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Definition and diagnostic criteria of clinical obesity
No full textInternational audienceCurrent BMI-based measures of obesity can both underestimate and overestimate adiposity and provide inadequate information about health at the individual level, which undermines medically-sound approaches to health care and policy. This Commission sought to define clinical obesity as a condition of illness that, akin to the notion of chronic disease in other medical specialties, directly results from the effect of excess adiposity on the function of organs and tissues. The specific aim of the Commission was to establish objective criteria for disease diagnosis, aiding clinical decision making and prioritisation of therapeutic interventions and public health strategies. To this end, a group of 58 experts—representing multiple medical specialties and countries—discussed available evidence and participated in a consensus development process. Among these commissioners were people with lived experience of obesity to ensure consideration of patients’ perspectives. The Commission defines obesity as a condition characterised by excess adiposity, with or without abnormal distribution or function of adipose tissue, and with causes that are multifactorial and still incompletely understood. We define clinical obesity as a chronic, systemic illness characterised by alterations in the function of tissues, organs, the entire individual, or a combination thereof, due to excess adiposity. Clinical obesity can lead to severe end-organ damage, causing life-altering and potentially life-threatening complications (eg, heart attack, stroke, and renal failure). We define preclinical obesity as a state of excess adiposity with preserved function of other tissues and organs and a varying, but generally increased, risk of developing clinical obesity and several other non-communicable diseases (eg, type 2 diabetes, cardiovascular disease, certain types of cancer, and mental disorders). Although the risk of mortality and obesity-associated diseases can rise as a continuum across increasing levels of fat mass, we differentiate between preclinical and clinical obesity (ie, health vs illness) for clinical and policy-related purposes. We recommend that BMI should be used only as a surrogate measure of health risk at a population level, for epidemiological studies, or for screening purposes, rather than as an individual measure of health. Excess adiposity should be confirmed by either direct measurement of body fat, where available, or at least one anthropometric criterion (eg, waist circumference, waist-to-hip ratio, or waist-to-height ratio) in addition to BMI, using validated methods and cutoff points appropriate to age, gender, and ethnicity. In people with very high BMI (ie, >40 kg/m2), however, excess adiposity can pragmatically be assumed, and no further confirmation is required. We also recommend that people with confirmed obesity status (ie, excess adiposity with or without abnormal organ or tissue function) should be assessed for clinical obesity. The diagnosis of clinical obesity requires one or both of the following main criteria: evidence of reduced organ or tissue function due to obesity (ie, signs, symptoms, or diagnostic tests showing abnormalities in the function of one or more tissue or organ system); or substantial, age-adjusted limitations of daily activities reflecting the specific effect of obesity on mobility, other basic activities of daily living (eg, bathing, dressing, toileting, continence, and eating), or both. People with clinical obesity should receive timely, evidence-based treatment, with the aim to induce improvement (or remission, when possible) of clinical manifestations of obesity and prevent progression to end-organ damage. People with preclinical obesity should undergo evidence-based health counselling, monitoring of their health status over time, and, when applicable, appropriate intervention to reduce risk of developing clinical obesity and other obesity-related diseases, as appropriate for the level of individual health risk. Policy makers and health authorities should ensure adequate and equitable access to available evidence-based treatments for individuals with clinical obesity, as appropriate for people with a chronic and potentially life-threatening illness. Public health strategies to reduce the incidence and prevalence of obesity at population levels must be based on current scientific evidence, rather than unproven assumptions that blame individual responsibility for the development of obesity. Weight-based bias and stigma are major obstacles in efforts to effectively prevent and treat obesity; health-care professionals and policy makers should receive proper training to address this important issue of obesity. All recommendations presented in this Commission have been agreed with the highest level of consensus among the commissioners (grade of agreement 90–100%) and have been endorsed by 76 organisations worldwide, including scientific societies and patient advocacy groups
Design and biological evaluation of bioinspired Lipidic AlkynylCarbinols for anti-TB drug discovery
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BrainAGE latent representation clustering is associated with longitudinal disease progression in early-onset Alzheimer's disease.
