Dual cross-linked COL1/HAp bionic gradient scaffolds containing human amniotic mesenchymal stem cells promote rotator cuff tendon-bone interface healing.

The tendon-bone interface heals through scar tissue, while the lack of a natural interface gradient structure and collagen fibre alignment leads to the occurrence of retearing. Therefore, the promotion of tendon healing has become the focus of regenerative medicine. The purpose of this study was to develop a gradient COL1/ hydroxyapatite (HAp) biomaterial loaded with human amniotic mesenchymal stem cells (hAMSCs). The performance of common cross-linking agents, Genipin, 1-ethyl-3-(3-dimethylaminopropyl) carbodiimide/N-hydroxysuccinimide (EDC/NHS), and dual cross-linked materials were compared to select the best cross-linking mechanism to optimize the biological and mechanical properties of the scaffold. The optimal COL1/HAp-loaded with hAMSCs were implanted into the tendon-bone rotator cuff interfaces in rats and the effect on the tendon-bone healing was assessed by micro-CT, histological analysis, and biomechanical properties. The results showed that Genipin and EDC/NHS dual cross-linked COL1/HAp had good biological activity and mechanical properties and promoted the proliferation and differentiation of hAMSCs. Animal experiments showed that the group using a scaffold loaded with hAMSCs had excellent continuity and orientation of collagen fibers, increased fibrocartilage and bone formation, and significantly higher biomechanical functions than the control group at the interface at 12 weeks post operation. This study demonstrated that dual cross-linked gradient COL1/HAp-loaded hAMSCs could promote interface healing, thereby providing a feasible strategy for tendon-bone interface regeneration.

Development and Validation of a Predictive Model for Chronic Postsurgical Pain After Arthroscopic Rotator Cuff Repair: A Prospective Cohort Study.

Purpose: Chronic pain management continues to present a significant challenge following arthroscopic shoulder surgery. Our purpose was to detect chronic postsurgical pain (CPSP) in patients who had undergone arthroscopic rotator cuff repair (ARCR) and develop a nomogram capable of predicting the associated risk. Patients and Methods: We collected the demographic and clinical data of 240 patients undergoing ARCR in our hospital from January 2021 to May 2022. The pain level was monitored and evaluated three months after ARCR. LASSO regression was used to screen out pain-predicting factors, which were subsequently used to construct a nomogram. Internal validation was carried out using Bootstrap resampling. The data of 78 patients who underwent ARCR in our hospital from August 2022 to December 2022 were also collected for external verification of the nomogram. The predictive model was evaluated using the receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA). Results: Age, duration of preoperative shoulder pain (DPSP), C-reactive protein (CRP), number of tear tendons, and American Shoulder and Elbow Surgical Score (ASES) were screened by LASSO regression as predictive factors for CPSP. These factors were then used to construct a chronic pain risk nomogram. The area under the curve (AUC) of the predictive and validation models were 0.756 (95% CI: 0.6386-0.8731) and 0.806 (95% CI: 0.6825-0.9291), respectively. Furthermore, the calibration curves and decision curve analysis (DCA) for both models indicated strong performance, affirming the reliability of this predictive model. Conclusion: The CPSP risk model that has been developed exhibits strong predictive capabilities and practical utility. It offers valuable support to clinical healthcare professionals in making informed treatment decisions, reducing the unnecessary use of analgesic drugs, and optimizing the allocation of medical resources.