Fabry disease is an X-linked lysosomal storage disorder caused by pathogenic variants in the α-galactosidase A (α-Gal A, GLA) gene. The disease exhibits substantial clinical heterogeneity, with renal injury representing one of its most prominent manifestations. Due to the scarcity of human renal specimens and the inability of conventional animal models to recreate patient-specific pathological features, the precise mechanism underlying renal-predominant Fabry disease remains poorly understood. In this study, we successfully established and comprehensively characterized a urine-derived induced pluripotent stem cell (iPSC) line from a 35-year-old male patient with classic Fabry disease with a typical renal-dominant phenotype. The patient carried the GLA c.1080_1082delTGG (p.Gly361del) variant. Non-integrating episomal reprogramming was used to generate monoclonal iPSCs, which were further validated for pluripotency, trilineage differentiation ability, genomic stability, and exogenous vector clearance. The established iPSC line stably retained the patient-specific pathogenic variant, exhibited full pluripotent properties, and showed no genomic abnormality or residual episomal integration. Therefore, this well-characterized renal-phenotype-specific iPSC line provides a reliable cellular platform for investigating the mechanisms of progressive Fabry disease nephropathy and can facilitate future targeted drug screening.
Guowei Li, Jing Luan, Zihan Li et al.· Intractable & Rare Diseases...· 0 citations
Question answering (QA) over irregular clinical time series (ICTS) plays a pivotal role in a wide range of healthcare applications. Although recent multimodal time-series large language models (LLMs) have shown considerable promise in general-purpose time-series QA, they remain poorly equipped to model the sparsity, asynchrony, and irregular sampling patterns of clinical observations. To fill this gap, we propose ClinPRISM, a cost-effective multimodal LLM reasoning framework for question answering over ICTS data. First, we devise an irregularity-aware multi-scale encoder to capture sparse clinical evidence at diverse temporal scales. Then, we propose a temporal evidence distiller to integrate representations across these scales and compress them into a small number of LLM-compatible tokens. Moreover, we introduce a progressive alignment strategy that sequentially aligns the irregular trajectories with the LLM's textual embedding space. To facilitate training, we construct 30,000 clinical time series paired with multi-scale descriptions, together with 41,000 instruction-tuning instances spanning 11 tasks. Using a 4-billion-parameter LLM backbone, ClinPRISM achieves state-of-the-art performance on the held-out evaluation benchmark while using only 16 time-series tokens and achieving an average inference latency of 0.15 seconds per question.
Frank Nie, Ethan B. Liu, Yuan Zhu et al.· 1 citation
CLINLENS is introduced, a benchmark of 200 executable tasks over five linked MIMIC resources spanning structured electronic health records, notes, electrocardiograms, chest radiographs, and echocardiograms, which exposes a substantial gap between runnable submissions and correct clinical analyses.
Yuan Zhu, Ethan B. Liu, Frank Nie et al.· arXiv.org· 0 citations
CLIR-Bench is introduced, a benchmark for irregular clinical time series QA constructed from de-identified ICU records through a principled four-stage pipeline, enabling evaluation of both answer accuracy and evidence use.
Frank Nie, Ethan B. Liu, Yuan Zhu et al.· arXiv.org· 2 citations
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