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Objective: To determine whether proximal sesamoid bone (PSB) microdamage and fracture toughness differ between Thoroughbred racehorses sustaining PSB fracture and controls.
Study Design: Cadaveric case-control.
Animals: Twenty-four Thoroughbred racehorses (n = 12 PSB fracture, n = 12 control).
Methods: Proximal sesamoid bones were dissected, and gross pathological changes and morphological measurements were documented. High-speed exercise history data were evaluated. Microdamage was assessed in fracture, fracture-contralateral limb (FXCL) and control PSBs using whole bone lead uranyl acetate (LUA) staining with micro-CT imaging or basic fuchsin histological analysis. Fracture toughness mechanical testing was carried out in 3-point-bending of microbeams created from PSB flexor cortices. Data were analyzed using ordinal logistic and linear regression models.
Results: Microdamage was detected most commonly in the articular subchondral region of PSBs via LUA micro-CT and basic fuchsin histology. There were no differences in microdamage between FXCL and control PSBs. Fracture toughness values were similar for FXCL (1.31 MPa√m) and control (1.35 MPa√m) PSBs. Exercise histories were similar except that horses sustaining fracture spent a greater percentage of their careers in rest weeks.
Conclusion: Microdamage was detected in the articular region of PSBs but was not greater in horses sustaining catastrophic PSB fracture. Fracture toughness of PSB flexor cortices did not differ between FXCL and control PSBs.
Clinical Significance: Although uncommon, microdamage is localized to the articular region of Thoroughbred racehorse PSBs. Catastrophic PSB failure is not associated with lower PSB flexor cortex fracture toughness.
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http://dx.doi.org/10.1111/vsu.13816 | DOI Listing |
ACS Appl Mater Interfaces
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College of Materials Science and Engineering, Hunan University, Changsha 410082, China.
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Department of Bioengineering, Imperial College London, London, UK.
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McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, TX 78712.
Many soft, tough materials have emerged in recent years, paving the way for advances in wearable electronics, soft robotics, and flexible displays. However, understanding the interfacial fracture behavior of these materials remains a significant challenge, owing to the difficulty of quantifying the respective contributions from viscoelasticity and damage to energy dissipation ahead of cracks. This work aims to address this challenge by labeling a series of polymer networks with fluorogenic mechanophores, subjecting them to T-peel tests at various rates and temperatures, and quantifying their force-induced damage using a confocal microscope.
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Key Laboratory of Leather Chemistry and Engineering of Ministry of Education, Sichuan University, Chengdu 610065, China.
Gel-based electronic skin (e-skin) has recently emerged as one of the most promising interfaces for human-machine interaction and wearable devices, owing to its exceptional flexibility, extensibility, transparency, biocompatibility, high-quality physiological signal monitoring, and system integration suitability. However, conventional hydrogel-based e-skins may exhibit limitations in mechanical strength and stretchability compatibility, as well as poor environmental stability. To address these challenges, following a top-down fabrication strategy, this study innovatively integrates poly(methacrylic acid), titanium sulfate, and ethylene glycol (EG) into the three-dimensional collagen fiber network structure of zeolite-tanned sheepskin to successfully develop an organogel (SMEMT) e-skin, which exhibits superior high toughness, environmental stability, high transparency (74% light transmittance at 550 nm), antibacterial properties and ecological compatibility.
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September 2025
Department of Orthopaedic Surgery, Vanderbilt University Medical Center, 1215 21st Ave. S., Suite 4200, Nashville, TN 37232, USA.
This study applied Raman spectroscopy (RS) to ex vivo human cadaveric femoral mid-diaphysis cortical bone specimens ( = 118 donors; age range 21-101 years) to predict fracture toughness properties via machine learning (ML) models. Spectral features, together with demographic variables (age, sex) and structural parameters (cortical porosity, volumetric bone mineral density), were fed into support vector regression (SVR), extreme tree regression (ETR), extreme gradient boosting (XGB), and ensemble models to predict fracture-toughness metrics such as crack-initiation toughness (K) and energy-to-fracture (J-integral). Feature selection was based on Raman-derived mineral and organic matrix parameters, such as νPhosphate (PO)/CH-wag, νPO/Amide I, and others, to capture the complex composition of bone.
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