X-ray computed tomography (XCT) and X-ray fluorescence (XRF) imaging are two non-invasive imaging techniques to study cellular structures and chemical element distributions, respectively. However, correlative X-ray computed tomography and fluorescence imaging for the same cell have yet to be routinely realized due to challenges in sample preparation and X-ray radiation damage. Here we report an integrated experimental and computational workflow for achieving correlative multi-modality X-ray imaging of a single cell. The method consists of the preparation of radiation-resistant single-cell samples using live-cell imaging-assisted chemical fixation and freeze-drying procedures, targeting and labeling cells for correlative XCT and XRF measurement, and computational reconstruction of the correlative and multi-modality images. With XCT, cellular structures including the overall structure and intracellular organelles are visualized, while XRF imaging reveals the distribution of multiple chemical elements within the same cell. Our correlative method demonstrates the feasibility and broad applicability of using X-rays to understand cellular structures and the roles of chemical elements and related proteins in signaling and other biological processes.
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http://dx.doi.org/10.1038/s42003-024-05950-y | DOI Listing |
J Med Internet Res
January 2025
Department of Health Policy and Management, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, United States.
Background: Uncertainty in the diagnosis of lung nodules is a challenge for both patients and physicians. Artificial intelligence (AI) systems are increasingly being integrated into medical imaging to assist diagnostic procedures. However, the accuracy of AI systems in identifying and measuring lung nodules on chest computed tomography (CT) scans remains unclear, which requires further evaluation.
View Article and Find Full Text PDFThe current study aimed to objectively evaluate the fit of a rectangular, tapered stem to the severely dysplastic hips on the basis of the proximal femoral anatomy and the dimensional properties of the stem. It was hypothesized that the stem size planned with accordance to the diaphyseal canal width alone can accommodate the distal femur successfully with no sizing mismatch. Forty-six patients (53 hips) suffering from secondary osteoarthritis due to hip dysplasia scheduled for total hip arthroplasty (THA) with a subtrochanteric transverse shortening osteotomy were included.
View Article and Find Full Text PDFJ Bone Miner Res
January 2025
Sahlgrenska Osteoporosis Centre, Department of Internal Medicine and Clinical Nutrition, Institute of Medicine, University of Gothenburg, Gothenburg, Sweden.
The socioeconomic burden of hip fractures, the most severe osteoporotic fracture outcome, is increasing and the current clinical risk assessment lacks sensitivity. This study aimed to develop a method for improved prediction of hip fracture by incorporating measurements of bone microstructure and composition derived from high-resolution peripheral quantitative computed tomography (HR-pQCT). In a prospective cohort study of 3028 community-dwelling women aged 75 to 80, all participants answered questionnaires and underwent baseline examinations of anthropometrics and bone by dual x-ray absorptiometry (DXA) and HR-pQCT.
View Article and Find Full Text PDFPLoS One
January 2025
School of Emergency Management, Institute of Disaster Prevention, Sanhe, Hebei, China.
With the increasing number of patients with Alzheimer's Disease (AD), the demand for early diagnosis and intervention is becoming increasingly urgent. The traditional detection methods for Alzheimer's disease mainly rely on clinical symptoms, biomarkers, and imaging examinations. However, these methods have limitations in the early detection of Alzheimer's disease, such as strong subjectivity in diagnostic criteria, high detection costs, and high misdiagnosis rates.
View Article and Find Full Text PDFIn 2019, the novel coronavirus swept the world, exposing the monitoring and early warning problems of the medical system. Computer-aided diagnosis models based on deep learning have good universality and can well alleviate these problems. However, traditional image processing methods may lead to high false positive rates, which is unacceptable in disease monitoring and early warning.
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