The growing prevalence of exercise-induced tibial stress fractures demands wearable sensors capable of monitoring dynamic musculoskeletal loads with medical-grade precision. While flexible pressure-sensing insoles show clinical potential, their development has been hindered by the intrinsic trade-off between high sensitivity and full-range linearity (R ² > 0.99 up to 1 MPa) in conventional designs. Inspired by the tactile sensing mechanism of human skin, where dermal stratification enables wide-range pressure adaptation and ion-channel-regulated signaling maintains linear electrical responses, we developed a dual-mechanism flexible iontronic pressure sensor (FIPS). This innovative design synergistically combines two bioinspired components: interdigitated fabric microstructures enabling pressure-proportional contact area expansion (∝ P ^1/3) and iontronic film facilitating self-adaptive ion concentration modulation (∝ P ^2/3), which together generate a linear capacitance-pressure response (C ∝ P). The FIPS achieves breakthrough performance: 242 kPa⁻¹ sensitivity with 0.997 linearity across 0–1 MPa, yielding a record linear sensing factor (LSF = 242,000). The design is validated across various substrates and ionic materials, demonstrating its versatility. Finally, the FIPS-driven design enables a smart insole demonstrating 1.8% error in tibial load assessment during gait analysis, outperforming nonlinear counterparts (6.5% error) in early fracture-risk prediction. The biomimetic design framework establishes a universal approach for developing high-performance linear sensors, establishing generalized principles for medical-grade wearable devices. Graphical Abstract

Globally, osteoarthritis (OA) is the most prevalent joint disease and is characterized by infiltration of M1 macrophages in the synovium, anabolic-catabolic imbalance of the extracellular matrix (ECM), increased articular shear force and overproduction of reactive oxygen species (ROS). Disease-modifying OA drugs are not yet available, and treatments for OA focus solely on reducing pain and inflammation and have limited therapeutic effect. Herein, we developed an injectable self-lubricating poly(N-acryloyl alaninamide) (PNAAA) hydrogel loaded with platelet lysate (PL) (termed "PNAAA@PL") for treating OA. Tribological and drug release tests revealed suitable lubrication properties and sustained release of bioactive factors in PNAAA@PL. In vitro experiments showed that PNAAA@PL alleviated interleukin-1β (IL-1β)-induced anabolic-catabolic imbalance of chondrocytes and repolarized pro-inflammatory M1 macrophages to the anti-inflammatory M2 phenotype via intracellular ROS scavenging. Additionally, the PNAAA@PL hydrogel enhanced the migratory capacity and chemotaxis ability of stem cells, which are essential for chondrogenesis. In vivo, the functionalized PNAAA@PL hydrogel acted like synovial fluid following intra-articular injection into a rat OA model with anterior cruciate ligament transection, ultimately attenuating cartilage degeneration and synovitis. According to molecular mechanism studies, PNAAA@PL repairs cartilage in the OA model by inhibiting the NF-ĸB pathway. Overall, this self-lubricating PNAAA@PL hydrogel offers a comprehensive strategy for preventing OA progression by engineering a biophysiochemical microenvironment to generate high-quality hyaline cartilage.

Background: This meta-analysis aimed to evaluate the current evidence on the diagnostic performance of MRI/MRA for detecting acetabular labral tears (ALT). Methods: We systematically searched the PubMed, Embase, and Cochrane library until February 5, 2021, to identify original research studies reporting the diagnostic performance of MRI/MRA for the detection of ALT. Study methodological quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool. The summary sensitivity (Se) and specificity (Sp) of the studies were estimated using a bivariate model. We calculated the post-test probability to assess the clinical utility of MRI/MRA. Univariate meta-regression and subgroup analyses were performed to assess between-study heterogeneity. Results: We included 22 studies (n = 1670 patients). The meta-analytic summary Se and Sp for MRI were 0.8 (95% CI 0.51–0.94) and 0.77 (95% CI 0.68–0.84), respectively, while for MRA they were 0.89 (95% CI 0.82–0.93) and 0.69 (95% CI 0.56–0.80). MRA showed a higher area under the summary receiver operating curve (SROC) (0.87 vs. 0.80) than MRI. MRI could increase the post-test probability to 0.78 and could decrease the post-test probability to 0.21, MRA could increase the post-test probability to 0.74 and could decrease the post-test probability to 0.14. Meta-regression analysis showed two significant factors affecting study heterogeneity: MR field strength and reference standard. After dividing the studies into two subgroups based on the MR field strength, we found that the Se values of 3.0 T MRI were very close to MRA (0.87 vs. 0.89), the Sp values of 3.0 T MRI were superior to MRA (0.77 vs. 0.69). Conclusions: Given that 3.0 T MRI could provide a non-invasive, fast and convenient method to recognize suspicious ALT cases, 3.0 T MRI is more recommended than MRA.

