Bile acids, once considered mere dietary surfactants, now emerge as critical modulators of macronutrient (lipid, carbohydrate, protein) metabolism and the systemic pro-inflammatory/anti-inflammatory balance. Bile acid metabolism and signaling pathways play a crucial role in protecting against, or if aberrant, inducing cardiometabolic, inflammatory, and neoplastic conditions, strongly influencing health and disease. No curative treatment exists for any bile acid influenced disease, while the most promising and well-developed bile acid therapeutic was recently rejected by the FDA. Here, we provide a bottom-up approach on bile acids, mechanistically explaining their biochemistry, physiology, and pharmacology at canonical and non-canonical receptors. Using this mechanistic model of bile acids, we explain how abnormal bile acid physiology drives disease pathogenesis, emphasizing how ceramide synthesis may serve as a unifying pathogenic feature for cardiometabolic diseases. We provide an in-depth summary on pre-existing bile acid receptor modulators, explain their shortcomings, and propose solutions for how they may be remedied. Lastly, we rationalize novel targets for further translational drug discovery and provide future perspectives. Rather than dismissing bile acid therapeutics due to recent setbacks, we believe that there is immense clinical potential and a high likelihood for the future success of bile acid therapeutics.
Understanding disease-gene associations is essential for unravelling disease mechanisms and advancing diagnostics and therapeutics. Traditional approaches based on manual curation and literature review are labour-intensive and not scalable, prompting the use of machine learning on large biomedical data. In particular, graph neural networks (GNNs) have shown promise for modelling complex biological relationships. To address limitations in existing models, we propose GLaDiGAtor (Graph Learning-bAsed DIsease-Gene AssociaTiOn pRediction), a novel GNN framework with an encoder-decoder architecture for disease-gene association prediction. GLaDiGAtor constructs a heterogeneous biological graph integrating gene-gene, disease-disease, and gene-disease interactions from curated databases, and enriches each node with contextual features from well-known language models (ProtT5 for protein sequences and BioBERT for disease text). In evaluations, our model achieves superior predictive accuracy and generalisation, outperforming 14 existing methods. Literature-supported case studies confirm the biological relevance of high-confidence novel predictions, highlighting GLaDiGAtor's potential to discover candidate disease genes. These results underscore the power of graph convolutional networks in biomedical informatics and may ultimately facilitate drug discovery by revealing new gene-disease links. The source code and processed datasets are publicly available at https://github.com/HUBioDataLab/GLaDiGAtor.
Traditional cellular force-sensing techniques, such as traction force microscopy (TFM), are predominantly limited to measuring linear tractions, overlooking and technically unable to capture the nanoscale torsional forces that are critical in cell-matrix interactions. Here, we introduce a nanodiamond-enabled torsion microscopy (DTM) that integrates nitrogen-vacancy (NV) centers as orientation markers with micropillar arrays to decouple and quantify nanoscale rotational and translational motions induced by cells. This approach achieves high precision (~1.47 degree rotational accuracy and ~3.13*10-15 Nm torque sensitivity), enabling reconstruction of cellular torsional force fields and twisting energy distributions previously underestimated. Our findings reveal the widespread presence of torsional forces in cell-matrix interactions, introducing "cellular mechanical modes" where different adhesion patterns dictate the balance between traction- and torque- mediated mechanical energy transferred to the substrate. Notably, in immune cells like macrophages that generally exert low linear tractions, torque overwhelmingly dominates traction, highlighting a unique mechanical output for specific cellular functions. By uncovering these differential modes, DTM provides a versatile tool to advance biomechanical investigations, with potential applications in disease diagnostics and therapeutics.
