Aug 2026· Intractable & Rare Diseases Research· Vol 15 3, pp.
232-246
· 0 citations
Medicine
TL;DR
The findings show that the physical characteristics of FXS are variable in the Asian group but similar to those in other populations and are not recommended for early recognition.
Abstract
Fragile X syndrome (FXS) is the most common genetic cause of inherited intellectual disabilities. Individuals with full mutation of FXS exhibit physical and behavioral symptoms in addition to other comorbidities. The clinical features of FXS have been widely studied in Caucasians; however, they remain limited in the Asian population. This study aimed to characterize the spectrum and variability of physical and behavioral phenotypes in Asian populations. A total of 5,830 studies from the PubMed, ScienceDirect, Scopus, and Cochrane/CENTRAL databases were screened using the Covidence software. We identified FXS-specific research studies conducted in Asia that reported the clinical characteristics of individuals with FXS. This review summarizes 51 studies from different Asian regions. The frequently reported physical characteristics were large and prominent ears (72.63%), an elongated face (57.49%), and macroorchidism (45.21%). The three most prevalent behavioral characteristics were intellectual disability (ID), hyperactivity, and social withdrawal, reported in 99%, 77%, and 55% of all cases, respectively. Our findings show that the physical characteristics of FXS are variable in the Asian group but similar to those in other populations and are not recommended for early recognition. Individuals with intellectual disabilities, especially when combined with autism spectrum disorders and large prominent ears, are suggestive of further genetic testing for FXS.
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James C. Davis, Kelechi G. Kalu, Huiyun Peng et al.· 1 citation
LLMs are increasingly used for code generation, yet they frequently hallucinate non-existent software packages, creating exploitable entry points into the software supply chain. We make four contributions to this problem. First, we show that prior evaluation methodologies systematically inflate hallucination rates by misclassifying standard-library modules as hallucinations in some languages. For Python, the overestimation reaches 9.4 percentage points. Second, we evaluate seven inference-time defenses for mitigating package hallucinations, including five guided decoding strategies (Greedy, Contrastive, DoLa, Nudging, and Active Layer-Contrastive Decoding), an iterative self-refinement approach (Self-Refine), and a Retrieval-Augmented Generation (RAG)-based defense.. Across eight models spanning five families and four programming languages (Python, JavaScript, Ruby, Rust), RAG reduces the package hallucination rate (PHR) in 18 of 32 model--language configurations. Third, we introduce Package Utility (PU) to assess whether defenses preserve valid and task-relevant recommendations. Among strategies evaluated, Greedy decoding provides the strongest average mitigation--utility trade-off. Fourth, we stress-test all strategies under adversarial prompts seeded with fabricated package names and find that PHR surges by up to 45 percentage points relative to standard prompts, with Ruby consistently the most vulnerable language (80.9--95.2\%). Under adversarial conditions, RAG and Self-Refine outperform all decoding-only strategies, indicating that robust defense requires either external grounding or iterative self-verification when prompts are actively hostile. Our results recast package hallucination as both a measurement problem and a decoding-time control problem, and they demonstrate that the choice of defense must be matched to the threat model and recommendation utility.
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