Ancient remains preserved in glaciers present a unique opportunity for us to advance our knowledge of human origins, diversity, and health, a central focus of anthropological studies. Cellular components of hard and soft tissue from frozen human remains dated between 1670 to 1850 cal AD recovered from a glacier in Canada were studied. Despite the expected ice crystal damage in some samples, regions of recognizable structure and ultrastructure were observed. We found that the state of preservation was tissue specific and that in some tissues the organelles were better preserved than in others. Skeletal, connective, nervous, and epithelial tissues were recognizable in some of the samples. DNA had been previously extracted from these remains and this study illustrates that the ability to successfully extract DNA may correlate with good preservation of histology.
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http://dx.doi.org/10.1002/ajpa.20864 | DOI Listing |
ACS Nano
January 2025
National Engineering Research Center for Biomaterials, College of Biomedical Engineering, Sichuan University, 29 Wangjiang Road, Chengdu 610064, P. R. China.
Osteoarthritis (OA) presents a significant therapeutic challenge, with few options for preserving joint cartilage and repairing associated tissue damage. Inflammation is a pivotal factor in OA-induced cartilage deterioration and synovial inflammation. Recently, exosomes derived from human umbilical cord mesenchymal stem cells (HucMSCs) have gained recognition as a promising noncellular therapeutic modality, but their use is hindered by the challenge of harvesting a sufficient number of exosomes with effective therapeutic efficacy.
View Article and Find Full Text PDFComput Biol Med
January 2025
University of Rwanda, Rwanda. Electronic address:
Deep learning methods have significantly improved medical image analysis, particularly in detecting COVID-19 chest X-rays. Nonetheless, these methodologies frequently inhibit some drawbacks, such as limited interpretability, extensive computational resources, and the need for extensive datasets. To tackle these issues, we introduced two novel algorithms: the Dynamic Co-Occurrence Grey Level Matrix (DC-GLM) and the Contextual Adaptation Multiscale Gabor Network (CAMSGNeT).
View Article and Find Full Text PDFInt J Biol Macromol
January 2025
College of Food Science and Engineering, Inner Mongolia Agricultural University, 306 Zhaowuda Road, Hohhot, Inner Mongolia 010010, China. Electronic address:
Biodegradable plastics are increasingly utilized in packaging, driven by green chemistry and environmental responsibility. Among them, poly(L-lactic acid) (PLLA) stands out due to its biodegradability and biocompatibility. However, its limited gas permeability and selectivity hinder its application in produce preservation.
View Article and Find Full Text PDFMed Image Anal
January 2025
Department of Computer and Data Science and Department of Biomedical Engineering, Case Western Reserve University, Cleveland, USA.
Accurate automatic polyp segmentation in colonoscopy is crucial for the prompt prevention of colorectal cancer. However, the heterogeneous nature of polyps and differences in lighting and visibility conditions present significant challenges in achieving reliable and consistent segmentation across different cases. Therefore, this study proposes a novel dynamic spectrum-driven hierarchical learning model (DSHNet), the first to specifically leverage image frequency domain information to explore region-level salience differences among and within polyps for precise segmentation.
View Article and Find Full Text PDFComput Biol Med
January 2025
Hamburg University of Technology, Hamburg, Germany.
The application of supervised models to clinical screening tasks is challenging due to the need for annotated data for each considered pathology. Unsupervised Anomaly Detection (UAD) is an alternative approach that aims to identify any anomaly as an outlier from a healthy training distribution. A prevalent strategy for UAD in brain MRI involves using generative models to learn the reconstruction of healthy brain anatomy for a given input image.
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