Publications

AURA: A Multi-Modal Medical Agent for Understanding, Reasoning&Annotation
Nima Fathi
Amar Kumar
Tal Arbel
Recent advancements in Large Language Models (LLMs) have catalyzed a paradigm shift from static prediction systems to agentic AI agents capa… (voir plus)ble of reasoning, interacting with tools, and adapting to complex tasks. While LLM-based agentic systems have shown promise across many domains, their application to medical imaging remains in its infancy. In this work, we introduce AURA, the first visual linguistic explainability agent designed specifically for comprehensive analysis, explanation, and evaluation of medical images. By enabling dynamic interactions, contextual explanations, and hypothesis testing, AURA represents a significant advancement toward more transparent, adaptable, and clinically aligned AI systems. We highlight the promise of agentic AI in transforming medical image analysis from static predictions to interactive decision support. Leveraging Qwen-32B, an LLM-based architecture, AURA integrates a modular toolbox comprising: (i) a segmentation suite with phase grounding, pathology segmentation, and anatomy segmentation to localize clinically meaningful regions; (ii) a counterfactual image-generation module that supports reasoning through image-level explanations; and (iii) a set of evaluation tools including pixel-wise difference-map analysis, classification, and advanced state-of-the-art components to assess diagnostic relevance and visual interpretability.
2025-07-22
ArXiv (prépublication)
doi.org
arxiv.org
AURA: A Multi-Modal Medical Agent for Understanding, Reasoning&Annotation
Nima Fathi
Amar Kumar
Tal Arbel
Recent advancements in Large Language Models (LLMs) have catalyzed a paradigm shift from static prediction systems to agentic AI agents capa… (voir plus)ble of reasoning, interacting with tools, and adapting to complex tasks. While LLM-based agentic systems have shown promise across many domains, their application to medical imaging remains in its infancy. In this work, we introduce AURA, the first visual linguistic explainability agent designed specifically for comprehensive analysis, explanation, and evaluation of medical images. By enabling dynamic interactions, contextual explanations, and hypothesis testing, AURA represents a significant advancement toward more transparent, adaptable, and clinically aligned AI systems. We highlight the promise of agentic AI in transforming medical image analysis from static predictions to interactive decision support. Leveraging Qwen-32B, an LLM-based architecture, AURA integrates a modular toolbox comprising: (i) a segmentation suite with phase grounding, pathology segmentation, and anatomy segmentation to localize clinically meaningful regions; (ii) a counterfactual image-generation module that supports reasoning through image-level explanations; and (iii) a set of evaluation tools including pixel-wise difference-map analysis, classification, and advanced state-of-the-art components to assess diagnostic relevance and visual interpretability.
2025-07-22
ArXiv (prépublication)
arxiv.org
arxiv.org
Penalty Learning for Optimal Partitioning using Multilayer Perceptron
Tung L. Nguyen
Toby Dylan Hocking
Changepoint detection is a technique used to identify significant shifts in sequences and is widely used in fields such as finance, genomics… (voir plus), and medicine. To identify the changepoints, dynamic programming (DP) algorithms, particularly Optimal Partitioning (OP) family, are widely used. To control the changepoints count, these algorithms use a fixed penalty to penalize the changepoints presence. To predict the optimal value of that penalty, existing methods used simple models such as linear or tree-based, which may limit predictive performance. To address this issue, this study proposes using a multilayer perceptron (MLP) with a ReLU activation function to predict the penalty. The proposed model generates continuous predictions -- as opposed to the stepwise ones in tree-based models -- and handles non-linearity better than linear models. Experiments on large benchmark genomic datasets demonstrate that the proposed model improves accuracy and F1 score compared to existing models.
2025-07-22
Statistics and Computing (publié)
doi.org
arxiv.org
Pharmaco-nutraceutical improvement of the response to obeticholic acid with omega-3 polyunsaturated fatty acids.
Audrey-Anne Lavoie
Ariane Thérien
Anisia Silva
Emanuel Paré
Anna Ciešlak
William Gagnon
Clémence Desjardins
Mélanie Verreault
J. Trottier
Marie-Claude Vohl
Jean-Philippe Drouin-Chartier
Jacques Corbeil
Alexandre Caron
Olivier Barbier
Obeticholic acid (OCA) is the second line therapy for primary biliary cholangitis. While efficient in promoting BA detoxification and limiti… (voir plus)ng liver fibrosis, its clinical use is restricted by severe dose-dependent side effects. We tested the hypothesis that adding n-3 polyunsaturated fatty acids, eicosapentaenoic (EPA) and docosahexaenoic (DHA) acids to OCA may improve the therapeutic effect of the low drug dosage. Several liver cell lines were exposed to vehicle, low or high OCA dose (1-20μM) in the presence or absence of EPA/DHA for 24H. To induce ER stress, apoptosis, and fibrosis, HepG2 cells were exposed to a 400μM BA mixture or to 2ng/mL TGF-β. For inflammation analyses, THP-1 cells were activated with 100ng/mL LPS. The impact OCA+EPA/DHA was assessed using transcriptomic (qRT-PCR), proteomic (ELISA, caspase-3), and metabolomic (LC-MS/MS) approaches. The addition of EPA/DHA reinforced the ability of low OCA dose to down-regulate the expression of genes involved in BA synthesis (CYP7A1, CYP8B1) and uptake (NTCP) and to up-regulate MRP2 & 3 genes expression. EPA/DHA also enhanced the anti-inflammatory response of the drug by reducing the expression of the LPS-induced cytokines: TNFα, IL-6, IL-1β and MCP-1 in THP-1 macrophages. OCA+EPA/DHA decreased the expression of BIP, CHOP and COL1A1 genes and the caspase-3 activity. EPA+DHA potentiate the response to low OCA doses on BA toxicity, and provide additional benefits on ER stress, apoptosis, inflammation and fibrosis. These observations support the idea that adding n-3 polyunsaturated fatty acids to the drug may reduce the risk of dose-related side effects in patients treated with OCA.
