Studies of interacting/overlapping genetic skeletal disorders are rare for populations today, but even more so for archaeological contexts. The skeletal remains of an adult female (EZ 3-7-1) were excavated in the 1980s from the Middle Woodland (50BC-AD400) context of the Elizabeth site (11PK512) in the lower Illinois Valley (LIV), USA. Reported here are the standard score (z-score) comparisons of the measured skeletal differences of EZ 3-7-1 with a reference sample and a re-analysis of the individual's pathological changes, with special consideration placed on refining the disease diagnosis. The impressive preservation and meticulous recovery of these skeletal remains have provided the opportunity to identify the first and earliest archaeological example of an individual (EZ 3-7-1) with a combined genetic skeletal dysplasia, Leri-Weill dyschondrosteosis and achondroplasia.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/j.ijpp.2017.06.001 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Effectiveness of a tailored forest package of interventions, including topical repellents, in reducing malaria incidence in Myanmar.
Malar J
January 2025
PMI Defeat Malaria Activity, University Research Co., LLC, Yangon, Burma.
Background: In Myanmar, progress towards malaria elimination has stagnated in some areas requiring deployment of new tools and approaches to accelerate malaria elimination. While there is evidence that networks of community-based malaria workers and insecticide-treated nets (ITNs) can reduce malaria transmission in a variety of settings, evidence for the effectiveness of other interventions, such as topical repellents, is limited. Since malaria transmission in Myanmar occurs outdoors, mainly among forest-goers, this study tested the effectiveness of topical repellents in combination with supplemental ITN distribution and strengthened networks of malaria workers.
View Article and Find Full Text PDFRandomized community trial to assess nutritional, socioeconomic, and health outcomes of a food forest initiative in Santa Elena Province, Ecuador: a study protocol.
Front Public Health
January 2025
Centro de Investigación, Instituto Superior Tecnológico Argos, Guayaquil, Ecuador.
Malnutrition is an escalating concern in low-and-middle-income countries (LMICs), including Ecuador, particularly within rural settings. To address this issue, food forests emerge as a promising intervention. This research protocol outlines a controlled intervention in the province of Santa Elena, aiming to evaluate the efficacy of a food forest in enhancing nutritional outcomes, with potential implications for broader replication.
View Article and Find Full Text PDFSpatiotemporal variation in population size and relative abundance of Kalij pheasant (Lophura leucomelanos) in scrub forest.
PLoS One
January 2025
Ministry of Climate Change, Islamabad Wildlife Management Board, Islamabad, Pakistan.
Most species of pheasants (Galliformes: Phasianidae) occur in Asia. In Pakistan, pheasants occur from 245-3050 m in altitude, and one of these, the Kalij pheasant (Lophura leucomelanos) is a large-bodied, brightly-colored habitat quality indicator species. The present study was designed to determine spatiotemporal variation, population size, and the relative abundance of Kalij pheasants in Haripur, Pakistan.
View Article and Find Full Text PDFUtilising causal inference methods to estimate effects and strategise interventions in observational health data.
PLoS One
January 2025
Applied Artificial Intelligence Institute (A2I2), Deakin University, Geelong, Australia.
Randomised controlled trials (RCTs) are the gold standard for evaluating health interventions but often face ethical and practical challenges. When RCTs are not feasible, large observational data sets emerge as a pivotal resource, though these data sets may be subject to bias and unmeasured confounding. Traditional statistical (or non-causal) learning methods, while useful, face limitations in fully uncovering causal effects, i.
View Article and Find Full Text PDFSurvival machine learning methods for mortality prediction after heart transplantation in the contemporary era.
PLoS One
January 2025
Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America.
Although prediction models for heart transplantation outcomes have been developed previously, a comprehensive benchmarking of survival machine learning methods for mortality prognosis in the most contemporary era of heart transplants following the 2018 donor heart allocation policy change is warranted. This study assessed seven statistical and machine learning algorithms-Lasso, Ridge, Elastic Net, Cox Gradient Boost, Extreme Gradient Boost Linear, Extreme Gradient Boost Tree, and Random Survival Forests in a post-policy cohort of 7,160 adult heart-only transplant recipients in the Scientific Registry of Transplant Recipients (SRTR) database who received their first transplant on or after October 18, 2018. A cross-validation framework was designed in mlr.
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!