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We discuss a novel diagnostic method for predicting the early recurrence of liver cancer with high accuracy for personalized medicine. The difficulty with cancer treatment is that even if the types of cancer are the same, the cancers vary depending on the patient. Thus, remarkable attention has been paid to personalized medicine. Unfortunately, although the Tokyo Score, the Modified JIS, and the TNM classification have been proposed as liver scoring systems, none of these scoring systems have met the needs of clinical practice. In this paper, we convert continuous and discrete data to categorical data and keep the natively categorical data as is. Then, we propose a discrete Bayes decision rule that can deal with the categorical data. This may lead to its use with various types of laboratory data. Experimental results show that the proposed method produced a sensitivity of 0.86 and a specificity of 0.49 for the test samples. This suggests that our method may be superior to the well-known Tokyo Score, the Modified JIS, and the TNM classification in terms of sensitivity. Additional comparative study shows that if the numbers of test samples in two classes are the same, this method works well in terms of the F1 measure compared to the existing scoring methods. [ABSTRACT FROM AUTHOR]

Background. Neonatal intrahepatic cholestasis caused by citrin deficiency (NICCD) is a Mendelian disorder arising from biallelic SLC25A13 mutations, and SLC25A13 genetic analysis was indispensable for its definite diagnosis. However, conventional SLC25A13 analysis could not detect all mutations, especially obscure large insertions/deletions. This paper aimed to explore the obscure SLC25A13 mutation in an NICCD infant. Methods. Genomic DNA was extracted to screen for 4 high-frequency SLC25A13 mutations, and then all 18 exons and their flanking sequences were analyzed by Sanger sequencing. Subsequently, cDNA cloning, SNP analyses, and semiquantitative PCR were performed to identify the obscure mutation. Results. A maternally inherited mutation IVS16ins3kb was screened out, and then cDNA cloning unveiled paternally inherited alternative splicing variants (ASVs) featuring exon 5 skipping. Ultimately, a large deletion c.329-1687_c.468+3865del5692bp, which has never been described in any other references, was identified via intensive study on the genomic DNA around exon 5 of SLC25A13 gene. Conclusions. An NICCD patient was definitely diagnosed as a compound heterozygote of IVS16ins3kb and c.329-1687_c.468+3865del5692bp. The large deletion enriched the SLC25A13 mutation spectrum, and its identification supported the concept that cDNA cloning analysis, along with other molecular tools such as semiquantitative PCR, could provide valuable clues, facilitating the identification of obscure SLC25A13 deletions. [ABSTRACT FROM AUTHOR]

Measuring stride variability and dynamics in children is useful for the quantitative study of gait maturation and neuromotor development in childhood and adolescence. In this paper, we computed the sample entropy (SampEn) and average stride interval (ASI) parameters to quantify the stride series of 50 gender-matched children participants in three age groups. We also normalized the SampEn and ASI values by leg length and body mass for each participant, respectively. Results show that the original and normalized SampEn values consistently decrease over the significance level of the Mann-Whitney U test (p<0.01) in children of 3–14 years old, which indicates the stride irregularity has been significantly ameliorated with the body growth. The original and normalized ASI values are also significantly changing when comparing between any two groups of young (aged 3–5 years), middle (aged 6–8 years), and elder (aged 10–14 years) children. Such results suggest that healthy children may better modulate their gait cadence rhythm with the development of their musculoskeletal and neurological systems. In addition, the AdaBoost.M2 and Bagging algorithms were used to effectively distinguish the children’s gait patterns. These ensemble learning algorithms both provided excellent gait classification results in terms of overall accuracy (≥90%), recall (≥0.8), and precision (≥0.8077). [ABSTRACT FROM AUTHOR]