Browsing by Author "Ossandon, Diego"

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    Genetic testing for inherited ocular conditions in a developing country
    (2020) Zanolli, Mario; Oporto, Joaquin I.; Verdaguer, Juan I.; Lopez, Juan Pablo; Zacharias, Sergio; Romero, Pablo; Ossandon, Diego; Denk, Oliver; Acuna, Olga; Lopez, Jose Manuel; Stevenson, Ricardo; alamos, Bernardita; Iturriaga, Hernan
    Background: Inherited ocular conditions are a frequent cause of blindness. Gene therapy has encouraged the development of genetic testing, currently able to detect up to 80% of mutations in contrast to the 5% sensitivity achieved a few decades ago. Materials and methods: One hundred sixty-three patients with suspected genetic ocular disorders who were referred to a single clinician between August 2014 and August 2019 underwent a thorough ophthalmologic examination. Those diagnosed with congenital cataract, retinoblastoma, anterior segment dysgenesis, autoimmune retinal disease, posterior microphthalmia, or cobalamin C deficiency were excluded, along with patients who opted against genetic testing. Included probands were classified into a diagnostic clinical category and offered genetic testing. Blood samples were sent to foreign accredited diagnostic laboratories, followed by clinical interpretation of the results. Results: Of the 163 patients referred, 104 were enrolled in the study. Median age at disease onset was 2 years (range, 0 to 43 years). A molecular diagnosis was established at a median age of 10 years (range, 0.4 to 50 years). Disease-causing genotypes were identified in 82 of the probands, indicating a mutation detection rate of 78.8%. Mutations were identified in 38 genes, ABCA4 being the most commonly affected (23% of mutations), followed by CRB1 (13% of mutations). Whole-exome sequencing was performed in 6 patients, resulting in a definite diagnosis in 3 (50%). Conclusions: Molecular testing for inherited ocular conditions is feasible in developing countries by sending samples to certified foreign laboratories, with a mutation detection rate comparable to published values in developed countries. Further studies to identify more disease-causing genes may improve the overall sensitivity.
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    Optimizing Retaining Walls through Reinforcement Learning Approaches and Metaheuristic Techniques
    (2023) Lemus-Romani, Jose; Ossandon, Diego; Sepulveda, Rocio; Carrasco-Astudillo, Nicolas; Yepes, Victor; Garcia, Jose
    The structural design of civil works is closely tied to empirical knowledge and the design professional's experience. Based on this, adequate designs are generated in terms of strength, operability, and durability. However, such designs can be optimized to reduce conditions associated with the structure's design and execution, such as costs, CO2 emissions, and related earthworks. In this study, a new discretization technique based on reinforcement learning and transfer functions is developed. The application of metaheuristic techniques to the retaining wall problem is examined, defining two objective functions: cost and CO2 emissions. An extensive comparison is made with various metaheuristics and brute force methods, where the results show that the S-shaped transfer functions consistently yield more robust outcomes.