Sensory nerves are long being recognized as collecting units of various outer stimuli; recent advances indicate that the sensory nerve also plays pivotal roles in maintaining organ homeostasis. Here, this study shows that sensory nerve orchestrates intervertebral disc (IVD) homeostasis by regulating its extracellular matrix (ECM) metabolism. Specifically, genetical sensory denervation of IVD results in loss of IVD water preserve molecule chondroitin sulfate (CS), the reduction of CS bio‐synthesis gene chondroitin sulfate synthase 1 (CHSY1) expression, and dysregulated ECM homeostasis of IVD. Particularly, knockdown of sensory neuros calcitonin gene‐related peptide (CGRP) expression induces similar ECM metabolic disorder compared to sensory nerve denervation model, and this effect is abolished in CHSY1 knockout mice. Furthermore, in vitro evidence shows that CGRP regulates nucleus pulposus cell CHSY1 expression and CS synthesis via CGRP receptor component receptor activity‐modifying protein 1 (RAMP1) and cyclic AMP response element‐binding protein (CREB) signaling. Therapeutically, local injection of forskolin significantly attenuates IVD degeneration progression in mouse annulus fibrosus puncture model. Overall, these results indicate that sensory nerve maintains IVD ECM homeostasis via CGRP/CHSY1 axis and promotes IVD repair, and this expands the understanding concerning how IVD links to sensory nerve system, thus shedding light on future development of novel therapeutical strategy to IVD degeneration.

Intervertebral disc degeneration (IDD) has been identified as one of the predominant factors leading to persistent low back pain and disability in middle-aged and elderly people. Dysregulation of Prostaglandin E2 (PGE2) can cause IDD, while low-dose celecoxib can maintain PGE2 at the physiological level and activate the skeletal interoception. Here, as nano fibers have been extensively used in the treatment of IDD, novel polycaprolactone (PCL) nano fibers loaded with low-dose celecoxib were fabricated for IDD treatment. In vitro studies demonstrated that the nano fibers had the ability of releasing low-dose celecoxib slowly and sustainably and maintain PGE2. Meanwhile, in a puncture-induced rabbit IDD model, the nano fibers reversed IDD. Furthermore, low-dose celecoxib released from the nano fibers was firstly proved to promote CHSY3 expression. In a lumbar spine instability-induced mouse IDD model, low-dose celecoxib inhibited IDD in CHSY3wt mice rather than CHSY3−/− mice. This model indicated that CHSY3 was indispensable for low-dose celecoxib to alleviate IDD. In conclusion, this study developed a novel low-dose celecoxib-loaded PCL nano fibers to reverse IDD by maintaining PGE2 at the physiological level and promoting CHSY3 expression.

Background Cervical spinal malalignment and instability are frequently occurring pathological conditions involving neck pain, radiculopathy, and myelopathy, often requiring surgical intervention. Accurate assessment of cervical alignment and instability are essential in surgical planning and evaluating postoperative outcomes. Purpose To automatically measure the sagittal alignment and instability of the cervical spine, we develop a novel deep‐learning model by detecting landmarks on cervical radiographs. Methods We introduce the transformer‐embedded residual network (ResNet) as the network's core to automatically identify vertebral landmarks on digital and film‐transformed cervical radiographs, and simultaneously measure the segmental Cobb angle and horizontal displacement. A Transformer Module was embedded into the latent space to extract the relationship between different vertebrae. Then a Rotating Attention Module was integrated between the encoder‐decoder pairs to highlight the key points and maintain more details. Finally, a Vector Loss Module was proposed to restrain the orientation of the adjacent vertebra to reduce misdetection. All images were obtained from local hospital. Digital images were split into training, validation, and test subsets (896, 225, and 353 images, respectively). Likewise, film‐transformed images were split into 404, 115, and 150 images, respectively. The results of the model were compared with manual measurements. Results Our deep learning algorithm achieved mean absolute difference (MAD) at a level of 2.20° and 2.33°, symmetric mean absolute error(SMAPE)at 16.63% and 19.35%, respectively, when measuring Cobb angle on digital images and films. On evaluating cervical instability, the diagnostic accuracy, sensitivity, specificity, precision, and F1‐score evaluation metrics were calculated. The corresponding values were 89.80%, 86.49%, 90.68%, 71.11%, and 78.05% on digital images, and 90.00%, 83.78%, 91.15%, 75.61%, and 79.49% on film‐transformed images, which were comparable to experienced surgeons. Visualization results demonstrated robust effectiveness in subjects with severe osteophytes or artifacts. Conclusion This study presents a novel and efficient deep‐learning model to assist landmarks identification and angulation and displacement calculation on lateral cervical spine radiographs, and demonstrates excellent accuracy in measuring cervical alignment and sound sensitivity and specificity in cervical instability diagnosis. It should be helpful for future research and clinical applications.