Current clinical agent built on small LLMs, such as TxAgent suffer from a \textit{Context Utilization Failure}, where models successfully retrieve biomedical evidence due to supervised finetuning but fail to ground their diagnosis in that information. In this work, we propose the Executor-Analyst Framework, a modular architecture that decouples the syntactic precision of tool execution from the semantic robustness of clinical reasoning. By orchestrating specialized TxAgents (Executors) with long-context foundation models (Analysts), we mitigate the reasoning deficits observed in monolithic models. Beyond simple modularity, we demonstrate that a Stratified Ensemble strategy significantly outperforms global pooling by preserving evidentiary diversity, effectively addressing the information bottleneck. Furthermore, our stress tests reveal critical scaling insights: (1) a \textit{Context-Performance Paradox}, where extending reasoning contexts beyond 12k tokens introduces noise that degrades accuracy; and (2) the \textit{Curse of Dimensionality} in action spaces, where expanding toolsets necessitates hierarchical retrieval strategies. Crucially, our approach underscores the potential of training-free architectural engineering, achieving state-of-the-art performance on CURE-Bench without the need for expensive end-to-end finetuning. This provides a scalable, agile foundation for the next generation of trustworthy AI-driven therapeutics. Code has been released on https://github.com/June01/CureAgent.
Sharjeel Tahir, Judith Johnson, Jumana Abu-Khalaf
et al.
A prevalent shortfall among current empathic AI systems is their inability to recognize when verbal expressions may not fully reflect underlying emotional states. This is because the existing datasets, used for the training of these systems, focus on surface-level emotion recognition without addressing the complex verbal-visual incongruence (mismatch) patterns useful for empathic understanding. In this paper, we present E-THER, the first Person-Centered Therapy-grounded multimodal dataset with multidimensional annotations for verbal-visual incongruence detection, enabling training of AI systems that develop genuine rather than performative empathic capabilities. The annotations included in the dataset are drawn from humanistic approach, i.e., identifying verbal-visual emotional misalignment in client-counsellor interactions - forming a framework for training and evaluating AI on empathy tasks. Additional engagement scores provide behavioral annotations for research applications. Notable gains in empathic and therapeutic conversational qualities are observed in state-of-the-art vision-language models (VLMs), such as IDEFICS and VideoLLAVA, using evaluation metrics grounded in empathic and therapeutic principles. Empirical findings indicate that our incongruence-trained models outperform general-purpose models in critical traits, such as sustaining therapeutic engagement, minimizing artificial or exaggerated linguistic patterns, and maintaining fidelity to PCT theoretical framework.
Francesca Ballatore, Lorenzo Scolaris, Chiara Giverso
Glioblastoma Multiforme (GBM) is a highly aggressive brain tumour with limited therapeutic options and poor prognosis. This study presents a mathematical framework to investigate the efficacy of immunotherapy strategies based on cytotoxic T-lymphocyte (CTL) infusion. The model couples tumour and immune dynamics through a system of partial differential equations (PDEs), incorporating cell proliferation, diffusion, and chemotactic migration in response to TGF-$β$, a tumour-secreted signalling molecule. A reduced ordinary differential equation (ODE) model is first analysed to derive threshold conditions for tumour eradication, identifying critical infusion levels consistent with clinical data. Numerical bifurcation analysis explores the impact of parameter variations. The full PDE model is solved using the finite element method on simplified 2D domains, followed by sensitivity analyses to quantify parameter influence on tumour mass and volume. The model is then applied to a realistic 3D brain geometry reconstructed from patient-specific MRI and DTI data, accounting for anatomical anisotropy and tissue heterogeneity. Therapeutic scenarios are simulated with spatially localised lymphocyte infusion. Results highlight spatial variations in tumour growth and treatment response, with infusion intensity and tumour location critically influencing therapeutic outcomes. These findings emphasise the importance of personalised, spatially informed modelling in optimising immunotherapy protocols for GBM.
Drug-drug interactions (DDIs) remain a major source of preventable harm, and many clinically important mechanisms are still unknown. Existing models either rely on pharmacologic knowledge graphs (KGs), which fail on unseen drugs, or on electronic health records (EHRs), which are noisy, temporal, and site-dependent. We introduce, to our knowledge, the first system that conditions KG relation scoring on patient-level EHR context and distills that reasoning into an EHR-only model for zero-shot inference. A fusion "Teacher" learns mechanism-specific relations for drug pairs represented in both sources, while a distilled "Student" generalizes to new or rarely used drugs without KG access at inference. Both operate under a shared ontology (set) of pharmacologic mechanisms (drug relations) to produce interpretable, auditable alerts rather than opaque risk scores. Trained on a multi-institution EHR corpus paired with a curated DrugBank DDI graph, and evaluated using a clinically aligned, decision-focused protocol with leakage-safe negatives that avoid artificially easy pairs, the system maintains precision across multi-institutuion test data, produces mechanism-specific, clinically consistent predictions, reduces false alerts (higher precision) at comparable overall detection performance (F1), and misses fewer true interactions compared to prior methods. Case studies further show zero-shot identification of clinically recognized CYP-mediated and pharmacodynamic mechanisms for drugs absent from the KG, supporting real-world use in clinical decision support and pharmacovigilance.
