Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 3 appraisals
Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
Genetic Creutzfeldt-Jakob disease associated with 5-octapeptide repeat insertion in the PRNP gene: case and pedigree report and literature review
BACKGROUND: Genetic prion diseases caused by octapeptide repeat insertions in the PRNP gene are rare. 5-octapeptide repeat insertion (5-OPRI) is an uncommon subtype that usually manifests as Creutzfeldt-Jakob disease (CJD) but shows marked variability in clinical phenotype and disease course. DISCUSSION: We report a patient with rapidly progressive gCJD caused by a 5-OPRI mutation. A systematic PubMed review identified genetically confirmed 5-OPRI cases with available clinical data. Demographic characteristics, clinical features, ancillary investigations, disease course, and PRNP codon 129 polymorphisms were analyzed. A total of 25 patients from 14 families were included. Age at onset ranged from 26 to 63 years (mean 44.8 ± 9.9 years). Disease duration ranged from 4 to 192 months. Dementia was present in all patients, and 84% showed frontotemporal dementia-like features. Cerebellar signs, extrapyramidal symptoms, and myoclonus occurred in 68%, 60%, and 44% of patients, respectively. Periodic sharp wave complexes were detected in 29.4% of EEGs, and cerebral atrophy was observed in all patients with neuroimaging. Most patients carried the MM genotype at PRNP codon 129 (83.3%). A short disease course was associated with more frequent akinetic mutism and myoclonus, while MM carriers had a younger age at onset than MV carriers. CONCLUSIONS: 5-OPRI-associated CJD exhibits marked heterogeneity in clinical and disease duration. The PRNP codon 129 polymorphism appears to influence disease onset and progression.
26 May 2026
Read appraisal →Scientific reports
LASSO-HHO two-stage hybrid gene selection framework for accurate Alzheimer's disease diagnosis
Selecting informative genes is essential for building accurate and efficient diagnostic models, especially for high-dimensional gene expression data such as those related to Alzheimer's disease. These datasets typically involve thousands of genes with limited samples, increasing the risk of overfitting and computational complexity. To address this, we propose a LASSO-HHO Gene Selection (LHGS) framework. LASSO is first applied to reduce dimensionality by filtering irrelevant genes. A conditional HHO-based optimization stage is then applied only when the LASSO-selected subset does not achieve sufficient accuracy or remains relatively large. Otherwise, the LASSO-selected features are directly used without further optimization. Experimental results show that the proposed method reduces the number of selected genes by up to 99.9% while maintaining high performance. The experimental results indicate that 100% accuracy can be achieved on specific datasets. In particular, GSE48350 and GSE36980 achieved 100% accuracy using LASSO alone, whereas GSE118553 and GSE132903 required the full LHGS framework to achieve the same performance. The framework also improves computational efficiency within a consistent experimental setup by reducing the optimization search space after LASSO filtering. Overall, LHGS provides a practical and efficient solution for gene selection in high-dimensional biomedical data.
28 Apr 2026
Read appraisal →JMIR aging
AI-Enhanced Automatic Life Story Structuring for Reminiscence Therapy in Older Adults: Technical Feasibility Study
BACKGROUND: Storytelling interventions have demonstrated substantial potential in improving emotional well-being, cognitive function, and quality of life for older adults. However, its effectiveness is often limited by the challenges of processing disorganized and redundant life stories, which impose substantial cognitive demands on caregivers. Although storytelling interventions are a well-established therapeutic approach, current practices depend heavily on manual narrative organization, restricting both the scalability and consistency of treatment delivery. Prior research has primarily focused on validating the clinical outcomes of storytelling interventions, with insufficient attention given to technological solutions that could enhance narrative processing while preserving therapeutic integrity. Digital approaches to life story structuring remain underexplored, despite their potential to amplify storytelling benefits by reducing cognitive load and improving recall accuracy. OBJECTIVE: This study aims to design an event timeline generation algorithm to optimize the prior work of the Story Mosaic system. The optimized system enables (1) the automatic extraction of event elements from life narratives, (2) the automatic organization of fragmented life stories into structured timelines, and (3) the preservation of clinically relevant contextual details during compression. The goal is to reduce manual intervention costs while increasing treatment efficacy through artificial intelligence-driven narrative structuring. METHODS: We have designed a novel method, CARE event timeline (CARE-ET), which combines a temporal attention mechanism with graph-based event relationship modeling. Furthermore, we used the CARE-ET algorithm to optimize existing story collage systems. The system uses multifeature extraction technology to capture event clues from oral histories, prioritizes the 6 elements of events through a hierarchical attention mechanism, and uses adaptive compression algorithms to reduce redundancy while maintaining narrative continuity. To verify the effectiveness of the CARE-ET method, this paper adopts a multidimensional evaluation framework, which encompasses event summary assessment, timeline quality evaluation, and usability testing of the optimized system. RESULTS: The proposed CARE-ET algorithm outperforms the baseline in both narrative flow and temporal accuracy. The Story Mosaic system, optimized by the CARE-ET algorithm, underwent usability evaluation by 10 caregivers recruited for this study. Based on standardized assessment metrics, the system received an A rating for usability. The comprehensive experimental results demonstrate that the CARE-ET method can effectively structure fragmented narratives from older adults, enhancing the usability of the Story Mosaic system. CONCLUSIONS: The proposed method enables the structured extraction of representative event summaries, transforming disorganized life stories into an event timeline for caregiver-supported older adult well-being interventions. Future research should investigate longitudinal effects on cognitive preservation and explore integration with existing dementia care protocols. This work establishes a critical foundation for intelligent assistive technologies in geriatric mental health interventions.
7 Apr 2026
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