Zygosity diagnosis has been performed in 79 pairs of twins using three methods. Simple sequence repeat length polymorphism (SSLP) analysis allows an efficient classification (MZ or DZ) with only a few markers following a simplified technique of extraction and amplification. A method based on a full questionnaire completed by parents about twin similarity correctly classifies 97.46% of the pairs; 92.41% are correctly classified using only four questions as suggested by logistic regression analysis. The third method, using dermatoglyphic analyses, correctly classifies 86.76% of pairs. To lower the cost of DNA diagnosis we stress the possibility of limiting its use to pairs with scores in the overlap area between MZ and DZ twins with a validated questionnaire.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1007/BF02361159 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
The Impact of Temperature on Extracting Information From Clinical Trial Publications Using Large Language Models.
Cureus
December 2024
Department of Radiation Oncology, Cantonal Hospital Winterthur, Winterthur, CHE.
Introduction The application of natural language processing (NLP) for extracting data from biomedical research has gained momentum with the advent of large language models (LLMs). However, the effect of different LLM parameters, such as temperature settings, on biomedical text mining remains underexplored and a consensus on what settings can be considered "safe" is missing. This study evaluates the impact of temperature settings on LLM performance for a named entity recognition and a classification task in clinical trial publications.
View Article and Find Full Text PDFRead-Across of Biotransformation Potential between Activated Sludge and the Terrestrial Environment: Toward Making It Practical and Plausible.
Environ Sci Technol
January 2025
Eawag, Swiss Federal Institute of Aquatic Science and Technology, Dübendorf 8600, Switzerland.
Recent emphasis on the development of safe-and-sustainable-by-design chemicals highlights the need for methods facilitating the early assessment of persistence. Activated sludge experiments have been proposed as a time- and resource-efficient way to predict half-lives in simulation studies. Here, this persistence "read-across" approach was developed to be more broadly and robustly applicable.
View Article and Find Full Text PDF%diag_test: a generic SAS macro for evaluating diagnostic accuracy measures for multiple diagnostic tests.
BMC Med Inform Decis Mak
January 2025
Institute of Mathematical Sciences Centre for Health Analytics and Modelling (CHaM), Strathmore University, Nairobi, Kenya.
Background: Measures of diagnostic test accuracy provide evidence of how well a test correctly identifies or rules-out disease. Commonly used diagnostic accuracy measures (DAMs) include sensitivity and specificity, predictive values, likelihood ratios, area under the receiver operator characteristic curve (AUROC), area under precision-recall curves (AUPRC), diagnostic effectiveness (accuracy), disease prevalence, and diagnostic odds ratio (DOR) etc. Most available analysis tools perform accuracy testing for a single diagnostic test using summarized data.
View Article and Find Full Text PDFMortality and morbidity patterns in Yaoundé, Cameroon: an ICD-11 classification-based analysis.
BMC Med Inform Decis Mak
January 2025
Higher Institute of Medical Technology, Yaoundé, Cameroon.
Background: In Cameroon, like in many other resource-limited countries, data generated by health settings including morbidity and mortality parameters are not always uniform. In the absence of a national guideline necessary for the standardization and harmonization of data, precision of data required for effective decision-making is therefore not guaranteed. The objective of the present study was to assess the reporting style of morbidity and mortality data in healthcare settings.
View Article and Find Full Text PDFStructural analysis of physical gel networks using graph neural networks.
Eur Phys J E Soft Matter
January 2025
Institut für Theoretische Physik 1, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, 91058, Bavaria, Germany.
We employ graph neural networks (GNN) to analyse and classify physical gel networks obtained from Brownian dynamics simulations of particles with competing attractive and repulsive interactions. Conventionally such gels are characterized by their position in a state diagram spanned by the packing fraction and the strength of the attraction. Gel networks at different regions of such a state diagram are qualitatively different although structural differences are subtile while dynamical properties are more pronounced.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!