Purpose Low bladder compliance (BC) poses a significant clinical challenge. Nevertheless, studies exploring pharmacological mechanisms to improve BC remain limited. We investigated the efficacy of a β3-adrenoceptor agonist, mirabegron, on BC in comparison with anticholinergics. Methods This prospective single-arm paired comparison trial included 14 patients with low BC (≤20 mL/cm H2O) despite anticholinergics treatment. After a 2-week anticholinergics-washout period, patients were treated with mirabegron for 8 weeks and then returned to 8 weeks of anticholinergics. Major treatment effect was assessed with urodynamic studies performed at baseline, 8 weeks after mirabegron treatment, and 8 weeks after consecutive anticholinergics treatment (McNemar test, Paired t-test; mean [95% confidence intervals]). Results Following mirabegron, 71.43% of patients exhibited a BC of >20 mL/cm H₂O, compared to 54.55% after switching back to anticholinergics (P=0.317). BC improved significantly from 12.02 (9.52–14.52) to 39.67 (21.60–57.73) mL/cm H2O after mirabegron treatment (P=0.007), but subsequently declined to 20.94 (15.78–26.10) mL/cm H₂O after reintroduction of anticholinergics (P=0.075). Maximum cystometric capacity increased from 352.21 (282.78–421.65) to 442.71 (348.95–536.48) mL after mirabegron (P=0.091), but decreased to 402.00 (315.92–488.08) mL after returning to anticholinergics (P=0.218). Notably, detrusor pressure at end-filling decreased significantly with mirabegron, from 30.50 (25.61–35.39) to 14.43 (10.79–18.06) cm H2O (P<0.001), while increasing to 20.36 (16.26–24.46) cm H2O after returning to anticholinergics (P=0.056). Conclusions A β3-adrenoceptor agonist, mirabegron, was more effective than anticholinergics in improving BC. Among the two components of improved BC—increased bladder volume and reduced detrusor filling pressure—the β3-adrenoceptor agonist showed a more pronounced effect on lowering detrusor filling pressure, compared to anticholinergics. These findings suggest that β3-adrenoceptor agonists might play an important role in reducing the tension of the bladder wall by controlling detrusor muscle tone, and this may be an important target for future research.
Abdullah Al Shafi, Rowzatul Zannat, Abdul Muntakim
et al.
Disease-symptom datasets are significant and in demand for medical research, disease diagnosis, clinical decision-making, and AI-driven health management applications. These datasets help identify symptom patterns associated with specific diseases, thus improving diagnostic accuracy and enabling early detection. The dataset presented in this study systematically compiles disease-symptom relationships from various online sources, medical literature, and publicly available health databases. The data was gathered through analyzing peer-reviewed medical articles, clinical case studies, and disease-symptom association reports. Only the verified medical sources were included in the dataset, while those from non-peer-reviewed and anecdotal sources were excluded. The dataset is structured in a tabular format, where the first column represents diseases, and the remaining columns represent symptoms. Each symptom cell contains a binary value, indicating whether a symptom is associated with a disease. Thereby, this structured representation makes the dataset very useful for a wide range of applications, including machine learning-based disease prediction, clinical decision support systems, and epidemiological studies. Although there are some advancements in the field of disease-symptom datasets, there is a significant gap in structured datasets for the Bangla language. This dataset aims to bridge that gap by facilitating the development of multilingual medical informatics tools and improving disease prediction models for underrepresented linguistic communities. Further developments should include region-specific diseases and further fine-tuning of symptom associations for better diagnostic performance
Medical artificial intelligence (AI) systems frequently lack systematic domain expertise integration, potentially compromising diagnostic reliability. This study presents an ontology-based framework for bone disease diagnosis, developed in collaboration with Ho Chi Minh City Hospital for Traumatology and Orthopedics. The framework introduces three theoretical contributions: (1) a hierarchical neural network architecture guided by bone disease ontology for segmentation-classification tasks, incorporating Visual Language Models (VLMs) through prompts, (2) an ontology-enhanced Visual Question Answering (VQA) system for clinical reasoning, and (3) a multimodal deep learning model that integrates imaging, clinical, and laboratory data through ontological relationships. The methodology maintains clinical interpretability through systematic knowledge digitization, standardized medical terminology mapping, and modular architecture design. The framework demonstrates potential for extension beyond bone diseases through its standardized structure and reusable components. While theoretical foundations are established, experimental validation remains pending due to current dataset and computational resource limitations. Future work will focus on expanding the clinical dataset and conducting comprehensive system validation.