2025-07-22
Biochemical Journal (publié)
doi.org
Pharmaco-nutraceutical improvement of the response to obeticholic acid with omega-3 polyunsaturated fatty acids
Audrey-Anne Lavoie
Ariane Thérien
Anisia Silva
Emanuel Paré
Anna Ciešlak
William Gagnon
Clémence Desjardins
Mélanie Verreault
Jocelyn Trottier
Marie-Claude Vohl
Jean-Philippe Drouin-Chartier
Jacques Corbeil
Alexandre Caron
Olivier Barbier
2025-07-22
Biochemical Journal (publié)
doi.org
The impact of statistical adjustment for assay performance on inferences from SARS-CoV-2 serological surveillance studies
Jiacheng Chen
Yuan Yu
Sheila F O’Brien
Carmen Charlton
Steven J. Drews
Jane M Heffernan
Amber M Smith
Yu Nakagama
Yasutoshi Kido
David Buckeridge
W Alton Russell
2025-07-22
American Journal of Epidemiology (publié)
doi.org
The impact of statistical adjustment for assay performance on inferences from SARS-CoV-2 serological surveillance studies
Jiacheng Chen
Yuan Yu
Sheila F O’Brien
Carmen Charlton
Steven J. Drews
Jane M Heffernan
Amber M Smith
Y. Nakagama
Yasutoshi Kido
David Buckeridge
W Alton Russell
Choice of immunoassay influences population seroprevalence estimates. Post-hoc adjustments for assay performance could improve comparability… (voir plus) of estimates across studies and enable pooled analyses. We assessed post-hoc adjustment methods using data from 2021–2023 SARS-CoV-2 serosurveillance studies in Alberta, Canada: one that tested 124,008 blood donations using Roche immunoassays (SARS-CoV-2 nucleocapsid total antibody and anti-SARS-CoV-2 S) and another that tested 214,780 patient samples using Abbott immunoassays (SARS-CoV-2 IgG and anti-SARS-CoV-2 S). Comparing datasets, seropositivity for antibodies against nucleocapsid (anti-N) diverged after May 2022 due to differential loss of sensitivity as a function of time since infection. The commonly used Rogen-Gladen adjustment did not reduce this divergence. Regression-based adjustments using the assays’ semi-quantitative results produced more similar estimates of anti-N seroprevalence and rolling incidence proportion (proportion of individuals infected in recent months). Seropositivity for antibodies targeting SARS-CoV-2 spike protein was similar without adjustment, and concordance was not improved when applying an alternative, functional threshold. These findings suggest that assay performance substantially impacted population inferences from SARS-CoV-2 serosurveillance studies in the Omicron period. Unlike methods that ignore time-varying assay sensitivity, regression-based methods using the semi-quantitative assay resulted in increased concordance in estimated anti-N seropositivity and rolling incidence between cohorts using different assays.
2025-07-22
American Journal of Epidemiology (publié)
doi.org
Corrigendum to "Child- and Proxy-reported Differences in Patient-reported Outcome and Experience Measures in Pediatric Surgery: Systematic Review and Meta-analysis" [Journal of Pediatric Surgery 60 (2025) 162172].
Zanib Nafees
Siena O'Neill
Alexandra Dimmer
Elena Guadagno
Julia Ferreira
Nancy Mayo
Dan Poenaru
2025-07-21
Journal of Pediatric Surgery (publié)
doi.org
Corrigendum to "Child- and Proxy-reported Differences in Patient-reported Outcome and Experience Measures in Pediatric Surgery: Systematic Review and Meta-analysis" [Journal of Pediatric Surgery 60 (2025) 162172].
Zanib Nafees
Siena O'Neill
Alexandra Dimmer
Elena Guadagno
Julia Ferreira
Nancy Mayo
Dan Poenaru
2025-07-21
Journal of Pediatric Surgery (publié)
doi.org
Corrigendum to "Virtual Reality for Pediatric Trauma Education - A Preliminary Face and Content Validation Study" [Journal of Pediatric Surgery 60 (2025) 161951].
F. Botelho
Said Ashkar
Shreenik Kundu
Tj Matthews
Elena Guadagno
Dan Poenaru
2025-07-21
Journal of Pediatric Surgery (publié)
doi.org
Corrigendum to "Virtual Reality for Pediatric Trauma Education - A Preliminary Face and Content Validation Study" [Journal of Pediatric Surgery 60 (2025) 161951].
F. Botelho
Said Ashkar
Shreenik Kundu
Tj Matthews
Elena Guadagno
Dan Poenaru
2025-07-21
Journal of Pediatric Surgery (publié)
doi.org
Employing Machine Learning to Predict Medical Trainees’ Psychophysiological Responses and Self- and Socially- Shared Regulated Learning Strategies While Completing Medical Simulations
Matthew Moreno
Keerat Grewal
Maria Cutumisu
Jason M. Harley
2025-07-18
Educational Psychology Review (publié)
doi.org