Biomedical research increasingly relies on heterogeneous, high-dimensional datasets, yet effective visualization remains hindered by fragmented code resources, steep programming barriers, and limited domain-specific guidance. Bizard is an open-source visualization code repository engineered to streamline data analysis in biomedical research. It aggregates a diverse array of executable visualization scripts, empowering researchers to select and tailor optimal graphical methods for their specific investigative demands. The platform features an intuitive interface equipped with sophisticated browsing and filtering capabilities, exhaustive tutorials, and interactive discussion forums that foster knowledge dissemination. Through its community-driven paradigm, Bizard promotes continual refinement and functional expansion, establishing itself as an essential resource for elevating biomedical data visualization and analytical standards. By harnessing Bizard's infrastructure, researchers can augment their visualization proficiency, propel methodological progress, and enhance interpretive rigor, ultimately accelerating precision medicine and personalized therapeutics. Bizard is freely accessible at https://openbiox.github.io/Bizard/.
Maureen Watt, Rose Chang, Louise Huafeng Yu
et al.
Abstract Background and Objective Hereditary angioedema presents as recurrent, unpredictable, and often debilitating attacks of cutaneous/submucosal swelling. This study assessed the characteristics and treatment patterns of patients receiving long-term prophylaxis with the plasma kallikrein inhibitor lanadelumab in US clinical practice. Methods This retrospective longitudinal study, based on a physician panel-based medical chart review, included patients with a diagnosis of hereditary angioedema due to C1 esterase inhibitor deficiency/dysfunction (HAE-C1INH-Type1/2), initiating lanadelumab in/after August 2018 (index date), and with ≥ 3 months’ post-index follow-up (Part 1, N = 186) and, additionally, a dosing interval extension after initiating lanadelumab 300 mg every 2 weeks (Part 2, N = 75). Results Patients in Part 1 were predominantly aged ≥ 18 years (95.7%) with HAE-CINH-Type1 (90.3%); Part 2 included a higher proportion of patients with HAE-C1INH-Type2 (28.0% vs 9.7%). In Part 1, 115/165 (69.7%) patients with hereditary angioedema attack information experienced 371 attacks in the 3 months pre-index; these were mostly mild/moderate (60.4%) and most commonly affected the lips (38.0%) and hands (32.9%). In total, 19/155 (12.3%) patients had 39 attacks during the post-index period (mean ± standard deviation [interquartile range] attack rate: 0.1 ± 0.3 [0.0, 0.0] per month). In Part 2, a dosing interval extension was enabled by well-controlled disease (74/75, 98.7%); most patients (86.7%) transitioned from every 2 weeks to every 4 weeks dosing. Among patients with attack information, 7/72 (9.7%) experienced a hereditary angioedema attack while receiving an initial every 2 weeks dosing regimen and 4/75 (5.3%) after an extended-interval dosing regimen. Conclusions Lanadelumab dosing intervals can be individualized to maintain effective disease control. A dosing interval extension may be considered in well-controlled disease.
Therapeutics. Pharmacology, Pharmacy and materia medica
Colorectal cancer (CRC) continues to be a significant global health burden, prompting the need for more effective and targeted therapeutic strategies. Nanoparticle-based drug delivery systems have emerged as a promising approach to address the limitations of conventional chemotherapy, offering enhanced specificity, reduced systemic toxicity, and improved therapeutic outcomes. This paper provides an in-depth review of the current advancements in the application of nanoparticles as vehicles for targeted drug delivery in CRC therapy. It covers a variety of nanoparticle types, including liposomes, polymeric nanoparticles, dendrimers, and mesoporous silica nanoparticles (MSNs), with a focus on their design, functionalization, and mechanisms of action. This review also examines the challenges associated with the clinical translation of these technologies and explores future directions, emphasizing the potential of nanoparticle-based systems to revolutionize CRC treatment.