Aniketh Garikaparthi, Manasi Patwardhan, Lovekesh Vig
et al.
The rapid advancement in capabilities of large language models (LLMs) raises a pivotal question: How can LLMs accelerate scientific discovery? This work tackles the crucial first stage of research, generating novel hypotheses. While recent work on automated hypothesis generation focuses on multi-agent frameworks and extending test-time compute, none of the approaches effectively incorporate transparency and steerability through a synergistic Human-in-the-loop (HITL) approach. To address this gap, we introduce IRIS: Interactive Research Ideation System, an open-source platform designed for researchers to leverage LLM-assisted scientific ideation. IRIS incorporates innovative features to enhance ideation, including adaptive test-time compute expansion via Monte Carlo Tree Search (MCTS), fine-grained feedback mechanism, and query-based literature synthesis. Designed to empower researchers with greater control and insight throughout the ideation process. We additionally conduct a user study with researchers across diverse disciplines, validating the effectiveness of our system in enhancing ideation. We open-source our code at https://github.com/Anikethh/IRIS-Interactive-Research-Ideation-System
Modeling disease progression through multiple stages is critical for clinical decision-making for chronic diseases, e.g., cancer, diabetes, chronic kidney diseases, and so on. Existing approaches often model the disease progression as a uniform trajectory pattern at the population level. However, chronic diseases are highly heterogeneous and often have multiple progression patterns depending on a patient's individual genetics and environmental effects due to lifestyles. We propose a personalized disease progression model to jointly learn the heterogeneous progression patterns and groups of genetic profiles. In particular, an end-to-end pipeline is designed to simultaneously infer the characteristics of patients from genetic markers using a variational autoencoder and how it drives the disease progressions using an RNN-based state-space model based on clinical observations. Our proposed model shows improvement on real-world and synthetic clinical data.
Despite the diversity of brain data acquired and advanced AI-based algorithms to analyze them, brain features are rarely used in clinics for diagnosis and prognosis. Here we argue that the field continues to rely on cohort comparisons to seek biomarkers, despite the well-established degeneracy of brain features. Using a thought experiment, we show that more data and more powerful algorithms will not be sufficient to identify biomarkers of brain diseases. We argue that instead of comparing patient versus healthy controls using single data type, we should use multimodal (e.g. brain activity, neurotransmitters, neuromodulators, brain imaging) and longitudinal brain data to guide the grouping before defining multidimensional biomarkers for brain diseases.
Jorge Iranzo-Sánchez, Javier Iranzo-Sánchez, Adrià Giménez
et al.
This work describes the participation of the MLLP-VRAIN research group in the shared task of the IWSLT 2025 Simultaneous Speech Translation track. Our submission addresses the unique challenges of real-time translation of long-form speech by developing a modular cascade system that adapts strong pre-trained models to streaming scenarios. We combine Whisper Large-V3-Turbo for ASR with the multilingual NLLB-3.3B model for MT, implementing lightweight adaptation techniques rather than training new end-to-end models from scratch. Our approach employs document-level adaptation with prefix training to enhance the MT model's ability to handle incomplete inputs, while incorporating adaptive emission policies including a wait-$k$ strategy and RALCP for managing the translation stream. Specialized buffer management techniques and segmentation strategies ensure coherent translations across long audio sequences. Experimental results on the ACL60/60 dataset demonstrate that our system achieves a favorable balance between translation quality and latency, with a BLEU score of 31.96 and non-computational-aware StreamLAAL latency of 2.94 seconds. Our final model achieves a preliminary score on the official test set (IWSLT25Instruct) of 29.8 BLEU. Our work demonstrates that carefully adapted pre-trained components can create effective simultaneous translation systems for long-form content without requiring extensive in-domain parallel data or specialized end-to-end training.
