Portail HAL de l'Université Picardie Jules Verne
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Dual AAV gene therapy achieves recovery of hearing and auditory processing in a DFNB16 mouse model
No full textInternational audienceBackgroundDFNB16, the second most common genetic cause of hearing loss, is caused by mutations of the STRC gene encoding stereocilin, a protein essential for the effective functioning of outer hair cells (OHCs) as cochlear amplifiers. Strc−/− mice, which lack stereocilin, display severe to profound deafness and constitute a relevant preclinical model for DFNB16.MethodsUsing Strc−/− mice, we developed a gene therapy strategy based on the use of dual AAV9-PHP.eB vectors to deliver the full-length Strc cDNA. Therapeutic efficacy was assessed by evaluating stereocilin expression, OHC bundle architecture, and their attachment to the tectorial membrane, together with functional recovery using distortion product otoacoustic emissions (DPOAEs), auditory brainstem responses (ABR) measurements and Go/No-Go behavioral testing with psychometric analysis.ResultsDual-AAV–mediated Strc gene delivery restored stereocilin expression, OHC bundle architecture and their attachment to the tectorial membrane, leading to the recovery of cochlear amplification and hearing to near normal thresholds, as confirmed by distortion product otoacoustic emission (DPOAE) and auditory brainstem response measurements. Behavioural assessment showed that treated Strc−/− mice regained normal frequency discrimination, indicating a restoration of higher-order auditory processing, up to 100 days post-treatment.ConclusionThese findings provide the first proof-of-principle that peripheral gene therapy can restore OHC function, cochlear amplification and central auditory perception in a DFNB16 model
Hematologic Landscape of Adult Patients With Diamond‐Blackfan Anemia Syndrome
No full textInternational audienceInformation about Diamond‐Blackfan anemia syndrome (DBAS), a ribosomopathy associated with anemia, congenital anomalies and cancer predisposition is limited in adults. Using the French DBAS registry, 235 adult patients with DBAS were analyzed for hematological outcomes. At last follow‐up, anemia, neutropenia, thrombocytopenia, and pancytopenia affected respectively 78%, 31%, 15%, and 11% of the patients. There was no severe aplastic anemia outside of clonal evolution. Among patients without DBAS‐specific treatment (e.g., steroids or red blood cell transfusions), the incidence of anemia, neutropenia, and thrombocytopenia was 52%, 35%, and 10%, highlighting that treatment independence does not mean hematologic remission. Four patients developed myelodysplastic syndrome (MDS) or acute myeloid leukemia (AML), all associated with poor‐risk features and dismal outcomes. Observed to expected ratios were 155 for MDS and 55.4 for AML, confirming a major risk‐excess despite stringent criteria for MDS diagnosis. With a median follow‐up of 32.2 years (IQR 26–44), overall survival (OS) was 98.0% (95% CI, 95.8–100), 90.6% (95% CI, 84.2–97.5), and 70.7% (95% CI, 57.3–87.2) at 30, 40, and 50 years respectively. Solid and hematologic cancers were the main cause of death. This study demonstrates, in a large cohort of adults with DBAS, that cytopenias beyond anemia are frequent and persistent. Myeloid neoplasms occur with a high incidence and dismal outcomes. These findings highlight the need for risk stratification, tailored surveillance, and optimized therapeutic strategies in this vulnerable population
Understory plant indicator values poorly perform at monitoring temporal changes in French forest soil chemical properties
No full textInternational audienceUnderstory plant communities are widely used to infer soil conditions through species indicator values (IVs), scores reflecting species’ ecological preferences for factors such as soil acidity, moisture, or nutrient levels. While their reliability to describe soil conditions along spatial gradients is well established, their ability to capture temporal changes in soil chemistry remains largely untested at both temporal and geographical scales. We combined 26 years of vegetation monitoring with two French national soil surveys (1993–1997; 2007–2012) across 102 permanent forest plots to assess the reliability of community soil indices (CSIs) as bioindicators of soil pH, carbon-to-nitrogen ratio (C/N), and extractable phosphorus. CSIs were computed by averaging species IVs for each plot and survey. Temporal dynamics of measured soil properties and CSIs were analyzed using mixed-effects models at the national scale, while local trends were estimated separately for each