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353. Severe clinical phenotype in Alport syndrome due to two COL4A4 exon skipping eventsJerica Pleško, Nika Kojc, Špela Kert, Sara Petrin, Alenka Matjašič, Anamarija Meglič, Andrej Zupan, 2025, other scientific articles Abstract: Alport syndrome (AS) is a genetically heterogeneous disorder caused by mutations in COL4A3, COL4A4, or COL4A5, leading to progressive renal dysfunction. While genetic screening has advanced, many cases remain undiagnosed due to deep intronic splice site variants. We report a male patient diagnosed with autosomal recessive AS, characterized by hematuria, proteinuria, and chronic kidney disease progression. Initial kidney biopsy at age 10 revealed glomerular basement membrane thinning and focal sclerosis, while targeted DNA sequencing failed to detect pathogenic variants. Over 15 years, renal function declined, and a second biopsy showed severe GBM abnormalities with multilamellated structures. Whole-transcriptome sequencing revealed two events of exon skipping, specifically at exons 27 and 38 of the COL4A4 gene, which were verified by exon-specific PCR and Sanger sequencing. Intronic regions analysis revealed two heterozygous variants positioned 78 bp downstream of exon 27 and 8 bp upstream of exon 38, though their role in aberrant splicing remains uncertain. Immunofluorescence analysis confirmed disrupted α3α4α5(IV) heterotrimer assembly. This is the first documented case of dual exon-skipping events in COL4A4, highlighting their contribution to disease severity. Our findings emphasize the need for RNA-based diagnostics and raise questions about potential benefit of exon-skipping therapy in autosomal recessive AS.
Keywords: Alport syndrome, exon skipping, whole transcriptome sequencing, kidney, COL4A4
Published in DiRROS: 24.11.2025; Views: 98; Downloads: 43
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360. The usefulness of wearable sensors for detecting freezing of gait in Parkinson’s disease : a systematic reviewMatic Gregorčič, Dejan Georgiev, 2025, review article Abstract: Background: Freezing of gait (FoG) is one of the most debilitating motor symptoms in Parkinson’s disease (PD). It often leads to falls and reduces quality of life due to the risk of injury and loss of independence. Several types of wearable sensors have emerged as promising tools for the detection of FoG in clinical and real-life settings. Objective: The main objective of this systematic review was to critically evaluate the current usability of wearable sensor technologies for FoG detection in PD patients. The focus of the study is on sensor types, sensor combinations, placement on the body and the applications of such detection systems in a naturalistic environment. Methods: PubMed, IEEE Explore and ACM digital library were searched using a search string of Boolean operators that yielded 328 results, which were screened by title and abstract. After the screening process, 43 articles were included in the review. In addition to the year of publication, authorship and demographic data, sensor types and combinations, sensor locations, ON/OFF medication states of patients, gait tasks, performance metrics and algorithms used to process the data were extracted and analyzed. Results: The number of patients in the reviewed studies ranged from a single PD patient to 205 PD patients, and just over 65% of studies have solely focused on FoG + PD patients. The accelerometer was identified as the most frequently utilized wearable sensor, appearing in more than 90% of studies, often in combination with gyroscopes (25.5%) or gyroscopes and magnetometers (20.9%). The best overall sensor configuration reported was the accelerometer and gyroscope setup, achieving nearly 100% sensitivity and specificity for FoG detection. The most common sensor placement sites on the body were the waist, ankles, shanks and feet, but the current literature lacks the overall standardization of optimum sensor locations. Real-life context for FoG detection was the focus of only nine studies that reported promising results but much less consistent performance due to increased signal noise and unexpected patient activity. Conclusions: Current accelerometer-based FoG detection systems along with adaptive machine learning algorithms can reliably and consistently detect FoG in PD patients in controlled laboratory environments. The transition of detection systems towards a natural environment, however, remains a challenge to be explored. The development of standardized sensor placement guidelines along with robust and adaptive FoG detection systems that can maintain accuracy in a real-life environment would significantly improve the usefulness of these systems.
Keywords: Parkinson’s disease, wearable sensors, freezing of gait, symptoms
Published in DiRROS: 24.11.2025; Views: 144; Downloads: 78
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