98%
921
2 minutes
20
Objectives: Strontium isotopes (Sr/Sr) have been used worldwide to track migrations and identify nonlocal individuals in the past. In South America, these studies often use comparative baseline maps, or isoscapes, established by samples from archaeological fauna and geologic formations. However, baseline research has focused on coastal Peru and the Central and South Andean Highlands. Currently, no comparable isoscape exists for Ecuador. Thus, scholars approximate baselines from predictive models and geologic studies, which may not accurately reflect the biologically available strontium in archaeological samples. This study tested the accuracy of predictive archaeological and geologic models for Ecuadorian strontium.
Materials And Methods: We collected 11 faunal samples from eight archaeological sites across three coastal regions and the northern highlands to test for Sr/Sr. All samples were collected from animals with narrow home ranges. Samples were processed at the University of North Carolina at Chapel Hill.
Results: Strontium values ranged from 0.704226 to 0.709764, with significant regional distribution. The lowest values came from highland samples (mean = 0.704296) and clustered by coastal region from north to south (central coast mean = 0.707561; south coast mean = 0.7064118; far south coast mean = 0.709764).
Discussion: This pilot study reveals two trends: First, strontium values cluster regionally despite stratigraphic volcanic influences, and second, values do not correspond to predictive models, particularly along the coast. We suggest that the unique geology of Ecuador means that predictive models based on Peruvian baselines are inappropriate for Ecuadorian strontium studies. There is a need for a large-scale baseline study of biologically available strontium in Ecuadorian archaeological samples.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC12183493 | PMC |
http://dx.doi.org/10.1002/ajpa.70074 | DOI Listing |
Crit Rev Ther Drug Carrier Syst
January 2025
Department of Pharmacology, PSG College of Pharmacy, Coimbatore 641004, Tamil Nadu, India.
Treating neurological disorders is challenging due to the blood-brain barrier (BBB), which limits therapeutic agents, including proteins and peptides, from entering the central nervous system. Despite their potential, the BBB's selective permeability is a significant obstacle. This review explores recent advancements in protein therapeutics for BBB-targeted delivery and highlights computational tools.
View Article and Find Full Text PDFNeural Netw
September 2025
School of Automation and Intelligent Sensing, Shanghai Jiao Tong University, Shanghai, 200240, China; Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, 200240, China; Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, 200240, China.
3D shape defect detection plays an important role in autonomous industrial inspection. However, accurate detection of anomalies remains challenging due to the complexity of multimodal sensor data, especially when both color and structural information are required. In this work, we propose a lightweight inter-modality feature prediction framework that effectively utilizes multimodal fused features from the inputs of RGB, depth and point clouds for efficient 3D shape defect detection.
View Article and Find Full Text PDFAtherosclerosis
September 2025
Department of Cardiothoracic and Macrovascular Surgery, Jingzhou Hospital Affiliated to Yangtze University, No.26 Chuyuan Avenue, Jingzhou District, Jingzhou City, Hubei Province, 434020, China. Electronic address:
Background And Aims: Aortic dissection (AD) is one of the most dangerous and tricky diseases in the field of cardiovascular surgery, severely affecting public health. Recent studies have found that SUMOylation is linked to the pathogenesis of cardiovascular diseases. However, we know very little about the molecular mechanisms of SUMOylation in AD.
View Article and Find Full Text PDFJ Med Internet Res
September 2025
Department of Information Systems and Cybersecurity, The University of Texas at San Antonio, 1 UTSA Circle, San Antonio, TX, 78249, United States, 1 (210) 458-6300.
Background: Adverse drug reactions (ADR) present significant challenges in health care, where early prevention is vital for effective treatment and patient safety. Traditional supervised learning methods struggle to address heterogeneous health care data due to their unstructured nature, regulatory constraints, and restricted access to sensitive personal identifiable information.
Objective: This review aims to explore the potential of federated learning (FL) combined with natural language processing and large language models (LLMs) to enhance ADR prediction.
Ann Intern Med
September 2025
Department of Medicine, Johns Hopkins University School of Medicine, and Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland (J.B.S.).
Electronic health record (EHR) data are increasingly used to develop prediction models that guide clinical decision making at the point of care. These include algorithms that use high-frequency data, like in sepsis prediction, as well as simpler equations, such as the Pooled Cohort Equations for cardiovascular outcome prediction. Although EHR data used in prediction models are often highly granular and more current than other data, there is systematic and nonsystematic missingness in EHR data as there is with most data.
View Article and Find Full Text PDF