plot to directly compare measured and vegetation-inferred changes. CSIs showed strong spatial correlations with measured pH and C/N, but weaker relationships for phosphorus. In contrast, no significant coupling was found between temporal changes in CSIs and measured soil parameters, with frequent mismatches in both direction and magnitude at the plot level. Measured soil dynamics were mainly driven by initial edaphic conditions and stand age, whereas CSI dynamics responded primarily to canopy openness and anthropogenic disturbances such tree harvesting. These findings confirm the value of IVs for spatial bioindication but highlight their limited sensitivity for detecting long-term soil changes, especially where soil changes remain modest
EUROPEAN MUTUAL BANKS AT A CROSSROADS: FACING CLIMATE CHALLENGES AND TRADITIONAL BANKS
No full textMutual banks hold a unique position in the European financial landscape, rooted in principles of solidarity and democratic governance. Despite their strong local ties and social mission, they face significant challenges in sustainability, particularly in transitioning to a low-carbon economy. This paper traces the historical evolution of mutual banking, analyzes the reasons behind their lag in decarbonization compared to traditional banks, and offers concrete recommendations to strengthen their climate engagement. The study highlights the sector's diverse climate performance through comparative analysis and the use of data science tools, emphasizing the need for targeted institutional reforms to accelerate the ecological transition.</div
Relationship between amyloid choroidopathy and neurological involvement severity scores in transthyretin amyloidosis
No full textInternational audienceBackground: Transthyretin amyloidosis (ATTR) is a disorder characterized by amyloid fibril deposits in various tissues, leading to dysfunction of one or multiple organs. Ocular manifestations include keratoconjunctivitis, secondary glaucoma, vitreous deposits, and amyloid choroidopathy. This study aims to describe the angiographic findings in 40 patients with either hereditary (ATTRv) or wild-type (ATTRwt) transthyretin amyloidosis, analyze the 3-year progression of choroidal involvement, and correlate these findings with neurological and cardiac involvement.Patients and methods: We retrospectively analyzed 79 eyes of 40 patients who underwent a comprehensive ophthalmological examination, including fluorescein angiography and indocyanine green angiography (ICGA), between 2018 and 2021. A neurological assessment (SFN-SIQ questionnaire, PND, FAP, and NIS scores) and cardiology evaluation (NYHA, LVEF) were systematically performed.Results: A total of 25 men and 15 women with a mean age of 65.8±16.8 years were included. Seventy-five percent had ATTRv, mostly with the Val30Met (p.Val50Met) mutation (35%). In 61.1% of cases, hyperfluorescent lesions were observed on ICGA. Only Val30Met (p.Val50Met) patients exhibited firework-like patterns on ICGA. There was no progression of choroidal involvement over 3 years. Ninety-five percent of patients showed neurological involvement. Diffuse choroidal involvement is associated with higher SFN-SIQ questionnaire value (P=0.02), FAP score (P=0.017) and NIS score (P=0.046). In contrast, no relationship was found between cardiac involvement and choroidal involvement.Conclusion: ICG may be used as a marker for neural components of the choroid in this disease. A prospective longitudinal study is needed to evaluate the progression of hyperfluorescent lesions on ICGA in choroidal neuropathy under treatment over time in ATTR
Ferroelectric KNbO3 nanoplatelets for thermally driven pyrocatalytic hydrogen evolution and dye degradation
No full textInternational audienceDay and night shift-induced thermal cycling offers a promising route toward free energy for green hydrogen production and dye degradation. Pyroelectric materials make this possible by converting temperature fluctuations into electrical charges that drive water splitting catalytic reactions and produce hydrogen fuel. Herein, we demonstrate an efficient pyrocatalytic hydrogen evolution reaction and Rhodamine B (RhB) degradation using ferroelectric potassium niobate (KNbO3) perovskite nanoplatelets (KN-np) with an orthorhombic phase. Under thermal cycling between 20 and 50 degrees C, KN-np produced a high hydrogen yield of 680 mu mol & sdot;g-1 over 30 thermal cycles, with an average hydrogen generation rate of approximately 22.67 mu mol g-1 per thermal cycle. Besides, KN-np pyrocatalytic activity enabled efficient degradation of the RhB dye up to 84 % after only 16 cycles with a high kinetic rate constant of 0.11 per thermal cycle. Our findings show that the excellent pyroelectric properties of KN-np are at the origin of the catalytic activity enhancement. This work lays the foundation for the future design of pyroelectric materials for clean energy production and environmental remediation