No full textInternational audienceIntroduction: Early-onset Alzheimer's disease (EOAD) population is a clinically, genetically and pathologically heterogeneous condition. Identifying biomarkers related to disease progression is crucial for advancing clinical trials and improving therapeutic strategies. This study aims to differentiate EOAD patients with varying rates of progression using Brain Age Gap Estimation (BrainAGE)-based clustering algorithm applied to structural magnetic resonance images (MRI).Methods: A retrospective analysis of a longitudinal cohort consisting of 142 participants who met the criteria for early-onset probable Alzheimer's disease was conducted. Participants were assessed clinically, neuropsychologically and with structural MRI at baseline and annually for 6 years. A Brain Age Gap Estimation (BrainAGE) deep learning model pre-trained on 3,227 3D T1-weighted MRI of healthy subjects was used to extract encoded MRI representations at baseline. Then, k-means clustering was performed on these encoded representations to stratify the population. The resulting clusters were then analyzed for disease severity, cognitive phenotype and brain volumes at baseline and longitudinally.Results: The optimal number of clusters was determined to be 2. Clusters differed significantly in BrainAGE scores (5.44 [± 8] years vs 15.25 [± 5 years], p < 0.001). The high BrainAGE cluster was associated with older age (p = 0.001) and higher proportion of female patients (p = 0.005), as well as greater disease severity based on Mini Mental State Examination (MMSE) scores (19.32 [±4.62] vs 14.14 [±6.93], p < 0.001) and gray matter volume (0.35 [±0.03] vs 0.32 [±0.02], p < 0.001). Longitudinal analyses revealed significant differences in disease progression (MMSE decline of -2.35 [±0.15] pts/year vs -3.02 [±0.25] pts/year, p = 0.02; CDR 1.58 [±0.10] pts/year vs 1.99 [±0.16] pts/year, p = 0.03).Conclusion: K-means clustering of BrainAGE encoded representations stratified EOAD patients based on varying rates of disease progression. These findings underscore the potential of using BrainAGE as a biomarker for better understanding and managing EOAD
Impact of Environmental Factors on the Distribution Patterns of Nephropathia Epidemica Cases in Western Europe
No full textInternational audienceBackground: Environmental factors, such as fluctuations of climatic conditions and land cover, play a pivotal role in driving infectious disease epidemics, particularly those originating from wildlife reservoirs. Orthohantavirus puumalaense, hosted by bank voles in Europe, is the causative agent of a form of hemorrhagic fever and renal syndrome called nephropathia epidemica. Despite two decades of consistent presence in western Europe, nephropathia epidemica outbreaks still pose challenges due to localized periodic occurrences and a lack of understanding of its environmental drivers.Objective: Our study aims to bridge this gap by investigating the specific ecological and climatic factors influencing nephropathia epidemica outbreaks in western Europe.Methods: We compiled monthly, serologically confirmed nephropathia epidemica case data obtained from public health authorities in Belgium, France, Germany, and the Netherlands for the period 2004-2012. Cases were georeferenced to the finest available administrative unit. We selected 28 covariates, including climatic variables, land cover, tree species distributions, and human population, and implemented a Bayesian spatiotemporal model using integrated nested Laplace approximation (INLA) with zero-inflated Poisson distribution, including fixed effects and spatial, temporal, and nonstructured random effects.Results: We identified key triggers for nephropathia epidemica outbreaks, particularly climate-mediated changes in all seasons up to 2 years before, favoring tree mast impacting bank vole abundance. Our findings revealed that while land-cover factors mostly determine hotspot locations, climatic fluctuation patterns rather tend to modulate outbreak intensity.Discussion: Crucially, our model allows for the generation of yearly maps showcasing nephropathia epidemica incidence and risk factors, aiding in public health preparedness against climate change-induced disease emergence. This work represents a significant step toward developing targeted forecasting tools for Orthohantavirus puumalaense outbreaks, offering valuable insights for epidemic control strategies
P-112 Predictive model of good quality blastocyst development based on static image of fresh mature oocytes
No full textMeeting abstractInternational audienceStudy question Can good quality blastocyst development be predicted from a static images of fresh mature oocytes? Summary answer At deep learning model can be used to predict the blastocyst outcome with the 0.694 confidence based on a static image of a mature oocyte. What is known already The application of deep learning in in vitro fertilization(IVF) laboratories has been a rapidly evolving area of research, aimed at improving the efficiency, accuracy, and outcomes of IVF treatments. One of the most significant applications of deep learning in IVF laboratories is the grading and selection of embryos for implantation. But recently, oocyte is gaining more interest in the context of fertility preservation and oocyte donation. The development of reliable models to predict blastocyst development from a single image of metaphase II oocyte may improve the counselling and management of fertility preservation cycles as well as oocyte donors and recipients. Study design, size, duration This study aimed to develop a model for good-quality blastocyst development prediction using non-invasive imaging of fresh metaphase II oocytes. A dataset of 747 oocyte images with an imbalanced class distribution (70% blastocyst, 30% non-blastocyst) was used. Images collected from one clinic required patient-wise data separation to ensure meaningful evaluation. Preprocessing included grayscale conversion, normalization to [0,1], resizing to 224 × 224pixels, and cross-validation with a stratified