Background: Huntington’s Disease (HD) is a neurodegenerative disorder caused by an abnormal extension of a CAG repeat stretch located in the exon 1 of the HTT (IT15) gene, leading to production of a mutated and misfolded Huntingtin protein (muHTT) with an abnormally elongated polyglutamine (polyQ) region. This mutation causes muHTT to oligomerize and aggregate in the brain, particularly in the striatum and cortex, causing alterations in intracellular trafficking, caspase activation, and ganglioside metabolism, ultimately leading to neuronal damage and death and causing signs and symptoms such as chorea and cognitive dysfunction. Currently, there is no available cure for HD patients; hence, there is a strong need to look for effective therapies. Methods: This study aims to investigate the efficacy of siRNA-containing nano-engineered lipopolymers in selectively silencing the HTT expression in a neuronal model expressing a chimeric protein formed by the human mutated exon 1 of the HTT gene, tagged with GFP. Toxicity of lipopolymers was assessed using MTT assay, while efficacy of silencing was monitored using qRT-PCR, as well as Western blotting/flow cytometry. Changes in muHTT-GFP aggregation were observed using fluorescence microscopy and image analyses. Results: Here, we show that engineered lipopolymers can be used as delivery vehicles for specific siRNAs, decreasing the transcription of the mutated gene, as well as the muHTT protein production and aggregation, with Leu-Fect C being the most effective candidate amongst the assessed lipopolymers. Conclusions: Our findings have profound implications for genetic disorder therapies, highlighting the potential of nano-engineered materials for silencing mutant genes and facilitating molecular transfection across cellular barriers. This successful in vitro study paves the way for future in vivo investigations with preclinical models, offering hope for previously considered incurable diseases such as HD.

BACKGROUND Large language models (LLMs) have entered medical education quickly, with students utilizing them for clinical reasoning and self-assessment. Whether they can be trusted on written examinations that anchor residency assessment remains unsettled, particularly in image-rich specialties such as orthopedic surgery. OBJECTIVE Synthesize published evidence on LLM performance on orthopedic written examinations. METHODS We conducted a rapid review following PRISMA 2020 guidance. We searched PubMed, Embase, and Google Scholar from 1 January 2023 through 1 August 2025 for primary studies evaluating any LLM on orthopedic board, in-training, licensure, or commercial-bank items reporting quantitative accuracy. We appraised bias risk across six pre-specified domains and synthesized results. As a review of previously published literature, this study did not require institutional review board approval. RESULTS Thirty-nine studies (2023-2025) evaluating approximately 14,700 questions across 74 model-level arms met inclusion criteria. GPT-3.5-series models achieved 29% to 61% accuracy, below pass thresholds. GPT-4 reached 60% to 82% on text-only items, with multiple studies meeting the American Board of Orthopedic Surgery (ABOS)-Orthopedic In-Training Examination (OITE) linking benchmark of 68.6%; GPT-4o ranged from 58% to 88%. Image-based accuracy was lower (38% to 74% for GPT-4). Eighteen of 39 studies (46%) carried elevated risk of bias for index-test conduct. CONCLUSIONS Current LLMs approach passing-level accuracy on text-only orthopedic examination items but remain unreliable on image-dependent questions. Included studies did not assess the fidelity of LLM-generated explanations. We recommend program-level guidelines for supervised LLM use, examination-body attention to item-security risks, and learner-outcome research before broader integration into orthopedic residency curricula.