A closed formula in the deformed affine nilHecke algebra
No full textInternational audienceThere is a q-deformation of the reflection representation of the affine symmetric group, which arises in the quantum geometric Satake equivalence, and in the study of the complex reflection groups . Demazure operators (often called divided difference operators) act on the polynomial ring of this deformed representation. When we prove an explicit closed formula for the scalar one obtains when applying a degree Demazure operator to a monomial of degree . We also prove a simpler formula for the scalar obtained after specializing q to a root of unity
Intelligence artificielle en hématologie biologique
No full textInternational audienceArtificial intelligence (AI) is reshaping laboratory hematology through machine learning and deep learning, enabling the analysis of complex, multimodal data from morphology, flow cytometry, and molecular biology. Algorithms can now classify blood cells, interpret high-dimensional cytometry panels, and integrate genetic information with human-level or superior performance. In cytology, AI supports automated identification of normal and malignant cells, particularly in acute leukemias. In flow cytometry, it automates gating, enhances detection of rare subsets, and improves reproducibility. In molecular biology, AI refines NGS and epigenomic data interpretation, advancing hematologic disease classification and prognosis. Yet, challenges remain regarding data quality, standardization, clinical validation, and model explainability. Future developments will rely on multimodal models combining cytologic, cytometric, molecular, and clinical data. These advances redefine the medical biologist’s role, emphasizing expertise, oversight, and ethical governance of AI in hematology.L’intelligence artificielle (IA) transforme profondément l’hématologie biologique en exploitant le machine learning et le deep learning pour analyser des données complexes issues de la morphologie, de la cytométrie en flux et de la biologie moléculaire. Les algorithmes permettent aujourd’hui de classer les cellules sanguines, d’interpréter des panels de cytométrie à haut débit et d’intégrer des données génétiques avec des performances proches, voire supérieures, à celles des experts humains. En cytologie, ils favorisent la reconnaissance automatisée des cellules normales ou pathologiques, notamment dans les leucémies aiguës. En cytométrie, l’IA automatise le gating, améliore la détection de populations rares et augmente la reproductibilité. En biologie moléculaire, elle optimise l’interprétation des profils NGS et épigénomiques, soutenant la classification pronostique des hémopathies. Malgré ces avancées, des défis persistent : qualité des données, standardisation, validation clinique et explicabilité. L’avenir réside dans des modèles multimodaux intégrant simultanément données cytologiques, cytométriques, moléculaires et cliniques. Ces innovations redéfinissent le rôle du biologiste médical, désormais acteur central de la supervision, de l’interprétation et de l’éthique de l’IA en hématologie
Hypothyroïdie congénitale et pollution : comment l’IA et le Big Data transforment la détection des signaux épidémiques
No full textInternational audienceObjective: To investigate how the integration of massive health data (French National Health Data System, SNDS, and data from Regional Neonatal Screening Centers, CRDN) and environmental data (Information System on Health and Environment for Water, SISE-Eaux, and the Air Quality Cartography from INERIS) can be used to explore associations between environmental pollution and congenital hypothyroidism (CH), and to enable early detection of large-scale epidemic signals. Methods: This synthesis is based on three peer-reviewed articles published by our team. The first study analyzes spatial and temporal trends in the incidence of congenital and acquired hypothyroidism in France from 2014 to 2019 using the SNDS, a medico-administrative database covering the entire French population. The second study, conducted at the regional level (Picardy), assesses the association between neonatal TSH concentrations and prenatal exposure to various air and water pollutants by linking neonatal screening data with local environmental data. The third study examines, at the national level, the relationship