group k-fold split to maintain class balance and patient separation across folds Participants/materials, setting, methods The designed machine learning model is based on a modified pre-trained VGG16-architecture to benefit from transfer learning. The model was trained to distinguish oocytes likely to develop into blastocysts. The imbalance class distribution was addressed using Focal Loss, enabling the model to prioritize harder-to-classify images while balancing minority class gradients. The training involved two phases: classifier-only training (three epochs) and end-to-end fine-tuning (ten epochs). Augmentation techniques like image rotations, zoom, and intensity adjustments enhanced robustness. Main results and the role of chance The preprocessing pipeline included grayscale conversion, normalization, and cross-validation with a stratified group k-fold split to ensure robust evaluation and prevent data leakage. Given the dataset’s small size, a data-centric approach was adopted, focusing on collecting high-quality oocyte images and cleaning to remove noise and artifacts, maximizing data utility and clinical relevance. The use of Focal Loss further addressed class imbalance, balancing sensitivity and specificity while prioritizing harder-to-classify cases. Building on these mentioned methods, the model demonstrated strong predictive performance. Indeed, the model achieved an AUC-ROC of 0.694, demonstrating good performance in predicting good quality blastocyst development (Grade A and B based on Gardner grading system). Sensitivity and specificity were balanced at 0.65 and 0.672, respectively, reflecting the model’s ability to handle the class imbalance. The negative predictive value (NPV) (non-blastocyst development) was 0.382, while the positive predictive value (PPV) (blastocyst development) reached 0.86, indicating superior performance in identifying good quality blastocyst development. These results were validated on an independent test set including 224 images with patient-wise separation, ensuring clinical relevance and mitigating potential data leakage. The balanced performance across sensitivity and specificity metrics supports the model’s potential as a non-invasive predicting support tool for embryologists and clinicians. Limitations, reasons for caution The dataset included oocytes that developed into blastocysts and oocytes that did not reach the blastocyst stage, excluding other developmental outcomes. Data from a single clinic limits generalizability. Additionally, the relatively low PPV underscores the need for larger, multi-clinic datasets to validate the model’s robustness and clinical applicability. Wider implications of the findings This study highlights the potential of AI in non-invasive oocyte quality evaluation, supporting professionals in counselling fertility preservation and IVF patients. By predicting good quality blastocyst development, the model is expected to reduce subjective assessment and improve the prediction of success rates. Expanding dataset will enhance clinical impact and generalizability. Trial registration number N
Host–parasite interactions after in vitro infection of human macrophages by Leishmania major: Dual analysis of microRNA and mRNA profiles reveals regulation of key processes through time kinetics
No full textInternational audienceMicro-RNAs are a class of small non-coding ribonucleic acids that concomitantly regulate the expression of tens to hundreds of genes. To reduce the host's defense, Leishmania parasites hijack the cellular functions of their macrophage's targets through gene expression regulation. Only few studies have attempted to correlate miRNAs and mRNAs expressions within the same samples in the context of cellular parasitism.In this study, the profiling of human macrophages, in vitro infected by L. major parasites, was performed at both the mRNA transcriptomic level and the expression of a set of 365 miRNAs, and we correlated their expressions in search for a common molecular signature.Both mRNA and miRNA profiles were monitored during the first 24 h post-infection to capture potential time-dependent fluctuations. We then cross-correlated the cellular biological processes and the pathways associated to the predicted targets of miRNAs and to the differentially expressed mRNAs at all time points of infection on the same samples.Besides revealing the classical activation of immune signaling pathways, the mRNA-micro-RNAs correlation study highlighted other common regulatory inflammatory biological processes, allowing identification of rapidly modulated pathways, and bringing further evidence on the early molecular cross talk that take place between Leishmania and infected cells
Summary of taxonomy changes ratified by the International Committee on Taxonomy of Viruses from the Plant Viruses Subcommittee, 2025
No full textInternational audienceIn March 2025, following the annual International Committee on Taxonomy of Viruses (ICTV) ratification vote, newly proposed taxa were added to those under the mandate of the Plant Viruses Subcommittee. In brief, 1 new order, 3 new families, 6 new genera, 2 new subgenera and 206 new species were created. Some taxa were reorganized. Genus Cytorhabdovirus in the family Rhabdoviridae was abolished and its taxa were redistributed into three new genera Alphacytorhabdovirus , Betacytorhabdovirus and Gammacytorhabdovirus . Genus Waikavirus in the family Secoviridae was reorganized into two subgenera ( Actinidivirus and Ritunrivirus ). One family and four previously unaffiliated genera were moved to the newly established order Tombendovirales . Twelve species not assigned to a genus were abolished. To comply with the ICTV mandate of a binomial format for virus species, eight species were renamed. Demarcation criteria in the absence of biological information were defined in the genus Ilarvirus (family Bromoviridae ). This article presents the updated taxonomy put forth by the Plant Viruses Subcommittee and ratified by the ICTV
Characterisation of the Novel HLA-B*35:627 Allele by Sequencing-Based Typing.
