RESUMEN

Impaired callus remodeling significantly contributes to the delayed healing of osteoporotic fractures; however, the underlying mechanisms remain unclear. Sensory neuronal signaling plays a crucial role in bone repair. In this study, we aimed to investigate the pathological mechanisms hindering bone remodeling in osteoporotic fractures, particularly focusing on the role of sensory neuronal signaling. We demonstrate that in ovariectomized (OVX) mice, the loss of CGRP+TrkA+ sensory neuronal signaling during callus remodeling correlates with increased Cx3cr1+iOCs expression within the bone callus. Conditional knockout of Cx3cr1+iOCs restored CGRP+TrkA+ sensory neuronal, enabling normal callus remodeling progression. Mechanistically, we further demonstrate that Cx3cr1+iOCs secrete Sema3A in the osteoporotic fracture repair microenvironment, inhibiting CGRP+TrkA+ sensory neurons' axonal regeneration and suppressing nerve-bone signaling exchange, thus hindering bone remodeling. Lastly, in human samples, we observed an association between the loss of CGRP+TrkA+ sensory neuronal signaling and increased expression of Cx3cr1+iOCs. In conclusion, enhancing CGRP+TrkA+ sensory nerve signaling by inhibiting Cx3cr1+iOCs activity presents a potential strategy for treating delayed healing in osteoporotic fractures. Inhibition of inflammatory osteoclasts enhances CGRP+TrkA+ signaling and accelerates callus remodeling in osteoporotic fractures.

RESUMEN

OBJECTIVE: Ultrasound localization microscopy (ULM) breaks the acoustic diffraction limit. However, the temporal resolution of ULM is relatively low because of a long data-acquisition time. METHODS: Inspired by super-resolution optical fluctuation imaging (SOFI), in this paper, we propose a method for ultrasound imaging with improved imaging performance, which is achieved by using cumulant analysis. Specifically, to eliminate the axial oscillations, here, the cumulant analysis framework is extended, which is used to process the complex-valued analytic signals rather than the real-valued signals. RESULTS: The results from the numerical simulations and in vitro physical phantom experiments indicate that by generalizing cumulant analysis to complex-valued signals, a high imaging performance is achieved with an improvement of ∼35%-42% (lateral direction) and ∼41%-42% (axial direction) in the resolution compared with the temporal mean envelope image, in terms of full-width-half-max (FWHM). In particularly, the axial oscillations appearing in the real cumulant images are effectively eliminated by the complex cumulant analysis. Moreover, the proposed method can easily take advantage of SOFI. In the phantom experiment, a short data-acquisition time (∼2 sec) is enough to obtain the improved spatial resolution. CONCLUSION: The proposed method offers an implementation of US with high spatial resolution, fast data-acquisition speed, and axial oscillations removal characteristics. SIGNIFICANCE: The method provides the potential in US imaging fast biological processes in vivo.

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RESUMEN

Single-view cone beam x-ray luminescence optical tomography (CB-XLOT) has the merit of short data acquisition time, which is important for resolving fast biological processes in vivo. However, challenges remain in the reconstruction of single-view CB-XLOT. In our previous work, by using the sparsity-based reconstruction method, we have demonstrated the feasibility of single-view CB-XLOT. But, when the imaging conditions become complicated (e.g. multiple adjacent nanophosphors (NPs) contained in imaged object), it is difficult to resolve each NP by the previous method. To solve the problem, we hereby present a sparsity reconstruction method based on group information, termed Group_YALL1. The imaging performance of single-view CB-XLOT can be further improved by utilizing the group sparsity characteristic of NPs as a priori knowledge of reconstruction constraint. To assess the capability of the method, we used a customized CB-XLOT/XCT system to perform the numerical simulation and physical phantom experiments. The experimental results demonstrate that compared with the former sparse reconstruction method (e.g. YALL1), the proposed Group_YALL1 method can accurately resolve the NPs embedded in the object, even if they are close to each other. The acquired location error is less than 1 mm. Hence, this method has the potential to greatly reduce the data acquisition time while preserving a high imaging quality.

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