Inflammatory myofibroblastic tumors of the bladder (IMTB) are rare neoplasms that can occur in children. These tumors have uncertain malignant potential and can present similarly to bladder sarcomas. It is important to differentiate between IMTB and bladder sarcomas using a careful immunohistochemical approach. We report a case of IMTB in a 12-year-old girl who presented with presyncope and gross hematuria. IMTB was diagnosed through immunohistochemical analysis, and clinical improvement was observed after resection of the tumor.
Objective: Our objective is to develop and validate TrajVis, an interactive tool that assists clinicians in using artificial intelligence (AI) models to leverage patients' longitudinal electronic medical records (EMR) for personalized precision management of chronic disease progression. Methods: We first perform requirement analysis with clinicians and data scientists to determine the visual analytics tasks of the TrajVis system as well as its design and functionalities. A graph AI model for chronic kidney disease (CKD) trajectory inference named DEPOT is used for system development and demonstration. TrajVis is implemented as a full-stack web application with synthetic EMR data derived from the Atrium Health Wake Forest Baptist Translational Data Warehouse and the Indiana Network for Patient Care research database. A case study with a nephrologist and a user experience survey of clinicians and data scientists are conducted to evaluate the TrajVis system. Results: The TrajVis clinical information system is composed of four panels: the Patient View for demographic and clinical information, the Trajectory View to visualize the DEPOT-derived CKD trajectories in latent space, the Clinical Indicator View to elucidate longitudinal patterns of clinical features and interpret DEPOT predictions, and the Analysis View to demonstrate personal CKD progression trajectories. System evaluations suggest that TrajVis supports clinicians in summarizing clinical data, identifying individualized risk predictors, and visualizing patient disease progression trajectories, overcoming the barriers of AI implementation in healthcare. Conclusion: TrajVis bridges the gap between the fast-growing AI/ML modeling and the clinical use of such models for personalized and precision management of chronic diseases.
The rising prevalence of vision-threatening retinal diseases poses a significant burden on the global healthcare systems. Deep learning (DL) offers a promising solution for automatic disease screening but demands substantial data. Collecting and labeling large volumes of ophthalmic images across various modalities encounters several real-world challenges, especially for rare diseases. Here, we introduce EyeDiff, a text-to-image model designed to generate multimodal ophthalmic images from natural language prompts and evaluate its applicability in diagnosing common and rare diseases. EyeDiff is trained on eight large-scale datasets using the advanced latent diffusion model, covering 14 ophthalmic image modalities and over 80 ocular diseases, and is adapted to ten multi-country external datasets. The generated images accurately capture essential lesional characteristics, achieving high alignment with text prompts as evaluated by objective metrics and human experts. Furthermore, integrating generated images significantly enhances the accuracy of detecting minority classes and rare eye diseases, surpassing traditional oversampling methods in addressing data imbalance. EyeDiff effectively tackles the issue of data imbalance and insufficiency typically encountered in rare diseases and addresses the challenges of collecting large-scale annotated images, offering a transformative solution to enhance the development of expert-level diseases diagnosis models in ophthalmic field.
Catarina Botelho, Alberto Abad, Tanja Schultz
et al.
Speech is a rich biomarker that encodes substantial information about the health of a speaker, and thus it has been proposed for the detection of numerous diseases, achieving promising results. However, questions remain about what the models trained for the automatic detection of these diseases are actually learning and the basis for their predictions, which can significantly impact patients' lives. This work advocates for an interpretable health model, suitable for detecting several diseases, motivated by the observation that speech-affecting disorders often have overlapping effects on speech signals. A framework is presented that first defines "reference speech" and then leverages this definition for disease detection. Reference speech is characterized through reference intervals, i.e., the typical values of clinically meaningful acoustic and linguistic features derived from a reference population. This novel approach in the field of speech as a biomarker is inspired by the use of reference intervals in clinical laboratory science. Deviations of new speakers from this reference model are quantified and used as input to detect Alzheimer's and Parkinson's disease. The classification strategy explored is based on Neural Additive Models, a type of glass-box neural network, which enables interpretability. The proposed framework for reference speech characterization and disease detection is designed to support the medical community by providing clinically meaningful explanations that can serve as a valuable second opinion.
