the SPICs experienced a general increase in SST and SLA, accompanied by marked within-EEZ heterogeneity in Chl-a and NPP variations, with Papua New Guinea exhibiting the largest within-EEZ inter-annual variability. In addition to monitoring, satellite data would help to constrain the uncertainty of CMIP6 results in the SPICs, subject to the accuracy of specific products. By 2100, Nauru might experience the most vulnerable EEZ, while the marine environment in the French Polynesian EEZ can keep relatively stable among all 12 EEZs. Meanwhile, CMIP6 projections in the Southeastern EEZs are more sensitive to satellite-based constraints, showing pronounced adjustments. Our results demonstrate the potential of combining validated satellite data with CMIP6 models to provide national-scale decision support for climate adaptation and marine resource management in the SPICs. Full article