Objective To investigate the outcomes of hip arthroscopy for femoroacetabular impingement (FAI) in patients over the age of 50 years. Method This is a therapeutic case series study. A total of 27 FAI patients over the age of 50 years who met inclusion and exclusion criteria and were being followed up for at least 2 years in the orthopaedics department at our hospital between January 2015 and October 2017 were recruited for a prospective analysis on the outcomes of hip arthroscopy. All patients underwent unilateral surgery. Of the patients included, there were 15 men and 12 women, who were aged 50–74 years old (57 ± 6.4 years). The outcomes were assessed using the visual analog scale (VAS), the modified Harris hip score (mHHS), and the International Hip Outcome Tool (iHOT‐12). Results A total of 27 patients were followed up for at least 2 years. The postoperative center‐edge angle, the alpha angle, and the offset decreased significantly compared with preoperative measurements (P < 0.01). The mHHS before surgery and at 1 year and 2 years after surgery was 62.19 ± 7.47, 86.70 ± 5.80, and 87.89 ± 5.08, respectively; iHOT‐12 scores were 30.44 ± 4.22, 73.56 ± 3.89, and 73.77 ± 3.72, respectively; VAS scores were 6.07 ± 0.78, 1.93 ± 0.73, and 1.59 ± 0.64, respectively. As compared with the condition before surgery, there was a significant improvement in the mHHS, iHOT‐12, and VAS scores at 1 year and 2 years after surgery (P < 0.01). The mHHS score at 2‐year follow up after surgery was higher than that at 1 year after surgery, and the difference observed was statistically significant (P = 0.04). One patient with severe acetabular and femoral cartilage damage underwent total hip replacement 11 months after surgery. Conclusion Hip arthroscopy considerably improved hip symptoms and function in Chinese FAI patients aged 50 years or older who did not have severe radiographic osteoarthritis. The conversion to THA and complications were low. Strict surgical indications and appropriate surgical strategies lay the foundation for satisfactory postoperative results in elderly patients with FAI.

Purpose: This retrospective study summarized the clinical, radiographic, and arthroscopic manifestation of synovial chondromatosis (SC) of the hip, along with the post-operative effect to discuss the curative effect of arthroscopic management of hip SC. Methods: Twenty-one patients who underwent arthroscopic surgery from the same surgeon for hip SC were followed up for an average of 45 months. T-shaped capsulotomy was routinely performed in each case. Visual analog scale, range of motion, modified Harris Hip Score, and International Hip Outcome Tool score were collected preoperatively and at the time of the latest follow-up. All patients’ demographics, radiographs, and arthroscopic images were collected to summarize and conclude the similarities and differences of their manifestation. Results: Large wedged clumps of loose bodies demonstrated distinguishable radiographic, arthroscopic appearance and demanded a different surgical strategy. Postoperative scores were all significantly improved. One case of residual pain and two cases of residual loose bodies with no symptom related were reported at the final follow-up. All but one patient were satisfied with the outcome. Conclusion: Arthroscopy treatment of hip SC with T-shaped capsulotomy has demonstrated a good result in terms of clinical outcome score, recurrence rate, and complication rate. On the basis of this study, we concluded the clinical performance of large wedged clumps of loose bodies of hip SC.

Musculoskeletal injuries induced by high-intensity and repetitive physical activities represent one of the primary health concerns in the fields of public fitness and sports. Musculoskeletal injuries, often resulting from unscientific training practices, are particularly prevalent, with the tibia being especially vulnerable to fatigue-related damage. Current tibial load monitoring methods rely mainly on laboratory equipment and wearable devices, but datasets combining both sources are limited due to experimental complexities and signal synchronization challenges. Moreover, wearable-based algorithms often fail to capture deep signal features, hindering early detection and prevention of tibial fatigue injuries. In this study, we simultaneously collected data from laboratory equipment and wearable insole sensors during in-place running by volunteers, creating a dataset named WearLab-Leg. Based on this dataset, we developed a machine learning model integrating Temporal Convolutional Network (TCN) and Transformer modules to estimate vertical ground reaction force (vGRF) and tibia bone force (TBF) using insole pressure signals. Our model’s architecture effectively combines the advantages of local deep feature extraction and global modeling, and further introduces the Weight-MSELoss function to improve peak prediction performance. As a result, the model achieved a normalized root mean square error (NRMSE) of 7.33% for vGRF prediction and 10.64% for TBF prediction. Our dataset and proposed model offer a convenient solution for biomechanical monitoring in athletes and patients, providing reliable data and technical support for early warnings of fatigue-induced injuries.