between prenatal exposure to specific pollutants (perchlorate, nitrates, particulate matter) and the incidence of CH, based on a national cohort derived from the SNDS. Results: The analyses reveal associations between several environmental pollutants and alterations in neonatal thyroid function. The combined use of SNDS and large-scale environmental data enables fine-grained detection of emerging epidemic signals across the country. Conclusions: Integrating massive health and environmental datasets opens new avenues for automated epidemi ological surveillance and for better understanding the environmental determinants of thyroid diseases.Objectif : Étudier comment l’intégration des données massives (Big Data) de santé (Système National des Données de Santé [SNDS] et données des Centres Régionaux de Dépistage Néonatal [CRDN]) et environnementales (Système d’Information en Santé-Environnement sur les Eaux, SISE-Eaux et Cartothèque de la Qualité de l’Air) permet d’explorer les associations entre pollution environnementale et hypothyroïdie congénitale, et de détecter précocement des signaux épidémiques à grande échelle. Méthodes : Ce travail de synthèse s’appuie sur trois études publiées par notre équipe. Le premier analyse les tendances spatiales et temporelles de l’incidence de l’hypothyroïdie congénitale et acquise en France entre 2014 et 2019 grâce au SNDS, base médico-administrative couvrant l’ensemble de la population française. Le second évalue, à l’échelle régionale (Picardie), l’association entre les concentrations de TSH néonatale et l’exposition prénatale à divers polluants de l’air et de l’eau, via un croisement entre données de dépistage néonatal et données environnementales locales. Le troisième examine, à l’échelle nationale, le lien entre exposition prénatale à certains polluants (perchlorate, nitrates, PM) et la survenue de l’hypothyroïdie congénitale à partir d’une cohorte issue du SNDS. Résultats : Les analyses mettent en évidence une association entre plusieurs polluants environnementaux et des anomalies de la fonction thyroïdienne néonatale. L’exploitation conjointe du SNDS et de données environnementales massives permet une détection fine de signaux épidémiques émergents sur l’ensemble du territoire. Conclusions : L’intégration des données massives de santé et d’environnement ouvre de nouvelles perspectives pour la surveillance épidémiologique automatisée et la compréhension des liens entre pollution et maladies thyroïdiennes
On the Limits of Alpine Plants: A Systematic Review of the Factors Behind Species' Elevational Range Limits
No full textInternational audienceUnderstanding the factors behind species' range limits is a fundamental objective in ecology. Recent research in alpine plant ecology has moved beyond the classical view that distributions are chiefly shaped by climate and competition. Specifically, broader sets of factors have been taken into account, comprising both biotic factors such as facilitation and herbivory as well as additional abiotic factors such as soil properties. However, an overview of the factors that have been identified and studied as important for elevational range limits of alpine plant species is lacking. In this systematic literature review, we synthesize evidence derived from 107 empirical studies on 226 vascular plant species occurring beyond elevational and latitudinal treelines. We find a persistent research focus on the upper elevational range limit (73% of the studies) and on the role of abiotic factors (54% of the studies), particularly temperature (36% of the studies), whereas research on inter‐ and intraspecific factors (40% and 25%, respectively), such as herbivory or phenology, remained comparatively rare. While temperature was clearly identified as a major factor influencing the upper range limit (29% of the studies), water availability (15% of the studies) was most commonly studied at the lower range limit. Even though a broad set of factors has been investigated, many potentially important factors remain poorly researched, such as the influence of gene flow and connectivity between populations, phenology and light (each only one or two studies). Our findings highlight the need to move beyond temperature and plant–plant interactions as factors influencing the elevational range limits of alpine plants and to integrate intraspecific (such as gene flow and adaptations) and edaphic factors more fully into future research. Improved methodological standardization and transparency and increased attention on lower range limits will be essential for explaining and predicting alpine plant responses under accelerating environmental change