Rare diseases pose significant challenges in diagnosis and treatment due to their low prevalence and heterogeneous clinical presentations. Unstructured clinical notes contain valuable information for identifying rare diseases, but manual curation is time-consuming and prone to subjectivity. This study aims to develop a hybrid approach combining dictionary-based natural language processing (NLP) tools with large language models (LLMs) to improve rare disease identification from unstructured clinical reports. We propose a novel hybrid framework that integrates the Orphanet Rare Disease Ontology (ORDO) and the Unified Medical Language System (UMLS) to create a comprehensive rare disease vocabulary. The proposed hybrid approach demonstrates superior performance compared to traditional NLP systems and standalone LLMs. Notably, the approach uncovers a significant number of potential rare disease cases not documented in structured diagnostic records, highlighting its ability to identify previously unrecognized patients.
Jia-Yuh Sheu, Jeff Shih-Chieh Chueh, Chao-Yuan Huang
et al.
Purpose: The purpose of this study was to demonstrate the usefulness of genetic analysis with short tandem repeats (STRs) to identify the cellular origin of an unusual allograft urothelial carcinoma (UC). Materials and Methods: A 30-year-old Taiwanese woman received a kidney transplant from her aunt in 2000. She was diagnosed with high-grade UC of her native upper urinary tract (urinary tract UC [UTUC]) in 2012. During a follow-up, tumors were discovered in both her native right ureter and graft ureter. The final pathology report identified this as a high-grade invasive UC. To investigate the origin of her allograft ureteral cancer to determine whether it originated from her own or the donor cells, we employed STR analysis because the recipient and donor were of the same gender. Results: We compared 23 autosomal STR loci and one amelogenin. Overall, the STR expression from the native right UTUC was identical to that of the recipient's buccal cells. The STR expression of the graft UTUC was similar to that of recipient cells, but importantly, some STR loci showed gene expressions that were only present in the donor's buccal cells. Conclusion: We concluded that the native right UTUC was of recipient origin and not metastatic from the donor. While we cannot be entirely sure of the tumor origin of the graft ureteral UC, we conjectured that it was not wholly from the donor source alone; either because of the intermixing with the donor stroma or due to microchimerism that developed after transplantation.
Osama Shalkamy, Mohamed Elsalhy, Saleh Mohammed Alghamdi
et al.
Abstract Purpose We aimed to compare the impact of urethral transection after different techniques of bulbar urethroplasty on erectile function outcome. Materials and methods We retrospectively reviewed the records for 245 patients who underwent different urethroplasty techniques for bulbar urethral stricture between February 2013 and January 2021. The comparison between the transecting and non-transecting cohorts included patients’ demographics, clinicopathological features of the urethral stricture, post-urethroplasty erectile function, and success of urethroplasty. Outcomes were erectile function status verified by IIEF5-15 score at preoperative, three months, and 12 months post-surgery. We defined Post-urethroplasty ED as a decrease of 5 points or more. Results The urethroplasty success rate of the entire cohort was 86.9% after a mean follow-up of 45.59 ± 21 months. Out of 245 patients, 18 (7.3%) experienced 90-day complications. Transecting bulbar urethroplasty techniques were performed in 74 patients (30.2%), while non-transecting techniques were performed in 171 patients (69.8%). there were no differences between the cohorts regarding urethroplasty success (87.8% Vs. 86.5%, Mantel-Cox test p = 0.93) or postoperative complications (8.1% Vs. 7%, p = 0.73). Transient ED was evident in the transecting cohort as reported in 8.1% compared to 2.9% for the non-transecting (p = 0.07).Still, but de novo permanent ED was comparable (4.1% Vs. 2.9%, p = 0.65), for transecting and non-transecting, respectively. Conclusions Unfortunately, some patients who undergo transecting techniques of bulbar urethroplasty experience transient erectile dysfunction that can improve within the first post- urethroplasty year; however, de novo permanent erectile dysfunction is uncommon after different techniques of bulbar urethroplasty and is not predisposed by urethral transection.