No full textInternational audienceHLA-B*35:627 differs from HLA-B*35:01:01:01 by one nucleotide substitution in codon 80 in exon 2
One step toward the understanding of potential albumin benefits in septic patients
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Dynamiques éco-évolutives des interactions hôte-parasite dans des environnements spatialement structurés
No full textWhy do parasites cause more or less harm to their hosts?Why some epidemics become pandemicswhile others remain localized?It is well known that parasites andinfectious diseases are long-standingenemies of humanity, and understandingtheir dynamics of emergence,spread, and evolution is a key leverfor managing epidemics (whether animalor plant) through public policies.Among all the environmental factorsthat shape the dynamics of parasitespread and evolution, spatial structureappears to be a key factor. Thethesis seeks to provide some answersto these questions through thedevelopment of theoretical modelsstudying the evolution and coevolutionof hosts and parasites in spatiallystructured populations. The modelsdeveloped describe populations subjectto hierarchical levels of organizationand highlight the selection pressuresacting at each of these levels.These selection pressures may act incontradictory directions, favoring thepersistence of parasites in environmentswhere contact between individualsor populations is highly restricted.However, when interactionsbetween populations intensify, selectionfavors local competition, generallyleading to the emergence ofmore virulent parasites. Furthermore,the models in the thesis identify thatselection towards locally high prevalencespromotes the spread of newparasitic strains at broader scales,which is supported by experimentalstudies.The models developed in this thesisare built in the scope of generalityand should be easily adaptable to avariety of real-world systems. However,the predictions made rely on specificassumptions, notably the considerationof fully connected populationnetworks, which simplifies theirmathematical treatment. Consideringmore realistic networks and interactionsspecific to documented biologicalsystems would ultimately allowfor more refined predictions and helpquantify the impact of interventionmeasures in real populations.Pourquoi les parasitesfont-ils plus ou moins de de malà leurs hôtes? Qu'est-ce qui déterminele fait que certaines épidémiesdeviennent pandémiques,quand d'autres restent localisées?S'il est notoire que les parasiteset les maladies infectieuses constituentdes ennemis de longue datede l'Humanité, comprendre leurs dynamiquesd'émergence, de propagationet d'évolution constitue un levierimportant pour la gestion des épidémies(animales ou végétales) par despolitiques publiques. Parmis tous lesfacteurs environnementaux qui façonnentles dynamiques de propagationet d'évolution des parasites,la structure spatiale apparaît commeun facteur clé. La thèse cherche àapporter des éléments de réponseà ces questions au moyen de modèlesthéoriques d'évolution et de coévolutiondes hôtes et de leurs parasitesdans des populations structuréesdans l'espace. Les modèlesainsi développés décrivent des populationssoumises à des niveauxd'organisation hiérarchisés et mettenten lumière les pressions de sélections'exerçant à chacun de ces niveaux.Ces pressions de sélecion agissentdans des directions contradictoires,et favorisent la persistence des parasitesdans des environnements oules contacts entre individus ou populationssont fortement limités. En revanche,lorsque les interactions entrepopulations s'intensifient, la sélectionfavorise alors la compétition locale,faisant émerger des parasites généralementplus virulents. Par ailleurs,les modèles de la thèse identifientque la sélection vers des prévalenceslocalement élevées favorisent la propagationde nouvelles souches parasitairesà des échelles plus larges, cequi semble confirmé par des résultatsexpérimentaux.Les modèles développés au cours dela thèse se veulent très généraux, etdevraient être facilement adaptablesà une diversité de systèmes réels.Toutefois, les prédictions réalisées reponsentsur des hypothèses spécifiques,notamment sur la considérationsde réseaux de populations pleinementconnectés, ce qui en facilitele traitement mathématique. Laconsidération de réseaux réalistes etd'interactions spécifiques à des systèmesbiologiques documentés permettraità terme de formuler desprédictions plus fines, et de quantifierl'impact de mesures d'interventiondans des populations réelles