Inserm

Rare-variant studies now yield more significant gene-phenotype associations than teams can place against prior evidence, and that placement has become the tighter bottleneck. PESTO combines large language model literature curation with structured Open Targets genetic evidence in one agentic pipeline that assigns Novel, Hypothesized, Existing or Established with supporting citations. Across 940 unique associations from two rare-variant studies, 7% were called Novel and 21% Hypothesized; 86% of 50 randomly paired negative controls fell into those two tiers.

Rare-variant studies now yield more significant gene-phenotype associations than teams can place against prior evidence, and that placement has become the tighter bottleneck. PESTO combines large language model literature curation with structured Open Targets genetic evidence in one agentic pipeline that assigns Novel, Hypothesized, Existing or Established with supporting citations. Across 940 unique associations from two rare-variant studies, 7% were called Novel and 21% Hypothesized; 86% of 50 randomly paired negative controls fell into those two tiers.

Segmenting cells in 3D fluorescence microscopy has meant choosing between two paths: deep learning is efficient but returns discrete voxel masks, while active surfaces give continuous shape but struggle with multiple objects. Nagini3D joins them, using a 3D U-Net to predict both object locations and spline surface parameters, so each object is one closed smooth parametric surface. On the synthetic CAPS set of non-star-convex objects, the refined Nagini3D+ reaches a mean IoU of 0.859 against StarDist3D's 0.779; on the real BONES set the two are close, but Nagini3D is the fastest tested, at 4.75 seconds on average.

Segmenting cells in 3D fluorescence microscopy has meant choosing between two paths: deep learning is efficient but returns discrete voxel masks, while active surfaces give continuous shape but struggle with multiple objects. Nagini3D joins them, using a 3D U-Net to predict both object locations and spline surface parameters, so each object is one closed smooth parametric surface. On the synthetic CAPS set of non-star-convex objects, the refined Nagini3D+ reaches a mean IoU of 0.859 against StarDist3D's 0.779; on the real BONES set the two are close, but Nagini3D is the fastest tested, at 4.75 seconds on average.

The work introduces the first framework that explicitly disentangles prompt dependence into prompt ambiguity (inter-user variability) and local sensitivity (interaction imprecision): a mixture density network models the image-conditioned distribution of plausible prompts and quantifies ambiguity by the trace of its covariance, while a stability margin captures the minimal perturbation under measurement noise needed to induce a mask change; evaluated on two female pelvic T2-weighted MRI datasets, UT-EndoMRI (81 patients with endometriosis, uterus segmentation) and MOGaMBO (94 patients with locally advanced cervical cancer, bladder segmentation), with MobileSAM and MedSAM, the two metrics correlate significantly and negatively with Dice, correlate little with each other, and conditional samp

The work introduces the first framework that explicitly disentangles prompt dependence into prompt ambiguity (inter-user variability) and local sensitivity (interaction imprecision): a mixture density network models the image-conditioned distribution of plausible prompts and quantifies ambiguity by the trace of its covariance, while a stability margin captures the minimal perturbation under measurement noise needed to induce a mask change; evaluated on two female pelvic T2-weighted MRI datasets, UT-EndoMRI (81 patients with endometriosis, uterus segmentation) and MOGaMBO (94 patients with locally advanced cervical cancer, bladder segmentation), with MobileSAM and MedSAM, the two metrics correlate significantly and negatively with Dice, correlate little with each other, and conditional samp

The work shows that semi-supervised domain generalization methods such as FBCSA and DGWM degrade substantially under long-tailed class distributions, and proposes IMaX: maximizing the mutual information between learned features and latent labels under supervision constraints from labeled samples, while replacing the standard marginal entropy term with a Tsallis α-entropy to relax the class-balance assumption; across two modalities (ESCA histopathology and diabetic retinopathy grading), three SSL frameworks and three SSDG methods, IMaX improves accuracy in all but one setting, with gains up to 7.3 points in the low-label regime (mL=5).

