In this study a well-characterized pathological mutation at nucleotide position 3243 of human mitochondrial DNA was introduced into human 0 teratocarcinoma (NT2) cells. In cloned and mixed populations of NT2 cells heteroplasmic for the mutation, mitotic segregation toward increasing levels of mutant mitochondrial DNA always occurred. Rapid segregation was frequently followed by complete loss of mitochondrial DNA. These findings support the idea that pathological mitochondrial DNA mutations are particularly deleterious in specific cell types, which can explain some of the tissue-specific aspects of mitochondrial DNA diseases. Moreover, these findings suggest that mitochondrial DNA depletion may be an important and widespread feature of mitochondrial DNA disease.

Current mental health services across the world remain expert-centric and are based on traditional workflows, mostly using impractical and ineffective electronic record systems or even paper-based documentation. The international network for digital mental health (IDMHN) is comprised of top-level clinicians, regulatory and ICT experts, genetic scientists, and support organizations. The IDMHN has been formed to enable the implementation of digital innovations in clinical practice, hereby facilitating the transformation of current mental health services to be more personalized and more responsive to patients and healthcare needs. This consensus statement summarizes the consortium's vision and strategy for further development of digital mental health.

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Galactica, a newly developed machine-learning system that utilizes a genetic algorithm for learning, was compared with discriminant analysis, logistic regression, k-means cluster analysis, a C4.5 decision-tree generator and a random bit climber hill-climbing algorithm. The methods were evaluated in the diagnosis of female urinary incontinence in terms of prediction accuracy of classifiers, on the basis of patient data. The best methods were discriminant analysis, logistic regression, C4.5 and Galactica. Practically no statistically significant differences existed between the prediction accuracy of these classification methods. We consider that machine-learning systems C4.5 and Galactica are preferable for automatic construction of medical decision aids, because they can cope with missing data values directly and can present a classifier in a comprehensible form. Galactica performed nearly as well as C4.5. The results are in agreement with the results of earlier research, indicating that genetic algorithms are a competitive method for constructing classifiers from medical data.

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