Jishizhan Chen

Despite long-standing uncertainty regarding the suitability of animal intervertebral disc (IVD) models for the study of human IVD pathophysiology, few cross-species studies have integrated hierarchical structure with mechanical function. In this study, we present a multiscale structure–mechanics framework combining synchrotron computed tomography (sCT), fibre tracing, digital volume correlation (DVC) with structure-based mechanical inference and finite-element modelling. Rat, mini-pig and human IVDs (n = 3/species) were imaged by sCT, with rat and mini-pig discs additionally subjected to in situ uniaxial compression and DVC. A Structure Index (SI), integrating local structural anisotropy and fibre orientation, was developed to regionally pinpoint the location of mechanically vulnerable regions. Cross-species comparison showed scale-dependent structural conservation: gross disc and lamellar organisation retained common regional features, whereas fibre-bundle architecture exhibited greater species- and regional divergence. Despite these differences, lower SI was consistently associated with elevated deformation in rat and mini-pig discs across multiple strain measures. Structure–strain inverse correlations became tighter within progressively higher-strain domains, reaching Spearman ρ values of approximately −0.5 to −0.7 for several strain components. Synthetic fibre phantoms confirmed that SI reflects a coupling of fibre orientation and organisation, with additional sensitivity to fibre number in poorly organised configurations. In human discs with varying degrees of degeneration, SI revealed spatial heterogeneity and redistribution of structurally vulnerable regions. Fibril-informed homogenisation provided anisotropic material properties for organ-scale finite-element modelling that independently supported SI-based predictions. Overall, these findings establish a cross-species framework linking hierarchical collagen architecture to mechanical vulnerability and provide a basis for non-invasive, structure-driven biomechanical assessment of human IVD degeneration and mechanical risk.

Background: CT and MRI provide detailed clinical assessment of the human spine; however, their millimeter-scale spatial resolution limits direct visualization of tissue microarchitecture and interfaces. Hierarchical phase-contrast tomography (HiP-CT) enables ex vivo non-destructive, micron-resolution imaging of intact human organs without exogenous contrast agents, extending anatomical assessment to spatial scales inaccessible to conventional clinical imaging. Purpose: To evaluate the capability of HiP-CT to visualize macro- to microanatomy of an intact adult human spine ex vivo . Materials and Methods: An intact spine (C1-L5) from a 62-year-old male donor without known skeletal disease was formalin-fixed and imaged by HiP-CT at the European Synchrotron. Whole-spine imaging was performed using 16.26 µm isotropic voxels, with selected regions of interest imaged using 2.2 µm voxels. Ex vivo MRI was performed for anatomical comparison. Three-dimensional segmentation and quantitative analyses included intervertebral disc (IVD) grading, annulus fibrosus fiber tracing, and vertebral trabecular micro-morphometry. Results: HiP-CT enabled high spatial-resolution, multiscale imaging of the intact human spine, simultaneously visualizing vertebral bone and endplate, IVDs, spinal cord and meninges, nerve roots and ganglia, vasculature, ligaments, and musculature. Three-dimensional segmentation and virtual sectioning mapped neurovascular anatomy and vertebral vascular supply, without exogenous contrast enhancement, while preserving their relationships with surrounding tissues. Whole-spine images enabled quantitative analysis of annulus fibrosus fiber organization and vertebral trabecular microarchitecture, permitting the detection of focal posterolateral fiber disruption at the L4-L5 IVD and regional differences in trabecular architecture between cervical and lumbar vertebral bodies. Local tomography resolved annulus fibrosus lamellae, vertebral endplate Haversian systems, and facet joint hyaline cartilage at micron resolution. Conclusions: HiP-CT enables non-destructive, multiscale three-dimensional imaging of the intact human spine while preserving tissue relationships across spatial scales and tissue boundaries. HiP-CT images could provide an ex vivo microanatomical reference for future correlation with clinical imaging investigations of spinal disease and in the discovery and validation of imaging biomarkers. Key results: Synchrotron-based hierarchical phase-contrast tomography enables non-destructive, multiscale three-dimensional imaging of an intact adult human spine using 16.26 µm isotropic voxels, resolving both mineralized and non-mineralized tissue. Local tomography using 2.2 µm isotropic voxels reveals additional microstructural detail without exogenous contrast agents or sectioning. Whole-spine imaging enables quantitative characterization of intervertebral disc fiber architecture and vertebral trabecular morphometry, alongside visualization of neurovascular anatomy while preserving spatial relationships across the intact specimen. Summary statement: Hierarchical phase-contrast tomography enables non-destructive multiscale imaging of the intact human spine, resolving microarchitecture across tissues and interfaces and providing a basis for bridging clinical imaging with underlying three-dimensional microanatomy.

Even though osteoarthritis is considered a disease of the whole joint, integrated analysis of articular calcified cartilage (ACC) behaviour with adjacent tissues remains a persistent limitation. Herein, we performed synchrotron X-ray computed tomography in intact loaded joints and coupled anatomical analysis with digital volume correlation to examine whether microarchitectural ACC features serve as predictive mechano-biomarkers of osteoarthritis. We reveal that male osteoarthritis-prone STR/Ort mice have thicker yet more porous ACC that contains larger but less spherical chondrocyte lacunae than their healthy parental-control CBA mice. This was linked to asymmetrical distribution of tensile and compressive strains generated under physiological loads in the ACC and underlying subchondral bone in tibial epiphyses of STR/Ort mice. Together these data suggest that murine ACC microarchitecture has a mechano-predictive role in age-related osteoarthritis.

