Osteocytes, the most abundant bone cells, form an interconnected network in the lacunar-canalicular pore system (LCS) buried within the mineralized matrix, which allows osteocytes to obtain nutrients from the blood supply, sense external mechanical signals, and communicate among themselves and with other cells on bone surfaces. In this study, we examined key features of the LCS network including the topological parameter and the detailed structure of individual connections and their variations in cortical and cancellous compartments, at different ages, and in two disease conditions with altered mechanosensing (perlecan deficiency and diabetes). LCS network showed both topological stability, in terms of conservation of connectivity among osteocyte lacunae (similar to the "nodes" in a computer network), and considerable variability the pericellular annular fluid gap surrounding lacunae and canaliculi (similar to the "bandwidth" of individual links in a computer network). Age, in the range of our study (15-32 weeks), affected only the pericellular fluid annulus in cortical bone but not in cancellous bone. Diabetes impacted the spacing of the lacunae, while the perlecan deficiency had a profound influence on the pericellular fluid annulus. The LCS network features play important roles in osteocyte signaling and regulation of bone growth and adaptation.
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http://dx.doi.org/10.1038/boneres.2015.9 | DOI Listing |
Artif Intell Med
December 2024
Department of Electrical and Computer Engineering, Duke University, Durham, NC, United States of America; Medical Physics Graduate Program, Duke University, Durham, NC, United States of America; Department of Radiology, Duke University, Durham, NC, United States of America; Department of Biomedical Engineering, Duke University, Durham, NC, United States of America; Department of Radiation Oncology, Duke University, Durham, NC, United States of America; Department of Pathology, Duke University, Durham, NC, United States of America. Electronic address:
In this paper, we introduce a novel concordance-based predictive uncertainty (CPU)-Index, which integrates insights from subgroup analysis and personalized AI time-to-event models. Through its application in refining lung cancer screening (LCS) predictions generated by an individualized AI time-to-event model trained with fused data of low dose CT (LDCT) radiomics with patient demographics, we demonstrate its effectiveness, resulting in improved risk assessment compared to the Lung CT Screening Reporting & Data System (Lung-RADS). Subgroup-based Lung-RADS faces challenges in representing individual variations and relies on a limited set of predefined characteristics, resulting in variable predictions.
View Article and Find Full Text PDFBrain Sci
October 2024
Unit of Neurology, Neurophysiology, Neurobiology and Psichiatry, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 21, 00128 Rome, Italy.
Background/objectives: Driving abilities require the synchronized activity of cerebral networks associated with sensorimotor integration, motricity, and executive functions. Drivers with Parkinson's disease (DwP) have impaired driving ability, but little is known about the impact of "wearing-off" and therapies in addition to L-DOPA on driving capacities. This study aimed to (i) compare driving performance between DwP during different motor states and healthy controls and (ii) assess the impact of add-on therapies on driving abilities.
View Article and Find Full Text PDFMed Phys
November 2024
Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA.
Background: Voxel-based analysis (VBA) for population level radiotherapy (RT) outcomes modeling requires topology preserving inter-patient deformable image registration (DIR) that preserves tumors on moving images while avoiding unrealistic deformations due to tumors occurring on fixed images.
Purpose: We developed a tumor-aware recurrent registration (TRACER) deep learning (DL) method and evaluated its suitability for VBA.
Methods: TRACER consists of encoder layers implemented with stacked 3D convolutional long short term memory network (3D-CLSTM) followed by decoder and spatial transform layers to compute dense deformation vector field (DVF).
Environ Monit Assess
November 2024
Department of Civil Engineering, National Aerosol FacilityIndian Institute of TechnologyUttar Pradesh, Kanpur, 208016, India.
J Ethnopharmacol
February 2025
Interdisciplinary Institute for Personalized Medicine in Brain Disorders, Jinan University, Guangzhou, 510632, China; The Guangdong-Hongkong-Macau Joint Laboratory of Traditional Chinese Medicine Regulation of Brain-Periphery Homeostasis and Comprehensive Health, Guangzhou, 510632, China; Zhuhai Institute of Jinan University, Zhuhai, 519070, China. Electronic address:
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