The work shows that semi-supervised domain generalization methods such as FBCSA and DGWM degrade substantially under long-tailed class distributions, and proposes IMaX: maximizing the mutual information between learned features and latent labels under supervision constraints from labeled samples, while replacing the standard marginal entropy term with a Tsallis α-entropy to relax the class-balance assumption; across two modalities (ESCA histopathology and diabetic retinopathy grading), three SSL frameworks and three SSDG methods, IMaX improves accuracy in all but one setting, with gains up to 7.3 points in the low-label regime (mL=5).

This study applied AI-guided phenotypic drug repurposing to drug-resistant Streptococcus pneumoniae, using ensembles of transformer, graph, and tree models trained on 1849 actives and 34 503 inactives to prospectively examine 6747 drugs, selecting 11 candidate antibiotics of which nine strongly reduced in vitro growth of S. pneumoniae R6 (IC50 ≤ 0.4 µg/mL), with the most potent drugs thiostrepton and ceftiofur showing IC50 values of 0.0001 µg/mL (60.1 pM) and 0.0004 µg/mL (764 pM), respectively, and thiostrepton remaining highly potent against multidrug-resistant strains.

This study applied AI-guided phenotypic drug repurposing to drug-resistant Streptococcus pneumoniae, using ensembles of transformer, graph, and tree models trained on 1849 actives and 34 503 inactives to prospectively examine 6747 drugs, selecting 11 candidate antibiotics of which nine strongly reduced in vitro growth of S. pneumoniae R6 (IC50 ≤ 0.4 µg/mL), with the most potent drugs thiostrepton and ceftiofur showing IC50 values of 0.0001 µg/mL (60.1 pM) and 0.0004 µg/mL (764 pM), respectively, and thiostrepton remaining highly potent against multidrug-resistant strains.

This work developed and evaluated a fully on-premise clinical AI agent that couples local operational control with a multi-perspective reliability framework to support selective autonomy, achieving 90.04% accuracy on a seven-disease task and 83.8% on a four-disease task across two MIMIC-IV-derived benchmarks, and finding that diagnostic behavioral consistency best discriminated correctness (AUC = 0.860, and AUC = 0.875 under stress testing), with a consistency threshold of 0.90 retaining 49.4% of cases at 98.9% diagnostic accuracy.

This work developed and evaluated a fully on-premise clinical AI agent that couples local operational control with a multi-perspective reliability framework to support selective autonomy, achieving 90.04% accuracy on a seven-disease task and 83.8% on a four-disease task across two MIMIC-IV-derived benchmarks, and finding that diagnostic behavioral consistency best discriminated correctness (AUC = 0.860, and AUC = 0.875 under stress testing), with a consistency threshold of 0.90 retaining 49.4% of cases at 98.9% diagnostic accuracy.

The study introduces a domain-agnostic paired-comparison approach that fine-tunes a large language model to emulate documented emergency triage decisions and then compares predictions on sex-swapped pairs in which only sex is flipped while documented clinical content is held constant, finding that otherwise identical presentations were more likely to receive a lower-severity (less urgent) predicted triage score as female than male, with a pooled per-pair rate of about 1.1% (95% CI 0.9–1.3) across more than 140,000 Bordeaux University Hospital admissions and a directionally consistent but larger 2.2% (1.7–2.7) in MIMIC-IV, while a model retrained on sex-neutralized inputs eliminated the between-sex prediction gap, indicating the asymmetry is mediated by explicit sex markers.

The study introduces a domain-agnostic paired-comparison approach that fine-tunes a large language model to emulate documented emergency triage decisions and then compares predictions on sex-swapped pairs in which only sex is flipped while documented clinical content is held constant, finding that otherwise identical presentations were more likely to receive a lower-severity (less urgent) predicted triage score as female than male, with a pooled per-pair rate of about 1.1% (95% CI 0.9–1.3) across more than 140,000 Bordeaux University Hospital admissions and a directionally consistent but larger 2.2% (1.7–2.7) in MIMIC-IV, while a model retrained on sex-neutralized inputs eliminated the between-sex prediction gap, indicating the asymmetry is mediated by explicit sex markers.