Coordinated load transfer across knee joint compartments underpins lifelong joint function, yet dysregulated mechanics are also widely implicated in the etiology of osteoarthritis (OA). How physiological loads are accommodated in the healthy joint and how regionalized architectural alterations reconfigure joint‐level mechanics to promote OA, however, remain unresolved. Here, we integrate in situ mechanical loading of murine tibial epiphyses with phase‐contrast synchrotron X‐ray computed tomography and digital volume correlation to quantify three‐dimensional, compartment‐specific load‐bearing behavior in intact healthy (CBA) and OA‐prone (STR/Ort) knee joints. We find that raised focal strain concentrations emerge within the subchondral plate and precede histological cartilage degeneration in STR/Ort joints at 10 weeks of age. In contrast, these strain concentrations are absent in both young and aging CBA mice, where mechanical strain is preferentially transmitted to locations distant from the articular surface. Finite element modeling further reveals that strain localization in STR/Ort joints is governed by region‐specific microstructural incongruities. Together, these findings demonstrate that epiphyseal microarchitecture preserves mechanical homeostasis during healthy aging, whereas spatial disorganization of subchondral microarchitecture renders the epiphysis susceptible to load‐induced failure. Collectively, this work identifies mechano‐architectural misalignment within the mineralized phase of the tibial epiphysis as an early mechanical promoter of OA emergence.

The severity stratification of carpal tunnel syndrome (CTS) relies on ultrasound morphological markers and electromyography. However, it remains unclear how structural imaging can reliably infer functional impairment. Clarifying the structure–function relationship is critical for efficient diagnostic pathways. A retrospective cohort of 55 patients with symptoms related to CTS was analyzed at the Shanghai Sixth People’s Hospital. All patients were subjected to ultrasound and EMG. 72.7% cases were diagnosed with CTS with a female predominance and equal left–right involvement. Random-forest classifiers were trained using surrogate splits, and performance was evaluated using predictions outside the bag. A full-feature model (34 candidate variables) was compared against a simplified model (8 core variables) capturing the core morphological and electrophysiological features. A residual-based framework was then used to characterize the structure–function mismatch within severity grades (1a–3c). The simplified model improved discriminative performance compared to the full-feature model (AUC 0.789 to 0.824). The simplified model achieved an overall accuracy of 77.3%. Analysis of predicted probability distributions and 10-bin calibration curves indicated stable and clinically interpretable risk estimation in most probability ranges. Permutation-based importance analysis confirmed that both ultrasound and electrophysiological features contributed substantively to prediction. Residual-based grading further revealed structure– function heterogeneity within each main severity grade. CTS severity can be stratified using a limited set of complementary morphological and electrophysiological features. Structure–function mismatch supports an imaging-led initial screening, with electrophysiology reserved for selected patients.

Current biomedical imaging focuses on spatial detail but overlooks time, limiting our understanding of disease progression. There is an unmet need for temporal atlases that align multiscale and multimodal data across defined timepoints, enabling dynamic mapping of pathophysiology. This framework will pave the way for more personalised, time-aware diagnostics and interventions.

Mechanical and anatomical interplay between the distinct tissues of the knee joint is essential for maintaining functional integrity during healthy ageing and contributes to the mechanisms that drive osteoarthritis (OA). In this study, we investigate how age- and disease-associated alterations in joint anatomy influence load transmission and tissue-level strain distribution. Using full-field synchrotron X-ray computed tomography coupled with digital volume correlation, we hierarchically characterised in situ nanoscale strains generated in response to mechanical loading across the tibial epiphysis. Our findings show that greater compressive strains accumulate in the articular condyle of male OA-prone (STR/Ort) epiphyses. Finite element modelling further demonstrated that these strain concentrations are associated with reduced load-bearing capacity, which arise from architectural differences localised to the subchondral bone plate. By coupling high-resolution imaging with computational modelling, our work provides new insights into how structural-function changes to joint anatomy contribute to the initiation and progression of mechanically driven OA. Our approach offers a means to identify early imaging biomarkers prior to OA diagnosis and has potential for monitoring interventions aimed at preserving joint mechanics while promoting healthy joint ageing.

The rapid advancement of Unmanned Aerial Vehicle (UAV) technology has facilitated dynamic, high-resolution remote sensing, significantly benefiting applications in agriculture, forestry, urban planning, and disaster management. However, detecting small objects in UAV imagery remains challenging due to severe scale variations and environmental complexities. While traditional detection methods and even many advanced YOLO variants achieve reasonable performance, they often either incur high computational costs or fail to preserve the fine-grained features essential for reliably detecting extremely small targets. To overcome these limitations, we propose SRD-YOLOv5, an enhanced version of the lightweight YOLOv5n model, distinguished by its novel multi-scale feature fusion framework. Our approach introduces two innovative modules: the Scale Sequence Feature Fusion Module (SSFF) and the Multi-Scale Feature Extraction Module (MSFE), which collaboratively capture global contextual information and preserve detailed semantic cues that are typically lost in conventional fusion techniques. Furthermore, we incorporate an Extremely Small Target Detection Layer (ESTDL) specifically designed to retain high-resolution features for micro-scale object detection. Additionally, the implementation of a Decoupled Head, which independently processes regression and classification tasks, further optimizes the detection of small targets by reducing task conflicts and improving localization precision. Experimental results demonstrate that SRD-YOLOv5 outperforms existing methods in detecting small targets within UAV remote sensing images. It achieves higher accuracy while maintaining low computational demands, making it suitable for real-time applications in UAV remote sensing.